Intelligent safety operation and maintenance method and device and storage medium

Abstract

The invention discloses an intelligent security operation and maintenance method, which comprises the following steps of: based on input key information of a user demand, interacting with a user through a virtual AI customer service to determine a specific operation and maintenance demand of the user; the virtual AI customer service associates the specific operation and maintenance demand with the complete list of the started plug-in functions, generates final operation and maintenance demand data and sends the final operation and maintenance demand data to the generative artificial intelligence module; in combination with a context environment formed in a user interaction process and final operation and maintenance demand data, the generative artificial intelligence module creates a pre-arranged plan and sends the pre-arranged plan to the virtual AI customer service for the user to confirm; the virtual AI customer service calls the operation and maintenance coordinator, and the operation and maintenance coordinator collects information by running codes of the selected plug-in and calling a corresponding application programming interface, executes a pre-arranged plan and generates a response report at the same time. Based on the distributed grid computing power platform, manual operation and maintenance operation is replaced by AI analysis and linkage execution, the operation and maintenance work efficiency can be improved, the operation and maintenance process can be standardized, and the operation and maintenance cost can be saved.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to an intelligent security operation and maintenance method and system. Background Art

[0002] At present, the field of security operation and maintenance has formed a comprehensive guarantee system including firewalls, intrusion detection, vulnerability scanning, virus prevention and other technical means. Traditional operation and maintenance operations are mainly implemented through the operation and maintenance audit system (bastion host). The principle is to use various technical means to monitor and record the operation behavior of operation and maintenance personnel on servers, network equipment, security equipment, databases and other equipment in the network in a specific network environment, so as to centrally alarm, handle and audit responsibilities in a timely manner, thereby protecting the network and data from intrusion and damage from external and internal users. This solution solves the controllability and auditability problems in operation and maintenance management, but does not involve solving the intelligence in security operation and maintenance. A large number of operation and maintenance personnel are still required to perform manual operations, resulting in the efficiency and cost of operation and maintenance being difficult to adapt to the needs of operation and maintenance services in a big data environment.

[0003] The current mainstream automated operation and maintenance management tools such as Puppet, Terraform, Ansible, and GitOps focus on supporting batch management of remote servers. Through complex configuration files and plug-ins, they help operation and maintenance personnel define and deploy infrastructure in a code-based manner, thereby improving the manageability and maintainability of the system. Although this has greatly improved the efficiency of operation and maintenance, it still requires the intervention of professional operation and maintenance personnel. With the surge in the number of alarms, the work pressure of operation and maintenance personnel has increased, their work efficiency has decreased, and the labor cost is high.

[0004] Based on this, a new solution is needed. Summary of the invention

[0005] The main purpose of the present invention is to provide an intelligent and safe operation and maintenance method to address the problem that the current operation and maintenance system requires a large number of operation and maintenance personnel to connect and interact with users, resulting in excessively high operation and maintenance costs, and to provide complete technical support for unified and centralized intelligent operation and maintenance management and decision-making of equipment.

[0006] To achieve the above object, the present invention provides an intelligent security operation and maintenance method, comprising:

[0007] Based on the key information of the input user needs, interact with the user through the virtual AI customer service to determine the user's specific operation and maintenance needs, wherein the user needs are input through text, voice and / or video;

[0008] The virtual AI customer service associates the specific operation and maintenance requirements with the complete list of enabled plug-in functions, generates final operation and maintenance requirement data, and sends it to the generative artificial intelligence module;

[0009] In combination with the context environment formed during the interaction with the user and the final operation and maintenance demand data, the generative artificial intelligence module creates a contingency plan and sends it to the virtual AI customer service for user confirmation;

[0010] After the user confirms the contingency plan, the virtual AI customer service calls the operation and maintenance coordinator, which collects information by running the code of the selected plug-in and calling the corresponding application programming interface, executes the contingency plan, and generates a response report.

