Intelligent operation and maintenance method and system based on MCP protocol

Abstract

The invention discloses an intelligent operation and maintenance method and system based on an MCP protocol, and belongs to the crossing field of artificial intelligence and operation and maintenance automation. The system comprises an AI Agent module, a central intelligent analysis engine module, a man-machine interaction module and a safety control module. The method comprises the following steps: collecting system operation data in real time through an AI Agent module, and packaging the system operation data into an MCP protocol format; the central intelligent analysis engine module predicts the load based on an LSTM model, and generates operation and maintenance decision suggestions in combination with log anomaly recognition and reinforcement learning; the man-machine interaction module realizes understanding of user input through a natural language large model, automatically converts the user input into an MCP instruction, and supports multiple rounds of interaction confirmation and instruction verification; the safety control module provides an MCP operation sandbox and semantic instruction verification, and the operation safety is guaranteed. The system can dynamically schedule resources and automatically dispose faults, realizes that non-technical personnel can execute complex operation and maintenance operations, and is suitable for application scenes with high reliability requirements, such as industrial data acquisition and chemical engineering.

Description

Technical Field

[0001] The present invention belongs to the intersection of artificial intelligence and operation and maintenance automation, and specifically relates to an intelligent operation and maintenance method and system based on the MCP (Model Context Protocol) protocol. Background Art

[0002] With the rapid development of emerging technologies such as 5G, the Internet of Things, edge computing, and large models, intelligent operations and maintenance (O&M) has become a crucial tool for improving quality and efficiency in key sectors such as power, industry, manufacturing, and communications. In particular, with the surge in equipment volume and increasingly complex fault types, traditional O&M methods suffer from low efficiency, delayed response, and high misjudgment rates, making them difficult to adapt to the comprehensive real-time, intelligent, and secure demands of the new O&M environment.

[0003] In the prior art, the Chinese invention patent with publication number CN119379263B discloses a multimodal large model of automated intelligent operation and maintenance method and system for power grid dispatching, which constructs a multimodal large model composed of a language processing model, an image recognition model, and an AIGC operation and maintenance model. This method generates a variety of operation and maintenance plan texts by processing input information from different sources, and matches historical operation and maintenance plans based on keywords in the input information to form a three-party confirmation mechanism. When the matching degree is lower than the threshold, the output result is marked as unreliable, thereby improving the credibility of the intelligent operation and maintenance results. The above method effectively improves the controllability and accuracy of the operation and maintenance results, and reduces the review pressure on the operation and maintenance personnel.

[0004] The above technologies have made progress in operation and maintenance strategies, but the following problems still exist: (1) Existing intelligent operation and maintenance systems still have the problem of decision lag. Especially in the context of microservice architecture and large-scale cluster deployment, fault location and processing under complex dependencies still rely mainly on manual analysis of logs and indicator data, which cannot achieve rapid response and closed-loop optimization, and easily leads to system downtime and business interruption. (2) Existing automation systems are mostly based on static rules and preset parameters, lacking dynamic perception and policy adaptation capabilities. They are unable to cope with complex scenarios such as sudden changes in system load and fluctuations in operating status, and are prone to resource allocation imbalance or scheduling delays. (3) Lack of deep correlation analysis capabilities for multi-dimensional data, unable to perform global diagnosis of system health status and generate operation and maintenance recommendations across nodes and services; (4) AI components are mostly used for basic tasks such as anomaly detection and trend prediction. They lack intelligent decision-making mechanisms and deduction capabilities for complex scenarios and are unable to dynamically adjust strategies based on actual conditions. (5) The high-risk operation control mechanism is weak, lacking sufficient multi-round security verification and multi-round interactive confirmation mechanisms, making it easy for risk events such as misoperation and configuration conflicts to occur; (6) Lack of multi-round interaction and intelligent feedback mechanisms. When faced with complex problems, traditional automated operation and maintenance systems often rely on single decision execution and lack flexible feedback and adjustment mechanisms. For complex faults or resource scheduling problems, existing systems are usually unable to conduct in-depth interactions with operation and maintenance personnel, nor can they optimize decisions based on feedback.

