Automatic server operation and maintenance method and system based on AI technology

Abstract

The invention is suitable for the technical field of automatic operation and maintenance, and provides a server automatic operation and maintenance method and system based on an AI technology. According to the invention, multi-source data acquisition and processing are carried out on the server; trend prediction and anomaly detection are carried out on the multi-source processing data, and whether a potential anomaly trend exists or not is judged; when the potential abnormal trend exists, operation and maintenance cost and operation and maintenance risk analysis is carried out based on the AI technology; after the security verification is passed, performing automatic actual operation and maintenance execution; and according to the comparison record of operation and maintenance execution, generating and visually displaying an operation and maintenance feedback report. Through trend prediction and anomaly detection, problems in the operation process of the server can be found in time, the operation and maintenance response speed is remarkably improved, the fault interruption time is shortened, intelligent operation and maintenance can replace part of manual work, so that the labor cost of a logistics park is reduced, operation and maintenance faults are automatically processed, repeated events can be rapidly processed, and the operation and maintenance efficiency is improved. The operation efficiency can be remarkably improved, and the manual processing time is shortened.

Description

Technical Field

[0001] The present invention relates to the field of automated operation and maintenance technology, and in particular to an AI-based server automated operation and maintenance method and system. Background Art

[0002] The server operation and maintenance of coal logistics parks usually have the following problems:

[0003] (1) In the existing server operation and maintenance management, manual operation and maintenance are usually used to handle problems. A large amount of server operation information monitoring is required, which leads to delays in timely detection and handling of problems. It has limitations when facing complex and diverse service problems, requires a lot of manpower to maintain, and is relatively costly.

[0004] (2) Existing operation and maintenance methods have limited ability to understand system abnormalities and provide accurate problem diagnosis. In actual applications, server abnormalities and failures often cause various problems in the production environment. Traditional operation and maintenance methods lack in-depth understanding and are unable to solve problems in a timely and effective manner. They can only manually handle and troubleshoot problems based on information feedback from production failures.

[0005] (3) The existing server operation and maintenance has a low ability to handle repeated events, and the operation and maintenance personnel are required to frequently intervene and operate the server. Large-scale manpower operations are prone to misjudgments and misoperations, which pose certain risks to the stability and security of the server operation. Summary of the Invention

[0006] The purpose of the embodiments of the present invention is to provide a server automated operation and maintenance method and system based on AI technology, aiming to solve the technical problems existing in the existing technology mentioned in the background technology.

[0007] The embodiment of the present invention is implemented as follows:

[0008] An AI-based automated server operation and maintenance method, the method specifically comprising the following steps:

[0009] Collect and process multi-source data on the server to obtain multi-source processed data;

[0010] According to a plurality of preset key indicators, trend prediction and anomaly detection are performed on the multi-source processed data to determine whether there is a potential abnormal trend;

[0011] When there are potential abnormal trends, AI technology is used to analyze operation and maintenance costs and risks, and to plan and optimize operation and maintenance strategies;

[0012] Perform security verification on the optimized operation and maintenance strategy, and after the security verification passes, automatically perform actual operation and maintenance on the optimized operation and maintenance strategy;

[0013] Compare and record the operation and maintenance execution according to multiple preset business indicators, and generate and visualize the operation and maintenance feedback report.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the multi-source data collection and processing on the server to obtain the multi-source processed data specifically includes the following steps:

[0015] Conduct environmental monitoring of the server room and obtain environmental monitoring data;

[0016] Perform hardware monitoring on the server and obtain hardware monitoring data;

[0017] Extract the server's operation logs to obtain operation monitoring data;

[0018] Anomalies are identified and filtered on the environmental monitoring data, the hardware monitoring data, and the operation monitoring data to generate multi-source processed data.

[0019] As a further limitation of the technical solution of the embodiment of the present invention, the performing trend prediction and anomaly detection on the multi-source processed data according to a plurality of preset key indicators to determine whether there is a potential abnormal trend specifically includes the following steps:

[0020] Extracting multi-source key data from the multi-source processed data according to a plurality of preset key indicators;

[0021] Performing trend prediction on the multi-source key data to obtain trend prediction results;

[0022] Identify the trend prediction results to determine whether there is a potential abnormal trend.

