Research on the Application of Large Language Models in Intelligent Operations and Maintenance

1 Background

With diversified business scenarios and extensive demand for digital transformation and upgrading, the financial industry O&M field has a foundation for the vertical landing of large language models, and large language model technology can promote the digital transformation of the financial industry O&M field through fast and accurate information integration and automated task processing. Given that the financial industry has extremely high requirements for system stability and data security, operation and maintenance work not only involves daily system maintenance, but also includes risk prevention and control, compliance checks and other aspects. In this context, this topic is aimed at the application of large language models in the field of intelligent operation and maintenance in the financial industry to carry out relevant research and practical exploration, research on the construction technology and application scenarios of the large language model of operation and maintenance, security control and privacy protection, cost optimization and efficiency enhancement, to promote the application of large language models in the field of operation and maintenance, to enrich the intelligent operation and maintenance tools, to further improve the quality of the work of operation and maintenance, and explore innovative solutions for the development of the field of intelligent operation and maintenance in the financial industry. The project will explore innovative solutions for the development of intelligent operation and maintenance in the financial industry.

2 Content and Achievements

This research project focuses on interactive Q&A, intelligent operation, code generation, and other operation and maintenance scenarios centered on big language model, and empowers the development of operation and maintenance business through large language model technology.

2.1 Knowledge Q&A Scenario

In the knowledge Q&A scenario, the O&M large language model introduces the enterprise's internal knowledge base, combines with the general knowledge map in the field of O&M for cross-validation, accurately locates the original source sentence by sentence, and quickly traces it back to specific reference documents, knowledge and experience, and other authoritative information, so that the information is "traceable" and the content is highly accurate and credible. The O&M model provides immediate answers to user-initiated questions, avoiding interactive questions that fail to understand the user's intent, and reflecting the application value of the O&M model in intelligent interaction and intelligent search through the timeliness of the response and the accuracy of the information.

2.2 Operation and Maintenance Intelligent Assistant Scenario

The combination of Copilot software (operation and maintenance software assistant) and operation and maintenance model provides a new operation and maintenance method for operation and maintenance personnel. This combination makes O&M work faster, more convenient, and improves O&M efficiency. O&M Copilot supports O&M personnel to use voice or text for interactive dialogue through natural language processing technology. This type of interaction is significantly different from traditional O&M software based on Windows, menus, and command button interfaces. Instead of memorizing the location of the window where the function is located, or searching for the desired operation option in a large number of menus and buttons, the operation and maintenance personnel can enter text or voice in the dialog box, and Copilot understands their intention and automatically performs the corresponding operation and maintenance operation. The O&M large language model has acquired rich O&M knowledge and experience through deep learning and analysis of a large amount of O&M data. In addition, O&M Copilot has self-learning and optimization capabilities, and can continuously optimize its own algorithms and models according to the feedback and operation records of O&M personnel to improve the accuracy and efficiency of interaction, enabling O&M Copilot to continuously adapt to new O&M scenarios and needs, providing O&M personnel with smarter and more efficient operation assistance, and improving the accuracy and standardization of the operation of O&M personnel, and Reduce O&M labor input. Third-party software can also interact with the Copilot interface of the O&M model to build a multi-party ecosystem of the O&M model.

2.3 Code Generation Scenario

The O&M large language model can learn and reason, and through the training of a large number of O&M scripts and data, it can master the rules and best practices of writing O&M scripts. When O&M personnel need to write scripts, they interact with the model in natural language, input relevant requirements and parameters, and the model automatically generates script code that meets the requirements based on its built-in knowledge base and reasoning ability. Secondly, the O&M model can check errors and optimize. During the scripting process, the model is able to check the correctness of the code, discover potential problems, and provide corresponding suggestions, which reduces the error rate of the O&M staff in scripting, improves the quality of the scripts, and pushes the management of O&M scripts to a more intelligent level. Traditional script management often relies on manual review and modification, which is less efficient. With the help of the O&M large language model, an automated script review and modification system can be established. When the operation and maintenance personnel write scripts and submit them to the model for review, the model will review the scripts and make optimization suggestions based on its built-in rules and best practices.

3 Summary and Outlook

This research topic has carried out research on the technology related to the large language model, combined with the business requirements in the field of operation and maintenance, built a large language model of operation and maintenance, and explored the scenarios of knowledge quiz, intelligent assistant of operation and maintenance, and code generation, etc., and realized the innovative application through the human-computer dialogue, text synthesis, and semantic recognition under the support of the capability of the large language model of operation and maintenance, and improved the online knowledge service through the expansion of the relevant corpus and the operation and maintenance knowledge base, and promoted the skill improvement of operation and maintenance personnel. By expanding the relevant corpus and operation and maintenance knowledge base, the online knowledge service can be improved, the skills of operation and maintenance personnel can be improved, and intelligent operation and maintenance can be promoted to a higher level. Looking forward to the application of large language model in the field of intelligent operation and maintenance, the next step will be to continue to carry out research and exploration, and practice in the areas of operation and maintenance scene mining, operation and maintenance data management, and operation and maintenance tool ecological construction.