A computer room maintenance monitoring system

Abstract

The present invention relates to the field of equipment monitoring technology, specifically to a computer room maintenance monitoring system, the system comprising a temperature threshold adjustment module, a temperature anomaly assessment module, a performance anomaly identification module and a maintenance sequence adjustment module. The present invention optimizes the evaluation process of temperature anomalies by dynamically adjusting the temperature threshold of computer room equipment monitoring based on the environment and the service life of the equipment, can instantly identify temperature anomalies at the early stage of temperature deviation, shorten the response time, and reduce the damage caused by equipment overheating. By comparing the current performance of the equipment with the benchmark performance, calculating the performance loss ratio and the performance degradation rate, and evaluating the performance anomaly accordingly, the maintenance team can respond quickly, reduce the spread of problems and losses, adjust the maintenance priority according to the importance of the equipment and the severity of the fault, realize the rationalization and systematization of maintenance work, effectively avoid the occurrence of major faults, and improve the overall stability and safety of the computer room equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment monitoring, and in particular to a machine room maintenance monitoring system. Background Art

[0002] The field of equipment monitoring technology aims to monitor and manage the performance and status of various mechanical, electronic and network equipment in real time. This technology field uses sensors, software and communication technologies to collect equipment operation data, analyze equipment health, predict potential failures, and implement preventive maintenance measures. Equipment monitoring systems can improve equipment reliability and service life, reduce maintenance costs, and improve overall operational efficiency.

[0003] Among them, the computer room maintenance monitoring system is a monitoring system specially designed for computer room equipment, and its main purpose is to maintain the stable operation of computer room computing facilities. The system monitors the operating status of computing equipment, detects and reports abnormal performance problems of computing equipment in real time, such as overheating and power failure, so as to guide maintenance personnel to maintain computing equipment immediately, ensure the high availability and security of the computer room, reduce downtime caused by equipment failure, and ensure the continuous circulation and storage of data.

[0004] Traditional maintenance monitoring systems use fixed monitoring parameters and lack adaptability to environmental changes and equipment aging factors. This results in the inability to accurately reflect the actual working status of the equipment during the actual monitoring process, increasing fault diagnosis errors and response delays. This rigid monitoring strategy limits the effectiveness of preventive maintenance measures, resulting in unnecessary maintenance cost increases and shortened equipment life. The lack of flexible maintenance strategies may also lead to insufficient response to emergency failures, increasing the overall risk of the system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a machine room maintenance monitoring system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a computer room maintenance monitoring system, the system comprising:

[0007] The temperature threshold adjustment module monitors the humidity and temperature data in the current computer room based on the operating environment of the computer room, and adjusts the abnormal temperature detection threshold in combination with the service life of the computing equipment to obtain temperature threshold adjustment information;

[0008] The temperature anomaly assessment module compares the current temperature of the computing device with the adjusted temperature detection threshold based on the temperature threshold adjustment information, assesses the degree of temperature deviation, and assesses the degree of temperature anomaly in combination with the duration of the temperature deviation to obtain abnormal temperature information;

[0009] The performance anomaly identification module monitors the operation performance data of the computing device based on the operation status of the computing device, calculates the performance degradation rate by analyzing the operation performance data over a period of time, and compares the current performance with the benchmark performance, calculates the performance loss ratio, evaluates the degree of performance anomaly, and obtains performance anomaly information;

[0010] The maintenance sequence adjustment module evaluates the abnormality level of the computing device based on the abnormal temperature information and the performance abnormality information, evaluates the maintenance priority of the computing device in combination with the computing device type, and performs priority sorting to obtain the computing device maintenance requirement information.

[0011] The present invention is improved in that the step of obtaining the temperature threshold adjustment information is:

[0012] Based on the operating environment of the computer room, collect the ambient temperature and humidity data in the current computer room to obtain real-time environmental data;

[0013] Based on the real-time environmental data, combined with the age of the computing equipment and the basic abnormal temperature detection threshold, the formula is:

[0014] ,

[0015] Calculate the adjusted temperature detection threshold , get the temperature threshold adjustment information, where, is the temperature adjustment factor, is the humidity adjustment factor, is the age adjustment factor, is the exponential coefficient, is the real-time ambient temperature, is the real-time ambient humidity, is the basic abnormal temperature detection threshold, is the current equipment age, The maximum service life designed for the equipment, is the standard ambient temperature, is the standard ambient humidity, is the adjusted temperature detection threshold.