[0023] As a further limitation of the technical solution of the embodiment of the present invention, when there is a potential abnormal trend, performing operation and maintenance cost and operation and maintenance risk analysis based on AI technology and planning an optimized operation and maintenance strategy specifically includes the following steps:

[0024] When there are potential abnormal trends, basic operation and maintenance planning is carried out to generate multiple basic operation and maintenance strategies;

[0025] Based on AI technology, the operation and maintenance cost of multiple basic operation and maintenance strategies is analyzed to determine the operation and maintenance costs of multiple strategies;

[0026] Based on AI technology, an operation and maintenance risk analysis is performed on multiple basic operation and maintenance strategies to determine the operation and maintenance risks of multiple strategies;

[0027] Comparing and arranging the plurality of basic operation and maintenance strategies according to the plurality of strategy operation and maintenance costs and the plurality of strategy operation and maintenance risks, and recording comparison and arrangement information;

[0028] An optimized operation and maintenance strategy is selected from the basic operation and maintenance strategies according to the comparison and arrangement information.

[0029] As a further limitation of the technical solution of the embodiment of the present invention, the security verification of the optimized operation and maintenance strategy and, after the security verification passes, the automated actual operation and maintenance execution of the optimized operation and maintenance strategy specifically include the following steps:

[0030] Build a virtual verification environment;

[0031] In the virtual verification environment, security verification and recording of the optimized operation and maintenance strategy are performed to obtain security verification results;

[0032] Based on the security verification results, partially deploy and verify the effectiveness of the optimized operation and maintenance strategy to obtain an effectiveness verification result;

[0033] According to the effect verification result, after the effect verification is passed, the optimized operation and maintenance strategy is fully deployed and automated actual operation and maintenance is executed.

[0034] As a further limitation of the technical solution of the embodiment of the present invention, the comparative recording of operation and maintenance execution according to multiple preset business indicators and the generation and visual display of the operation and maintenance feedback report specifically include the following steps:

[0035] Compare and record the operation and maintenance execution according to multiple preset business indicators and generate comparison record information;

[0036] Standardize and organize the comparison record information and generate an operation and maintenance feedback report;

[0037] Get the management feedback address;

[0038] The operation and maintenance feedback report is visually displayed according to the management feedback address.

[0039] An AI-based server automated operation and maintenance system, comprising a multi-source data acquisition module, a trend anomaly detection module, an operation and maintenance strategy planning module, an operation and maintenance verification execution module, and an operation and maintenance feedback processing module, wherein:

[0040] Multi-source data acquisition module, used to collect and process multi-source data from the server and obtain multi-source processed data;

[0041] A trend anomaly detection module is used to perform trend prediction and anomaly detection on the multi-source processed data according to a plurality of preset key indicators to determine whether there is a potential abnormal trend;

[0042] The operation and maintenance strategy planning module is used to analyze operation and maintenance costs and risks based on AI technology when there are potential abnormal trends, and to plan and optimize operation and maintenance strategies;

[0043] An operation and maintenance verification execution module is used to perform security verification on the optimized operation and maintenance strategy, and after the security verification passes, automatically perform actual operation and maintenance on the optimized operation and maintenance strategy;

[0044] The operation and maintenance feedback processing module is used to compare and record operation and maintenance execution according to multiple preset business indicators, and generate and visually display operation and maintenance feedback reports.

[0045] As a further limitation of the technical solution of the embodiment of the present invention, the multi-source data acquisition module specifically includes:

[0046] Environmental monitoring unit, used to monitor the environment of the server room and obtain environmental monitoring data;

[0047] Hardware monitoring unit, used to monitor the server hardware and obtain hardware monitoring data;

[0048] A log extraction unit is used to extract the operation log of the server and obtain operation monitoring data;

[0049] The anomaly identification and filtering unit is used to identify and filter anomalies of the environmental monitoring data, the hardware monitoring data and the operation monitoring data to generate multi-source processing data.

[0050] As a further limitation of the technical solution of the embodiment of the present invention, the operation and maintenance strategy planning module specifically includes:

[0051] The basic operation and maintenance planning unit is used to carry out basic operation and maintenance planning when there are potential abnormal trends and generate multiple basic operation and maintenance strategies;

[0052] An operation and maintenance cost analysis unit, configured to perform operation and maintenance cost analysis on the plurality of basic operation and maintenance strategies based on AI technology, and determine the operation and maintenance costs of the plurality of strategies;

[0053] An operation and maintenance risk analysis unit, configured to perform operation and maintenance risk analysis on the plurality of basic operation and maintenance strategies based on AI technology, and determine the operation and maintenance risks of the plurality of strategies;

[0054] a comparison and ranking unit, configured to compare and rank the plurality of basic operation and maintenance strategies according to the plurality of strategy operation and maintenance costs and the plurality of strategy operation and maintenance risks, and record comparison and ranking information;

[0055] An optimization selection unit is used to select an optimized operation and maintenance strategy from the basic operation and maintenance strategies according to the comparison arrangement information.

