Operation and maintenance control method and system based on automation strategy

Abstract

The invention relates to the technical field of operation and maintenance control, in particular to an operation and maintenance control method and system based on an automation strategy, and the method comprises the following steps: based on server state monitoring data obtained in real time, recording the current energy consumption and load data of a data center, recognizing the stable operation state and potential faults of a server, and determining the state of the server; and performing preventive maintenance on the equipment identified as the fault risk, and generating an equipment maintenance list. According to the invention, by automatically collecting the state data of the server and carrying out real-time performance evaluation and fault prediction on operation and maintenance management, early identification and preventive maintenance of fault risks and automatic generation and adjustment of a resource configuration table are realized, the data processing capability is directly influenced, continuous operation of key applications is ensured, energy consumption is optimized, and the service life of the server is prolonged. Automatic load balancing not only optimizes resource allocation, but also reduces errors and delay caused by manual intervention, and improves operation accuracy and response speed.

Description

Technical Field

[0001] The present invention relates to the field of operation and maintenance control technology, and in particular to an operation and maintenance control method and system based on automation strategy. Background Art

[0002] Operation and maintenance control technology involves monitoring, managing and automating information technology systems (including hardware, software and networks) to ensure their efficient, secure and continuous operation. This technical field includes health monitoring, performance optimization, fault recovery, and configuration management. Automation plays a key role in this field, helping to reduce human errors, improve response speed, and free up human resources to handle more complex tasks. By using scripting languages, automation tools and predefined policies, the operation and maintenance team can more effectively prevent failures, respond to problems and perform routine maintenance.

[0003] Among them, the operation and maintenance control method based on automation strategy is to use automation technology to improve and optimize various aspects of IT operation and maintenance control, emphasizing the use of algorithms and software tools to automate routine operation and maintenance tasks, such as system monitoring, log management, fault detection and response, and resource scheduling. Its purpose is to reduce dependence on manual operations, improve operation and maintenance accuracy and efficiency, while reducing operation and maintenance costs and ensuring continuous service availability and business continuity.

[0004] Traditional operation and maintenance control relies on regular manual inspections and fault responses, lacks sufficient flexibility and immediacy, and cannot effectively respond to the increasing scale and complexity of data centers. It shows its limitations in high data flow and complex environments, such as long processing time, high error rate, and untimely response. There is a lack of automated strategies, and the fault recovery and resource optimization processes are cumbersome and error-prone, which often lead to service interruptions and business losses, increase the overall cost of operation and maintenance, and limit the ability of data centers to adapt to business needs and the ever-changing technological environment. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose an operation and maintenance control method and system based on an automation strategy.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: an operation and maintenance control method based on an automation strategy, comprising the following steps:

[0007] S1: Based on the server status monitoring data obtained in real time, record the current energy consumption and load data of the data center, identify the stable operation status and potential failures of the server, perform preventive maintenance on the equipment identified as failure risks, and generate an equipment maintenance list;

[0008] S2: Based on the equipment maintenance list, dynamically sort resource priorities, determine the highest priority of key equipment, and automatically adjust and optimize server performance according to real-time operating parameters to generate a resource configuration table;

[0009] S3: Based on the resource configuration table, automatically adjust the load distribution of the data center server, optimize the overload and idle problems caused by uneven load, reduce the probability of failure, and generate load balancing status information;

[0010] S4: Based on the load balancing status information, the performance monitoring rules of the data center are upgraded, continuous monitoring is implemented, potential performance bottlenecks are identified and predicted, and the monitored data is analyzed, the maintenance process and resource allocation are adjusted and optimized, and operation and maintenance optimization adjustment results are generated.

[0011] The present invention is improved in that the steps of recording the current energy consumption and load data are specifically as follows:

[0012] S111: Based on the server status monitoring data acquired in real time, the current and voltage data of the data center server are collected using the formula:

[0013] P=VI

[0014] Calculate the instantaneous power P of each server to obtain an instantaneous power data set, where V is voltage and I is current;

[0015] S112: Record the CPU and memory usage of the server, analyze the running load of each server, and obtain a load data set;

[0016] S113: Analyze the instantaneous power data set using the formula:

[0017]

[0018] Calculate the average energy consumption E, and combine it with the load data set to obtain the current energy consumption and load data of the data center, where P t It represents the instantaneous power of the server at time t, and N is the number of servers.

