In today's era of digitalization sweeping the world, artificial intelligence technology is penetrating into all walks of life at an unprecedented speed, bringing revolutionary changes to the traditional working model. As the guardian of social security, the public security organs are facing increasingly complex crime situations and massive data processing needs. How to use advanced technology to improve case handling efficiency and enhance the accuracy of combating crime has become an important issue facing every public security worker.

Recently, DeepSeek big models have been successfully applied in various places . This marks that China's public security work has officially entered a new era of big model empowerment, and also provides valuable practical experience for the application of artificial intelligence technology in public security organs across the country. In a case where the criminal investigation department of a local public security bureau used DeepSeek to screen information in tens of millions of data, the time required to locate the suspect's trajectory was greatly reduced from 8 hours to 15 minutes. We have seen the huge potential of AI technology in public security work.

However, for most grassroots public security agencies, how to choose a suitable big model, how to build application scenarios that suit their needs, and how to ensure data security are still a series of urgent problems to be solved. This article will introduce the application of DeepSeek big models in public security case handling in an easy-to-understand way, providing a complete big model application solution for public security agencies from aspects such as AI popularization, base model selection, application scenario construction, knowledge base creation, intelligent body construction, browser tool development to security assurance.

Let us explore together how to make DeepSeek , the top domestic large model, a powerful assistant to public security police and jointly open a new chapter of " smart policing " .

1. AI popularization: Let intelligence become a powerful assistant in public security work

In today's era of information explosion, public security organs are faced with the task of processing and analyzing massive amounts of data every day. From surveillance videos, call records, social media information to various case reports, these data are like a vast ocean, containing key clues to solving cases, but also making police officers exhausted. The traditional " sweat policing " model can no longer meet the needs of modern public security work, and it is urgent to transform to " smart policing " . The emergence of artificial intelligence technology, especially large model technology, provides strong technical support for this transformation.

The inevitable shift from "sweat policing" to "smart policing"

For a long time, China's public security work has been adhering to the traditional concept of " solving cases by footwork " . Police officers obtain clues through visits and investigations, squatting and waiting, and rely on personal experience and intuition to analyze cases. Although this method is effective, its limitations are becoming increasingly prominent in the context of an information society: on the one hand, criminal methods are constantly being updated, and the criminal chain is becoming increasingly complex, which is difficult to deal with by relying solely on manpower; on the other hand, a large amount of basic and repetitive work takes up a lot of police time and energy, affecting the efficiency of case handling.

Take a typical telecommunications fraud case as an example. In traditional investigation methods, police officers need to manually sort out the flow of funds from hundreds of suspicious accounts and analyze thousands of call records, a process that may take weeks. However, after the introduction of AI technology, the system can complete data analysis within a few hours, automatically identify suspicious transaction patterns and key node figures, greatly shortening the investigation cycle.

Basic application forms of AI technology in the public security field

At present, the application of AI technology in the public security field mainly takes the following forms:

1.  Intelligent identification and analysis

Computer vision technology can analyze surveillance videos in real time and automatically identify suspicious persons, vehicles and behaviors; natural language processing technology can perform semantic analysis on case materials, interrogation records and other texts to extract key information; speech recognition technology can convert recording materials into text for subsequent analysis. These technologies have greatly reduced the basic workload of police officers and improved information processing efficiency.

2.  Intelligent prediction and early warning

By analyzing historical case data, the AI ​​system can predict crime hotspots and high-incidence periods to assist in police deployment. At the same time, the system can also monitor abnormal behaviors of specific groups of people, promptly identify potential risks, and achieve a shift from " post-event crackdown " to " pre-event prevention . "

3.  Intelligent decision support

The AI ​​system can automatically match similar cases based on case characteristics and provide references. It can also provide guidance on the case handling process based on laws, regulations and case handling standards to reduce procedural errors and improve case handling quality.

4.  Intelligent collaboration and knowledge sharing

The AI ​​system can break down information barriers between departments and realize data sharing and collaborative case handling; at the same time, the system can also precipitate and pass on case handling experience, enabling new police officers to quickly master the case handling skills and methods of their predecessors.

Big Model Technology: A New Level of AI Application

In recent years, large model technologies represented by ChatGPT and DeepSeek have made breakthrough progress, bringing new possibilities for public security work. Compared with traditional AI technology, large models have the following advantages:

1.  Strong understanding and reasoning skills

By learning from massive amounts of text data, the big model has powerful language comprehension and logical reasoning capabilities. It can understand complex case descriptions, conduct multi-dimensional analysis, and even discover details and connections that humans may overlook.

2.  Multimodal information processing capabilities

Advanced large models can not only process text information, but also understand multimodal data such as images and audio, and realize all-round analysis of case materials. For example, the system can simultaneously analyze the behavior of people in surveillance videos and the voice content in call records to find related clues.

3.  Knowledge integration and transfer capabilities

The big model can integrate knowledge from multiple fields such as laws and regulations, criminological theories, historical cases, etc., and apply this knowledge to specific cases, providing comprehensive knowledge support for case handling.

4.  Continuous learning and evolution capabilities

The big model can continuously improve its capabilities by constantly learning new cases and knowledge, and adapt to the ever-changing crime situation and case handling needs.

Challenges and countermeasures facing the popularization of AI

Although AI technology has broad application prospects in the public security field, its popularization still faces some challenges:

1.  Cognitive impairment

Some grassroots police officers lack understanding of AI technology and have resistance to it. In this regard, we can enhance the police's understanding and acceptance of AI technology through training lectures and case sharing .

2.  Technical threshold

The deployment and use of AI systems requires a certain technical foundation, but grassroots public security agencies often lack professional technical personnel. This requires the development of simpler and easier-to-use AI tools to lower the threshold for use.

3.  Data Quality

The effectiveness of AI systems depends largely on data quality, but historical data in the public security system is often incomplete and non-standard. Therefore, it is necessary to strengthen data governance and improve data quality.

4.  Ethics and safety

The application of AI technology involves ethical issues such as privacy protection and responsibility attribution, and also faces risks such as data security and system security. This requires the establishment of sound laws, regulations and technical standards to ensure that AI technology is applied within a legal and compliant framework.

By solving these challenges, we can accelerate the popularization of AI technology in the public security field, allowing more police officers to enjoy the convenience brought by technological progress, and realize the gorgeous transformation from " sweat policing " to " smart policing " . In the next chapter, we will introduce in detail how to choose a pedestal model suitable for public security work, especially the advantages and characteristics of the DeepSeek model.

2. Choose a large base model: DeepSeek’s unique advantages

With the rapid development of artificial intelligence, large model technology has become the core engine to promote the digital transformation of various industries. For public security organs, choosing a suitable base large model is crucial, which is not only related to the effect of intelligent application, but also to data security and system stability. Among the many large models, the domestically developed DeepSeek large model is becoming the first choice of public security organs with its excellent performance and unique advantages.

Key considerations for large model selection

When choosing a large model of a pedestal for public security work, several key factors need to be considered:

1.  Performance and Capabilities

The basic capabilities of the model are the primary consideration, including language comprehension, logical reasoning, knowledge coverage, etc. For public security work, the model needs to be able to understand professional terminology, master legal and regulatory knowledge, and have strong logical analysis capabilities to effectively assist in case handling.

2.  Security and Compliance

Public security work involves a large amount of sensitive data, so the security and compliance of the model are particularly important. It is necessary to consider whether the model supports local deployment, whether there is a complete data protection mechanism, and whether it complies with relevant national laws and regulations.

3.  Customization and expansion capabilities

The business needs of different public security departments vary, and the model needs to have good customization and expansion capabilities, and be able to be adjusted and optimized according to specific needs.

4.  Deployment and maintenance costs

The deployment and maintenance costs of the model are also important considerations, including hardware requirements, operational efficiency, technical support, etc. For grassroots public security agencies, low-cost, high-efficiency models are more attractive.

5.  Ecosystem and community support

The ecosystem and community support of the model are crucial for long-term development. An active developer community and a rich application ecosystem can provide more technical support and application references for public security agencies.

The core advantages of DeepSeek large models

Based on the above considerations, the DeepSeek large model demonstrates significant competitive advantages, making it an ideal choice for public security agencies.

1.  Excellent performance

DeepSeek large models have performed well in multiple benchmarks, especially in mathematical reasoning, code generation, and natural language understanding. Taking DeepSeek-V3 as an example, it has 671 billion parameters, but only 37 billion parameters are activated for each reasoning, which significantly reduces the computational cost while maintaining high performance. In mathematical competitions (such as AIME 2024 and MATH-500 ) and code generation tasks (such as Codeforces ), DeepSeek-V3 's performance is close to or even exceeds that of international top models such as Claude Sonnet and GPT-4o .

This powerful reasoning ability is particularly important for public security work. For example, in the analysis of complex cases, DeepSeek can extract key information from massive amounts of evidence, establish logical connections, and assist police officers in quickly identifying suspects; in the writing of legal documents, DeepSeek can ensure that the documents are logically rigorous and comply with legal regulations.