[0011] In the intelligent security operation and maintenance method provided by the present invention, the steps of creating a contingency plan by the generative artificial intelligence module and sending it to the virtual AI customer service for user confirmation in combination with the context environment formed in the process of interaction with the user and the final operation and maintenance demand data include:

[0012] The generative artificial intelligence module queries the knowledge base according to the final operation and maintenance demand data;

[0013] When there is an existing plan matching the final operation and maintenance demand data in the knowledge base, the matched existing plan is used as the plan and sent to the virtual AI customer service for user confirmation;

[0014] When there is no existing plan matching the final operation and maintenance demand data in the knowledge base, the generative artificial intelligence module combines the contextual environment formed during the interaction with the user, uses a preset artificial intelligence model to generate the plan and sends it to the virtual AI customer service for user confirmation.

[0015] In the intelligent security operation and maintenance method provided by the present invention, when there is an existing plan matching the final operation and maintenance demand data in the knowledge base, the step of using the matched existing plan as the plan and sending it to the virtual AI customer service for user confirmation includes:

[0016] Calculating the similarity between the existing plans in the knowledge base and the final operation and maintenance demand data;

[0017] Sorting multiple existing plans whose similarities meet a set threshold, and feeding them back to the virtual AI customer service for the user to choose;

[0018] If the user agrees, the existing plan selected by the user will be used as the plan; otherwise, the existing plan will be updated according to the user's modification suggestions for the user's reference until the user confirms, and the plan confirmed by the user will be added to the knowledge base for subsequent calls.

[0019] In the intelligent security operation and maintenance method provided by the present invention, after the user confirms the plan, the virtual AI customer service calls the operation and maintenance coordinator, and the operation and maintenance coordinator collects information by running the code of the selected plug-in and calling the corresponding application programming interface, executes the plan, and generates a response report at the same time. The steps include:

[0020] According to the contingency plan, the operation and maintenance coordinator runs the code of the selected plug-in and calls the corresponding application programming interface to receive a response, wherein the response is the record information of the integrated multiple cooperative operation and maintenance applications collecting and executing the corresponding operation and maintenance operations according to the API call;

[0021] The operation and maintenance coordinator binds the response with the input user demand and sends it to the generative artificial intelligence module;

[0022] The generative artificial intelligence module writes a response report using a preset large language model.

[0023] In the intelligent security operation and maintenance method provided by the present invention, after the step of the generative artificial intelligence module writing a response report using a preset large language model, the method further includes:

[0024] Review the generated response reports using pre-set text content analysis models;

[0025] After the response report is reviewed and approved, the response report is sent to the user through the virtual AI customer service, and the corresponding operation and maintenance execution process and results are archived in the form of a report;

[0026] When malicious or inappropriate content is detected, an error message is generated and a prompt message is provided to prompt the response report to be rewritten.

[0027] In the intelligent security operation and maintenance method provided by the present invention, in the step where the operation and maintenance coordinator runs the code of the selected plug-in and calls the corresponding application programming interface to receive a response according to the emergency plan, the emergency plan is decomposed by the distributed parallel grid computing power support platform and handed over to different cluster working nodes for execution through the cluster resource manager. The cluster working nodes will split the tasks into different jobs and distribute them to different computer nodes that support multi-processor parallel computing for processing, and then aggregate the running results step by step upward to generate the response.

[0028] The present invention also provides an intelligent safety operation and maintenance device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the intelligent safety operation and maintenance method as described above are implemented.

[0029] The present invention also provides a storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the intelligent security operation and maintenance method as described above.

[0030] The intelligent security operation and maintenance method provided by the present invention has the following beneficial effects: the present invention is supported by a distributed parallel grid computing power platform, integrates speech recognition, NLP, and image / video analysis technology, provides unified and centralized operation and maintenance management for equipment, and realizes self-service intelligent security operation and maintenance management services; replaces manual operation and maintenance operations with AI analysis and linkage execution, which is conducive to improving operation and maintenance work efficiency, standardizing operation and maintenance processes, and saving operation and maintenance costs; at the same time, under the support of the designed distributed parallel grid computing power platform, AI technology is used to support feature analysis of multimodal data, and the operation and maintenance system is intelligently upgraded, thereby improving the industry's operation and maintenance service level and competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the provided drawings without creative work:

[0032] Figure 1 The figure shows the logical architecture diagram of the intelligent security operation and maintenance system provided by the present invention;

[0033] Figure 2 Shown Figure 1 The logical architecture diagram of the AI ​​intelligent decision-making layer shown;

[0034] Figure 3 Shown is a flow chart of the intelligent security operation and maintenance method provided according to the invention. DETAILED DESCRIPTION