[0056] As a further limitation of the technical solution of the embodiment of the present invention, the operation and maintenance verification execution module specifically includes:

[0057] An environment construction unit, used for constructing a virtual verification environment;

[0058] A security verification unit is used to perform security verification and record the optimized operation and maintenance strategy in the virtual verification environment to obtain a security verification result;

[0059] A partial deployment unit, configured to perform partial deployment and effect verification on the optimized operation and maintenance strategy according to the security verification result, and obtain an effect verification result;

[0060] The complete deployment unit is used to perform automated actual operation and maintenance execution of the optimized operation and maintenance strategy in full deployment according to the effect verification result after the effect verification is passed.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The embodiments of the present invention collect and process multi-source data from the server; perform trend prediction and anomaly detection on the multi-source processed data to determine whether there are potential abnormal trends; when there are potential abnormal trends, conduct operation and maintenance cost and operation and maintenance risk analysis based on AI technology; after passing security verification, perform automated actual operation and maintenance execution; compare and record the operation and maintenance execution, and generate and visualize operation and maintenance feedback reports. Through trend prediction and anomaly detection, problems in the server operation process can be discovered in a timely manner, significantly improving the operation and maintenance response speed and reducing the interruption time of failures. Intelligent operation and maintenance can replace some manual work, thereby reducing the labor cost of the logistics park. By automating the processing of operation and maintenance failures and being able to quickly handle repeated events, it can significantly improve operational efficiency and reduce manual processing time. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flowchart of a server automated operation and maintenance method based on AI technology provided by an embodiment of the present invention is shown;

[0064] Figure 2 A flowchart of obtaining multi-source processing data in a method provided by an embodiment of the present invention is shown;

[0065] Figure 3 A flowchart of trend prediction and anomaly detection in the method provided by an embodiment of the present invention is shown;

[0066] Figure 4 A flowchart of analyzing operation and maintenance costs and risks in the method provided by an embodiment of the present invention is shown;

[0067] Figure 5 A flowchart showing the automated actual operation and maintenance execution in the method provided by an embodiment of the present invention is shown;

[0068] Figure 6A flowchart showing comparison records of operation and maintenance execution in the method provided by an embodiment of the present invention is shown;

[0069] Figure 7 The following is an application architecture diagram of a server automated operation and maintenance system based on AI technology provided by an embodiment of the present invention;

[0070] Figure 8 Shows a structural block diagram of a multi-source data acquisition module in a system provided by an embodiment of the present invention;

[0071] Figure 9 It shows a structural block diagram of the operation and maintenance strategy planning module in the system provided by an embodiment of the present invention;

[0072] Figure 10 The structure block diagram of the operation and maintenance verification execution module in the system provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0074] It is understandable that the server operation and maintenance of coal logistics parks usually have the following problems: (1) In the existing server operation and maintenance management, manual operation and maintenance are usually used to handle problems. A large amount of server operation information is monitored, and there are delays in timely detection and handling of problems. It shows limitations when facing complex and diverse service problems. It requires a lot of manpower to maintain and the cost is relatively high; (2) The existing operation and maintenance methods are limited in their ability to understand system abnormalities and provide accurate problem judgments. In actual applications, server abnormalities and failures often cause various problems to the production environment. Traditional operation and maintenance methods lack in-depth understanding capabilities, resulting in the inability to solve problems in a timely and effective manner. They can only manually handle and troubleshoot problems based on information feedback from production failures; (3) The existing server operation and maintenance has a low ability to handle repeated events, requiring operation and maintenance personnel to frequently interfere with and operate the server. A large amount of manual operation is prone to misjudgment and misoperation, which poses certain risks to the stability and security of server operation.

[0075] To solve the above problems, the embodiment of the present invention discloses a server automated operation and maintenance method and system based on AI technology. The method collects and processes multi-source data from the server to obtain multi-source processed data; performs trend prediction and anomaly detection on the multi-source processed data according to multiple preset key indicators to determine whether there is a potential abnormal trend; when there is a potential abnormal trend, conducts operation and maintenance cost and operation and maintenance risk analysis based on AI technology, and plans an optimized operation and maintenance strategy; performs security verification on the optimized operation and maintenance strategy, and after the security verification passes, performs automated actual operation and maintenance execution on the optimized operation and maintenance strategy; performs comparative records of operation and maintenance execution according to multiple preset business indicators, and generates and visually displays an operation and maintenance feedback report. Through trend prediction and anomaly detection, problems in the server operation process can be discovered in a timely manner, significantly improving the operation and maintenance response speed and reducing the fault interruption time. Intelligent operation and maintenance can replace some manual work, thereby reducing the labor cost of the logistics park. By automating the processing of operation and maintenance failures and being able to quickly handle repeated events, it can significantly improve operational efficiency and reduce manual processing time.