[0016] The present invention is improved in that the method for evaluating the degree of temperature deviation is:

[0017] The current real-time temperature of the computing device is collected through the temperature sensor built into the computing device to obtain the real-time device temperature information;

[0018] Based on the real-time device temperature information and the temperature threshold adjustment information, the formula:

[0019] ,

[0020] Calculate the degree of temperature deviation , get the temperature deviation information, where, is the device sensitivity coefficient, is the environmental impact factor, To calculate the current temperature of the device, is the adjusted temperature detection threshold, is the degree of temperature deviation.

[0021] The present invention is improved in that the step of obtaining the abnormal temperature information is:

[0022] Based on the degree of temperature deviation , collect the duration data of temperature deviation and obtain the temperature anomaly related data;

[0023] Based on the temperature anomaly correlation data, the formula:

[0024] ,

[0025] Calculate the degree of temperature anomaly in, is the time sensitivity coefficient, is the time offset constant, is the degree of temperature deviation, The duration of temperature deviation is the degree of temperature anomaly, is the base of natural logarithms;

[0026] Based on the degree of temperature abnormality ,according to The value is used to evaluate the severity of the temperature anomaly of the computing device and obtain the abnormal temperature information.

[0027] The present invention is improved in that the method for calculating the performance degradation rate is:

[0028] Based on the running status of the computing device, monitor the running performance data of the computing device, the running performance data includes CPU usage, memory usage and response time;

[0029] Based on the operating performance data, the formula:

[0030] ,

[0031] Calculate the performance degradation rate ,in, is the number of data points, It's time point Performance data, is the time interval between data points, It's time point The weight of For baseline performance data, is the performance degradation rate.

[0032] The present invention is improved in that the step of obtaining the performance abnormality information is:

[0033] Collect the current performance data of the computing device and compare it with the baseline performance data through the formula:

[0034] ,

[0035] Calculate the performance loss ratio ,in, To calculate the current performance data of the device, For baseline performance data, is the performance loss ratio;

[0036] Based on the performance loss ratio Combined performance degradation rate , through the formula:

[0037] ,

[0038] Calculate the degree of abnormal performance , get performance abnormality information, where is the weight coefficient, is the performance degradation rate, The degree of performance abnormality, is the performance loss ratio.

[0039] The present invention is improved in that the method for evaluating the abnormal level of a computing device is:

[0040] Based on the abnormal temperature information and the performance abnormality information, the degree of temperature abnormality is extracted. and performance abnormality , get the abnormal level associated data;

[0041] Based on the abnormal level association data, by formula:

[0042] ,

[0043] Calculating the anomaly level , get the abnormal level information, where is the degree of temperature anomaly, The degree of performance abnormality, and is the weight coefficient, is the nonlinear adjustment factor, It is an abnormal level.

[0044] The present invention is improved in that the step of obtaining the computing device maintenance requirement information is as follows:

[0045] Based on the abnormal level , combined with the computing device type, through the formula:

[0046] ,

[0047] Calculating maintenance priorities ,in, is the device type coefficient, For abnormal level, is the weight coefficient, To maintain priority, is the base of natural logarithms;

[0048] Based on the maintenance priorities ,according to The value is used to prioritize differentiated computing devices and obtain computing device maintenance demand information.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are:

[0050] In the present invention, by dynamically adjusting the temperature threshold of the equipment monitoring in the computer room based on the environment and the age of the equipment, the evaluation process of temperature anomalies is optimized, and the temperature anomaly can be immediately identified at the early stage of the temperature deviation, thereby shortening the response time and reducing the damage caused by equipment overheating. By comparing the current performance of the equipment with the benchmark performance, the performance loss ratio and the performance degradation rate are calculated, and the performance anomaly is evaluated accordingly, so that the maintenance team can respond quickly and reduce the spread of problems and losses. According to the importance of the equipment and the severity of the fault, the maintenance priority is adjusted to achieve the rationalization and systematization of the maintenance work, effectively avoiding the occurrence of major faults, and at the same time improving the overall stability and safety of the equipment in the computer room. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a system flow chart of the present invention;