[0035] In order to facilitate understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Typical embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0037] Figure 1The figure shows the logical architecture of the intelligent security operation and maintenance system provided by the present invention. Figure 1 As shown in the figure, the intelligent security operation and maintenance system architecture consists of a resource monitoring and collection layer, a data storage layer, an AI intelligent decision-making layer, and an application display layer. Among them:

[0038] Resource monitoring and collection layer: Through various sensors and multi-source data collection equipment, the parameter information of the normal working state, warning state and fault state of the operation and maintenance equipment is collected and monitored. Through a unified interface protocol and the transmission channel established by the signal system, the monitoring information required by the intelligent operation and maintenance system is uploaded in a unified manner. The uploaded information includes power supply detection information, network traffic information, equipment status information, fault alarm information, etc.

[0039] Data storage layer: stores the data collected by the access aggregation layer and establishes a corresponding database. The database classifies fault information such as alarm information of field equipment, network traffic information, current and voltage analog information, and fault record data, and passes it to the AI ​​intelligent decision-making layer for calculation;

[0040] AI intelligent decision-making layer: This layer is the core layer of this intelligent security operation and maintenance system. Its main function is to provide corresponding intelligent operation and maintenance operations based on user needs by using the intelligent analysis and decision-making capabilities provided by AI under the support of the distributed parallel grid computing platform;

[0041] Application display layer: mainly provides an interface for users to interact with the system and display operation and maintenance results. It can also be used to display the real-time monitoring status of operation and maintenance equipment, equipment warning status, and equipment failure status.

[0042] Figure 2 Shown Figure 1 The logical architecture diagram of the AI ​​intelligent decision-making layer is shown in Figure 1. Figure 2 As shown in the figure, the AI ​​intelligent decision-making layer includes:

[0043] Distributed parallel grid computing power platform: The core function of the computing power platform of the present invention is to decompose tasks using a distributed parallel grid operating system under the support of the underlying multi-level distributed parallel computing (including CPU level, computer level and cluster node level) mechanism, and assign them to different cluster work nodes for operation through the cluster resource manager. The cluster work nodes then split the tasks into different jobs and distribute them to different computer nodes that support multi-processor parallel computing for processing, and then aggregate the operation results step by step upward, thereby greatly improving the computing efficiency, and then providing computing power support for the upper-level AI analysis through the computing power scheduling engine;

[0044] AI analysis module: provides NLP, speech recognition, image analysis, video analysis, and AIGC analysis submodules required for user interaction, as well as AIGC for generating operation and maintenance solutions;

[0045] Intelligent operation and maintenance service module: provides business services required for intelligent operation and maintenance, including system management, event management, problem management, change management, work order management, configuration management, asset management, knowledge base management, and other functions.

[0046] Furthermore, the AI ​​analysis module includes: 1) NLP module: supports API access for natural language processing (NLP) of large language models (LLM), can read, decipher and understand human language, and enables users to interact with it safely using natural language; 2) Speech analysis module: supports automatic speech recognition (ASR), speech synthesis, speech detection, keyword location, translation and audio classification; 3) Image analysis module: supports target recognition, edge detection and automatic classification based on images of the operating status of various devices in operation and maintenance scenarios; 4) Video analysis module: supports common tasks in the field of video understanding in operation and maintenance scenarios, such as action recognition, time-series action detection, spatiotemporal action detection, etc.; 5) AIGC module: supports the generation of operation and maintenance solution content related to the operation and maintenance conditions or guidance input by the user. For example, by entering keywords, descriptions or samples, AIGC can generate matching solutions, and the solution display forms include text, images, audio and video, etc.