[0005] In view of this, it is very necessary to provide an intelligent operation and maintenance method and system based on the MCP protocol to solve the above-mentioned defects in the prior art. Summary of the Invention

[0006] The present invention aims to address existing O&M systems, which suffer from delayed decision-making responses, limited automation capabilities, incomplete system status awareness, insufficient operational security, and a lack of intelligent interaction and feedback optimization mechanisms. To address these technical deficiencies, an intelligent O&M method and system based on the MCP protocol is provided. By integrating natural language parsing, intelligent policy generation, standardized control command conversion, and feedback-driven optimization mechanisms, this method improves the accuracy, response speed, and system adaptability of O&M decisions, enabling efficient, secure, and intelligent O&M process management.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: An intelligent operation and maintenance method based on the MCP protocol: Step 100: Initialization and data collection phase: deploying an AI agent on the target server or service node to collect operational data and encapsulate it into a standard format. Step 200: Intelligent analysis and strategy generation phase, where the central intelligent analysis engine comprehensively processes the collected operational data and generates decision recommendations; Step 300: Human-computer interaction and semantic understanding phase, providing a natural language interaction interface for operation and maintenance personnel, facilitating non-technical users to efficiently participate in operation and maintenance decision-making; Step 400: Security control and policy execution phase, ensuring the legality, security and traceability of all control operations; Step 500: Result feedback and model optimization phase, using the operation results as feedback samples for optimization learning.

[0008] In addition, the present invention also provides an intelligent operation and maintenance system based on the MCP protocol, including an AI Agent module, a central intelligent analysis engine module, a human-computer interaction module and a security control module.

[0009] The AI ​​Agent module is deployed on each server node and can collect real-time operational data such as CPU utilization, memory usage, network indicators, log information, and service call chains, and uniformly encapsulate it into a data structure in the MCP protocol format and report it to the central intelligent analysis engine.

[0010] The central intelligent analysis engine module uses deep learning models (such as LSTM, Transformer, etc.) and traditional algorithms (such as K-means, decision trees, etc.) to extract features, predict trends, detect anomalies and diagnose faults on the collected data, and generate operation and maintenance decision recommendations.

[0011] The human-computer interaction module incorporates a large natural language model. Through semantic parsing, prompt templates, and dialogue enhancement technology, it understands natural language questions or commands entered by users and automatically converts them into control commands that comply with the MCP protocol specification. This module supports multi-round interactive confirmation, command clarification, and key parameter inference, enabling even non-expert users to complete complex operation and maintenance tasks. For complex or high-risk operations, the system provides manual confirmation and adjustment capabilities. Operations and maintenance personnel can review suggestions in the manual review interface and confirm or adjust instructions as needed to ensure accuracy and safety. The system also supports feedback optimization after executing suggestions. Operations and maintenance personnel can use execution results to optimize subsequent decision recommendations, and automatic execution rules can be configured for key scenarios. A semantic command verification mechanism verifies the rationality of commands and potential risks, ensuring that each operation is fully verified. The system possesses a certain degree of strategic flexibility to adapt to the needs of different scenarios and has continuous optimization capabilities. User feedback, execution records, and other information are fed back to the central intelligent analysis engine to enable model parameter updates and adaptive evolution of policy recommendations.

[0012] The security control module incorporates a dual security protection mechanism, including an MCP operation sandbox and semantic instruction verification. Before executing an instruction, simulation testing is performed in the sandbox environment to verify the feasibility and risks of the instruction, ensuring that the instruction will not negatively impact the system. Furthermore, the system supports user interaction and interface access for easy integration.

[0013] The beneficial effects of the present invention are: This system implements conversational O&M control, automatically converting natural language commands into MCP protocol-compliant commands. It supports a multi-round dialogue confirmation mechanism for secondary verification of high-risk operations. It also implements intelligent resource scheduling, using LSTM models to predict service load and dynamically trigger Kubernetes cluster scaling. It uses the MCP protocol to obtain real-time cross-system performance metrics (QPS, latency, and error rate) for automated fault handling. It builds a fault knowledge graph, enabling a closed-loop self-healing system for common problems (such as database connection pool exhaustion). It uses reinforcement learning to optimize handling strategies, effectively reducing mean time to repair (MTTR).

[0014] Deep modeling and trend analysis of multi-dimensional data using a central intelligent analysis engine significantly improves fault detection accuracy and resource optimization efficiency, shortens problem location time, and reduces manual troubleshooting costs, thereby improving decision-making efficiency. Leveraging a large language model and natural language interface, users can efficiently control the system without requiring specialized commands, significantly lowering the operational and maintenance threshold. Dynamic policy adjustments can be made to address changes in business load or unexpected failures, enhancing system adaptability.

[0015] Through the MCP protocol's encapsulation and execution mechanism, the system achieves standardized operations and cross-platform compatibility, facilitating horizontal system expansion and cluster cascade control. Furthermore, the system's built-in security mechanisms, including operation sandboxes, semantic validation, and multi-round confirmations, significantly enhance the security and controllability of high-risk operations, reduce the risk of misoperation, and avoid the cascading problems caused by blind automation, effectively minimizing the risk of misoperation.