[0076] Specifically, Figure 1 A flowchart of an AI-based automated server operation and maintenance method provided by an embodiment of the present invention is shown.

[0077] In a preferred embodiment of the present invention, a server automated operation and maintenance method based on AI technology comprises the following steps:

[0078] Step S101: collect and process multi-source data on the server to obtain multi-source processed data.

[0079] In an embodiment of the present invention, the temperature, humidity, smoke and other environmental conditions of the server room are monitored to obtain environmental monitoring data, and the hardware conditions of the server, such as CPU / GPU temperature, fan speed, power consumption, etc., are monitored to obtain hardware monitoring data, and logs of the server are extracted to obtain operation monitoring data. Then, anomalies are identified and filtered for the environmental monitoring data, hardware monitoring data and operation monitoring data to generate multi-source processing data.

[0080] It is understandable that the operation monitoring data extracted from the logs may include data such as order processing volume and inventory turnover rate extracted from the log contents of the WMS (warehouse management system) and TMS (transportation management system) of the coal logistics park.

[0081] Specifically, Figure 2 A flow chart of obtaining multi-source processing data in the method provided by an embodiment of the present invention is shown.

[0082] In another preferred embodiment of the present invention, the step of collecting and processing multi-source data from a server to obtain multi-source processed data specifically includes the following steps:

[0083] Step S1011: Perform environmental monitoring on the server room to obtain environmental monitoring data.

[0084] Step S1012: Perform hardware monitoring on the server to obtain hardware monitoring data.

[0085] Step S1013: extract the operation log of the server to obtain operation monitoring data.

[0086] Step S1014: perform abnormality identification and filtering on the environmental monitoring data, the hardware monitoring data, and the operation monitoring data to generate multi-source processed data.

[0087] Furthermore, the server automated operation and maintenance method based on AI technology also includes the following steps:

[0088] Step S102: performing trend prediction and anomaly detection on the multi-source processed data according to a plurality of preset key indicators to determine whether there is a potential abnormal trend.

[0089] In an embodiment of the present invention, according to multiple preset key indicators (for example, server temperature, CPU utilization, log problems, etc.), multi-source key data is extracted from multi-source processing data, and then the LSTM neural network is trained through historical data to perform trend prediction on the multi-source key data, obtain trend prediction results, identify the trend prediction results, and determine whether there is a potential abnormal trend.

[0090] Specifically, Figure 3 A flow chart of trend prediction and anomaly detection in the method provided by an embodiment of the present invention is shown.

[0091] In another preferred embodiment of the present invention, the trend prediction and anomaly detection of the multi-source processed data according to a plurality of preset key indicators to determine whether there is a potential abnormal trend specifically includes the following steps:

[0092] Step S1021 : extracting multi-source key data from the multi-source processed data according to a plurality of preset key indicators.

[0093] Step S1022: Perform trend prediction on the multi-source key data to obtain trend prediction results.

[0094] Step S1023: Identify the trend prediction result to determine whether it has a potential abnormal trend.

[0095] Furthermore, the server automated operation and maintenance method based on AI technology also includes the following steps:

[0096] Step S103: When there is a potential abnormal trend, operation and maintenance cost and risk analysis is performed based on AI technology to plan and optimize operation and maintenance strategies.

[0097] In an embodiment of the present invention, in the case of potential abnormal trends, basic operation and maintenance planning is carried out to generate multiple basic operation and maintenance strategies. Then, based on AI technology, operation and maintenance cost analysis is performed on the multiple basic operation and maintenance strategies to determine the operation and maintenance costs of the multiple strategies. Based on AI technology, operation and maintenance risk analysis is performed on the multiple basic operation and maintenance strategies to determine the operation and maintenance risks of the multiple strategies. Then, according to the operation and maintenance costs and the operation and maintenance risks of the multiple strategies, the multiple basic operation and maintenance strategies are compared and arranged, and the comparison and arrangement information is recorded. According to the comparison and arrangement information, the basic operation and maintenance strategy ranked first is selected from the multiple basic operation and maintenance strategies and marked as the optimized operation and maintenance strategy.

[0098] It is understandable that operation and maintenance cost analysis includes analysis of server power consumption, downtime losses, etc.; operation and maintenance risk analysis can be analysis of data loss.

[0099] Specifically, Figure 4 A flow chart showing operation and maintenance cost and operation and maintenance risk analysis in the method provided by an embodiment of the present invention is shown.