[0047] Furthermore, the intelligent operation and maintenance service modules include: 1) System management module: mainly including user management, department management, role management and authority management; 2) Configuration management module: mainly including basic configuration information, configuration management information, configuration attribute information and configuration maintenance information; 3) Equipment management module: mainly through centralized management methods to uniformly manage various types of equipment, applications, databases, etc. in the data center, and to organize, summarize and manage assets such as equipment, applications, and data; 4) Knowledge base management module: mainly used to maintain the entry, approval, use and query of various operation and maintenance solutions; through the accumulation of common fault data, the fault diagnosis knowledge system is used for reasoning and judgment, and the initial knowledge base is formed according to the corresponding maintenance methods of on-site faults, and in the operation and maintenance process, the knowledge base is continuously updated through AIGC; the knowledge base can reserve operation and maintenance knowledge and improve the efficiency and level of operation and maintenance; 5) Problem management module: Problem management is a service management process for the intelligent operation and maintenance system to analyze and solve the root causes of IT resource failures; problem management analyzes the most common or major faults in IT resources with the support of AI to help The user finds the root cause of the failure and generates a change request (RFC), workaround or recommended preventive measures to prevent the failure from happening again, changing passive maintenance to active prevention; 6) Event management module: Event management functions include event reception and recording, event classification and online support, event investigation and diagnosis, event resolution and recovery, event satisfaction survey and event closure; Event management supports multiple event entry methods and supports dispatching methods integrated with other systems; 7) Virtual AI customer service module: Virtual AI customer service provides users with online submission of service requests based on user agents, tracks service status at any time, and provides online feedback on service satisfaction; 8) Coordination management module: Responsible for running the code of the selected plug-in / function and calling the appropriate application programming interface (API) to collect information and take actions to execute the plan; 9) Automatic orchestration management module: Responsible for automatically binding the user's operation and maintenance demand prompts with the response from the API call received by the coordinator; 10) Content review management module: Responsible for reviewing and managing the content of the response report generated by the AIGC module, and using the preset AI model to find any malicious, hate speech and other content in the report.

[0048] Furthermore, the distributed parallel grid computing platform provides the computing power support required by the AI ​​intelligent auxiliary decision-making layer for voice, image, video, NLP and plan generation, including sub-modules that support parallel computing, distributed computing, storage and computing integration, full switching network, space-time association memory and other mechanisms. Among them, parallel support provides support for various forms of parallel computing such as processor parallelism, computer parallelism, cluster node parallelism, etc.; distributed support provides support for distributed computing architectures such as grid computing; storage and computing integration support provides support for storage and computing integration mechanisms based on brain-like models; full switching network support provides support for full switching communication technology to achieve high-speed multi-board and multi-chip communication; space-time association memory support: based on the hippocampal memory mechanism, it supports the coupling of space-time memory, forms the memory of state / operation mapping pairs, and supports self-attention association and long-term and short-term memory mechanisms.

[0049] Figure 3 FIG. 1 is a flow chart of an intelligent security operation and maintenance method provided by an embodiment of the present invention. Figure 3 As shown, based on Figure 1 The intelligent safety operation and maintenance system shown in the figure, the intelligent safety operation and maintenance method comprises the following steps:

[0050] Step S1: Based on the key information of the input user needs, interact with the user through the virtual AI customer service to determine the user's specific operation and maintenance needs;

[0051] Specifically, in one embodiment of the present invention, the operation and maintenance plan or operation requirements input by the user to the user agent module in the form of text or voice are first received. After verifying the user's identity, the user agent module will send the user's input requirements to the background virtual AI customer service. If it is in text form, the virtual customer service directly calls the NLP (natural language processing) module in the AI ​​analysis module for recognition and processing. If it is in voice form, the voice analysis module in the AI ​​analysis module is first called to convert it into the corresponding text, and then the NLP module is called for processing.

[0052] Specifically, in one embodiment of the present invention, the NLP module will extract the key information of the user's input requirements, and interact with the user through the virtual AI customer service to further clarify the user's specific operation and maintenance requirements. During the interaction process, the user can input the images and videos required for the corresponding operation and maintenance, and the virtual AI customer service will call the corresponding image analysis module and video analysis module for targeted processing, extracting the key information in the images and videos for use in interacting with the user and generating specific operation and maintenance requirements.

[0053] Step S2: the virtual AI customer service associates the specific operation and maintenance requirements with the complete list of enabled plug-in functions, generates final operation and maintenance requirement data, and sends it to the generative artificial intelligence module;

[0054] Specifically, in one embodiment of the present invention, after clarifying the user's final specific operation and maintenance requirements, the virtual AI customer service will bundle the clarified user requirements and a complete list of enabled plug-in functions together, and send them to the AIGC (generative artificial intelligence) module, requesting it to formulate a contingency plan to meet the user's request.