2.  Comprehensive localization support

As a domestically developed large-scale model, DeepSeek fully complies with the country's strategic requirements for independent control of key technologies. It supports deployment on domestic GPU servers, does not rely on foreign technology, and ensures the autonomy and security of data processing. This feature is particularly important for public security agencies that handle sensitive information, as it can fundamentally eliminate the risk of data leakage.

3.  Open source and local deployment

DeepSeek is completely open source, follows the MIT protocol, and supports free commercial use and customized development. Public security agencies can adjust and optimize the model according to their own needs to create exclusive intelligent applications. More importantly, DeepSeek supports local deployment and can run in a physically isolated environment to ensure that data does not leave the computer room, thus ensuring data security from the source.

4.  Low cost and high efficiency

The training cost of DeepSeek is only $ 5.57 million, far lower than the $ 100 million of GPT-4o . Its inference cost is also very competitive, with the input / output cost per million tokens being only 1/10 of that of Sonnet-3.5 . This low-cost and high-performance feature enables even grassroots public security agencies with limited resources to afford large-scale model applications and realize technological empowerment.

5.  Innovative architecture design

DeepSeek uses a sparse hybrid expert architecture, activating only a small number of parameters ( 5%-10% ) for each inference, significantly reducing the amount of computation and video memory usage. This innovative design enables DeepSeek to run efficiently on limited hardware resources, making large-scale deployment possible for public security agencies.

6.  Rich version selection

DeepSeek provides multiple versions to meet different needs:

DeepSeek-R1 : Designed specifically for code generation and mathematical problems, it is extremely fast and highly accurate, and is suitable for handling complex data analysis and algorithm optimization tasks.

DeepSeek-V3 : Suitable for general knowledge question and answer, text creation and learning assistance, with a wide coverage, suitable for daily office and knowledge query.

DeepSeek- Internet Edition : It can obtain the latest information in real time and is suitable for tracking news, hot topics, academic trends and other time-sensitive needs.

Public security organs can choose the appropriate version according to specific scenarios, or deploy multiple versions at the same time to complement each other and comprehensively improve work efficiency.

7.  Active ecology and community

DeepSeek has an active developer community and rich application ecosystem. HuggingFace, the world's largest open source platform , has announced that it will replicate all pipeline functions of DeepSeek-R1 and open source all training data and scripts. Well-known academic institutions such as the University of California, Berkeley and the Hong Kong University of Science and Technology are also actively researching and applying DeepSeek technology. This active ecological environment provides the public security organs with rich technical resources and application references.

DeepSeek's successful practice in the public security field

The DeepSeek big model has been successfully implemented in many public security agencies and has achieved remarkable results. Taking the XX Public Security Bureau in a certain place as an example, the police DeepSeek big model was deployed and applied to multiple scenarios such as police situation analysis, investigation and crackdown, and risk prevention and control.

In terms of data foundation, the XX Public Security Bureau of a certain place has built a branch data service platform, which brings together data from public security, government affairs, social resources and the Internet, with a total of more than 9.4 billion pieces of data, and an average of 20 million new pieces of data are added every day. These rich data resources provide a solid foundation for the training and application of DeepSeek large models.

In terms of technical implementation, the XX police in a certain place adopted a domestic computing cluster with domestic GPU servers as the core, which provided a strong driving force for the local deployment, training and reasoning of large models in the police. At the same time, they adopted strict measures such as local deployment and physical isolation to ensure that the entire process of data transmission, processing, reasoning and storage is in an absolutely safe environment.

In terms of application effects, the DeepSeek large model has significantly improved the efficiency and quality of public security work. For example, the criminal investigation department of the YY Public Security Bureau uses DeepSeek to screen information in tens of millions of data, and the time required to locate the suspect's trajectory has been greatly reduced from 8 hours to 15 minutes, greatly improving the efficiency of criminal investigation.

These successful practices fully demonstrate the huge potential and value of the DeepSeek large model in the public security field, and provide valuable experience for the application of other public security agencies.

How to choose the DeepSeek version that suits you

For public security agencies, how to choose the DeepSeek version that suits their needs is an important issue. Here are some suggestions:

1.  Choose based on business needs

If the main requirements are complex data analysis, algorithm optimization, and precise reasoning, DeepSeek-R1 is recommended .

If the main needs are daily office work, knowledge query and text generation, it is recommended to choose DeepSeek-V3 .

If you need to obtain the latest information and dynamics in real time, it is recommended to choose DeepSeek- Internet version.

2.  Selection based on resource conditions

For provincial and municipal public security agencies with sufficient resources, they can consider deploying the full version of DeepSeek-V3 to give full play to its powerful performance.

For county-level public security agencies with limited resources, they can consider deploying lightweight DeepSeek models, or migrating the capabilities of large models to small models through distillation technology.

3.  Choose based on security requirements

For departments with extremely high security requirements, it is recommended to choose a completely local deployment solution to ensure that the data does not leave the computer room.

For scenarios with relatively low security requirements, a hybrid deployment solution can be considered, where some functions are deployed locally and some functions are called from the cloud.

By scientifically selecting and rationally deploying the DeepSeek big model, the public security organs can give full play to its technical advantages and achieve a dual improvement in case handling efficiency and quality. In the next chapter, we will discuss in detail the specific application scenarios of the DeepSeek big model in public security case handling.

3. Finding scenarios: Application scenarios of DeepSeek large models in public security case handling

After selecting a suitable base model, how to apply it to the daily police work becomes the key link to determine the success or failure of the AI ​​project. Finding the right application scenario can not only maximize the technical advantages of the DeepSeek model, but also effectively solve the pain points in public security work and achieve the goal of technology empowering policing. This chapter will discuss in detail the typical application scenarios of the DeepSeek model in public security case handling.

Police situation analysis: extracting key clues from massive data

Police situation analysis is a basic part of public security work, which directly affects the direction and efficiency of case detection. Traditional police situation analysis mainly relies on the experience and intuition of police officers. Faced with the increasingly complex criminal situation and massive amounts of information data, this method has become inadequate.

Application scenario description:

The DeepSeek large model can conduct multi-dimensional and refined analysis of police situations by connecting to the data service platform, quickly and automatically generate police situation analysis reports, and intelligently give work instruction suggestions. Specific applications include:

Police information extraction and integration : DeepSeek can extract key information from multiple sources of data such as alarm records, on-site transcripts, and surveillance videos to form a structured description of the police situation, avoiding information omissions and misunderstandings.

Case correlation analysis : Through a deep understanding of case characteristics, DeepSeek can automatically identify similar cases, discover potential clues to a series of cases, and provide support for the investigation of serial cases.

Suspect portrait generation : Based on information such as crime scene characteristics, modus operandi, and physical evidence left behind, DeepSeek can generate a preliminary portrait of the suspect, including possible age group, professional background, behavioral characteristics, etc., to provide a reference for the direction of investigation.

Police deployment recommendations : Based on the nature, urgency, and possible development trends of the case, DeepSeek can provide optimized recommendations for police deployment to ensure that limited police resources are used most efficiently.

Practical application cases:

The Intelligence and Command Center of a local XX Public Security Bureau used the DeepSeek big model to connect to the data service platform, realizing intelligent analysis and judgment of police situations. In a burglary case, the system quickly linked it to five similar cases that occurred in the jurisdiction by analyzing the time, location, and method of the crime, and generated a detailed case analysis report, providing the task force with valuable investigative clues. In the past, such correlation analysis might have taken experienced police officers several days to complete, but now it only takes a few minutes.

Investigation and crackdown: accurately targeting criminal suspects

Investigation and crackdown are the core links of public security work and are directly related to whether a case can be successfully solved. In a complex criminal environment, how to quickly identify the real criminal suspect from a large number of clues is the biggest challenge facing investigation work.

Application scenario description:

The DeepSeek big model, combined with reinforcement learning of the criminal investigation knowledge base, can quickly and intelligently mine relevant intelligence clues of illegal and criminal activities based on public security big data. Specific applications include:

Clue mining and screening : DeepSeek can automatically identify and extract case-related clues from massive data, and evaluate and prioritize the clues to help investigators focus on the most valuable clues.

Relationship network analysis : By analyzing communication records, social media data, capital flows and other information, DeepSeek can build a suspect’s social network and behavior trajectory, and discover potential accomplices and criminal chains.

Evidence chain construction : DeepSeek can assist investigators in sorting out existing evidence, identifying weak links and missing parts in the evidence chain, and making suggestions for supplementary evidence collection to ensure the integrity and rigor of the evidence chain.

Investigative strategy generation : Based on case characteristics and existing clues, DeepSeek can generate a variety of possible investigative strategies and analyze the advantages and disadvantages of each strategy to provide reference for investigative decision-making.