[0019] The present invention is improved in that the steps of obtaining the equipment maintenance list are specifically as follows:

[0020] S121: Compare the real-time data with the performance threshold, identify the server with excessive power or abnormal load, and use the conditional expression P>P max or L>L max , where P is the instantaneous power, P max is the power threshold, L is the load rate, L max is the load threshold, and a list of servers at risk of failure is obtained;

[0021] S122: Perform health checks on the devices in the failure risk server list, including hardware checks, software configuration audits, and performance log analysis, using the formula:

[0022] SY=α Y HY+β Y CY+γ Y LY

[0023] Score the severity of the problem SY and generate maintenance demand data, where HY, CY, and LY represent hardware failure indicators, configuration problem indicators, and performance log abnormality indicators, respectively. Y , β Y and γ Y is the weight coefficient corresponding to the indicator;

[0024] S123: Perform preventive maintenance according to the maintenance requirement data, the severity score of the equipment and the availability of maintenance resources to obtain an equipment maintenance list.

[0025] The present invention is improved in that the steps for determining the highest priority of the key equipment are specifically as follows:

[0026] S211: Based on the equipment maintenance list, collect the maintenance urgency score and maintenance timestamp of each equipment to obtain an overview of the equipment status and requirements;

[0027] S212: Based on the status and demand overview of the equipment, the formula is used:

[0028]

[0029] Calculate the priority score of each device UP i , among which, UQ i represents the maintenance urgency score of equipment i, UT i is the time that has passed since device i was last maintained;

[0030] S213: Sort the devices according to the priority scores, and determine the highest priority device after sorting.

[0031] The present invention is improved in that the step of obtaining the resource configuration table is specifically as follows:

[0032] S221: monitor the real-time operating parameters of the server, including CPU usage, memory usage, disk I / O, and network traffic, and evaluate the current performance status of each server;

[0033] S222: Based on the current performance status, automatically adjust and optimize the performance configuration of the server, using the formula:

[0034] VO i =αV P cpu +β V P mem +γ V P disk +δ V P net

[0035] Get the resource allocation table, where VO i represents the optimization operation of server i, P cpu , P mem , P disk and P net Represents CPU usage, memory usage, disk I / O and network traffic, α V , β V , γ V and δ V is the adjustment weight of the performance parameter.

[0036] The present invention is improved in that the step of acquiring the load balancing status information is specifically as follows:

[0037] S311: Based on the resource configuration table, extract the current load data of each server to obtain a real-time load data set;

[0038] S312: Based on the real-time load data set, automatically adjust the load distribution of the data center server, using the formula:

[0039]

[0040] Get the load balancing factor ZB i , among which ZL i is the current load of server i, ZL max is the highest load in the server;

[0041] S313: Based on the load balancing factor, dynamically adjust the task and resource allocation between servers, balance the load and reduce the overload or idle state, and generate load balancing state information.

[0042] The present invention is improved in that the steps of identifying and predicting the potential performance bottleneck are specifically as follows:

[0043] S411: Based on the load balancing status information, verify the existing performance monitoring rules, determine whether there is a monitoring blind spot or excessive monitoring, and collect key performance indicators;

[0044] S412: Based on the key performance indicators, the performance monitoring rules are updated, the monitoring threshold is dynamically adjusted according to the real-time data, the response speed to the abnormal state is refined, and the updated monitoring rules are obtained;

[0045] S413: Based on the updated monitoring rules, continuous monitoring is performed using the formula:

[0046]

[0047] Automatically identify and predict performance bottlenecks, where DP i Represents the performance bottleneck prediction index, V current is the currently monitored performance value, V baseline is the baseline performance value, SD historical is the standard deviation of the historical data.

[0048] The present invention is improved in that the steps of obtaining the operation and maintenance optimization adjustment result are specifically as follows:

[0049] S421: Analyze the monitored data, identify areas with poor performance and improper resource allocation, and obtain a list of key areas;

[0050] S422: Based on the key area list, adjust and optimize the maintenance process and resource allocation, using the formula:

[0051]

[0052] Get the operation and maintenance optimization adjustment results, among which XR new It is the adjusted resource allocation, XR current is the current resource configuration, XP target is the target performance level, XP current is the current performance level.