[0100] In another preferred embodiment of the present invention, when there is a potential abnormal trend, performing operation and maintenance cost and operation and maintenance risk analysis based on AI technology and planning an optimized operation and maintenance strategy specifically includes the following steps:

[0101] Step S1031: When there is a potential abnormal trend, basic operation and maintenance planning is performed to generate multiple basic operation and maintenance strategies.

[0102] Step S1032: Based on AI technology, perform operation and maintenance cost analysis on the multiple basic operation and maintenance strategies to determine the operation and maintenance costs of the multiple strategies.

[0103] Step S1033: Based on AI technology, perform operation and maintenance risk analysis on the multiple basic operation and maintenance strategies to determine the operation and maintenance risks of multiple strategies.

[0104] Step S1034: compare and rank the plurality of basic operation and maintenance strategies according to the plurality of strategy operation and maintenance costs and the plurality of strategy operation and maintenance risks, and record comparison and ranking information.

[0105] Step S1035: Select an optimized operation and maintenance strategy from the basic operation and maintenance strategies based on the comparison and arrangement information.

[0106] Furthermore, the server automated operation and maintenance method based on AI technology also includes the following steps:

[0107] Step S104: perform security verification on the optimized operation and maintenance strategy, and after the security verification passes, perform automated actual operation and maintenance execution on the optimized operation and maintenance strategy.

[0108] In an embodiment of the present invention, a virtual verification environment is constructed, in which the optimized operation and maintenance strategy is security verified (for example: simulating data consistency after disk replacement) and recorded to obtain a security verification result. Based on the security verification result, the optimized operation and maintenance strategy is partially deployed (for example: deploying 5% of the servers) and the effect is verified to obtain the effect verification result. Then, based on the effect verification result, after the effect verification is passed, the optimized operation and maintenance strategy is fully deployed for automated actual operation and maintenance execution. The entire process is automated and does not require manual work, thereby reducing the labor cost of the logistics park, reducing manual processing time, and significantly improving the operational efficiency of the coal logistics park.

[0109] Specifically, Figure 5 A flowchart of automated actual operation and maintenance execution in the method provided by an embodiment of the present invention is shown.

[0110] Among them, in another preferred embodiment provided by the present invention, the security verification of the optimized operation and maintenance strategy, and after the security verification passes, the automated actual operation and maintenance execution of the optimized operation and maintenance strategy specifically includes the following steps:

[0111] Step S1041: Build a virtual verification environment.

[0112] Step S1042: In the virtual verification environment, perform security verification and record the optimized operation and maintenance strategy to obtain a security verification result.

[0113] Step S1043: Based on the security verification result, partially deploy and verify the effect of the optimized operation and maintenance strategy to obtain the effect verification result.

[0114] Step S1044: Based on the effect verification result, after the effect verification is passed, the optimized operation and maintenance strategy is fully deployed and automated actual operation and maintenance is executed.

[0115] Furthermore, the server automated operation and maintenance method based on AI technology also includes the following steps:

[0116] Step S105: perform comparative records of operation and maintenance execution according to a plurality of preset business indicators, and generate and visually display an operation and maintenance feedback report.

[0117] In an embodiment of the present invention, a comparative record of operation and maintenance execution is performed according to a plurality of preset business indicators (for example, processing success rate, server downtime, etc.), and comparative record information is generated. Then, the comparative record information is standardized and organized according to a preset standardized report template to generate an operation and maintenance feedback report, and a management feedback address is obtained. According to the management feedback address, the operation and maintenance feedback report is visually displayed.

[0118] Specifically, Figure 6 A flowchart of comparison records of operation and maintenance execution in the method provided by an embodiment of the present invention is shown.

[0119] In another preferred embodiment of the present invention, the comparative recording of operation and maintenance execution according to multiple preset business indicators and the generation and visual display of the operation and maintenance feedback report specifically include the following steps:

[0120] Step S1051: Perform comparative records of operation and maintenance execution according to a plurality of preset business indicators to generate comparative record information.

[0121] Step S1052: Standardize and organize the comparison record information to generate an operation and maintenance feedback report.

[0122] Step S1053: Obtain the management feedback address.

[0123] Step S1054: Visually display the operation and maintenance feedback report according to the management feedback address.

[0124] Further, Figure 7 The application architecture diagram of the server automated operation and maintenance system based on AI technology provided by an embodiment of the present invention is shown.

[0125] Specifically, in another preferred embodiment provided by the present invention, a server automated operation and maintenance system based on AI technology includes:

[0126] The multi-source data acquisition module 101 is used to collect and process multi-source data from the server and obtain multi-source processed data.