Practical application cases:

The criminal investigation department of the YY Public Security Bureau uses DeepSeek to screen information in tens of millions of data, and the time required to locate the suspect's trajectory has been greatly reduced from 8 hours to 15 minutes, greatly improving the efficiency of criminal investigation. In a cross-provincial telecommunications fraud case, the system quickly locked down a fraud gang of more than 20 people by analyzing the suspect's call records, bank transactions and travel trajectories, and accurately located the activity area of ​​the gang members, providing key support for the successful arrest.

Risk prevention and control: prediction and early warning, proactive attack

The shift from passive response to active prevention and control is an important shift in the modern police model. Through early identification and early warning of risks, public security organs can take intervention measures before problems occur, effectively preventing crime and maintaining social stability.

Application scenario description:

By leveraging the deep reasoning capabilities of the DeepSeek large model and combining it with multi-source data resources, public security agencies can build an accurate risk prediction system. Specific applications include:

Monitoring of high-risk individuals : DeepSeek can analyze the behavioral patterns of specific groups of people (such as released prisoners, drug addicts, etc.), identify abnormal behaviors and high-risk signals, and issue timely warnings.

Regional security situation awareness : By analyzing regional police data, population mobility, public opinion information, etc., DeepSeek can assess regional security situation, predict possible security risks, and provide a basis for police deployment.

Mass incident warning : DeepSeek can monitor public opinion trends on social media, online forums and other platforms, identify signs that may lead to mass incidents, and provide a time window for timely intervention.

Security monitoring of key locations : For key locations such as schools, hospitals, and transportation hubs, DeepSeek can combine video surveillance, crowd flow data, and other information to assess security conditions in real time and identify potential risks.

Practical application cases:

The XX Public Security Bureau in a certain place built an accurate risk prediction system using the DeepSeek big model, which achieved early detection, quick handling, and proper placement of risk events. During the Spring Festival in 2025 , the system accurately predicted several high-risk areas where theft cases might occur by analyzing historical police data, population mobility, and social media information. Based on this, the public security organs strengthened patrols in these areas and successfully prevented many theft cases. The number of theft cases in the jurisdiction decreased by 30% year-on-year .

Security work: data collision, improved accuracy

Security of large-scale events is an important part of public security work. How to ensure the safety and order of events under limited police force conditions is the core challenge of security work.

Application scenario description:

In the security work of large-scale events, DeepSeek large models obtain accurate intelligence support by accessing various types of diverse data. Specific applications include:

Personnel risk screening : DeepSeek can conduct background screening on people participating in activities, identify potential security risks, and provide targeted guidance for on-site security checks.

Abnormal behavior identification : Combining video surveillance and behavior analysis technology, DeepSeek can identify abnormal behaviors in the crowd in real time, such as wandering, gathering, conflict, etc., and promptly warn of possible safety hazards.

Emergency plan generation : Based on factors such as activity characteristics, site layout, number of participants, etc., DeepSeek can generate targeted emergency plans, including evacuation routes, police deployment, emergency response, etc.

Real-time situation analysis : During the activity, DeepSeek can analyze the on-site situation in real time, assess the security situation, and provide support for command decision-making.

Practical application cases:

In the security work of a large-scale music festival in a certain place in 2025 , the XX Public Security Bureau of a certain place used the DeepSeek big model to screen the participants for risks and successfully identified three people with bad criminal records. At the same time, the system optimized the allocation of police resources through data collision and analysis, and deployed limited police forces to the areas where they were most needed. During the event, the system also monitored the on-site situation in real time, discovered and dealt with many potential risks in a timely manner, and ensured the smooth progress of the event.

Emergency response: intelligent deduction and efficient response

Handling emergencies is an important test of the emergency response capabilities of public security organs. How to make correct decisions quickly in emergency situations is directly related to the effectiveness of incident handling and its social impact.

Application scenario description:

In the handling of emergencies, the DeepSeek big model plays an important role in intelligent analysis and prediction. Specific applications include:

Scenario deduction and prediction : DeepSeek can deduce the possible development path and results of events based on known information, helping commanders prepare in advance.

Resource scheduling optimization : Based on the nature and urgency of the incident, DeepSeek can provide the optimal resource scheduling plan, including police deployment, equipment call-up, and rescue force coordination.

Decision support : In complex and ever-changing emergency environments, DeepSeek can quickly analyze the pros and cons of various decision options and provide an objective basis for command decisions.

Information integration and sharing : DeepSeek can integrate information from different channels to form a unified understanding of the situation and ensure that all participating departments obtain consistent information.

Practical application cases:

In a chemical plant leak accident, the XX Public Security Bureau in a certain place used the DeepSeek large model to quickly analyze the accident site, meteorological conditions and surrounding environment, and accurately predicted the diffusion range and speed of toxic gases. Based on this, the system generated the optimal evacuation plan and police deployment suggestions to guide on-site disposal work. At the same time, DeepSeek also assisted the command center in formulating multiple emergency plans to deal with different possible developments. In the end, the accident was handled quickly and effectively without causing casualties.

Law enforcement and case handling: standardize review and improve quality

Standardization of law enforcement is a basic requirement of public security work. How to ensure that law enforcement procedures are legal, evidence materials are complete, and legal documents are standardized is the focus and difficulty of law enforcement and case handling.

Application scenario description:

The comprehensive use of OCR recognition technology and the powerful semantic analysis and logical reasoning capabilities of the DeepSeek large model can assist legal personnel in conducting normative reviews of law enforcement and case handling materials. Specific applications include:

Intelligent generation of legal documents : DeepSeek can automatically generate standardized legal documents, such as interrogation records, search warrants, detention warrants, etc., based on case facts and applicable laws, to reduce clerical errors.

Review of evidence materials : DeepSeek can conduct a comprehensive review of evidence materials to check for omissions, contradictions or illegalities, and ensure the legality and integrity of the evidence.

Query and application of laws and regulations : DeepSeek can quickly retrieve laws, regulations and judicial interpretations related to the case, provide accurate legal basis, and assist case handlers in correctly applying the law.

Procedural compliance check : DeepSeek can conduct procedural compliance checks on the entire case handling process to ensure that each link complies with legal provisions and avoid case quality issues caused by procedural flaws.

Practical application cases:

The Legal Affairs Brigade of the XX Public Security Bureau in a certain place used the DeepSeek big model to conduct a normative review of law enforcement and case handling materials, which significantly improved the quality of cases. In a complex economic crime case, the system analyzed a large amount of evidence materials and legal documents, found many problems of loose evidence links and inaccurate application of laws, and made timely modification suggestions. At the same time, the system also assisted case handlers in generating standardized legal documents to ensure the smooth transfer of cases for review and prosecution. Since the application of this system, the case return rate of the branch has dropped by 40% , and the quality of cases has been significantly improved.

Intelligent assisted office: improve efficiency and reduce burden

Public security organs are faced with a large amount of daily office work, such as filling out reports, organizing materials, and statistics. Although these tasks are simple, they are very time-consuming and take up a lot of the police's energy.

Application scenario description:

The DeepSeek large model can be used as an intelligent office assistant to assist police officers in completing various daily office tasks. Specific applications include:

Intelligent document generation : DeepSeek can automatically generate various reports, summaries, notifications and other documents based on simple instructions or provided data, reducing the burden of paperwork.

Data analysis and visualization : DeepSeek can analyze and process various types of data, generate intuitive charts and reports, and help leaders and police officers quickly understand the trends and patterns behind the data.

Meeting minutes and task tracking : DeepSeek can record meeting content, extract key decisions and task division, and automatically generate meeting minutes to track task execution.

Knowledge management and retrieval : DeepSeek can build a public security knowledge base to realize intelligent retrieval and push of information such as rules and regulations, work guidelines, case experiences, etc.

Practical application cases:

A grassroots police station introduced the DeepSeek big model as an intelligent office assistant, which significantly improved office efficiency. The police only need to provide simple information and instructions, and the system can automatically generate standardized work reports, situation descriptions, summary materials and other documents. After a community safety publicity event, the system automatically generated a detailed activity summary report based on the activity photos and simple descriptions, including the activity background, process, effect and experience summary. The quality is no less than that of manual writing, but the time is shortened from the original 2 hours to 5 minutes. This greatly reduces the clerical workload of police officers, allowing them to devote more energy to actual police work.

Through the exploration and practice of the above application scenarios, we can see the great potential of DeepSeek big model in public security case handling. It can not only improve case handling efficiency and reduce the burden on police officers, but also improve case quality and enhance the accuracy of combating crime. With the continuous development of technology and the deepening of application, DeepSeek big model will play a role in more scenarios and become a powerful assistant for public security police.

In the next chapter, we will discuss in detail how to build a professional knowledge base for the DeepSeek large model to further improve its application effect in the public security field.

4. Building a knowledge base: injecting professional genes into the DeepSeek model

The power of big model technology lies in its general language understanding and generation capabilities, but in the application of professional fields, especially in special fields such as public security case handling, general capabilities alone are far from enough. How to make the DeepSeek big model truly understand public security business and master professional knowledge has become a key factor in determining its application effect. This requires building a professional, comprehensive, and accurate knowledge base for the big model to inject public security genes into it.