[0053] An operation and maintenance control system based on an automation strategy, the system comprising:

[0054] The status monitoring module records the current energy consumption and load data of the data center based on the server status monitoring data obtained in real time, analyzes the stability and failure risk of the equipment, performs preventive maintenance on the equipment identified as having failure risks, and generates an equipment maintenance list;

[0055] The resource optimization module dynamically evaluates and sorts the resource priorities of the servers in the data center based on the equipment maintenance list, determines the maintenance priority of key equipment, and adjusts the performance of key servers according to real-time operating parameters to generate a resource configuration table;

[0056] The load adjustment module automatically adjusts the load distribution of the data center server based on the resource configuration table, optimizes the overload and idle problems caused by uneven load, reduces the probability of failure, and generates load balancing status information;

[0057] The performance monitoring update module updates the data center performance monitoring rules based on the load balancing status information, optimizes the continuous monitoring capability, continuously tracks the server performance, predicts the performance bottleneck, and generates the performance monitoring records;

[0058] The maintenance strategy optimization module analyzes and identifies performance bottlenecks and monitoring data based on the performance monitoring records, adjusts maintenance processes and resource allocation, refines the operation and maintenance processes, and generates operation and maintenance optimization adjustment results.

[0059] Compared with the prior art, the advantages and positive effects of the present invention are:

[0060] In the present invention, by automatically collecting server status data, real-time performance evaluation and fault prediction are performed on operation and maintenance management, early identification of fault risks and preventive maintenance are achieved, and automatic generation and adjustment of resource allocation tables directly affect data processing capabilities, ensuring the continuous operation of key applications while optimizing energy consumption. Automatic load balancing not only optimizes resource allocation, but also reduces errors and delays introduced by manual intervention, improves the accuracy and response speed of operations, and the automated upgrade of performance monitoring and continuous monitoring strategies effectively predict and identify potential performance bottlenecks, promote continuous optimization of maintenance processes and resource allocation, and bring higher operating efficiency and lower operation and maintenance costs to the data center. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flow chart of an operation and maintenance control method based on an automation strategy is proposed for the present invention;

[0062] Figure 2 It is a flow chart for recording current energy consumption and load data in the present invention;

[0063] Figure 3 A flowchart for obtaining the equipment maintenance list in the present invention;

[0064] Figure 4 A flow chart for determining the highest priority of key equipment in the present invention;

[0065] Figure 5 A flowchart for obtaining a resource configuration table in the present invention;

[0066] Figure 6 A flowchart for obtaining load balancing status information in the present invention;

[0067] Figure 7 A flowchart for identifying and predicting potential performance bottlenecks in the present invention;

[0068] Figure 8 The present invention is a flowchart for obtaining the operation and maintenance optimization adjustment results. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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.

[0070] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0071] Example

[0072] See also Figure 1 The present invention provides a technical solution: an operation and maintenance control method based on an automation strategy, comprising the following steps:

[0073] S1: Based on the server status monitoring data obtained in real time, record the current energy consumption and load data of the data center, identify the stable operation status and potential failures of the server, perform preventive maintenance on the equipment identified as failure risks, and generate an equipment maintenance list;

[0074] S2: Based on the equipment maintenance list, dynamically sort resource priorities, determine the highest priority of key equipment, and automatically adjust and optimize server performance based on real-time operating parameters to generate a resource configuration table;

[0075] S3: Based on the resource configuration table, automatically adjust the load distribution of the data center server, optimize the overload and idle problems caused by uneven load, reduce the probability of failure, and generate load balancing status information;

[0076] S4: Based on the load balancing status information, upgrade the performance monitoring rules of the data center, implement continuous monitoring, identify and predict potential performance bottlenecks, analyze the monitored data, adjust and optimize the maintenance process and resource allocation, and generate operation and maintenance optimization adjustment results.

[0077] The equipment maintenance list includes fault identification, maintenance frequency, and responsibility allocation. The resource allocation table includes performance priority, resource quota, and allocation plan. The load balancing status information includes balancing efficiency information, load ratio, and adjustment frequency. The operation and maintenance optimization adjustment results include performance monitoring data, maintenance strategy update information, and resource reconfiguration status.