[0055] Step S3: combining the context environment formed during the interaction with the user and the final operation and maintenance demand data, the generative artificial intelligence module creates a contingency plan and sends it to the virtual AI customer service for user confirmation;

[0056] Specifically, in one embodiment of the present invention, after the AIGC module receives the user's final operation and maintenance demand data, it combines the contextual environment formed during the interaction with the user, matches the demand with the available operation and maintenance functions, and creates a plan to meet the user's operation and maintenance request, and submits it to the user for confirmation. Among them, when the AIGC module receives the user's final operation and maintenance demand data bundled with a complete list of enabled plug-in functions from the virtual AI customer service, it first queries its own knowledge base to find out whether there is a plan that meets the user's needs. If the existing plans in the knowledge base cannot meet the user's needs, the AIGC module uses the preset AI model to intelligently generate a corresponding operation and maintenance plan in the language that the customer understands (multi-language support) based on the user's needs and the current contextual environment with the support of the computing power platform, and submits it to the virtual AI customer service for user selection. Therefore, step S3 includes:

[0057] Step S31, the generative artificial intelligence module queries the knowledge base according to the final operation and maintenance demand data;

[0058] Step S32: when there is an existing plan matching the final operation and maintenance demand data in the knowledge base, the matched existing plan is used as the plan and sent to the virtual AI customer service for user confirmation;

[0059] Step S33: When there is no existing plan matching the final operation and maintenance demand data in the knowledge base, the generative artificial intelligence module generates the plan by using a preset artificial intelligence model in combination with the context environment formed during the interaction with the user and sends it to the virtual AI customer service for user confirmation;

[0060] Furthermore, in one embodiment of the present invention, when the existing plans in the knowledge base can meet the user's needs, similarity calculation is performed by setting a threshold, and the execution plan that meets the threshold is selected. If there are multiple plans, they are sorted according to relevance and fed back to the virtual AI customer service for the user to choose. Therefore, step S31 includes:

[0061] Step S311, calculating the similarity between the existing plans in the knowledge base and the final operation and maintenance demand data;

[0062] Step S312: sort multiple existing plans whose similarities meet the set threshold, and feed them back to the virtual AI customer service for user selection;

[0063] Step S313: If the user agrees, the existing plan selected by the user is used as the plan; otherwise, the existing plan is updated according to the user's modification suggestions for the user's reference until the user confirms, and the plan confirmed by the user is added to the knowledge base for subsequent calls.

[0064] Furthermore, in one embodiment of the present invention, if the user confirms the proposed contingency plan, the virtual AI client calls the operation and maintenance coordinator to collect information and take actions to implement the contingency plan and generate corresponding logs by running the code of the selected plug-in / function and calling the appropriate application programming interface (API); if the user is not satisfied with the plan, he or she may make an improvement request. After receiving the improvement request, the virtual AI client will update the contingency plan based on the current context and submit it to the user again, and through multiple interactions until a solution that satisfies the user is generated. After the user confirms the solution to the plan, the new plan is added to the plan library for subsequent calls.

[0065] Step S4: After the user confirms the contingency plan, the virtual AI customer service calls the operation and maintenance coordinator, and the operation and maintenance coordinator collects information by running the code of the selected plug-in and calling the corresponding application programming interface, executes the contingency plan, and generates a response report.

[0066] Further, in one embodiment of the present invention, according to the plan generated by the virtual AI customer service, the integrated various cooperative operation and maintenance applications collect information and perform operations according to the API call, and send the response back to the operation and maintenance coordinator. The operation and maintenance coordinator receives the response from the API call, and before sending the final response to the user, it uses the orchestrator to bundle the response with the original demand prompt and send it to the AIGC module. After receiving the response bound to the user's demand, the AIGC module uses the powerful function of its preset LLM (large language model) to write a response report in the language that the customer understands. The generated report is reviewed using a preset text content analysis model, which will find any malicious, hate speech and other content; if there is no problem with the detection, the response report will be sent to the user through the virtual AI customer service, and the corresponding operation and maintenance execution process and results will be formed into a report archive; if malicious or inappropriate content is detected, an error message and prompt information will be generated, and the response report will be returned to regenerate. The user receives the response report sent by the virtual AI customer service through the user agent to complete this operation and maintenance. Therefore, step S4 includes:

[0067] Step S41: According to the emergency plan, the operation and maintenance coordinator runs the code of the selected plug-in and calls the corresponding application programming interface to receive a response, wherein the response is a record of the integrated multiple cooperative operation and maintenance applications collecting and executing corresponding operation and maintenance operations according to the API call; further, the emergency plan is decomposed by the distributed parallel grid computing power platform and handed over to different cluster working nodes for operation through the cluster resource manager. The cluster working nodes will split the tasks into different jobs and distribute them to different computer nodes supporting multi-processor parallel computing for processing, and then aggregate the operation results step by step upward to generate the response;

[0068] Step S42: the operation and maintenance coordinator binds the response with the input user demand and sends it to the generative artificial intelligence module;

[0069] Step S43: The generative artificial intelligence module writes a response report using a preset large language model;

[0070] Step S44: reviewing the generated response report using a preset text content analysis model;

[0071] Step S45: After the response report is reviewed and approved, the response report is sent to the user through the virtual AI customer service, and the corresponding operation and maintenance execution process and results are archived as a report;

[0072] Step S46: When malicious or inappropriate content is detected, an error message is generated and a prompt message is provided to prompt the user to rewrite the response report.

[0073] In view of the current enterprises and institutions, especially the information centers of small and medium-sized enterprises and institutions, daily operation and maintenance work mainly relies on manual work, and the work is heavy, inefficient and costly. The present invention is supported by a distributed parallel grid computing power platform, integrating speech recognition, NLP, and image / video analysis technology to provide unified and centralized operation and maintenance management for equipment, and realize self-service intelligent and safe operation and maintenance management services; AI analysis and linkage execution replace manual operation and maintenance operations, solve the problems caused by insufficient operation and maintenance personnel, such as failure to handle in time, poor user experience, difficulty in accurately locating faults, difficulty in establishing a nationwide service system, and high maintenance costs, which is conducive to improving the efficiency of operation and maintenance work, standardizing the operation and maintenance process, and saving operation and maintenance costs. At the same time, it improves the defects of the existing operation and maintenance system that only analyzes indicators, logs or call chains. Under the support of the designed distributed parallel grid computing power platform, AI technology is used to support the feature analysis of multimodal data, and the operation and maintenance system is intelligently upgraded, which improves the industry's operation and maintenance service level and competitiveness.

[0074] The embodiment of the present invention further provides an intelligent security operation and maintenance device, which may include:

[0075] Memory for storing computer programs;

[0076] The processor can implement the following steps when executing the computer program stored in the above memory:

[0077] Based on the key information of the input user needs, the virtual AI customer service interacts with the user to determine the user's specific operation and maintenance needs; the virtual AI customer service associates the specific operation and maintenance needs with the complete list of enabled plug-in functions, generates final operation and maintenance demand data and sends it to the generative artificial intelligence module; based on the contextual environment formed in the process of interaction with the user and the final operation and maintenance demand data, the generative artificial intelligence module creates a contingency plan and sends it to the virtual AI customer service for user confirmation; after the user confirms the contingency plan, the virtual AI customer service calls the operation and maintenance coordinator, and the operation and maintenance coordinator collects information by running the code of the selected plug-in and calling the corresponding application programming interface, executes the contingency plan, and generates a response report.

[0078] The embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps can be implemented;

[0079] Based on the key information of the input user needs, the virtual AI customer service interacts with the user to determine the user's specific operation and maintenance needs; the virtual AI customer service associates the specific operation and maintenance needs with the complete list of enabled plug-in functions, generates final operation and maintenance demand data and sends it to the generative artificial intelligence module; based on the contextual environment formed in the process of interaction with the user and the final operation and maintenance demand data, the generative artificial intelligence module creates a contingency plan and sends it to the virtual AI customer service for user confirmation; after the user confirms the contingency plan, the virtual AI customer service calls the operation and maintenance coordinator, and the operation and maintenance coordinator collects information by running the code of the selected plug-in and calling the corresponding application programming interface, executes the contingency plan, and generates a response report.

[0080] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM)> a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0081] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0082] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0083] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0084] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.