[0016] In addition, by forming a learning loop through user feedback, the system has the ability to continuously learn and self-optimize, maintain a high level of intelligent decision-making performance for a long time, provide global optimization capabilities, integrate multi-dimensional data, and provide optimization suggestions for resource scheduling across service nodes, demonstrating the system's global optimization capabilities.

[0017] This application uses the fusion architecture of the MCP protocol and the AI ​​large model to break through the operational technical barriers of traditional operation and maintenance tools; designs a dual security protection mechanism: MCP operation sandbox + semantic instruction verification to ensure the safety of system operations; and improves the success rate of non-technical personnel in completing complex operation and maintenance operations.

[0018] In summary, the intelligent operation and maintenance method based on the MCP protocol provided by the present invention constructs an intelligent operation and maintenance closed loop of perception-analysis-execution-feedback, breaks through the barriers between natural language understanding, AI strategy generation and MCP protocol control, and is suitable for a variety of operation and maintenance scenarios with high requirements for intelligence, automation, security and flexibility, and has significant engineering application value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for the description. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flowchart of an intelligent operation and maintenance method based on the MCP protocol provided by the present invention; Figure 2 This is a functional block diagram of an intelligent operation and maintenance system based on the MCP protocol provided by the present invention; Figure 3 This is a decision flow chart of an intelligent operation and maintenance system based on the MCP protocol provided by the present invention. DETAILED DESCRIPTION

[0021] The present invention will be described in detail below with reference to the accompanying drawings and through specific embodiments. The following embodiments are intended to explain the present invention, but the present invention is not limited to the following implementation modes.

[0022] Example 1: like Figure 1 As shown, the present invention provides an intelligent operation and maintenance method based on the MCP protocol, comprising the following steps: Step 100: Initialization and data collection phase: deploying an AI agent on the target server or service node to collect operational data and encapsulate it into a standard format. Step 101: Start the AI ​​Agent service to continuously monitor the running status of the node, collecting data including but not limited to CPU usage, memory usage, disk I / O, network throughput, interface response time, log information, and call chain; Step 102: After preliminary cleaning and formatting, the collected raw data is converted into a data structure that complies with the MCP (Model Context Protocol) protocol to facilitate standardized communication in subsequent analysis and command control. Step 103: The collected data is uploaded to the central intelligent analysis engine through a secure channel and stored in a database according to data type. Performance data, time series indicators, log events, etc. are stored in different feature buffer pools. Step 200: Intelligent analysis and strategy generation phase, where the central intelligent analysis engine comprehensively processes the collected operational data and generates decision recommendations; Step 300: Human-computer interaction and semantic understanding phase, providing a natural language interaction interface for operation and maintenance personnel, facilitating non-technical users to efficiently participate in operation and maintenance decision-making; Step 400: Security control and policy execution phase, ensuring the legality, security and traceability of all control operations; Step 500: Result feedback and model optimization phase, using the operation results as feedback samples for optimization learning.

[0023] Example 2: like Figure 1 As shown, the present invention provides an intelligent operation and maintenance method based on the MCP protocol, comprising the following steps: Step 100: Initialization and data collection phase: deploying an AI agent on the target server or service node to collect operational data and encapsulate it into a standard format. Step 101: Start the AI ​​Agent service to continuously monitor the running status of the node, collecting data including but not limited to CPU usage, memory usage, disk I / O, network throughput, interface response time, log information, and call chain; Step 102: After preliminary cleaning and formatting, the collected raw data is converted into a data structure that complies with the MCP (Model Context Protocol) protocol to facilitate standardized communication in subsequent analysis and command control. Step 103: The collected data is uploaded to the central intelligent analysis engine through a secure channel and stored in a database according to data type. Performance data, time series indicators, log events, etc. are stored in different feature buffer pools. Step 200: Intelligent analysis and strategy generation phase, where the central intelligent analysis engine comprehensively processes the collected operational data and generates decision recommendations; Step 201: The central intelligent analysis engine uses a built-in prediction model (such as an LSTM network) to perform trend prediction on key indicators to determine whether the system will face performance bottlenecks or resource shortage risks in the future. Step 202: Based on the log pattern recognition algorithm and anomaly detection model, identify potential abnormal behaviors during operation, configure a pre-trained fault classification model, and generate decision recommendations through algorithms such as time series analysis and association rule mining; Step 203: For different types of risks or fault symptoms, the rule engine and reinforcement learning strategy are combined to automatically generate executable prioritized O&M decision recommendations. The recommendations include resource expansion and contraction, fault root cause analysis, configuration optimization, component restart, load balancing adjustment, etc., and each recommendation is assigned a priority and confidence score. Step 204: All decision suggestions are stored in the form of structured data and used for semantic prompts and automatic decision making in subsequent human-computer interaction processes; Step 300: Human-computer interaction and semantic understanding phase, providing a natural language interaction interface for operation and maintenance personnel, facilitating non-technical users to efficiently participate in operation and maintenance decision-making; Step 400: Security control and policy execution phase, ensuring the legality, security and traceability of all control operations; Step 500: Result feedback and model optimization phase, using the operation results as feedback samples for optimization learning.