[0052] Figure 2 A flow chart of obtaining temperature threshold adjustment information of the present invention;

[0053] Figure 3 A flow chart for evaluating the degree of temperature deviation for the present invention;

[0054] Figure 4 A flow chart of obtaining abnormal temperature information according to the present invention;

[0055] Figure 5 A flow chart of calculating the performance degradation rate of the present invention;

[0056] Figure 6 A flow chart of obtaining performance abnormality information of the present invention;

[0057] Figure 7 A flow chart for evaluating anomaly levels of computing devices for the present invention;

[0058] Figure 8 A flow chart of obtaining computing equipment maintenance requirement information according to the present invention. DETAILED DESCRIPTION

[0059] 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.

[0060] 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.

[0061] See also Figure 1 The present invention provides a technical solution: a computer room maintenance monitoring system, the system comprising:

[0062] The temperature threshold adjustment module monitors the humidity and temperature data in the current computer room based on the operating environment of the computer room, and adjusts the abnormal temperature detection threshold in combination with the service life of the computing equipment to obtain temperature threshold adjustment information;

[0063] The temperature anomaly assessment module compares the current temperature of the computing device with the adjusted temperature detection threshold based on the temperature threshold adjustment information, assesses the degree of temperature deviation, and assesses the degree of temperature anomaly in combination with the duration of the temperature deviation to obtain abnormal temperature information;

[0064] The performance anomaly identification module monitors the operation performance data of the computing device based on the operation status of the computing device, calculates the performance degradation rate by analyzing the operation performance data over a period of time, and compares the current performance with the benchmark performance, calculates the performance loss ratio, evaluates the degree of performance anomaly, and obtains performance anomaly information;

[0065] The maintenance sequence adjustment module evaluates the abnormal level of the computing equipment based on the abnormal temperature information and the performance abnormality information, evaluates the maintenance priority of the computing equipment in combination with the computing equipment type, and sorts the priorities to obtain the computing equipment maintenance demand information.

[0066] The temperature threshold adjustment information includes the adjusted temperature detection threshold and the corresponding device type. The abnormal temperature information includes the abnormal level, abnormal duration and temperature deviation value. The performance abnormality information includes the device identification, performance degradation rate and performance loss ratio value. The computing device maintenance requirement information includes the maintenance priority, maintenance urgency and computing device identification.

[0067] See also Figure 2 , the steps for obtaining the temperature threshold adjustment information are:

[0068] Based on the operating environment of the computer room, collect the ambient temperature and humidity data in the current computer room to obtain real-time environmental data;

[0069] Based on real-time environmental data, combined with the age of computing equipment and the basic abnormal temperature detection threshold, the formula is:

[0070] ,

[0071] Calculate the adjusted temperature detection threshold , get the temperature threshold adjustment information, where, is the temperature adjustment coefficient, is the humidity adjustment factor, is the age adjustment factor, is the exponential coefficient, is the real-time ambient temperature, is the real-time ambient humidity, is the basic abnormal temperature detection threshold, is the current equipment age, The maximum service life designed for the equipment, is the standard ambient temperature, is the standard ambient humidity, is the adjusted temperature detection threshold.

[0072] formula: ,

[0073] Parameter details and how to obtain them: :Basic abnormal temperature detection threshold, usually set by the equipment manufacturer based on the most ideal operating conditions and can be obtained from the equipment operation manual or technical specification.

[0074] and :It is the real-time temperature and humidity data of the computer room environment, which is obtained through real-time monitoring by sensors installed in the computer room to ensure the real-time and accuracy of the data.

[0075] and : Standard ambient temperature and humidity values, set according to the operating environment recommendations provided by the manufacturer.

[0076] , , and : Weight coefficients and indices, usually obtained through historical data analysis and experimental adjustment to optimize the performance response of the equipment.

[0077] and : is the age of the current equipment, which can be calculated from the equipment installation date and the current date. It is the maximum service life of the equipment designed, usually provided by the manufacturer.