[0078] See also Figure 2 , the specific steps for recording the current energy consumption and load data are as follows:

[0079] S111: Based on the server status monitoring data acquired in real time, the current and voltage data of the data center server are collected using the formula:

[0080] P=VI

[0081] Calculate the instantaneous power P of each server to obtain an instantaneous power data set, where V is voltage and I is current;

[0082] If the following data is collected, V = 230 volts as the server's power supply voltage, I = 2 amps as the current server's current consumption, then the server's instantaneous power P can be calculated using the formula:

[0083] P=230V×2A=460

[0084] The result shows that the current instantaneous power of the server is 460 watts, which reflects the energy consumption status of the server at a specific point in time. For data centers, it is important data for evaluating overall energy consumption and formulating energy-saving measures.

[0085] S112: Record the CPU and memory usage of the server, analyze the running load of each server, and obtain a load data set;

[0086] The real-time power data set reflects the instantaneous energy consumption of each server in the data center. The server load data is obtained by recording the usage of the CPU and memory, including the percentage of processor usage and the amount of memory in use. It is crucial for judging the server's workload and its energy efficiency performance. The data can be used to monitor the energy consumption performance of the server under different workloads and provide a basis for subsequent energy efficiency optimization.

[0087] S113: Analyze the instantaneous power data set using the formula:

[0088]

[0089] Calculate the average energy consumption E and combine it with the load data set to obtain the current energy consumption and load data of the data center, where P t represents the instantaneous power of the server at time t, and N is the number of servers;

[0090] If the following data is collected, P1 = 460 watts, P2 = 480 watts, P3 = 500 watts, three servers, that is, N = 3, the average energy consumption of the data center can be calculated:

[0091]

[0092] The results show that the current average power of the data center is 480 watts, which shows the overall energy efficiency performance of the data center, provides basic data for energy use and cost-benefit analysis, and helps data center managers to formulate further energy efficiency improvement measures based on energy consumption performance.

[0093] See also Figure 3 , the specific steps for obtaining the equipment maintenance list are:

[0094] S121: Compare the real-time data with the performance threshold, identify the server with excessive power or abnormal load, and use the conditional expression P>P max or L>L max , where P is the instantaneous power, P max is the power threshold, L is the load rate, L max is the load threshold, and a list of servers at risk of failure is obtained;

[0095] Compare real-time data with performance thresholds to identify servers with excessive power or abnormal load. The process involves continuous monitoring of the server's instantaneous power and load rate, using the conditional expression P>P max or L>L max , where P max and L max It is a set performance threshold. The threshold is set based on the server's maximum tolerance and historical performance data. The purpose is to ensure that the server runs in the optimal state of safety and efficiency. When the real-time monitoring data exceeds the threshold, the server is automatically identified as a device with a high risk of failure, and a list of servers with failure risks is generated. This list is the basis for subsequent maintenance work to ensure timely response to hardware failures or performance degradation.

[0096] S122: Perform health checks on devices in the failure risk server list, including hardware checks, software configuration reviews, and performance log analysis, using the formula:

[0097] SY=α Y HY+β Y CY+γ Y LY

[0098] Score the severity of the problem SY and generate maintenance demand data, where HY, CY, and LY represent hardware failure indicators, configuration problem indicators, and performance log abnormality indicators, respectively. Y , β Y and γ Y is the weight coefficient corresponding to the indicator;

[0099] If the following data is available, the hardware failure index HY = 0.7, the configuration problem index CY = 0.2, the performance log abnormality index LY = 0.1, and the weight coefficient is set to α Y =0.5,βY =0.3,γ Y =0.2, the calculation process is as follows:

[0100] SY=0.5×0.7+0.3×0.2+0.2×0.1=0.35+0.06+0.02=0.43

[0101] The result shows that the severity score of the given server is 0.43, indicating that its maintenance priority is medium and needs to be scheduled based on resource availability.