[0024] Example 3: like Figure 1 As shown, the present invention provides an intelligent operation and maintenance method based on the MCP protocol, comprising the following steps: Step 100: Initialization and data collection phase: deploying an AI agent on the target server or service node to collect operational data and encapsulate it into a standard format. Step 101: Start the AI ​​Agent service to continuously monitor the running status of the node, collecting data including but not limited to CPU usage, memory usage, disk I / O, network throughput, interface response time, log information, and call chain; Step 102: After preliminary cleaning and formatting, the collected raw data is converted into a data structure that complies with the MCP (Model Context Protocol) protocol to facilitate standardized communication in subsequent analysis and command control. Step 103: The collected data is uploaded to the central intelligent analysis engine through a secure channel and stored in a database according to data type. Performance data, time series indicators, log events, etc. are stored in different feature buffer pools. Step 200: Intelligent analysis and strategy generation phase, where the central intelligent analysis engine comprehensively processes the collected operational data and generates decision recommendations; Step 201: The central intelligent analysis engine uses a built-in prediction model (such as an LSTM network) to perform trend prediction on key indicators to determine whether the system will face performance bottlenecks or resource shortage risks in the future. Step 202: Based on the log pattern recognition algorithm and anomaly detection model, identify potential abnormal behaviors during operation, configure a pre-trained fault classification model, and generate decision recommendations through algorithms such as time series analysis and association rule mining; Step 203: For different types of risks or fault symptoms, the rule engine and reinforcement learning strategy are combined to automatically generate executable prioritized O&M decision recommendations. The recommendations include resource expansion and contraction, fault root cause analysis, configuration optimization, component restart, load balancing adjustment, etc., and each recommendation is assigned a priority and confidence score. Step 204: All decision suggestions are stored in the form of structured data and used for semantic prompts and automatic decision making in subsequent human-computer interaction processes; Step 300: Human-computer interaction and semantic understanding phase, providing a natural language interaction interface for operation and maintenance personnel, facilitating non-technical users to efficiently participate in operation and maintenance decision-making; Step 301: Provide a visual interface to display decision suggestions and an interactive interface for manual confirmation or adjustment. Operations personnel confirm the suggestions by inputting and then execute them. The execution results are recorded to optimize subsequent recommendations. Step 302: Use a large language model to perform semantic understanding on the natural language input by the user, and extract the operation and maintenance intent, target component, and operation type. Step 303: Match the analysis results with the operation and maintenance suggestions generated by the central intelligent analysis engine, and automatically construct a control instruction structure in the MCP format, including the control target, execution action, expected effect, and parameter configuration; Step 304: The generated control instruction enters the semantic verification module to check whether the instruction logic is complete and the parameters are legal. The module also assesses the potential risk level based on the current status. Low-risk recommendations can be automatically executed. Step 400: Security control and policy execution phase, ensuring the legality, security and traceability of all control operations; Step 500: Result feedback and model optimization phase, using the operation results as feedback samples for optimization learning.