[0078] Calculation example:

[0079] Assumptions: ,

[0080] calculate :

[0081] ,

[0082] Calculated , represents the adjusted abnormal temperature threshold, which means that, considering the increase in the age of the equipment and the current ambient temperature and humidity being higher than the standard values, the abnormal temperature threshold of the equipment should be adjusted to a higher value to prevent false alarms caused by environmental changes and equipment aging.

[0083] See also Figure 3 , the method for evaluating the degree of temperature deviation is:

[0084] The current real-time temperature of the computing device is collected through the temperature sensor built into the computing device to obtain the real-time device temperature information;

[0085] Based on the real-time device temperature information and temperature threshold adjustment information, the formula is:

[0086] ,

[0087] Calculate the degree of temperature deviation , get the temperature deviation information, where is the device sensitivity coefficient, is the environmental impact factor, To calculate the current temperature of the device, is the adjusted temperature detection threshold, is the degree of temperature deviation.

[0088] formula: ,

[0089] Parameter details and how to obtain them: Current real-time temperature: This is obtained through real-time monitoring by temperature sensors installed on computing devices. These sensors can provide accurate temperature readings for monitoring the operating status of the equipment.

[0090] Adjusted temperature detection threshold: calculated in the previous step.

[0091] Equipment sensitivity coefficient: It is a coefficient set according to the equipment model and the manufacturer's test data to indicate the equipment's sensitivity to temperature changes. In practical applications, this coefficient can be obtained from the equipment's technical manual or through experimental determination.

[0092] Environmental impact factor: reflects the impact of environmental conditions (such as humidity and dust level) on equipment performance. This factor is set based on data from the environmental monitoring system and usually involves statistical analysis of long-term environmental data.

[0093] Calculation example:

[0094] Assume the following specific values:

[0095] (Current real-time temperature of the device), (Adjusted temperature detection threshold), (device sensitivity coefficient), (Environmental impact factors).

[0096] Calculation process: ;

[0097] calculate The difference and divided by : ;

[0098] calculate , calculated as natural logarithm: ;

[0099] Compute products within square roots: ;

[0100] calculate : ,

[0101] Calculation results , indicating the degree of deviation between the current device temperature and the preset threshold. The smaller the value, the smaller the deviation.

[0102] See also Figure 4 , the steps to obtain abnormal temperature information are:

[0103] Based on the degree of temperature deviation , collect the duration data of temperature deviation and obtain the temperature anomaly related data;

[0104] Based on the temperature anomaly correlation data, the formula is: ,

[0105] Calculate the degree of temperature anomaly in, is the time sensitivity coefficient, is the time offset constant, is the degree of temperature deviation, The duration of temperature deviation is the degree of temperature anomaly, is the base of natural logarithms;

[0106] Based on the degree of temperature abnormality ,according to The value is used to evaluate the severity of the temperature anomaly of the computing device and obtain the abnormal temperature information.

[0107] formula: ,

[0108] Parameter details and how to obtain them:

[0109] : The degree of temperature deviation, calculated in the previous step.

[0110] : Represents the duration of the temperature deviation, starting from the time when the temperature begins to deviate from the set threshold, usually in minutes or hours. This duration is recorded and calculated in the monitoring system through the timestamp.

[0111] : Time sensitivity coefficient, which adjusts the influence of time variables. This coefficient is usually obtained through experiments or data analysis based on the historical failure data of the equipment and the environmental response characteristics.

[0112] : Time offset constant, used to adjust the model's sensitivity to initial temperature changes so that the model responds appropriately to abnormal evaluations of temperature changes in a short period of time. Set based on device characteristics and environmental conditions.

[0113] Calculation example:

[0114] Assumed parameter values: : Assumption deviation degree, : Assuming the temperature deviation has lasted for 120 minutes, : time sensitivity coefficient, Minutes: Time offset constant.

[0115] Formula calculation process: ;

[0116] Calculate the exponential part: ;

[0117] Calculate the denominator: ;

[0118] calculate : ,

[0119] Calculated , which represents the actual degree of temperature anomaly after considering the influence of the duration of temperature deviation, indicating that although the deviation lasted for a long time, due to the small time sensitivity coefficient, the regulatory effect of time offset made the final increase in the degree of anomaly not very large.