[0102] S123: Perform preventive maintenance based on the maintenance demand data, the severity score of the equipment and the availability of maintenance resources, and obtain an equipment maintenance list;

[0103] Based on the maintenance demand data, preventive maintenance is performed according to the severity score of the equipment and the availability of maintenance resources. This process involves the formulation of specific maintenance plans. The maintenance team will prioritize each device based on its severity score. The allocation of resources takes into account the availability of existing technicians and necessary maintenance tools to ensure that key equipment receives priority maintenance, thereby maximizing maintenance efficiency and reducing potential system downtime risks. Maintenance activities are performed according to the plan, and the results are presented in the form of an equipment maintenance checklist, which lists all scheduled maintenance tasks, including maintenance time, maintenance personnel, and expected maintenance results, to ensure that each maintenance task is completed on time and with quality.

[0104] See also Figure 4 , the steps to determine the highest priority of key equipment are as follows:

[0105] S211: Based on the equipment maintenance list, the maintenance urgency score and maintenance timestamp of each equipment are collected to obtain an overview of the equipment status and requirements;

[0106] Based on the equipment maintenance list, the maintenance urgency score and maintenance timestamp of each device are collected, including accessing the maintenance database and extracting data containing the last maintenance date and fault records. The data reflects the current status and maintenance needs of each device, and generates a status and needs overview. The overview will be used for subsequent data analysis and equipment priority assessment to ensure that the status of all equipment is recorded.

[0107] S212: Based on the equipment status and demand overview, use the formula:

[0108]

[0109] Calculate the priority score of each device UP i , among which, UQ i represents the maintenance urgency score of equipment i, UT iis the time that has passed since device i was last maintained;

[0110] If the maintenance urgency score of equipment A is UQ A 5, 12 months have passed since the last maintenance, i.e. UT A =12, substitute into the formula to calculate:

[0111]

[0112] The result shows that the priority score of equipment A is 0.385, and the lower score means that the maintenance of this equipment can be performed later compared to other more urgent needs.

[0113] S213: sorting the devices according to the priority scores, and determining the highest priority device after sorting;

[0114] According to the priority score, a sorting algorithm is applied to prioritize all devices. By comparing the priority score of each device, they are arranged from high to low. The sorting result directly affects the allocation and scheduling of maintenance resources. The purpose of sorting is to ensure that resources can be used most effectively. For the highest priority device in the sorting result, immediate maintenance scheduling will be carried out to prevent further production losses or greater maintenance costs caused by equipment failure.

[0115] See also Figure 5 , the specific steps for obtaining the resource configuration table are:

[0116] S221: monitor the real-time operating parameters of the server, including CPU usage, memory usage, disk I / O, and network traffic, and evaluate the current performance status of each server;

[0117] Monitor the real-time operating parameters of the server, including CPU usage, memory usage, disk I / O and network traffic, and obtain the server's operating data in real time. By analyzing the parameters, the operation and maintenance team can evaluate the current performance status of the server and understand the performance bottlenecks or resource overload problems of each server. Information is the basis for performance optimization and resource allocation, ensuring that the data center can effectively respond to changing needs and maintain operating efficiency and system stability.

[0118] S222: Based on the current performance status, automatically adjust and optimize the performance configuration of the server using the formula:

[0119] VO i =α V P cpu +β V P mem +γ V P disk +δ V Pnet

[0120] Get the resource allocation table, where VO i represents the optimization operation of server i, P cpu , P mem , P disk and P net Represents CPU usage, memory usage, disk I / O and network traffic, α V , β V , γ V and δ V is the adjustment weight of the performance parameter;

[0121] There is a server with a current CPU usage of P cpu =75%, memory usage P mem =60%, Disk I / OP disk =300MB / s and network traffic P net =120MB / s, weight coefficient is α V =0.5, β V =0.2,γ V =0.2 and δ V =0.1, substitute into the formula to calculate:

[0122] VO i =0.5×75+0.2×60+0.2×300+0.1×120

[0123] VO i =37.5+12+60+12=121.5

[0124] The result shows that the given server optimization operation value is 121.5, which reflects the current overall performance status of the server and guides the operation and maintenance team to make necessary adjustments to ensure optimal performance.

[0125] See also Figure 6 ,The specific steps for obtaining load balancing status information are:

[0126] S311: extracting the current load data of each server based on the resource configuration table to obtain a real-time load data set;

[0127] Based on the resource configuration table, real-time load data of each server is collected, including extracting key performance indicators such as CPU usage, memory usage, disk I / O and network utilization from the server. The data is the basis for evaluating the current operating efficiency and resource usage of each server. Collecting data is crucial for subsequent load analysis and optimization adjustments, ensuring the integrity and real-time nature of the data set.