[0127] In an embodiment of the present invention, the multi-source data acquisition module 101 monitors the environmental conditions of the server room, such as temperature, humidity, and smoke, to obtain environmental monitoring data, and monitors the hardware conditions of the server, such as CPU / GPU temperature, fan speed, and power consumption, to obtain hardware monitoring data, and extracts logs from the server to obtain operation monitoring data, and then identifies and filters anomalies in the environmental monitoring data, hardware monitoring data, and operation monitoring data to generate multi-source processed data.

[0128] Specifically, Figure 8FIG. 1 shows a structural block diagram of the multi-source data acquisition module 101 in the system provided by an embodiment of the present invention.

[0129] In another preferred embodiment of the present invention, the multi-source data acquisition module 101 specifically includes:

[0130] The environment monitoring unit 1011 is used to perform environment monitoring on the server room and obtain environment monitoring data.

[0131] The hardware monitoring unit 1012 is used to perform hardware monitoring on the server and obtain hardware monitoring data.

[0132] The log extraction unit 1013 is used to extract the operation log of the server and obtain operation monitoring data.

[0133] The abnormality identification and filtering unit 1014 is used to identify and filter abnormalities of the environmental monitoring data, the hardware monitoring data, and the operation monitoring data to generate multi-source processing data.

[0134] Furthermore, the server automated operation and maintenance system based on AI technology also includes:

[0135] The trend anomaly detection module 102 is used to perform trend prediction and anomaly detection on the multi-source processed data according to a plurality of preset key indicators to determine whether there is a potential abnormal trend.

[0136] In an embodiment of the present invention, the trend anomaly detection module 102 extracts multi-source key data from multi-source processing data according to multiple preset key indicators (for example, server temperature, CPU utilization, log problems, etc.), and then trains the LSTM neural network through historical data to perform trend prediction on the multi-source key data, obtain trend prediction results, identify the trend prediction results, and determine whether there is a potential abnormal trend.

[0137] The operation and maintenance strategy planning module 103 is used to perform operation and maintenance cost and operation and maintenance risk analysis based on AI technology when there is a potential abnormal trend, and to plan and optimize the operation and maintenance strategy.

[0138] In an embodiment of the present invention, in the case of potential abnormal trends, the operation and maintenance strategy planning module 103 performs basic operation and maintenance planning, generates multiple basic operation and maintenance strategies, and then based on AI technology, performs operation and maintenance cost analysis on the multiple basic operation and maintenance strategies to determine the operation and maintenance costs of the multiple strategies, and based on AI technology, performs operation and maintenance risk analysis on the multiple basic operation and maintenance strategies to determine the operation and maintenance risks of the multiple strategies, and then compares and arranges the multiple basic operation and maintenance strategies according to the operation and maintenance costs and the operation and maintenance risks of the multiple strategies, records the comparison and arrangement information, and selects the basic operation and maintenance strategy ranked first from the multiple basic operation and maintenance strategies according to the comparison and arrangement information, and marks it as the optimized operation and maintenance strategy.

[0139] Specifically, Figure 9 It shows a structural block diagram of the operation and maintenance strategy planning module 103 in the system provided by an embodiment of the present invention.

[0140] In another preferred embodiment of the present invention, the operation and maintenance strategy planning module 103 specifically includes:

[0141] The basic operation and maintenance planning unit 1031 is used to perform basic operation and maintenance planning when there is a potential abnormal trend and generate multiple basic operation and maintenance strategies.

[0142] The operation and maintenance cost analysis unit 1032 is used to perform operation and maintenance cost analysis on the multiple basic operation and maintenance strategies based on AI technology to determine the operation and maintenance costs of the multiple strategies.

[0143] The operation and maintenance risk analysis unit 1033 is used to perform operation and maintenance risk analysis on the multiple basic operation and maintenance strategies based on AI technology to determine the operation and maintenance risks of multiple strategies.

[0144] The comparison and ranking unit 1034 is configured to compare and rank the plurality of basic operation and maintenance strategies according to the plurality of strategy operation and maintenance costs and the plurality of strategy operation and maintenance risks, and record comparison and ranking information.

[0145] The optimization selection unit 1035 is configured to select an optimized operation and maintenance strategy from the basic operation and maintenance strategies according to the comparison and arrangement information.

[0146] Furthermore, the server automated operation and maintenance system based on AI technology also includes:

[0147] The operation and maintenance verification execution module 104 is used to perform security verification on the optimized operation and maintenance strategy, and after the security verification passes, automatically perform actual operation and maintenance on the optimized operation and maintenance strategy.

