Construction of a professional knowledge base for public security: DeepSeek big model technology empowers a new future for smart policing

introduction

In the digital age, public security work faces severe challenges such as the intelligence of criminal methods and the complexity of case types. How to quickly identify new criminal terms and accurately grasp the rules of cases has become the key to improving the efficiency of public security organs in handling cases. Traditional knowledge management methods have been unable to cope with the needs of massive information processing, and the public security professional knowledge base built based on big model technology is gradually becoming the core infrastructure of smart policing . This article will deeply explore the construction path and value of this innovative tool from the aspects of knowledge base architecture design, technical implementation, application scenarios and future prospects.

Chapter 1 Urgent Need for Public Security Professional Knowledge Base

1.1 The increasing complexity of public security work

In recent years, new types of cases such as telecommunications fraud, online money laundering, and cross-border crimes have occurred frequently, and criminals have used technical means to constantly update their terms and methods of committing crimes. For example, the term "running points" has evolved from a computer performance test to a money laundering jargon, and "Canon" has evolved from a piano piece to a bank card provider for fraud gangs. The concealment and dynamism of these terms have put forward higher requirements for the rapid response capabilities of the police.

1.2 Analysis of pain points of knowledge management

Public security organs need to rely on a lot of professional knowledge in the process of handling cases, but the traditional knowledge base has the following problems:

• Information fragmentation : term definitions are scattered in various documents and cases, lacking unified standards;

• Update lag : New criminal methods emerge in an endless stream, but knowledge base updates rely on manual input, which is inefficient;

• Inefficient retrieval : Police officers need to query information across multiple systems, making it difficult to quickly locate key content.

1.3 The empowering potential of large model technology

The Large Language Model (LLM) has powerful semantic understanding, knowledge association and dynamic learning capabilities, which can effectively solve the above pain points. By building a professional knowledge base for public security and combining it with large model technology, it can realize automatic knowledge update, intelligent retrieval and multi-dimensional reasoning, providing real-time and accurate decision-making support for case handling.

Chapter 2 Architecture Design of Public Security Professional Knowledge Base

The construction of the public security professional knowledge base needs to cover two core modules: the police terminology base and the police common sense base . The two complement each other and jointly support intelligent case handling.

2.1 Police Terminology Database: Cracking the “Code of Black Language”

The core task of the terminology database is to establish standardized mapping relationships for criminal terms, including original meanings, new definitions in crime scenarios, and related cases.

• Running the score : semantic transfer from computer performance testing to money laundering activities;

• GOIP devices : criminal instrumentalization from intellectual property platforms to virtual dialing tools;

• Cat Pool : Technological alienation from bulk management of phone cards to mass sending of fraudulent text messages.

The terminology database needs to combine natural language processing (NLP) technology to dynamically identify ambiguous words in the context and establish a network of associations between terms through knowledge graphs. For example, "water room" and "running points" both involve money laundering, and the system can automatically prompt their association.

2.2 Police knowledge base: building case reasoning logic

The common sense database focuses on structured knowledge such as case types, methods of committing crimes, and legal provisions. Its design must follow the following principles:

• Classification standardization : For example, telecommunications fraud is divided into seven categories, including identity impersonation, shopping, inducement, etc.

• Completeness of elements : Property infringement cases must include entities such as the criminal subject, the amount involved, and the time of the crime;

• Dynamic scalability : supports automatic identification of new fraud methods (such as AI face-changing fraud) through machine learning.

2.3 Technical Architecture: Knowledge Engine Driven by Large Models

The technical implementation of the knowledge base is based on the three-layer architecture of "data layer-model layer-application layer":

Chapter 3 Knowledge Base Construction Process and Key Technologies

3.1 Knowledge Acquisition: From Multi-Source Data to Standardized Knowledge

• Data cleaning : remove duplicate and contradictory information and unify terminology (e.g. normalize “counterfeit identity fraud” and “impersonation fraud”);

• Entity extraction : Use named entity recognition (NER) technology to extract case elements (such as bank card numbers involved in the case and fraud APP names);

• Relationship mining : Through co-occurrence analysis, the implicit association between terms and case types is discovered (for example, “cat pool” is often used in phishing SMS scams).

3.2 Knowledge Representation: From Text to Structured Graph

Taking "Identity Fraud" as an example, its knowledge graph nodes include:

• Case type : Impersonating leaders, relatives, friends, public security, procuratorate and courts;

• Related terms : GOIP equipment, cat pool;

• Legal basis : Article 266 of the Criminal Law on fraud.

After the map is constructed, police officers can quickly trace the crime chain through a visual interface.

3.3 Knowledge Update: Closed-Loop Mechanism of Dynamic Learning

• Active learning : The system automatically monitors case records and public opinion data to identify new terms (such as "AI face-changing fraud");

• Manual review : Police officers confirm the new knowledge recommended by the system to ensure its authority;

• Model fine-tuning : Iteratively optimize large model parameters based on feedback data to improve the accuracy of knowledge reasoning.

Chapter 4 Application Scenarios: From Theory to Practice

4.1 Intelligent Question and Answer: Police Officers’ “Portable Think Tank”

The police officer input “the legal consequences of running points” and the system automatically returned relevant laws, sentencing standards and similar cases, and prompted that “it may be related to the water room money laundering process.”

4.2 Case Reasoning: Intelligent Restoration of the Crime Chain

Taking a cross-border telecommunications fraud case as an example, the knowledge base automatically infers the money laundering path of "Cannon-Water Room-Running Points" by analyzing the bank card transactions, call records and other data involved in the case, and generates investigative suggestions.

4.3 Risk warning: taking precautions before they happen

The system analyzes the keyword "GOIP equipment rental" in social media and combines it with historical case data to warn of possible fraud dens in a certain area and guide the police to deploy control in advance.

Chapter 5 Challenges and Response Strategies

5.1 Data Security and Privacy Protection

• Desensitization : sensitive data such as the information of persons involved in the case and bank card numbers must be encrypted and stored;

• Authority control : Set knowledge access rights according to the rank of police officers to prevent information leakage.

5.2 Dynamic Evolution of Terminology

• Establish a terminology version management mechanism : record the evolution of "Canon" from a music term to a criminal term;

• Introducing multimodal learning : combining non-text data such as dark web forum pictures and voice chat records to capture new variants of terms.

5.3 Cross-regional knowledge collaboration

• Build a unified national knowledge base : break regional information silos and realize cross-provincial case knowledge sharing;

• Multi-language support : To address cross-border crimes, we develop multi-language terminology comparison functions such as Chinese, English, and Burmese.

Chapter 6 Future Outlook: Knowledge-Base Driven Smart Police Ecosystem

With the deep integration of big models with the Internet of Things and blockchain technology, the public security knowledge base will show the following trends:

• Virtual-real fusion : AR glasses can be used to identify the "cat pool" equipment used by suspects in real time, and knowledge prompts can be superimposed;

• Global intelligence : The knowledge base is linked with the Skynet system and traffic monitoring to achieve crime prediction and precise strikes;

• Public participation : Open an anti-fraud knowledge query interface, encourage the public to report suspicious terms, and build a "national anti-fraud" knowledge network.

Conclusion

The construction of the public security professional knowledge base is not only a technological upgrade, but also a revolution in the policing model. By transforming scattered knowledge into structured and intelligent decision-making resources, public security organs are able to gain an advantage in the ever-changing criminal battlefield. In the future, with the continuous iteration of technology, this knowledge base will become the "smart brain" to protect social security and provide solid support for the realization of the vision of "no fraud in the world".