The Importance of Knowledge Base: Making Big Models "Knowledgeable"

In the work of public security case handling, the importance of knowledge base is mainly reflected in the following aspects:

1.  Filling the expertise gap in general models

Although large models such as DeepSeek have learned a large amount of general knowledge during the pre-training stage, their coverage and accuracy are often insufficient for professional knowledge in the public security field, such as laws and regulations, case handling standards, and professional terminology. By building a professional knowledge base, this gap can be effectively filled, allowing the model to accurately understand and apply public security professional knowledge.

2.  Ensure the authority and accuracy of the answer

Public security work is of great importance and cannot tolerate any mistakes. With the support of the knowledge base, the DeepSeek model can give answers based on authoritative information instead of relying on the knowledge that may be biased in its pre-training stage, ensuring the authority and accuracy of the output content.

3.  Provide localization and customization capabilities

Public security work in different regions and departments has its own particularities. By building a targeted knowledge base, the DeepSeek large model can adapt to local needs, understand local policies, become familiar with local cases, and provide more practical services.

4.  Realize the accumulation and inheritance of knowledge

A large amount of valuable experience and cases have been accumulated in public security work. Systematically organizing and precipitating them in the form of a knowledge base can not only provide learning materials for large models, but also realize the inheritance and sharing of experience, so that new police officers can quickly acquire the wisdom of their predecessors.

Core components of the public security knowledge base

A complete public security knowledge base should include the following core components:

1.  Knowledge of laws and regulations

These include laws and regulations related to public security work, such as the Criminal Law, the Criminal Procedure Law, the Public Security Administration Punishment Law, and judicial interpretations and normative documents issued by the Supreme People's Court, the Supreme People's Procuratorate, the Ministry of Public Security, etc. These are the basic basis for public security law enforcement and the basis for the big model to conduct legal analysis and suggestions.

2.  Case handling standards and procedures

It includes the case filing standards, investigation procedures, evidence requirements, document formats, and other normative content for various types of cases. These contents can guide the big model to generate standardized legal documents and provide compliant case handling suggestions.

3.  Professional term dictionary

Public security work involves a large number of professional terms and jargon. Building a professional term dictionary can help the big model accurately understand and use these terms and avoid communication barriers.

4.  Typical Case Library

Collect various typical cases, including case description, investigation process, evidence analysis, legal application, etc. These cases can be used as references for large-scale model analysis of similar cases to improve the accuracy and pertinence of the analysis.

5.  Expert experience database

The case-handling experience, skills and methods of senior police officers are collated to form an experience database. These experiences can help the big model provide more practical and effective suggestions, especially in some non-standard situations that require experience judgment.

6.  Local knowledge

This includes the local social environment, crime characteristics, geographic information and other special knowledge. This knowledge can help the big model better understand the local case background and provide more practical analysis.

Methods and processes for building a knowledge base

To build a high-quality public security knowledge base, we need to follow scientific methods and processes:

1.  Demand analysis and planning

First, it is necessary to clarify the goals and scope of the knowledge base, analyze user needs, and determine the structure and content framework of the knowledge base. At this stage, it is necessary to fully investigate the actual needs of public security work to ensure the pertinence and practicality of the knowledge base.

2.  Data collection and organization

According to the plan, various materials are collected, including legal and regulatory texts, normative documents, case materials, experience summaries, etc. These materials can come from various channels such as the internal public security system, public publications, and Internet resources. After collection, preliminary sorting is required to remove duplicate and irrelevant content to ensure the quality and relevance of the materials.

3.  Knowledge extraction and structuring

The collected data is processed for knowledge extraction and structuring to form knowledge items that meet the knowledge base standards. This process can be done by automatically extracting key information with the help of natural language processing technology, or by manual editing by professionals. Structured knowledge should contain clear categories, attributes, relationships and other information to facilitate the understanding and application of large models.

4.  Knowledge review and verification

The extracted knowledge is reviewed and verified to ensure its accuracy, authority, and timeliness. This stage usually requires the participation of domain experts to check the knowledge content. Controversial or uncertain knowledge should be marked or temporarily excluded from the knowledge base.

5.  Knowledge base construction and deployment

Import the approved knowledge into the knowledge base system, build indexing and retrieval mechanisms, and achieve docking with the DeepSeek large model. The deployment of the knowledge base should take into account security, scalability, and ease of use to ensure the stable operation of the system.

6.  Continuous Update and Maintenance

The knowledge base is not static and needs to be continuously updated and maintained based on the update of laws and regulations, the emergence of new cases, the accumulation of new experiences, etc. Establish a regular update mechanism to ensure the timeliness and accuracy of the knowledge base.

How to connect the knowledge base with the DeepSeek large model

After the knowledge base is built, how to effectively connect it with the DeepSeek large model is the key to realizing knowledge empowerment. There are mainly the following ways to connect:

1.  Retrieval Enhanced Generation ( RAG )

This is the most commonly used docking method. When a user asks a question, the system first searches for relevant content in the knowledge base, and then provides the search results as context to the DeepSeek big model to assist it in generating answers. This method can effectively combine the professionalism of the knowledge base and the generation capabilities of the big model to provide accurate and natural answers.

2.  Fine-tuning training

By fine-tuning the DeepSeek large model, it can learn the professional knowledge in the knowledge base. This method can make the model internalize knowledge, and no additional retrieval is required when answering questions, and the response speed is faster. However, fine-tuning requires higher computing resources, and retraining is required when updating knowledge.

3.  Knowledge Distillation

The knowledge in the knowledge base is transferred to a small model through knowledge distillation to form a lightweight model in a professional field. This method can be deployed in an environment with limited resources and is suitable for use by grassroots public security agencies.

4.  Hybrid enhancement method

Combining the above methods, the most suitable docking method can be selected according to different types of knowledge and application scenarios. For example, basic legal knowledge can be internalized into the model through fine-tuning, and frequently updated regulations and cases can be generated through retrieval enhancement.

Practical cases of building a public security knowledge base

The XX Public Security Bureau in a certain place has actively explored the construction of the DeepSeek large model knowledge base and achieved remarkable results.

The branch has built a multi-level, multi-field public security knowledge base, which includes laws and regulations, case handling norms, typical cases, expert experience, etc. The total number of entries in the knowledge base exceeds 500,000 , covering professional knowledge of multiple police types such as criminal, public security, traffic, and network security.

In terms of knowledge extraction and structuring, they adopted a " human-machine combination " approach, using natural language processing technology to automatically extract basic knowledge, which was then reviewed and supplemented by professional police officers to ensure the accuracy and professionalism of the knowledge.

In connecting the knowledge base with the DeepSeek big model, they mainly adopted the retrieval enhanced generation ( RAG ) approach and optimized it according to the characteristics of the public security field, such as increasing the weight setting of knowledge and improving the filtering ability of sensitive information.

The application effect is remarkable. After the DeepSeek large model is connected to the knowledge base, the accuracy of answering public security professional questions has increased from the original 70% to more than 95% , greatly enhancing the professionalism and practicality of the model.

Challenges and Countermeasures in Knowledge Base Construction

In the process of building a public security knowledge base, you may face the following challenges:

1.  Data quality and standardization issues

The quality of historical data in the public security system is uneven and the formats are diverse, making it difficult to use it directly in knowledge base construction. To address this, data cleaning and standardization tools can be used to establish unified data standards and improve data quality.

2.  Knowledge updating and maintenance issues

Laws, regulations and policies are frequently updated, and keeping the knowledge base up to date is a major challenge. An automated knowledge update mechanism can be established to regularly obtain the latest content from authoritative sources and update it to the knowledge base after review by professionals.

3.  Security and privacy protection issues

The public security knowledge base contains a large amount of sensitive information, so ensuring security is an important consideration. A strict access control and authority management mechanism should be established to desensitize sensitive information and ensure the safe use of the knowledge base.

4.  Knowledge representation and retrieval efficiency issues

How to effectively represent and retrieve complex public security knowledge is a technical challenge. Advanced knowledge graph and vector retrieval technology can be used to improve the accuracy of knowledge representation and the efficiency of retrieval.

By building and maintaining a public security knowledge base through scientific methods, we can significantly improve the application effect of the DeepSeek model in the public security field, making it a real intelligent assistant for police officers. In the next chapter, we will explore how to build an intelligent agent based on the DeepSeek model to further improve its autonomy and practicality in public security work.

5. Constructing an intelligent agent: Making DeepSeek an autonomous case-handling assistant

With the continuous development of big model technology, it is far from being able to meet the complex needs of public security case handling by simply using it as a passive question-answering tool. How to make the DeepSeek big model more autonomous and proactive, and able to complete tasks autonomously in a complex case-handling environment, has become a new direction for the intelligent construction of public security. This requires building an agent based on the DeepSeek big model to upgrade it from a simple " question-answering machine " to a true " case-handling assistant " .

The concept and value of intelligent agents

An agent is an AI system that has the ability to perceive the environment, make autonomous decisions, and execute actions . Compared with traditional large models, agents can not only understand and answer questions, but also actively plan action paths according to goals, call tools to complete tasks, and even collaborate with humans and other agents.