[0128] S312: Based on the real-time load data set, automatically adjust the load distribution of the data center server, using the formula:

[0129]

[0130] Get the load balancing factor ZB i , among which ZL i is the current load of server i, ZL max is the highest load in the server;

[0131] There are three servers, and their current loads are ZL1=70%, ZL2=50%, and ZL3=80%. The maximum load ZL max = 80%, calculate the load balancing factor for the first server:

[0132]

[0133] This result shows that the load balancing factor of server 1 is 0.25, indicating that it has a lower priority for load adjustment relative to other servers under the current configuration.

[0134] S313: Based on the load balancing factor, dynamically adjust the task and resource allocation between servers, balance the load and reduce the overload or idle state, and generate load balancing state information;

[0135] Based on the load balancing factor, the task and resource allocation between servers is dynamically adjusted. By migrating tasks from high-load servers to low-load servers, the workload of the entire data center is balanced. The adjustment not only reduces server overload and idle status, but also optimizes the overall resource utilization efficiency. The generated load balancing status information will reflect the operating status of each server after adjustment in real time, providing a basis for the operation and maintenance management of the data center.

[0136] See also Figure 7 , the specific steps for identifying and predicting potential performance bottlenecks are:

[0137] S411: Based on the load balancing status information, verify the existing performance monitoring rules to determine whether there is a monitoring blind spot or over-monitoring phenomenon, and collect key performance indicators, including CPU load, memory usage, disk operation times, and network throughput;

[0138] Based on the load balancing status information, the existing performance monitoring rules are verified, including a comprehensive review of the current monitoring to determine whether there are monitoring blind spots or over-monitoring. At the same time, key performance indicators are collected, such as CPU load, memory usage, number of disk operations, and network throughput. Indicators are basic parameters for evaluating performance. By analyzing the data, you can fully understand the operating status of the data center and determine whether the monitoring rules need to be adjusted to more accurately reflect the actual performance status.

[0139] S412: Based on the key performance indicators, the performance monitoring rules are updated, the monitoring threshold is dynamically adjusted according to the real-time data, the response speed to the abnormal state is refined, and the updated monitoring rules are obtained;

[0140] Based on the collected key performance indicators, performance monitoring rules are updated, which involves adjusting monitoring thresholds according to real-time data to refine responses to abnormal conditions, ensure that monitoring can dynamically adapt to environmental changes, and improve the accuracy and efficiency of early warnings. By setting monitoring thresholds, potential performance issues can be more effectively identified and responded to quickly to avoid service interruptions or performance degradation.

[0141] S413: Based on the updated monitoring rules, implement continuous monitoring using the formula:

[0142]

[0143] Automatically identify and predict performance bottlenecks, where DP i Represents the performance bottleneck prediction index, V current is the currently monitored performance value, V baseline is the baseline performance value, SD historical is the standard deviation of the historical data;

[0144] If the following data is available, V current =75%, V baseline =50%, SD historical =10%, substitute into the formula to calculate:

[0145]

[0146] The result shows that the current performance value deviates significantly from the baseline, with the deviation being 2.5 times the historical standard deviation, indicating a performance bottleneck.

[0147] See also Figure 8 , the specific steps for obtaining the operation and maintenance optimization adjustment results are:

[0148] S421: Analyze the monitored data, identify areas with poor performance and improper resource allocation, and obtain a list of key areas;

[0149] Conduct in-depth analysis of monitored data to identify areas with poor performance and improper resource allocation. This involves the collection and processing of data, including but not limited to performance logs and resource usage. Through data mining techniques such as cluster analysis, indicators that significantly deviate from the normal range are identified. Indicators indicate hardware failure, configuration errors, or insufficient resources. The analysis results will directly affect resource allocation and maintenance decisions, and a list of key areas is obtained;

[0150] S422: Based on the key area list, adjust and optimize the maintenance process and resource allocation, using the formula:

[0151]

[0152] Get the operation and maintenance optimization adjustment results, among which XR new It is the adjusted resource allocation, XR current is the current resource configuration, XP target is the target performance level, XP current is the current performance level;

[0153] If the current resource configuration XR current The target performance XP is 100 target 80% of current performance XP current is 50%, substitute it into the formula and calculate as follows:

[0154]

[0155] This result shows that in order to achieve the target performance level, the resource allocation needs to increase from 100 to 160, reflecting the dynamic approach of resource allocation based on performance feedback to ensure an optimal match between resource usage and demand.