Claims

1. An intelligent security operation and maintenance method, characterized in that: The following steps are involved: Based on the key information of the input user needs, interact with the user through the virtual AI customer service to determine the user's specific operation and maintenance needs, wherein the user needs are input through text, voice and / or video; The virtual AI customer service associates the specific operation and maintenance requirements with the complete list of enabled plug-in functions, generates final operation and maintenance requirement data, and sends it to the generative artificial intelligence module; In combination with the context environment formed during the interaction with the user and the final operation and maintenance demand data, the generative artificial intelligence module creates a contingency plan and sends it to the virtual AI customer service for user confirmation; After the user confirms the contingency plan, the virtual AI customer service calls the operation and maintenance coordinator, which collects information by running the code of the selected plug-in and calling the corresponding application programming interface, executes the contingency plan, and generates a response report.

2. The intelligent security operation and maintenance method according to claim 1, characterized in that: In combination with the context environment formed during the interaction with the user and the final operation and maintenance demand data, the generative artificial intelligence module creates a contingency plan and sends it to the virtual AI customer service for user confirmation, including: The generative artificial intelligence module queries the knowledge base according to the final operation and maintenance demand data; When there is an existing plan matching the final operation and maintenance demand data in the knowledge base, the matched existing plan is used as the plan and sent to the virtual AI customer service for user confirmation; When there is no existing plan matching the final operation and maintenance demand data in the knowledge base, the generative artificial intelligence module combines the contextual environment formed during the interaction with the user, uses a preset artificial intelligence model to generate the plan and sends it to the virtual AI customer service for user confirmation.

3. The intelligent security operation and maintenance method according to claim 2, characterized in that: When there is an existing plan matching the final operation and maintenance demand data in the knowledge base, the step of using the matched existing plan as the plan and sending it to the virtual AI customer service for user confirmation includes: Calculating the similarity between the existing plans in the knowledge base and the final operation and maintenance demand data; Sorting multiple existing plans whose similarities meet a set threshold, and feeding them back to the virtual AI customer service for the user to choose; If the user agrees, the existing plan selected by the user will be used as the plan; otherwise, the existing plan will be updated according to the user's modification suggestions for the user's reference until the user confirms, and the plan confirmed by the user will be added to the knowledge base for subsequent calls.

4. The intelligent security operation and maintenance method according to claim 1, characterized in that: After the user confirms the contingency plan, the virtual AI customer service calls the operation and maintenance coordinator, and the operation and maintenance coordinator collects information by running the code of the selected plug-in and calling the corresponding application programming interface, executes the contingency plan, and generates a response report at the same time, including the following steps: According to the contingency plan, the operation and maintenance coordinator runs the code of the selected plug-in and calls the corresponding application programming interface to receive a response, wherein the response is the record information of the integrated multiple cooperative operation and maintenance applications collecting and executing the corresponding operation and maintenance operations according to the API call; The operation and maintenance coordinator binds the response with the input user demand and sends it to the generative artificial intelligence module; The generative artificial intelligence module writes a response report using a preset large language model.

5. The intelligent security operation and maintenance method according to claim 1, characterized in that: After the step of the generative artificial intelligence module writing a response report using a preset large language model, the method further includes: Review the generated response reports using pre-set text content analysis models; After the response report is reviewed and approved, the response report is sent to the user through the virtual AI customer service, and the corresponding operation and maintenance execution process and results are archived in the form of a report; When malicious or inappropriate content is detected, an error message is generated and a prompt message is provided to prompt the response report to be rewritten.

6. The intelligent security operation and maintenance method according to claim 5, characterized in that: In the step where the operation and maintenance coordinator runs the code of the selected plug-in and calls the corresponding application programming interface to receive a response according to the plan, the distributed parallel grid computing power support platform decomposes the plan and sends it to different cluster working nodes for execution through the cluster resource manager. The cluster working nodes will split the task into different jobs and distribute them to different computer nodes that support multi-processor parallel computing for processing, and then aggregate the running results step by step upward to generate the response.

7. An intelligent security operation and maintenance device, characterized in that: It includes a processor and a memory, the memory stores a computer program, and when the computer program is executed by the processor, the steps of the intelligent security operation and maintenance method as claimed in any one of claims 1 to 6 are implemented.

8. A storage medium, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the intelligent security operation and maintenance method of any one of claims 1 to 6.

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