[0025] Example 4: like Figure 1 As shown, the present invention provides an intelligent operation and maintenance method based on the MCP protocol, comprising the following steps: Step 100: Initialization and data collection phase: deploying an AI agent on the target server or service node to collect operational data and encapsulate it into a standard format. Step 101: Start the AI ​​Agent service to continuously monitor the running status of the node, collecting data including but not limited to CPU usage, memory usage, disk I / O, network throughput, interface response time, log information, and call chain; Step 102: After preliminary cleaning and formatting, the collected raw data is converted into a data structure that complies with the MCP (Model Context Protocol) protocol to facilitate standardized communication in subsequent analysis and command control. Step 103: The collected data is uploaded to the central intelligent analysis engine through a secure channel and stored in a database according to data type. Performance data, time series indicators, log events, etc. are stored in different feature buffer pools. Step 200: Intelligent analysis and strategy generation phase, where the central intelligent analysis engine comprehensively processes the collected operational data and generates decision recommendations; Step 201: The central intelligent analysis engine uses a built-in prediction model (such as an LSTM network) to perform trend prediction on key indicators to determine whether the system will face performance bottlenecks or resource shortage risks in the future. Step 202: Based on the log pattern recognition algorithm and anomaly detection model, identify potential abnormal behaviors during operation, configure a pre-trained fault classification model, and generate decision recommendations through algorithms such as time series analysis and association rule mining; Step 203: For different types of risks or fault symptoms, the rule engine and reinforcement learning strategy are combined to automatically generate executable prioritized O&M decision recommendations. The recommendations include resource expansion and contraction, fault root cause analysis, configuration optimization, component restart, load balancing adjustment, etc., and each recommendation is assigned a priority and confidence score. Step 204: All decision suggestions are stored in the form of structured data and used for semantic prompts and automatic decision making in subsequent human-computer interaction processes; Step 300: Human-computer interaction and semantic understanding phase, providing a natural language interaction interface for operation and maintenance personnel, facilitating non-technical users to efficiently participate in operation and maintenance decision-making; Step 301: Provide a visual interface to display decision suggestions and an interactive interface for manual confirmation or adjustment. Operations personnel confirm the suggestions by inputting and then execute them. The execution results are recorded to optimize subsequent recommendations. Step 302: Use a large language model to perform semantic understanding on the natural language input by the user, and extract the operation and maintenance intent, target component, and operation type. Step 303: Match the analysis results with the operation and maintenance suggestions generated by the central intelligent analysis engine, and automatically construct a control instruction structure in the MCP format, including the control target, execution action, expected effect, and parameter configuration; Step 304: The generated control instruction enters the semantic verification module to check whether the instruction logic is complete and the parameters are legal. The module also assesses the potential risk level based on the current status. Low-risk recommendations can be automatically executed. Step 400: Security control and policy execution phase, ensuring the legality, security and traceability of all control operations; Step 401: For high-risk instructions, such as service offline, data clearing, and migration operations, a multi-round confirmation mechanism is triggered, and semantic interaction confirmation is performed twice or three times with the user. If necessary, the user is required to provide administrator authorization or identity authentication; Step 402: Invoke the MCP sandbox environment to test run the control instructions in a simulation environment, predict the resource impact and state changes after execution, and determine whether there are potential anomalies or failure risks. Step 403: If the security verification passes, the control instruction is officially issued to the target node, and the execution agent completes the corresponding operation and synchronously feeds back the execution status and log; Step 404: Record all control actions, user confirmation processes, execution results, and related data for subsequent model optimization, exception backtracking, or responsibility attribution. Step 500: Result feedback and model optimization phase, using the operation results as feedback samples for optimization learning.