[0156] An operation and maintenance control system based on an automation strategy, the system comprising:

[0157] The status monitoring module records the current energy consumption and load data of the data center based on the server status monitoring data obtained in real time, analyzes the stability and failure risk of the equipment, performs preventive maintenance on the equipment identified as having failure risks, and generates an equipment maintenance list;

[0158] The resource optimization module dynamically evaluates and sorts the resource priorities of servers in the data center based on the equipment maintenance list, determines the maintenance priority of key equipment, and adjusts the performance of key servers based on real-time operating parameters to generate a resource configuration table;

[0159] The load adjustment module automatically adjusts the load distribution of the data center servers based on the resource configuration table, optimizes the overload and idle problems caused by uneven load, reduces the probability of failure, and generates load balancing status information;

[0160] The performance monitoring update module upgrades the data center performance monitoring rules based on the load balancing status information, optimizes the continuous monitoring capability, and continuously tracks the server performance, predicts the performance bottleneck, and generates the performance monitoring records;

[0161] The maintenance strategy optimization module analyzes and identifies performance bottlenecks and monitoring data based on performance monitoring records, adjusts maintenance processes and resource allocation, refines operation and maintenance processes, and generates operation and maintenance optimization adjustment results.

[0162] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An operation and maintenance control method based on automation strategy, characterized in that: The following steps are involved: Based on the real-time server status monitoring data, the current energy consumption and load data of the data center are recorded, the stable operation status and potential failures of the server are identified, and preventive maintenance is performed on the equipment identified as failure risks, and an equipment maintenance list is generated; Based on the equipment maintenance list, dynamically sort resource priorities, determine the highest priority of key equipment, and automatically adjust and optimize server performance according to real-time operating parameters to generate a resource configuration table; Based on the resource configuration table, the load distribution of the data center server is automatically adjusted to optimize the overload and idle problems caused by uneven load, reduce the probability of failure, and generate load balancing status information; Based on the load balancing status information, the performance monitoring rules of the data center are upgraded, continuous monitoring is implemented, potential performance bottlenecks are identified and predicted, and the monitored data is analyzed, the maintenance process and resource allocation are adjusted and optimized, and operation and maintenance optimization adjustment results are generated.

2. The operation and maintenance control method based on automation strategy according to claim 1, characterized in that: The specific steps of recording the current energy consumption and load data are as follows: Based on the real-time server status monitoring data, the current and voltage data of the data center server are collected using the formula: P=VI Calculate the instantaneous power P of each server to obtain an instantaneous power data set, where V is voltage and I is current; Record the CPU and memory usage of the server, analyze the operating load of each server, and obtain the load data set; The instantaneous power data set is analyzed using the formula: Calculate the average energy consumption E, and combine it with the load data set to obtain the current energy consumption and load data of the data center, where P t It represents the instantaneous power of the server at time t, and N is the number of servers.

3. The operation and maintenance control method based on automation strategy according to claim 1, characterized in that: The steps for obtaining the equipment maintenance list are specifically as follows: Compare real-time data with performance thresholds to identify servers with excessive power or abnormal loads, using the conditional expression P>P max or L>L max , where P is the instantaneous power, P max is the power threshold, L is the load rate, L max is the load threshold, and a list of servers at risk of failure is obtained; Perform health checks on the devices in the failure risk server list, including hardware checks, software configuration audits, and performance log analysis, using the formula: SY=α Y HY+β Y CY+γ Y LY Score the severity of the problem SY and generate maintenance demand data, where HY, CY, and LY represent hardware failure indicators, configuration problem indicators, and performance log abnormality indicators, respectively. Y , β Y and γ Y is the weight coefficient corresponding to the indicator; According to the maintenance demand data, preventive maintenance is performed according to the severity score of the equipment and the availability of maintenance resources to obtain an equipment maintenance list.