In the field of public security case handling, the value of intelligent agents is mainly reflected in the following aspects:

1.  Complete complex tasks autonomously

Traditional large models require humans to constantly ask questions and provide guidance to complete tasks, while intelligent agents can autonomously plan and execute a series of actions based on given goals, greatly reducing the operational burden on police officers. For example, given a task of " analyzing a suspect's social network , " an intelligent agent can autonomously collect data, analyze relationships, and generate reports without human intervention at each step.

2.  Active discovery and early warning

Intelligent agents can proactively monitor data changes and anomalies and issue early warnings in a timely manner, rather than passively waiting for human inquiries. This is particularly important for risk prevention and control and emergency response, and can greatly improve the timeliness and accuracy of early warnings.

3.  Tool collaboration and capability expansion

The intelligent agent can call various external tools and APIs , such as database query, web search, image recognition, etc., which greatly expands its capabilities. This allows the intelligent agent to handle more complex and diverse tasks, no longer limited to text understanding and generation.

4.  Continuous learning and self-optimization

The intelligent agent can continuously learn and optimize from the task execution process, accumulate experience and improve efficiency. This continuous learning ability enables the intelligent agent to adapt to the ever-changing crime situation and case handling needs.

The core capability construction of the public security intelligent body

To build an efficient public security intelligent body, the following core capabilities are required:

1.  Task understanding and planning ability

The intelligent agent needs to be able to accurately understand the task objectives, decompose them into executable subtasks, and formulate a reasonable execution plan. This requires the DeepSeek large model to have strong language comprehension and logical reasoning capabilities, and be able to understand complex task descriptions and implicit requirements.

For example, when receiving the task of " investigating a telecommunications fraud case " , the intelligent agent should be able to automatically decompose it into subtasks such as collecting case information, analyzing communication records, tracking the flow of funds, identifying suspects, and determine a reasonable execution order.

2.  Ability to use tools

The agent needs to be able to select and use appropriate tools to complete the task. These tools can be database query tools, network search engines, document processing tools, image recognition systems, etc. The agent should be able to understand the function and usage of each tool, select the tool that best suits the current task, and call and use it correctly.

For example, when analyzing surveillance videos, the agent should be able to call video analysis tools to extract character features; when querying criminal records, it should be able to correctly construct database query statements.

3.  Environmental perception and adaptability

The intelligent agent needs to be able to perceive and understand the working environment, including data status, system feedback, changes in user needs, etc., and be able to adjust its action strategy according to environmental changes. This adaptability enables the intelligent agent to maintain efficient operation in a complex and changing case handling environment.

For example, when a data source is found to be inaccessible, the agent should be able to automatically switch to an alternative data source; when the task priority changes, the execution plan should be adjusted in a timely manner.

4.  Interaction and collaboration capabilities

Intelligent agents need to be able to interact and collaborate effectively with human users and other intelligent agents. This includes understanding human instructions, providing clear feedback, explaining decision reasons, accepting human intervention, etc. Good interaction capabilities are the basis for intelligent agents to gain user trust and collaborate effectively.

For example, when performing complex tasks, the agent should be able to regularly report progress to the police; when encountering situations that require human judgment, it should be able to ask clear questions and provide necessary background information.

5.  Safety and ethical awareness

Public security agents deal with sensitive data and important decisions, and must have a strong sense of security and ethics. This includes protecting data privacy, complying with laws and regulations, avoiding bias and discrimination, and maintaining transparency in decision-making.

For example, when processing personal information, intelligent agents should strictly comply with data protection regulations; when providing decision-making recommendations, they should avoid bias based on factors such as race and gender.

Method of building police intelligence agent based on DeepSeek

To build a police intelligence agent based on the DeepSeek large model, the following methods can be used:

1. ReAct framework: combining thinking and action

The ReAct ( Reasoning and Acting ) framework is an effective way to build intelligent agents, which combines the reasoning ability of large models with the action execution ability. In this framework, the intelligent agent follows a " think - act - observe " cycle: first think about the current state and goal, then decide on the next action, observe the results after executing the action, and then think based on the new observation.

This method is particularly suitable for public security case handling scenarios, because the case handling process usually requires continuous collection of information, analysis and judgment, taking action, and evaluating results. It is a typical cyclic decision-making process.

2.  Tool-augmented agents

Equip DeepSeek with a rich tool set, enabling it to call on a variety of professional tools to complete tasks. These tools can include:

Database query tool: used to retrieve case records, personnel information, etc.

Document processing tools: for generating and analyzing legal documents

Multimedia analysis tools: used to process images, videos, audio and other evidence materials

Web search tools: for obtaining public information and the latest developments

Professional analysis tools: such as social network analysis, communication record analysis, etc.

By connecting these tools to the DeepSeek large model through an API interface , the intelligent agent can call appropriate tools as needed, greatly expanding its capabilities.

3.  Memory-enhanced agents

Equip the agent with a long-term memory system so that it can remember past interactions and task execution history, forming continuous learning and experience accumulation. This is especially important for handling long-term and complex cases. The agent can remember the development context of the case and previous analysis results to avoid duplication of work.

The memory system can use a vector database to store historical information and quickly find related memories through similarity retrieval to assist in the execution of the current task.

4.  Multi-agent collaborative system

Build multiple specialized agents to form a collaborative network. Each agent is responsible for a specific field or task, such as intelligence collection agent, evidence analysis agent, legal consulting agent, etc. They collaborate with each other through protocols and interfaces to complete complex tasks together.

This division of labor and cooperation is similar to a professional police team, which can handle complex cases more efficiently and facilitate specialized optimization in different areas.

5.  Human-machine collaboration to enhance learning

Through human-machine collaboration, the capabilities of the intelligent agent are continuously optimized. Police officers can evaluate and provide feedback on the performance of the intelligent agent, and the intelligent agent adjusts its behavior patterns based on this feedback, gradually improving its performance.

This enhanced learning method can enable the intelligent agent to better adapt to the working habits and needs of specific departments and specific police officers, and provide more personalized services.

Typical application scenarios of police intelligence agents

The police agent built based on DeepSeek can be applied in a variety of scenarios:

1.  Case Assistant Agent

As a personal assistant for police officers, it assists in handling daily cases. The agent can automatically collect and organize case materials, provide case analysis and suggestions, generate standardized legal documents, track case progress, and remind key nodes and deadlines.

For example, after receiving a burglary case, the intelligent agent can automatically collect on-site photos, victim statements, surrounding surveillance videos and other materials, conduct preliminary analysis, generate a case summary and investigative recommendations, and prepare case filing documents, greatly reducing the workload of the police.

2.  Intelligence Analysis Agent

Intelligent agents focused on intelligence collection and analysis can continuously monitor multiple data sources, such as police data, social media, news reports, etc., automatically identify potential threats and criminal clues, and generate intelligence analysis reports.

For example, intelligence analysis agents can monitor changes in police data in a specific area, discover abnormal growth trends in theft cases, and by analyzing features such as the time, location, and method of the crime, determine that it may be the work of the same gang and issue timely warnings to relevant departments.

3.  Evidence Review Agent

The intelligent agent specifically responsible for reviewing and organizing evidence can check the integrity, legality and relevance of evidence, identify weak links in the chain of evidence, and make suggestions for additional evidence collection.

For example, in an economic crime case, the evidence review agent can automatically check bank statements, contract documents, witness testimonies and other evidence materials, find timeline conflicts or logical contradictions, and remind investigators to further verify and supplement them.

4.  Legal Consulting Agent

An intelligent agent that provides professional legal advice can answer legal questions, provide legal basis, analyze the legal risks of a case, and recommend applicable legal clauses, etc.

For example, when police officers encounter a complex case characterization problem, the legal consulting agent can analyze the facts of the case, retrieve relevant laws and regulations and judicial interpretations, provide professional legal analysis and advice, and help police officers make accurate legal judgments.

5.  Training the Coach Agent

The training agent for new police officers can provide personalized learning guidance, simulate various case-handling scenarios, evaluate learning effects, and provide improvement suggestions.

For example, the training coach agent can create virtual case handling scenarios for new police officers, guide them through the entire process from receiving the call to closing the case, provide guidance and feedback at key points, and help new police officers quickly master case handling skills and processes.

Practical cases of building police intelligence agents

The XX Public Security Bureau in a certain place has conducted innovative explorations in building a public security intelligent entity based on DeepSeek and developed a " Smart Criminal Investigation Assistant " system.

The system is based on the DeepSeek-V3 large model and integrates a variety of professional tools, including portrait recognition, vehicle recognition, social network analysis, communication record analysis, etc. The system adopts the ReAct framework, which can autonomously plan and execute reconnaissance operations according to mission objectives and continuously optimize performance through human-machine collaboration.