[0026] Example 5: like Figure 1 As shown, the present invention provides an intelligent operation and maintenance method based on the MCP protocol, comprising the following steps: Step 100: Initialization and data collection phase: deploying an AI agent on the target server or service node to collect operational data and encapsulate it into a standard format. Step 101: Start the AI ​​Agent service to continuously monitor the running status of the node, collecting data including but not limited to CPU usage, memory usage, disk I / O, network throughput, interface response time, log information, and call chain; Step 102: After preliminary cleaning and formatting, the collected raw data is converted into a data structure that complies with the MCP (Model Context Protocol) protocol to facilitate standardized communication in subsequent analysis and command control. Step 103: The collected data is uploaded to the central intelligent analysis engine through a secure channel and stored in a database according to data type. Performance data, time series indicators, log events, etc. are stored in different feature buffer pools. Step 200: Intelligent analysis and strategy generation phase, where the central intelligent analysis engine comprehensively processes the collected operational data and generates decision recommendations; Step 201: The central intelligent analysis engine uses a built-in prediction model (such as an LSTM network) to perform trend prediction on key indicators to determine whether the system will face performance bottlenecks or resource shortage risks in the future. Step 202: Based on the log pattern recognition algorithm and anomaly detection model, identify potential abnormal behaviors during operation, configure a pre-trained fault classification model, and generate decision recommendations through algorithms such as time series analysis and association rule mining; Step 203: For different types of risks or fault symptoms, the rule engine and reinforcement learning strategy are combined to automatically generate executable prioritized O&M decision recommendations. The recommendations include resource expansion and contraction, fault root cause analysis, configuration optimization, component restart, load balancing adjustment, etc., and each recommendation is assigned a priority and confidence score. Step 204: All decision suggestions are stored in the form of structured data and used for semantic prompts and automatic decision making in subsequent human-computer interaction processes; Step 300: Human-computer interaction and semantic understanding phase, providing a natural language interaction interface for operation and maintenance personnel, facilitating non-technical users to efficiently participate in operation and maintenance decision-making; Step 301: Provide a visual interface to display decision suggestions and an interactive interface for manual confirmation or adjustment. Operations personnel confirm the suggestions by inputting and then execute them. The execution results are recorded to optimize subsequent recommendations. Step 302: Use a large language model to perform semantic understanding on the natural language input by the user, and extract the operation and maintenance intent, target component, and operation type. Step 303: Match the analysis results with the operation and maintenance suggestions generated by the central intelligent analysis engine, and automatically construct a control instruction structure in the MCP format, including the control target, execution action, expected effect, and parameter configuration; Step 304: The generated control instruction enters the semantic verification module to check whether the instruction logic is complete and the parameters are legal. The module also assesses the potential risk level based on the current status. Low-risk recommendations can be automatically executed. Step 400: Security control and policy execution phase, ensuring the legality, security and traceability of all control operations; Step 401: For high-risk instructions, such as service offline, data clearing, and migration operations, a multi-round confirmation mechanism is triggered, and semantic interaction confirmation is performed twice or three times with the user. If necessary, the user is required to provide administrator authorization or identity authentication; Step 402: Invoke the MCP sandbox environment to test run the control instructions in a simulation environment, predict the resource impact and state changes after execution, and determine whether there are potential anomalies or failure risks. Step 403: If the security verification passes, the control instruction is officially issued to the target node, and the execution agent completes the corresponding operation and synchronously feeds back the execution status and log; Step 404: Record all control actions, user confirmation processes, execution results, and related data for subsequent model optimization, exception backtracking, or responsibility attribution. Step 500: Result feedback and model optimization phase, using the operation results as feedback samples for optimization learning; Step 501: Evaluate whether the operation is successful, collect the changes in status before and after the operation, and determine the effectiveness of the suggestion and the accuracy of the strategy; In step 502, effective feedback data is used to fine-tune the reinforcement learning strategy network or analysis model to improve the accuracy and reliability of the next round of recommendation generation, forming a complete operation and maintenance knowledge closed loop.

[0027] Figure 3This paper describes the decision-making and execution process of an intelligent operation and maintenance system based on the MCP protocol. The system first receives the user's natural language instructions and parses the operation intention. The natural language analysis module extracts the operation requirements, generates corresponding operation instructions, and standardizes them according to the MCP protocol. The instructions are converted into API call sequences, which interact with other systems to perform operations such as capacity expansion or load migration. After the operation is completed, the system records the execution results and provides feedback, reporting success or failure. Finally, the system uses a machine learning optimization module to adjust the decision-making strategy based on historical feedback to improve the accuracy and efficiency of subsequent decisions.

[0028] Example 6: like Figure 2 As shown, the present invention provides an intelligent operation and maintenance system based on the MCP protocol, including: an AI Agent module 1, a central intelligent analysis engine module 2, a human-computer interaction module 3 and a security control module 4, which realizes the full process control of data perception, intelligent analysis and interactive execution of operation and maintenance tasks.

[0029] AI Agent Module 1, deployed on the target server or service node, is responsible for collecting data including but not limited to CPU usage, memory usage, network bandwidth, and log information. This module encapsulates the collected data into a data structure that complies with the MCP protocol and uploads it to the system through a secure channel.

[0030] The central intelligent analysis engine module 2 is used to receive the operating data and process the data based on the deep learning model to generate operation and maintenance decision recommendations including load forecasting, anomaly detection, fault diagnosis, and resource scheduling. The central intelligent analysis engine module performs time series forecasting through the LSTM model, performs anomaly detection on logs through traditional algorithms such as the Transformer deep learning algorithm or K-means clustering, and optimizes resource scheduling strategies through reinforcement learning.

[0031] Human-computer interaction module 3 displays the aforementioned decision recommendations and supports manual confirmation or semi-automated execution by operations and maintenance personnel. Users enter their operational intent through a graphical interface or natural language. The system uses a large language model to identify and understand the input intent and semantics, matching it with the recommended content and automatically generating a control instruction structure that conforms to the MCP protocol format. For low-risk operations, the system directly executes them after user confirmation. For high-risk instructions, the system uses multiple rounds of interactive confirmation and semantic verification, combined with sandbox environment simulation test results, to ensure the legality, safety, and controllability of the operation.