[0120] See also Figure 5 , the method for calculating the performance degradation rate is:

[0121] Based on the running status of the computing device, monitor the running performance data of the computing device, the running performance data includes CPU usage, memory usage and response time;

[0122] Based on the operating performance data, the formula:

[0123] ,

[0124] Calculate the performance degradation rate ,in, is the number of data points, It's time point Performance data, is the time interval between data points, It's time point The weight of For baseline performance data, is the performance degradation rate.

[0125] formula:

[0126] Parameter details and how to obtain them:

[0127] : Baseline performance data, usually measured under no-load or standard operating conditions of the equipment. Usually occurs during the equipment commissioning phase or periodic maintenance inspections to ensure that the performance of the equipment under ideal conditions is recorded.

[0128] : At time point The data is directly obtained from the system monitoring tool, which usually includes indicators such as CPU usage and memory usage to reflect the device operation status at a specific time point.

[0129] : The total number of data points. The value indicates the total number of performance data points included in the analysis period. This value is determined by the settings of the monitoring period. For example, if data is recorded every 10 minutes and the monitoring lasts for one hour, then is 6.

[0130] : The time interval between data points, which is set according to the monitoring configuration. For example, if data is recorded every 10 minutes, For 10 minutes.

[0131] : Time point Weights are assigned based on the reliability of the data point or its importance in the business process. For example, data points during peak hours may be given higher weights.

[0132] Calculation Example

[0133] Assume the following parameter values: : Benchmark performance, such as processing 100 tasks per hour.

[0134] : Performance data at three time points.

[0135] : Three data points.

[0136] : Record data every hour.

[0137] : The weights at different time points reflect the importance of the business at different times.

[0138] Calculation process: ;

[0139] Calculated , indicating that when the time weight is taken into account, the average performance degradation rate per unit time is 16.67 tasks per hour, which indicates the average degradation rate of device performance.

[0140] See also Figure 6 , the steps to obtain performance abnormality information are:

[0141] Collect the current performance data of the computing device and compare it with the baseline performance data through the formula:

[0142] ,

[0143] Calculate the performance loss ratio ,in, To calculate the current performance data of the device, For baseline performance data, is the performance loss ratio;

[0144] Based on the performance loss ratio Combined performance degradation rate , through the formula:

[0145] ,

[0146] Calculate the degree of abnormal performance , get performance abnormality information, where is the weight coefficient, is the performance degradation rate, The degree of performance abnormality, is the performance loss ratio.

[0147] formula: and Parameter details and how to obtain them

[0148] :Current performance indicators, obtained through real-time monitoring, usually include key performance indicators such as CPU usage and memory usage. For example, if the monitoring system is set to automatically record performance data once a minute, This is the latest record.

[0149] : A baseline performance data point, usually measured at no load during equipment installation or maintenance, used as a baseline for performance comparison. This is performance data under a set of standard operating conditions obtained when the equipment is initially configured or after major maintenance.

[0150] : Performance degradation rate, calculated by the previous step.

[0151] : The weight coefficient is an empirical value obtained by adjusting the sensitivity of device performance and historical data, and is used to adjust the impact of the performance degradation rate on the final abnormality level.

[0152] : The degree of performance abnormality is a comprehensive indicator that combines the performance loss ratio and performance degradation rate to evaluate whether the performance status of the device is abnormal.

[0153] Calculation example:

[0154] Assumed parameter values:

[0155] : The benchmark performance unit is the number of tasks processed per hour.

[0156] : Current performance.

[0157] : Indicates that the performance decreases by 0.05 per hour on average.

[0158] : Weight coefficient.

[0159] Calculate performance loss ratio : ;

[0160] Calculate the degree of abnormal performance : ;

[0161] Calculated , which means that after taking into account the performance degradation rate, the performance abnormality degree is 0.201. The larger the value, the more serious the performance abnormality.