In a cross-provincial telecommunications fraud case, the " Smart Criminal Investigation Assistant " demonstrated its powerful capabilities. After receiving the task, the system independently planned the investigation path: first analyzing the victim's bank statements to track the flow of funds; then collecting and analyzing relevant communication records to identify suspicious numbers; then through social network analysis, discovering the connection between numbers; and finally generating a detailed analysis report, including the suspect's relationship network, activity trajectory and possible hiding places.

Throughout the process, the system was able to continuously adjust the direction of investigation based on newly discovered clues, proactively raise questions that needed further verification, and give clear reasons and suggestions. In the end, with the assistance of the system, the police successfully identified a fraud gang of 15 people and arrested them within three days, saving at least a week compared to traditional investigation methods.

Challenges and countermeasures in building police intelligence

Although police agents show great potential, their construction and application still face some challenges:

1.  Decision transparency and explainability

The decision-making process of intelligent agents is often a " black box " that is difficult to understand and verify. To address this, we can require intelligent agents to provide detailed reasoning processes and decision-making basis to enhance transparency; at the same time, we can establish a human supervision mechanism to review key decisions.

2.  Error accumulation and correction

In long-term tasks, the intelligent agent may have the problem of error accumulation, where small errors in the early stage lead to large deviations in the later stage. In this regard, regular checkpoints can be set to allow human experts to evaluate and correct the state of the intelligent agent; at the same time, error detection and self-correction mechanisms can be established to enable the intelligent agent to identify and correct its own errors.

3.  Safety of tool use

The use of tools by agents may bring security risks, such as improper access to sensitive data, execution of dangerous operations, etc. In this regard, a strict permission control and security audit mechanism should be established to limit the tool usage permissions of agents and record all operation behaviors.

4.  System resource consumption

Complex intelligent systems may consume a lot of computing resources, affecting response speed and usage costs. To this end, technologies such as model quantization and knowledge distillation can be used to reduce resource requirements; at the same time, resources can be dynamically allocated according to the importance and urgency of the task to ensure the response speed of key tasks.

By solving these challenges, the police agent will be able to serve the police more safely and efficiently and become a capable assistant to the police. In the next chapter, we will explore how to develop and apply browser tools similar to large models to further expand the application scenarios of DeepSeek in police case handling.

6. Browser tools similar to big models: intelligent assistants for information acquisition

In the work of public security, the Internet has become an important source of information and work platform. Police officers need to use browsers to search for case-related information, query laws and regulations, and access various professional systems. However, traditional browsers are limited in functionality and inefficient when facing the professional needs of public security work. How to develop and apply browser tools similar to large models to provide more intelligent and efficient information acquisition and processing capabilities for public security has become an important part of smart policing construction.

Limitations of Traditional Browsers

In the daily work of public security, traditional browsers have the following limitations:

1.  Low efficiency in obtaining information

Police officers need to manually input keywords, filter search results, and read a large number of web pages to find the information they need, which is time-consuming and laborious. Especially in emergency cases, this inefficient way of obtaining information may lead to missing the best time.

2.  Lack of professional understanding

Traditional browsers are unable to understand public security professional terms and needs, and are unable to provide targeted search suggestions and result filtering, resulting in a mismatch between search results and actual needs.

3.  Weak information integration capabilities

Police officers usually need to obtain information from multiple web pages and systems, and then manually integrate and analyze it, which not only increases the workload but may also lead to misjudgment due to information fragmentation.

4.  Inadequate safety risk management

Traditional browsers lack security protection mechanisms for public security work and may face risks such as data leakage and malicious attacks when accessing Internet resources.

Core features of the large model browser tool

The browser tool based on the DeepSeek large model can provide the following core functions for public security investigations:

1.  Intelligent search and information extraction

The DeepSeek browser can understand the natural language queries of the police, automatically convert them into precise search statements, extract key information from the search results, and generate structured summaries. For example, when the police enter " recent new methods of telecommunications fraud " , the system can understand that this is a professional query about criminal methods, automatically expand related keywords, and extract specific fraud methods, characteristics, cases and other information from the search results to form a concise report.

2.  Multi-source information integration and analysis

DeepSeek Browser can access multiple information sources (such as news websites, professional databases, internal systems, etc.) at the same time, automatically integrate relevant information, and conduct cross-verification and comprehensive analysis. For example, when investigating a suspect, the system can simultaneously search public social media, news reports, and internal case libraries, integrating this information into a comprehensive background investigation report.

3.  Professional knowledge assistance

DeepSeek browser has a built-in public security professional knowledge base, which can provide professional explanations and background information when police officers browse the web. For example, when a police officer reads a report involving new drugs, the system can automatically identify related terms and provide supplementary information such as drug characteristics, legal provisions, typical cases, etc., to help the police officer better understand the content.

4.  Intelligent form filling and document processing

DeepSeek Browser can assist police officers in quickly filling out various online forms and processing electronic documents. The system can understand the form structure and requirements, and automatically complete the form based on the basic information provided by the police, greatly improving work efficiency. For example, when filling out a case report form, the police only need to provide the core information, and the system can automatically fill in other fields based on the context and historical records.

5.  Safe browsing and risk prevention

The DeepSeek browser has powerful security features that can automatically identify and block risks such as malicious websites and phishing links to protect the safety of police officers' online operations. At the same time, the system can also automatically adjust security policies according to the sensitivity of the accessed content, such as enabling encrypted channels and disabling script execution.

6.  Multimodal Information Processing

DeepSeek browser can process various forms of web content, including text, images, audio, video, etc. For example, the system can automatically identify and analyze images in web pages to extract key information; it can transcribe video content into text for easy retrieval and analysis; it can translate foreign language web pages to break down language barriers.

Customized development of public security browser

In order to meet the special needs of public security work, the DeepSeek browser tool needs to be customized:

1.  Public Security Knowledge Graph Integration

Integrate the police professional knowledge map into the browser so that it can understand police terminology, legal terms, case handling procedures and other professional content. This knowledge map should contain multi-dimensional information such as crime types, laws and regulations, evidence rules, case handling procedures, etc., and establish the relationship between various concepts.

2.  Internal and external network security isolation mechanism

Design a strict internal and external network isolation mechanism to ensure that internal sensitive information will not be leaked when accessing Internet resources. Sandbox technology, virtualization technology and other means can be used to strengthen security protection while ensuring functionality.

3.  Permission classification and behavior audit

Different browser function permissions are set according to the duties and powers of the police, and a comprehensive behavior audit mechanism is established to record key operations and ensure traceability. For example, for information inquiries on sensitive cases, the system will record information such as the inquirer, query content, query time, etc., and require the reason for the query.

4.  Professional tool plug-in system

Develop a series of professional plug-ins for public security work, such as portrait comparison plug-ins, license plate recognition plug-ins, electronic evidence collection plug-ins, etc., so that the browser can become an integrated case handling tool platform. These plug-ins can be configured according to the needs of different police types and departments to achieve personalized services.

5.  Offline working mode

Considering that public security work may be carried out in an environment with limited network conditions, the browser should support offline working mode and pre-cache necessary knowledge bases and tools to ensure that basic services can still be provided in an environment without network conditions.

Application scenarios of large model browser in public security case handling

The DeepSeek browser tool can be applied to a variety of public security case handling scenarios:

1.  Case background investigation

When receiving a new case, police officers need to quickly understand the relevant background information. DeepSeek Browser can automatically search and integrate relevant news reports, social media information, historical cases, etc. based on key case information (such as names, locations, event descriptions, etc.), and generate a comprehensive background investigation report to help police officers quickly understand the case.

For example, in a corporate fraud case, the police only need to enter the name of the company involved, and the DeepSeek browser can automatically collect multi-dimensional information such as the company's industrial and commercial information, news reports, court judgments, social media comments, etc., and conduct intelligent analysis to identify possible illegal clues and related persons.

2.  Query of laws and regulations

In the process of handling cases, police officers often need to consult relevant laws, regulations and judicial interpretations. DeepSeek Browser can understand the professional questions of police officers, accurately locate relevant laws and interpretations, and provide case references and application suggestions.

For example, when a police officer asks " How much amount of money is considered a ' huge amount ' for telecommunications fraud ? " , the DeepSeek browser can not only find the specific amount standard in the relevant judicial interpretation, but also provide the differences in execution standards in different regions, typical cases and sentencing references, to help the police officer accurately apply the law.

3.  Network investigation assistance

In cybercrime investigations, the DeepSeek browser can assist police officers in conducting deep network searches, information correlation analysis, and digital footprint tracking.

For example, in an online fraud case, the DeepSeek browser can automatically search for related accounts and activity records on major platforms based on known suspect information (such as online names, accounts, etc.), analyze their social networks and behavior patterns, discover potential accomplice relationships and activity patterns, and provide direction for investigation.

4.  Public opinion monitoring and analysis

For major cases and sensitive events, DeepSeek Browser can monitor and analyze online public opinion in real time, helping public security organs to promptly grasp public reactions and public opinion trends, and to guide and respond to public opinion.

For example, after a criminal case with a major social impact occurs, the DeepSeek browser can automatically monitor related reports and discussions on major news websites and social media platforms, analyze public opinion sentiment and opinion distribution, identify possible rumors and negative information, and provide decision-making support for the public security organs' response to public opinion.