[0148] In an embodiment of the present invention, the operation and maintenance verification execution module 104 constructs a virtual verification environment, and performs security verification on the optimized operation and maintenance strategy in the virtual verification environment (for example: simulating data consistency after disk replacement), and records the results to obtain security verification results. According to the security verification results, the optimized operation and maintenance strategy is partially deployed (for example: deploying 5% of the servers) and the effect is verified to obtain the effect verification results. Then, according to the effect verification results, after the effect verification is passed, the optimized operation and maintenance strategy is fully deployed for automated actual operation and maintenance execution. The entire process is automated and does not require manual work, thereby reducing the labor cost of the logistics park, reducing manual processing time, and significantly improving the operational efficiency of the coal logistics park.

[0149] Specifically, Figure 10It shows a structural block diagram of the operation and maintenance verification execution module 104 in the system provided by an embodiment of the present invention.

[0150] In another preferred embodiment of the present invention, the operation and maintenance verification execution module 104 specifically includes:

[0151] The environment construction unit 1041 is used to construct a virtual verification environment.

[0152] The security verification unit 1042 is used to perform security verification and record the optimized operation and maintenance strategy in the virtual verification environment to obtain a security verification result.

[0153] The partial deployment unit 1043 is used to perform partial deployment and effect verification on the optimized operation and maintenance strategy according to the security verification result, and obtain an effect verification result.

[0154] The full deployment unit 1044 is configured to perform automated actual operation and maintenance execution of the optimized operation and maintenance strategy in full deployment according to the effect verification result after the effect verification passes.

[0155] Furthermore, the server automated operation and maintenance system based on AI technology also includes:

[0156] The operation and maintenance feedback processing module 105 is used to perform comparative records of operation and maintenance execution according to multiple preset business indicators, and generate and visually display an operation and maintenance feedback report.

[0157] In an embodiment of the present invention, the operation and maintenance feedback processing module 105 performs comparative records of operation and maintenance execution according to multiple preset business indicators (for example, processing success rate, server downtime, etc.), generates comparative record information, and then standardizes the comparative record information according to a preset standardized report template to generate an operation and maintenance feedback report, and obtains a management feedback address, and visually displays the operation and maintenance feedback report according to the management feedback address.

[0158] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0159] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0160] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A server automated operation and maintenance method based on AI technology, characterized in that: The method specifically comprises the following steps: Collect and process multi-source data on the server to obtain multi-source processed data; According to a plurality of preset key indicators, trend prediction and anomaly detection are performed on the multi-source processed data to determine whether there is a potential abnormal trend; When there are potential abnormal trends, AI technology is used to analyze operation and maintenance costs and risks, and to plan and optimize operation and maintenance strategies; Perform security verification on the optimized operation and maintenance strategy, and after the security verification passes, automatically perform actual operation and maintenance on the optimized operation and maintenance strategy; Compare and record the operation and maintenance execution according to multiple preset business indicators, and generate and visualize the operation and maintenance feedback report.

2. The server automated operation and maintenance method based on AI technology according to claim 1, characterized in that: The multi-source data collection and processing on the server to obtain multi-source processed data specifically includes the following steps: Conduct environmental monitoring of the server room and obtain environmental monitoring data; Perform hardware monitoring on the server and obtain hardware monitoring data; Extract the server's operation logs to obtain operation monitoring data; Anomalies are identified and filtered on the environmental monitoring data, the hardware monitoring data, and the operation monitoring data to generate multi-source processed data.

3. The server automated operation and maintenance method based on AI technology according to claim 1, characterized in that: The method of performing trend prediction and anomaly detection on the multi-source processed data according to a plurality of preset key indicators to determine whether there is a potential abnormal trend specifically includes the following steps: Extracting multi-source key data from the multi-source processed data according to a plurality of preset key indicators; Performing trend prediction on the multi-source key data to obtain trend prediction results; Identify the trend prediction results to determine whether there is a potential abnormal trend.

4. The server automated operation and maintenance method based on AI technology according to claim 1, characterized in that: When there is a potential abnormal trend, the operation and maintenance cost and operation and maintenance risk analysis based on AI technology and the planning and optimization of the operation and maintenance strategy specifically include the following steps: When there are potential abnormal trends, basic operation and maintenance planning is carried out to generate multiple basic operation and maintenance strategies; Based on AI technology, the operation and maintenance cost of multiple basic operation and maintenance strategies is analyzed to determine the operation and maintenance costs of multiple strategies; Based on AI technology, an operation and maintenance risk analysis is performed on multiple basic operation and maintenance strategies to determine the operation and maintenance risks of multiple strategies; Comparing and arranging the plurality of basic operation and maintenance strategies according to the plurality of strategy operation and maintenance costs and the plurality of strategy operation and maintenance risks, and recording comparison and arrangement information; An optimized operation and maintenance strategy is selected from the basic operation and maintenance strategies according to the comparison and arrangement information.