[0162] See also Figure 7 ,The method for evaluating the abnormal level of computing equipment is:

[0163] Extract the degree of temperature anomaly based on abnormal temperature information and performance anomaly information and performance abnormality , get the abnormal level associated data;

[0164] Based on the abnormal level correlation data, through the formula:

[0165] ,

[0166] Calculating the anomaly level , get the abnormal level information, where is the degree of temperature anomaly, The degree of performance abnormality, and is the weight coefficient, is the nonlinear adjustment factor, It is an abnormal level.

[0167] formula:

[0168] Parameter details and how to obtain them:

[0169] : The degree of temperature anomaly, calculated by the previous steps.

[0170] : The degree of performance abnormality, calculated by the previous step.

[0171] and : Weight coefficients used to adjust the impact of temperature anomalies and performance anomalies in calculating the total anomaly level. These weight coefficients are set based on equipment type and sensitivity through historical data analysis.

[0172] : Nonlinear adjustment factor used to adjust the sensitivity and complexity of the anomaly level calculation to ensure that the model can effectively distinguish between minor and severe anomalies.

[0173] Calculation example:

[0174] Assume the following parameter values:

[0175] Indicates the current temperature abnormality of the device.

[0176] : Indicates the degree of abnormal performance of the device.

[0177] : Weight coefficient of temperature anomaly.

[0178] : Weight coefficient of performance anomaly.

[0179] : Nonlinear adjustment factor.

[0180] Formula calculation process: ;

[0181] First calculate the parts, using natural logarithms: ;

[0182] Compute the weighted sum: ;

[0183] Apply a nonlinear adjustment factor: ,

[0184] Calculated This means that after comprehensively considering temperature and performance anomalies, the overall abnormality level of the device is 0.267. This value is in a relatively low range, indicating that although there are certain anomalies, the overall abnormality level is not particularly serious.

[0185] See also Figure 8,The steps for obtaining computing equipment maintenance requirement information are:

[0186] Based on abnormal level , combined with the computing device type, through the formula:

[0187] ,

[0188] Calculating maintenance priorities ,in, is the device type coefficient, For abnormal level, is the weight coefficient, To maintain priority, is the base of natural logarithms;

[0189] Based on the maintenance priorities ,according to The value is used to prioritize differentiated computing devices and obtain computing device maintenance demand information.

[0190] formula:

[0191] Parameter details and how to obtain them:

[0192] : Abnormality level, calculated by the previous step.

[0193] : Weight coefficient, which is a coefficient based on the importance of the equipment type and the sensitivity of the impact on the business. The coefficient is set through historical data analysis and evaluation of the impact of equipment failure on the business.

[0194] :The index adjustment coefficient of the equipment type. Different coefficient values ​​are preset according to the different equipment types to reflect the business importance and maintenance urgency of different equipment types.

[0195] Calculation example:

[0196] Assume the following specific parameters:

[0197] : The abnormal level of the device.

[0198] : Weight coefficient, indicating that the abnormality level has a higher importance in the maintenance priority calculation.

[0199] : Assume the device type is server.

[0200] Calculation process: ,

[0201] Calculate the parts: ,

[0202] Then calculate the weighted sum: ,

[0203] Calculated This indicates that the maintenance priority score for this server is 0.6588, considering the abnormality level and type of the device. This value is in the upper-middle range, indicating that the device should be considered a relatively high priority for inspection and maintenance. This calculation method ensures timely maintenance of critical equipment, helping to prevent business interruptions.