5.  Cross-language information acquisition

In foreign-related cases and international cooperation, the DeepSeek browser can break through language barriers and help police officers obtain and understand foreign language information.

For example, in a case involving a foreign suspect, the DeepSeek browser can automatically translate and analyze foreign web pages, social media, and news reports to help police understand the suspect's background and activities and provide clues for investigation.

6.  Professional training and learning

DeepSeek Browser can also be used as a tool for professional learning and training of police officers, providing personalized learning resource recommendations and knowledge answers.

For example, when police officers browse professional articles or cases, DeepSeek Browser can automatically identify key concepts and knowledge points, provide extended reading and in-depth explanations, and help police officers deepen their understanding and expand their knowledge. At the same time, the system can also recommend targeted learning resources based on the police officers' work priorities and knowledge weaknesses.

Practical examples of large model browsers

The XX Public Security Bureau in a certain place conducted innovative exploration in the application of DeepSeek browser tools and developed a " Smart Police Browsing Assistant " system.

The system is based on the DeepSeek-V3 large model and open source browser kernel, integrating the public security professional knowledge base and a variety of professional tool plug-ins. The system adopts a two-layer architecture design: the browser front end is responsible for user interaction and basic functions, and the back-end DeepSeek large model is responsible for intelligent analysis and decision support.

In actual application, the " Smart Police Browsing Assistant " has demonstrated powerful capabilities:

In the investigation of a cross-border telecommunications fraud case, the police officer entered several key clues (the suspect's online name, common contact information, etc.) through the system, and the system automatically conducted an in-depth search on multiple platforms and found traces of the suspect's activities on overseas social media. Through intelligent analysis of this public information, the system successfully identified the suspect's true identity and approximate location, providing key clues for subsequent arrests.

In their daily legal consultation work, grassroots police officers can quickly obtain the latest laws, regulations and judicial interpretations through this system. The system can not only accurately locate relevant clauses, but also provide applicable suggestions and similar case references based on the specific case, greatly improving the accuracy and standardization of law enforcement.

During the security work of a large-scale event, the system assisted the command center in real-time monitoring of online public opinion, and promptly discovered and dealt with several pieces of false information that could trigger mass incidents, effectively maintaining the safety of the event and social stability.

The development trend of large model browser tools

With the continuous development of technology, the application of DeepSeek browser tools in the public security field will show the following trends:

1.  Enhanced multimodal understanding capabilities

In the future, DeepSeek browsers will have stronger multimodal understanding capabilities, and will be able to simultaneously process and analyze information in multiple forms, such as text, images, audio, and video, and achieve cross-modal information association and reasoning. For example, the system can simultaneously analyze the characteristics of people in surveillance videos and text descriptions on social media to discover potential associations.

2.  Active reasoning and prediction capabilities

DeepSeek Browser will evolve from passive response to active reasoning and prediction, and will be able to infer possible development paths and outcomes based on existing information, and make early warnings and suggestions. For example, when analyzing a series of cases, the system can predict the possible next actions and goals of criminals.

3.  Improved collaborative work capabilities

DeepSeek browser will better support teamwork, realize information sharing, division of labor and cooperation, and integration of results. Police officers in different positions can share investigation findings and analysis results through the system, forming a collaborative working mode.

4.  Edge computing and localized processing

In order to improve response speed and ensure data security, DeepSeek Browser will adopt more edge computing and localized processing technologies, complete some computing and analysis tasks on local devices, and reduce data transmission and cloud dependence.

Through continuous innovation and optimization, the DeepSeek browser tool will become an indispensable intelligent assistant for public security police, comprehensively improving the ability to obtain and process information, and providing strong technical support for public security case handling. In the next chapter, we will explore the security issues and solutions of DeepSeek large models in public security applications.

7. Security issues of large models: building a strong protective wall for public security data

In the process of public security organs using DeepSeek big models to assist in handling cases, security issues are a key link that cannot be ignored. Public security work involves a large amount of sensitive data and state secrets. How to ensure data security and system security while enjoying the convenience brought by big models has become an important prerequisite for the implementation of applications. This chapter will explore in depth the security challenges and solutions of big models in the application of public security.

Security risks in large model applications

The main security risks faced by using DeepSeek large models in the public security field include:

1.  Data leakage risk

The data generated and used in the daily work of public security organs are extremely sensitive, including case information, suspect information, victim information, investigation plans, etc. If these data are leaked during the interaction with the big model, it may lead to serious consequences, including obstruction of investigation work, violation of the privacy of the parties involved, and threat to national security.

Data leakage may occur in multiple links: data transmission is intercepted during transmission, model service providers obtain sensitive information, model output is accessed by unauthorized personnel, etc. Especially when using large models deployed in the cloud, data security risks are more prominent.

2.  Model security risks

Large models such as DeepSeek also face security risks, mainly including:

Prompt word injection attack : The attacker uses carefully designed prompt words to induce the model to bypass security restrictions and output sensitive or harmful content.

Adversarial sample attack : By adding specific perturbations to the input, the model produces incorrect outputs, affecting decision accuracy.

Model stealing : Obtaining model knowledge through a large number of queries, rebuilding similar models, or extracting training data.

Backdoor attack : implanting a backdoor into the model so that it produces a preset erroneous output under specific input.

3.  System integration risks

Integrating the DeepSeek large model into the public security business system also brings new security challenges:

Interface security : If the model API interface is improperly designed or insufficiently protected, it may be accessed or abused by unauthorized persons.

Permission management : The access rights of police officers in different roles and positions to the model need to be strictly controlled to avoid unauthorized access.

System Dependency : Excessive reliance on large models may lead to single points of failure and affect business continuity.

4.  Compliance and ethical risks

The use of large models also faces compliance and ethical risks:

Legal compliance : The use of large models to process personal information must comply with the Personal Information Protection Law and other laws and regulations.

Decision-making transparency : The process of large-scale model-assisted decision-making is often opaque, difficult to explain and hold accountable.

Bias and discrimination : Models may inherit biases from the training data, leading to unfair analytical results.

Large model security protection system in the public security field

In response to the above risks, public security organs need to build a comprehensive security protection system when applying the DeepSeek large model:

1.  Data security protection

On-premises deployment and privatization

For highly sensitive public security business, local deployment or private deployment of DeepSeek large models should be given priority to avoid sensitive data leakage. Although this deployment method has high hardware requirements, it can maximize data security.

This approach is adopted by a local XX Public Security Bureau. They deployed a lightweight version of the DeepSeek-7B model within the bureau , and all data processing is completed on the intranet to ensure that sensitive information does not leave the LAN.

Data desensitization and anonymization

Before using the big model, the input data is desensitized to remove or replace sensitive information such as personal identity information and case numbers. For example, the real name is replaced with a code name, the specific address is blurred, and the precise time is replaced with a time period.

End-to-end encryption

In the process of data transmission and storage, end-to-end encryption technology is used to ensure that even if the data is intercepted, it cannot be interpreted. At the same time, the output of the model is also encrypted and protected, and only authorized users can view it.

Access Control and Auditing

Establish a strict access control mechanism and assign permissions based on the " principle of least privilege " to ensure that each user can only access the minimum data set required for their work. At the same time, record all data access and usage behaviors, conduct regular audits, and detect anomalies in a timely manner.

2.  Model security protection

Safe fine-tuning and alignment

The DeepSeek basic model is fine-tuned and aligned to enhance its ability to resist attacks such as prompt word injection. A large number of adversarial samples are constructed for training, so that the model can identify and reject malicious input.

Input filtering and validation

Before user input enters the model, a multi-layer filtering mechanism is set up to detect and intercept possible malicious prompt words and attack patterns. For example, a sensitive word library and attack pattern library are established to perform real-time inspection of input.

Output review and control

Review the content generated by the model to ensure that it does not contain sensitive information or harmful content. You can set up automatic review rules, manually review suspicious outputs, or limit the model's output capabilities in certain sensitive areas.

Model monitoring and updating

Continuously monitor the behavior and performance of the model to detect abnormal patterns in a timely manner. Regularly update the model and security rules to patch known vulnerabilities and respond to emerging security threats.

3.  System integration security

Security architecture design

Adopting the security architecture of " defense in depth " , we set up security measures at all levels of the system. For example, we set up firewalls and intrusion detection systems at the network layer, implement identity authentication and access control at the application layer, and perform encryption and backup at the data layer.

API Security Management

Implement strict security management on the model API interface, including identity authentication, access frequency limit, request content verification, etc. Use API keys, OAuth and other mechanisms to ensure that only authorized systems and users can call the interface.

Disaster Recovery and Backup

Establish a complete disaster recovery and backup mechanism to ensure rapid recovery when the model service is interrupted. You can use multi-model redundant deployment or prepare backup traditional algorithms as emergency plans.