5. The server automated operation and maintenance method based on AI technology according to claim 1, characterized in that: The security verification of the optimized operation and maintenance strategy and, after the security verification passes, the automated actual operation and maintenance execution of the optimized operation and maintenance strategy specifically include the following steps: Build a virtual verification environment; In the virtual verification environment, security verification and recording of the optimized operation and maintenance strategy are performed to obtain security verification results; Based on the security verification results, partially deploy and verify the effectiveness of the optimized operation and maintenance strategy to obtain an effectiveness verification result; According to the effect verification result, after the effect verification is passed, the optimized operation and maintenance strategy is fully deployed and automated actual operation and maintenance is executed.

6. The server automated operation and maintenance method based on AI technology according to claim 1, characterized in that: The comparative record of operation and maintenance execution according to multiple preset business indicators and the generation and visualization of the operation and maintenance feedback report specifically include the following steps: Compare and record the operation and maintenance execution according to multiple preset business indicators and generate comparison record information; Standardize and organize the comparison record information and generate an operation and maintenance feedback report; Get the management feedback address; The operation and maintenance feedback report is visually displayed according to the management feedback address.

7. A server automated operation and maintenance system based on AI technology, characterized in that: The system includes a multi-source data acquisition module, a trend anomaly detection module, an operation and maintenance strategy planning module, an operation and maintenance verification execution module, and an operation and maintenance feedback processing module, wherein: Multi-source data acquisition module, used to collect and process multi-source data from the server and obtain multi-source processed data; A trend anomaly detection module is used to perform trend prediction and anomaly detection on the multi-source processed data according to a plurality of preset key indicators to determine whether there is a potential abnormal trend; The operation and maintenance strategy planning module is used to analyze operation and maintenance costs and risks based on AI technology when there are potential abnormal trends, and to plan and optimize operation and maintenance strategies; An operation and maintenance verification execution module is used to perform security verification on the optimized operation and maintenance strategy, and after the security verification passes, automatically perform actual operation and maintenance on the optimized operation and maintenance strategy; The operation and maintenance feedback processing module is used to compare and record operation and maintenance execution according to multiple preset business indicators, and generate and visually display operation and maintenance feedback reports.

8. The server automated operation and maintenance system based on AI technology according to claim 7, characterized in that: The multi-source data acquisition module specifically includes: Environmental monitoring unit, used to monitor the environment of the server room and obtain environmental monitoring data; Hardware monitoring unit, used to monitor the server hardware and obtain hardware monitoring data; A log extraction unit is used to extract the operation log of the server and obtain operation monitoring data; The anomaly identification and filtering unit is used to identify and filter anomalies of the environmental monitoring data, the hardware monitoring data and the operation monitoring data to generate multi-source processing data.

9. The server automated operation and maintenance system based on AI technology according to claim 7, characterized in that: The operation and maintenance strategy planning module specifically includes: The basic operation and maintenance planning unit is used to carry out basic operation and maintenance planning when there are potential abnormal trends and generate multiple basic operation and maintenance strategies; An operation and maintenance cost analysis unit, configured to perform operation and maintenance cost analysis on the plurality of basic operation and maintenance strategies based on AI technology, and determine the operation and maintenance costs of the plurality of strategies; An operation and maintenance risk analysis unit, configured to perform operation and maintenance risk analysis on the plurality of basic operation and maintenance strategies based on AI technology, and determine the operation and maintenance risks of the plurality of strategies; a comparison and ranking unit, configured to compare and rank the plurality of basic operation and maintenance strategies according to the plurality of strategy operation and maintenance costs and the plurality of strategy operation and maintenance risks, and record comparison and ranking information; An optimization selection unit is used to select an optimized operation and maintenance strategy from the basic operation and maintenance strategies according to the comparison arrangement information.

10. The server automated operation and maintenance system based on AI technology according to claim 7, characterized in that: The operation and maintenance verification execution module specifically includes: An environment construction unit, used for constructing a virtual verification environment; A security verification unit is used to perform security verification and record the optimized operation and maintenance strategy in the virtual verification environment to obtain a security verification result; A partial deployment unit, configured to perform partial deployment and effect verification on the optimized operation and maintenance strategy according to the security verification result, and obtain an effect verification result; The complete deployment unit is used to perform automated actual operation and maintenance execution of the optimized operation and maintenance strategy in full deployment according to the effect verification result after the effect verification is passed.

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