4. The operation and maintenance control method based on automation strategy according to claim 1, characterized in that: The steps for determining the highest priority of the critical equipment are specifically as follows: Based on the equipment maintenance list, the maintenance urgency score and maintenance timestamp of each equipment are collected to obtain an overview of the equipment status and requirements; Based on the status and requirements overview of the equipment, the formula is used: Calculate the priority score of each device UP i , among which, UQ i represents the maintenance urgency score of equipment i, UT i is the time that has passed since device i was last maintained; According to the priority scores, the devices are sorted, and the highest priority device after sorting is determined.

5. The operation and maintenance control method based on automation strategy according to claim 1 is characterized in that: The steps for obtaining the resource configuration table are specifically as follows: Monitor the real-time operating parameters of the server, including CPU usage, memory usage, disk I / O, and network traffic, and evaluate the current performance status of each server; Based on the current performance status, the performance configuration of the server is automatically adjusted and optimized using the formula: VO i =α V P cpu +β V P mem +γ V P disk +δ V P net Get the resource allocation table, where VO i represents the optimization operation of server i, P cpu , P mem , P disk and P net Represents CPU usage, memory usage, disk I / O and network traffic, α V , β V , γ V and δ V is the adjustment weight of the performance parameter.

6. The operation and maintenance control method based on automation strategy according to claim 1 is characterized in that: The steps for acquiring the load balancing status information are specifically as follows: Based on the resource configuration table, extract the current load data of each server to obtain a real-time load data set; Based on the real-time load data set, the load distribution of the data center server is automatically adjusted using the formula: Get the load balancing factor ZB i , among which ZL i is the current load of server i, ZL max is the highest load in the server; Based on the load balancing factor, the task and resource allocation between servers is dynamically adjusted to balance the load and reduce overload or idle state, and generate load balancing state information.

7. The operation and maintenance control method based on automation strategy according to claim 1 is characterized in that: The steps for identifying and predicting potential performance bottlenecks are as follows: Based on the load balancing status information, verify the existing performance monitoring rules, determine whether there are monitoring blind spots or over-monitoring phenomena, and collect key performance indicators; Based on the key performance indicators, the performance monitoring rules are updated, the monitoring thresholds are dynamically adjusted according to the real-time data, the response speed to the abnormal state is refined, and the updated monitoring rules are obtained; Based on the updated monitoring rules, continuous monitoring is implemented using the formula: Automatically identify and predict performance bottlenecks, where DP i Represents the performance bottleneck prediction index, V current is the currently monitored performance value, V baseline is the baseline performance value, SD historical is the standard deviation of the historical data.

8. The operation and maintenance control method based on automation strategy according to claim 1, characterized in that: The steps for obtaining the operation and maintenance optimization adjustment results are specifically as follows: Analyze the monitored data to identify areas of poor performance and improper resource allocation, and obtain a list of key areas; Based on the above list of key areas, adjust and optimize the maintenance process and resource allocation, using the formula: Get the operation and maintenance optimization adjustment results, among which XR new It is the adjusted resource allocation, XR current is the current resource configuration, XP target is the target performance level, XP current is the current performance level.

9. An operation and maintenance control system based on automation strategy, characterized in that: According to the operation and maintenance control method based on automation strategy according to any one of claims 1 to 8, the system comprises: The status monitoring module records the current energy consumption and load data of the data center based on the server status monitoring data obtained in real time, analyzes the stability and failure risk of the equipment, performs preventive maintenance on the equipment identified as having failure risks, and generates an equipment maintenance list; The resource optimization module dynamically evaluates and sorts the resource priorities of the servers in the data center based on the equipment maintenance list, determines the maintenance priority of key equipment, and adjusts the performance of key servers according to real-time operating parameters to generate a resource configuration table; The load adjustment module automatically adjusts the load distribution of the data center server based on the resource configuration table, optimizes the overload and idle problems caused by uneven load, reduces the probability of failure, and generates load balancing status information; The performance monitoring update module updates the data center performance monitoring rules based on the load balancing status information, optimizes the continuous monitoring capability, continuously tracks the server performance, predicts the performance bottleneck, and generates the performance monitoring records; The maintenance strategy optimization module analyzes and identifies performance bottlenecks and monitoring data based on the performance monitoring records, adjusts maintenance processes and resource allocation, refines the operation and maintenance processes, and generates operation and maintenance optimization adjustment results.

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