[0204] 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. A computer room maintenance monitoring system, characterized in that: The system comprises: The temperature threshold adjustment module monitors the humidity and temperature data in the current computer room based on the operating environment of the computer room, and adjusts the abnormal temperature detection threshold in combination with the service life of the computing equipment to obtain temperature threshold adjustment information; The steps for obtaining the temperature threshold adjustment information are as follows: Based on the operating environment of the computer room, collect the ambient temperature and humidity data in the current computer room to obtain real-time environmental data; Based on the real-time environmental data, combined with the age of the computing equipment and the basic abnormal temperature detection threshold, the formula is: ， Calculate the adjusted temperature detection threshold , get the temperature threshold adjustment information, where, is the temperature adjustment coefficient, is the humidity adjustment factor, is the age adjustment factor, is the exponential coefficient, is the real-time ambient temperature, is the real-time ambient humidity, is the basic abnormal temperature detection threshold, is the current equipment age, The maximum service life designed for the equipment, is the standard ambient temperature, is the standard ambient humidity, is the adjusted temperature detection threshold; The temperature anomaly assessment module compares the current temperature of the computing device with the adjusted temperature detection threshold based on the temperature threshold adjustment information, assesses the degree of temperature deviation, and assesses the degree of temperature anomaly in combination with the duration of the temperature deviation to obtain abnormal temperature information; The method for evaluating the degree of temperature deviation is: The current real-time temperature of the computing device is collected through the temperature sensor built into the computing device to obtain the real-time device temperature information; Based on the real-time device temperature information and the temperature threshold adjustment information, the formula: ， Calculate the degree of temperature deviation , get the temperature deviation information, where is the device sensitivity coefficient, is the environmental impact factor, To calculate the current temperature of the device, is the adjusted temperature detection threshold, is the degree of temperature deviation; The steps for obtaining the abnormal temperature information are as follows: Based on the degree of temperature deviation , collect the duration data of temperature deviation and obtain the temperature anomaly related data; Based on the temperature anomaly correlation data, the formula: ， Calculate the degree of temperature anomaly ,in, is the time sensitivity coefficient, is the time offset constant, is the degree of temperature deviation, is the duration of temperature deviation, is the degree of temperature anomaly, is the base of natural logarithms; Based on the degree of temperature abnormality ,according to The value is used to evaluate the severity of the temperature anomaly of the computing equipment and obtain the abnormal temperature information; The performance anomaly identification module monitors the operating performance data of the computing device based on the operating status of the computing device, calculates the performance degradation rate by analyzing the operating performance data over a period of time, and compares the current performance with the benchmark performance, calculates the performance loss ratio, evaluates the degree of performance anomaly, and obtains performance anomaly information; The maintenance sequence adjustment module evaluates the abnormality level of the computing device based on the abnormal temperature information and the performance abnormality information, evaluates the maintenance priority of the computing device in combination with the computing device type, and performs priority sorting to obtain the computing device maintenance requirement information.

2. The computer room maintenance monitoring system according to claim 1, characterized in that: The method for calculating the performance degradation rate is: Based on the running status of the computing device, monitor the running performance data of the computing device, the running performance data includes CPU usage, memory usage and response time; Based on the operating performance data, the formula: ， Calculate the performance degradation rate ,in, is the number of data points, It's time point Performance data, is the time interval between data points, It's time point The weight of For baseline performance data, is the performance degradation rate.

3. The computer room maintenance monitoring system according to claim 2, characterized in that: The steps for obtaining the performance abnormality information are as follows: Collect the current performance data of the computing device and compare it with the baseline performance data using the formula: ， Calculate the performance loss ratio ,in, To calculate the current performance data of the device, For baseline performance data, is the performance loss ratio; Based on the performance loss ratio Combined performance degradation rate , through the formula: ， Calculate the degree of abnormal performance , get performance abnormality information, where is the weight coefficient, is the performance degradation rate, The degree of performance abnormality, is the performance loss ratio.

4. The computer room maintenance monitoring system according to claim 1, characterized in that: The method for evaluating the abnormality level of a computing device is: Based on the abnormal temperature information and the performance abnormality information, the degree of temperature abnormality is extracted. and performance abnormality , get the abnormal level associated data; Based on the abnormal level association data, by formula: ， Calculating the anomaly level , get the abnormal level information, where, is the degree of temperature anomaly, The degree of performance abnormality, and is the weight coefficient, is the nonlinear adjustment factor, It is an abnormal level.

5. The computer room maintenance monitoring system according to claim 4, characterized in that: The steps for obtaining the computing device maintenance requirement information are as follows: Based on the abnormal level , combined with the computing device type, through the formula: ， Calculating maintenance priorities ,in, is the device type coefficient, For abnormal level, is the weight coefficient, To maintain priority, is the base of natural logarithms; Based on the maintenance priorities ,according to The value is used to prioritize differentiated computing devices and obtain computing device maintenance demand information.

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