4.  Compliance and ethical assurance

Compliance Assessment and Audit

Before deploying a large model, conduct a comprehensive compliance assessment to ensure compliance with relevant laws, regulations and industry standards. Conduct compliance audits regularly and adjust non-compliant functions and processes in a timely manner.

Transparency and explainability

Improve the transparency and explainability of large model decision-making processes, so that users can understand why the model makes a specific recommendation or judgment. For example, require the model to provide both the basis and reasoning process when making recommendations.

Human-machine collaboration and manual supervision

Establish a " human-machine collaboration " working mode, use large models as auxiliary tools, and retain the dominant position of humans in key decision-making. Manually review the important outputs of the model to ensure accuracy and appropriateness.

Public Security Big Model Safety Practice Case

In the process of applying the DeepSeek big model, the XX Public Security Bureau of a certain place has taken a series of security measures and formed a relatively complete security practice system:

1. " Three-layer isolation " network architecture

The branch adopts a three-layer isolated network architecture of " Internet zone - exchange zone - intranet zone " . The DeepSeek large model is deployed in the exchange zone and has limited communication with the intranet and the Internet through strict data exchange rules. This architecture ensures that sensitive data will not be directly exposed to the Internet, and also prevents external attacks from directly penetrating the intranet.

2.  Data classification processing mechanism

According to the sensitivity of the data, public security data is divided into four levels: top secret, confidential, secret and general. Different levels of data use different processing procedures:

Top secret data: prohibited from entering large models, can only be processed by authorized personnel in a specific isolated environment.

Confidential data: After being desensitized, it can only be used in small models deployed locally.

Secret-level data: After being desensitized, it can be used in large models deployed on the intranet.

General-level data: can be processed using large cloud models after security review.

This hierarchical processing mechanism ensures a balance between data security and model capabilities.

3. "AI+ Manual " Dual Review

The branch has established a dual review mechanism of "AI + manual " . All interactions with the big model are pre-reviewed by AI and necessary manual review:

Input review: The AI ​​system automatically detects sensitive information in the input and reminds users to desensitize or refuse to process highly sensitive content.

Output review: The AI ​​system automatically checks model outputs and marks possible sensitive information or inappropriate content. Important outputs can only be used after being reviewed by a dedicated person.

This double review mechanism effectively prevents the leakage of sensitive information and the generation of inappropriate content.

4.  Full process security audit

The branch implemented a full-process security audit mechanism to record every step of the interaction with the big model:

User identity and authority verification records

Input content and desensitization records

Model call and parameter setting records

Output content and usage records

These records are regularly analyzed and audited to promptly identify and address potential safety hazards.

5.  Regular security assessment and updates

The branch has established a regular safety assessment mechanism and conducts a comprehensive safety assessment of large model applications every quarter, including:

Vulnerability scanning and penetration testing

Data processing compliance checks

User behavior analysis and anomaly detection

Research and response to new security threats

Based on the assessment results, security strategies and protection measures are updated in a timely manner to ensure the continued effectiveness of system security.

Future development trends of large model safety

With the development of technology and the deepening of its application, the big model security in the public security field will show the following development trends:

1.  Federated Learning and Privacy Computing

Federated learning allows multiple parties to jointly train models without sharing original data. This technology will be widely used in public security big models. Local public security agencies can jointly train and optimize national public security-specific big models while protecting local data privacy.

At the same time, privacy computing technologies such as homomorphic encryption and secure multi-party computing will also be combined with large models to realize the security concept of " data available but invisible " .

2.  Trusted Execution Environment

Trusted Execution Environment ( TEE ) technology will provide hardware-level security for large models. Models and data running in TEE are protected by hardware encryption, and even system administrators cannot access the plaintext data being processed, fundamentally solving the risk of data leakage.

3.  Adaptive security protection

AI - based adaptive security protection systems will become a trend. Such systems can learn normal usage patterns, automatically identify abnormal behavior, and take corresponding measures based on the threat level to achieve active defense rather than passive response.

4.  Safety standards and certification system

As the application of large models in sensitive fields such as public security increases, special safety standards and certification systems will be established. These standards will regulate the safety requirements, test methods and certification processes of large models, and provide an objective basis for application selection.

5.  Optimize the balance between safety and performance

Future research will focus more on the balance between security measures and model effectiveness. Through algorithm optimization and architecture innovation, the impact of security mechanisms on model performance can be reduced to achieve the ideal state of " both safe and efficient " .

Implementation path of safety construction

For public security agencies that plan to apply the DeepSeek large model, they can refer to the following implementation paths to gradually establish a complete security protection system:

1.  Security assessment and planning

First, conduct a comprehensive security risk assessment to identify potential risk points and security needs. Based on the assessment results, develop a detailed security plan to clarify security goals, measures, and division of responsibilities.

2.  Infrastructure security construction

Build a secure network environment and computing infrastructure, including basic security measures such as network isolation, access control, and encrypted transmission. Choose the appropriate deployment method (on-premises deployment, private cloud, or hybrid deployment) based on data sensitivity and business needs.

3.  Establishment of data security system

Establish a data classification and grading system to clarify the processing rules for different types of data. Implement data security management throughout its life cycle, including security controls in all aspects such as collection, transmission, storage, use, and destruction.

4.  Improved model security capabilities

Enhance the security of the DeepSeek model, including security fine-tuning, defense training, input and output filtering, etc. Establish a model security testing mechanism and regularly evaluate the model's security performance.

5.  Operation monitoring and emergency response

Establish a comprehensive security monitoring system to monitor system operation status and security incidents in real time. Develop a detailed security emergency plan, clarify the response process and responsible persons, and conduct regular drills.

6.  Security awareness and capacity building

Strengthen user safety awareness training to improve police officers' awareness of the safe use of large models. Cultivate professional AI security talents to be responsible for the security maintenance and optimization of the system.

Through the above measures, public security organs can effectively control security risks and ensure the safety and reliability of systems and data while enjoying the convenience brought by DeepSeek big models. Security is not a one-time job, but a long-term task that requires continuous investment and optimization. Only by integrating the concept of security throughout the entire process of big model application can we build a strong protective wall for public security data and provide solid protection for the construction of smart policing.

Conclusion: A new chapter in smart policing - DeepSeek big model empowers police to handle cases

With the rapid development of artificial intelligence technology, big models are changing the way of working in all walks of life at an unprecedented speed and depth. In the field of public security case handling, the application of DeepSeek big models is not only a technological innovation, but also a change in the police model. It is opening a new chapter in smart policing.

The comprehensive value of large model applications

Through the systematic explanation in this article, we can clearly see the multiple values ​​of the DeepSeek model in public security case handling:

1.  Efficiency improvement: from " human wave tactics " to " intelligent warfare "

Traditional police investigations often rely on " human-wave tactics " , with a large amount of manpower invested in information collection, material organization, document production, etc. The application of DeepSeek's large model makes these tasks highly automated and intelligent, greatly improving the efficiency of case handling.

Take document production as an example. In the past, police officers had to spend a lot of time writing various legal documents. Now, with the help of DeepSeek big model, they only need to provide key information, and the system can automatically generate standardized and complete documents, which improves work efficiency by 3-5 times. In terms of case analysis, the big model can complete the processing and analysis of massive data in a few minutes, which may have taken days or even weeks of manual operation in the past.

2.  Quality improvement: from " experience judgment " to " data decision-making "

Traditional case handling relies heavily on the personal experience and subjective judgment of police officers, which is inevitably affected by individual differences and subjective factors. DeepSeek's big model is based on massive data and advanced algorithms, which can provide more objective and comprehensive analysis and suggestions, reducing human bias and errors.

For example, in case correlation analysis, the big model can discover hidden correlations and patterns that are difficult for humans to detect, helping to solve a series of cases; in terms of the application of law, the big model can comprehensively consider relevant laws, regulations and judicial interpretations, provide more accurate legal advice, and reduce law enforcement deviations.

3.  Capacity improvement: from " single point breakthrough " to " comprehensive empowerment "

The DeepSeek big model is not a simple single-point tool, but a comprehensive empowerment platform that can provide intelligent support in all aspects of public security work, including the entire process of receiving alarms, filing cases, investigation, interrogation, evidence collection, and document preparation.

This comprehensive empowerment has enabled public security work to transform from fragmented and isolated information processing to systematic and intelligent knowledge management and application, greatly enhancing the overall combat capability of public security organs and their ability to deal with complex cases.

4.  Innovation and improvement: from " tool application " to " model change "

The application of DeepSeek big model is not only an update at the tool level, but also a fundamental change in the working mode. It is promoting the transformation of public security work from the traditional " manual + tool " mode to the intelligent mode of " human-machine collaboration " .

Under this new model, police officers are freed from heavy basic work and can devote more energy to key links that require human wisdom and judgment, such as strategic decision-making, complex analysis, psychological analysis, etc., to achieve complementary advantages between man and machine and jointly improve the quality and efficiency of case handling.

Strategic thinking for future development

Looking ahead, the application of DeepSeek big models in the field of public security investigation will show the following development trends:

1.  From general capabilities to specialized customization