[0032] The security control module 4 is designed with a dual security protection mechanism, including MCP operation sandbox and semantic instruction verification. Before the operation instruction is executed, simulation testing is performed in the sandbox environment to verify the feasibility and risk of the instruction, ensuring that the instruction will not have a negative impact on the system.

[0033] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments are sufficient. The methods disclosed in the embodiments are described briefly because they correspond to the systems disclosed in the embodiments. For relevant details, refer to the method description.

[0034] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0035] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0036] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0037] In addition, the functional modules in the various embodiments of the present invention may be integrated into one processing unit, or each module may exist physically separately, or two or more modules may be integrated into one unit.

[0038] Similarly, each processing unit in each embodiment of the present invention may be integrated into one functional module, or each processing unit may exist physically, or two or more processing units may be integrated into one functional module.

[0039] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0040] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0041] The above disclosure is only a preferred embodiment of the present invention, but the present invention is not limited thereto. Any non-creative changes that can be thought of by those skilled in the art, as well as several improvements and modifications made without departing from the principles of the present invention, should fall within the scope of protection of the present invention.

Claims

1. An intelligent operation and maintenance method based on the MCP protocol, characterized in that: The steps include: Step 100, initialization and data collection phase, deploys AI Agent on the target server or service node to collect operation data and encapsulate it into a standard format; Step 200, intelligent analysis and strategy generation phase, the central intelligent analysis engine comprehensively processes the collected operational data and generates decision recommendations; Step 300, the human-computer interaction and semantic understanding phase, provides a natural language interaction interface for operation and maintenance personnel, facilitating non-technical users to efficiently participate in operation and maintenance decision-making; Step 400, security control and policy execution phase, ensures the legality, security and traceability of all control operations; Step 500, the result feedback and model optimization stage, uses the operation results as feedback samples for optimization learning.

2. The intelligent operation and maintenance method according to claim 1, wherein: The step 100 specifically includes: Step 101: Start the AI ​​Agent service, continuously monitor the running status of the node, and collect CPU usage, memory usage, disk I / O, network throughput, interface response time, log information, and call chain data; Step 102: After preliminary cleaning and format unification, the collected raw data is converted into a data structure that complies with the MCP protocol to facilitate standardized communication in subsequent analysis and command control. In step 103, the collected data is uploaded to the central intelligent analysis engine through a secure channel and stored in the database according to data type classification. Performance data, time series indicators, and log events are respectively stored in different feature cache pools.

3. The intelligent operation and maintenance method according to claim 1, wherein: The step 200 specifically includes: Step 201: The central intelligent analysis engine uses the built-in prediction model to predict the trend of key indicators and determine whether the system will face performance bottlenecks or resource shortage risks in the future. Step 202: Based on the log pattern recognition algorithm and anomaly detection model, identify potential abnormal behaviors during operation, configure a pre-trained fault classification model, and generate decision recommendations through time series analysis and association rule mining algorithms; Step 203: For different types of risks or failure symptoms, the rule engine and reinforcement learning strategy are combined to automatically generate executable prioritized operation and maintenance decision recommendations. The recommendations include resource expansion and contraction, failure root cause analysis, configuration optimization, component restart, and load balancing adjustment. Each recommendation is assigned a priority and confidence score. In step 204 , all decision suggestions are stored in the form of structured data and used for semantic prompts and automatic decision making in subsequent human-computer interaction processes.

4. The intelligent operation and maintenance method according to claim 1, wherein: The step 300 specifically includes: Step 301: Provide a visual interface to display decision suggestions and an interactive interface for manual confirmation or adjustment. Operations personnel confirm the suggestions by inputting and then execute them. The execution results are recorded to optimize subsequent recommendations. Step 302: Use a large language model to perform semantic understanding on the natural language input by the user, and extract the operation and maintenance intent, target component, and operation type. Step 303: Match the analysis results with the operation and maintenance suggestions generated by the central intelligent analysis engine, and automatically construct a control instruction structure in the MCP format, including the control target, execution action, expected effect, and parameter configuration; In step 304, the generated control instruction enters the semantic verification module to check whether the instruction logic is complete and the parameters are legal, and evaluate the potential risk level based on the current status; low-risk suggestions can be automatically executed.

5. The intelligent operation and maintenance method according to claim 1, wherein: The step 400 specifically includes: Step 401: For high-risk instructions, such as service offline, data clearing, and migration operations, a multi-round confirmation mechanism is triggered, requiring a second or third semantic interaction confirmation with the user, requiring the user to provide administrator authorization or identity authentication; Step 402: Invoke the MCP sandbox environment to test run the control instructions in a simulation environment, predict the resource impact and state changes after execution, and determine whether there are potential anomalies or failure risks. Step 403: After the security verification is passed, the control instruction is officially sent to the target node. The execution agent completes the corresponding operation and synchronously feeds back the execution status and log. Step 404: Record all control actions, user confirmation processes, execution results, and related data for subsequent model optimization, exception backtracking, or responsibility attribution.

6. The intelligent operation and maintenance method according to claim 1, wherein: The step 500 specifically includes: Step 501: Evaluate whether the operation is successful, collect the changes in status before and after the operation, and determine the effectiveness of the suggestion and the accuracy of the strategy; In step 502, effective feedback data is used to fine-tune the reinforcement learning strategy network or analysis model to improve the accuracy and reliability of the next round of recommendation generation, forming a complete operation and maintenance knowledge closed loop.

7. The intelligent operation and maintenance method according to claim 1, wherein: The central intelligent analysis engine uses a combination of deep learning models and traditional algorithms, and can generate customized strategies for different operating environments and fault types. Through multi-dimensional data correlation analysis, it generates global optimization suggestions. The optimization suggestions cover data analysis and resource scheduling across nodes and services, ensuring the optimization of the overall system performance. The AI ​​Agent can continuously monitor the system operation status and adjust the data collection strategy in real time based on dynamically changing data to ensure the integrity and real-time nature of the data. Human-computer interaction provides a multi-round confirmation mechanism to ensure that operation and maintenance personnel can conduct sufficient review and confirmation when performing high-risk operations or important decisions. The method supports the continuous optimization of generated decision suggestions based on user feedback, and improves the accuracy and reliability of subsequent decisions through a closed-loop learning model. The method standardizes data transmission between modules through the MCP protocol to ensure cross-platform data compatibility and operational consistency.

8. The intelligent operation and maintenance method according to claim 1, wherein: The decision-making process of the method is as follows: after receiving a monitoring alarm or user instruction, the input content is first parsed into natural language. If the parsing fails, the user is prompted to clarify the semantics through interaction to ensure that the operation intention is clearly obtained. If the parsing is successful, the MCP protocol matching process is entered to determine whether the input is a standardized operation instruction. For standard operations, the corresponding API call sequence is directly generated for execution. For complex or unclearly classified scenarios, the pre-trained AI prediction model is called to perform strategy deduction and decision generation. After the matching is completed, the resource scheduling action is executed according to the generated control instruction. After the instruction is executed, the status change and result feedback are collected to determine whether the established service level target is achieved. If the goal is achieved, the success log of the operation is recorded and the process ends. If not, the compensation mechanism is triggered and a prompt is issued to the user, notifying manual intervention to complete the problem resolution.

9. An intelligent operation and maintenance system based on the MCP protocol, characterized in that: The system adopts an intelligent operation and maintenance method based on the MCP protocol as described in any one of claims 1 to 8; the system comprises: an AI Agent module (1), a central intelligent analysis engine module (2), a human-computer interaction module (3) and a security control module (4).

10. The system according to claim 9, characterized in that The AI ​​Agent module (1) is deployed on the target server or service node to collect operational data such as CPU usage, memory usage, network bandwidth, and log content; the collected data is encapsulated into a data structure that complies with the MCP protocol and uploaded to the system through a secure channel; The central intelligent analysis engine module (2) is used to receive the operation data and process the data based on the deep learning model to generate operation and maintenance decision suggestions including load prediction, anomaly detection, fault diagnosis, and resource scheduling, wherein the central intelligent analysis engine module performs time series prediction through the LSTM model, performs anomaly detection on the log through the Transformer deep learning algorithm or the K-means clustering traditional algorithm, and optimizes the resource scheduling strategy through reinforcement learning; The human-computer interaction module (3) is used to display the above-mentioned decision suggestions and support manual confirmation or semi-automatic execution by operation and maintenance personnel; the user inputs the operation intention through a graphical interface or natural language, and the system uses a large language model to perform intention recognition and semantic understanding of the input sentence, and matches it with the suggested content, and automatically generates a control instruction structure that conforms to the MCP protocol format; for low-risk operations, the system can directly execute after user confirmation; for high-risk instructions, the system will use multiple rounds of interactive confirmation and semantic verification mechanisms, combined with sandbox environment simulation test results, to ensure that the operation is legal, safe, and controllable; The security control module (4) designs a dual security protection mechanism, including MCP operation sandbox and semantic instruction verification. Before the operation instruction is executed, simulation testing is performed in the sandbox environment to verify the feasibility and risk of the instruction and ensure that the instruction will not have a negative impact on the system.

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