A computer room intelligent monitoring method, device and electronic equipment

Abstract

A computer room intelligent monitoring method, device and electronic equipment, relating to the field of computer room monitoring. The method comprises: obtaining multiple historical operation data groups of a target entity system collected by multiple data acquisition devices, the target entity system comprising multiple computer room devices, one computer room device corresponding to one historical operation data group; based on the multiple historical operation data groups, obtaining a first operation rule of the target entity system; calculating the first predicted operation data of the target entity system in a preset first time period according to the first operation rule; obtaining the first actual operation data of the target entity system in the preset first time period; adjusting the calculation parameters in the first operation rule according to the comparison result of the first predicted operation data and the first actual operation data, and analyzing to obtain a second operation rule of the target entity system; calculating the second predicted operation data of the target entity system in a preset second time period according to the second operation rule, the preset second time period being after the preset first time period; judging whether the target entity system has an abnormality in the preset second time period according to the second predicted operation data, if it is determined that the target entity system has an abnormality in the preset second time period, then sending the second actual operation data of the target entity system collected in the preset second time period to a remote monitoring terminal. The technical solution of the present application solves the problem that only through manual inspection, when the monitoring data is missed or wrongly detected during the inspection process, the monitoring result will be inaccurate.

Description

Technical Field

[0001] The present application relates to the field of computer room monitoring, and in particular to a computer room intelligent monitoring method, device and electronic equipment. Background Art

[0002] Computer room monitoring refers to the process of real-time monitoring and management of the computer room environment and equipment. Its purpose is to ensure the normal operation of the equipment in the computer room, the stability of the environment, and to promptly discover and solve problems that may affect the operation of the computer room, so as to ensure the stability, security and reliability of the data center.

[0003] Nowadays, computer room monitoring is usually carried out based on manual inspections, that is, specialized operation and maintenance personnel regularly inspect the computer room environment and equipment status to achieve the purpose of real-time monitoring and management of the computer room environment and equipment. However, if only manual inspections are performed, when monitoring data is missed or misdetected during the inspection process, the monitoring results will be inaccurate.

[0004] Therefore, there is an urgent need for an intelligent monitoring method, device and electronic equipment for a computer room. Summary of the invention

[0005] The present application provides a method, device and electronic equipment for intelligent monitoring of a computer room, which solves the problem that inaccurate monitoring results may occur when monitoring data is missed or misdetected during the inspection process only through manual inspection.

[0006] In a first aspect of the present application, a method for intelligent monitoring of a computer room is provided, the method comprising: obtaining multiple historical operation data groups of a target entity system collected by multiple data collection devices, the target entity system comprising multiple computer room devices, one computer room device corresponding to one historical operation data group; based on the multiple historical operation data groups, obtaining a first operation rule of the target entity system; calculating first predicted operation data of the target entity system in a preset first time period according to the first operation rule; obtaining first actual operation data of the target entity system in the preset first time period; adjusting calculation parameters in the first operation rule according to a comparison result between the first predicted operation data and the first actual operation data, and analyzing to obtain a second operation rule of the target entity system; calculating second predicted operation data of the target entity system in a preset second time period according to the second operation rule, the preset second time period being after the preset first time period; judging whether an abnormality occurs in the target entity system in the preset second time period according to the second predicted operation data, and if it is determined that an abnormality occurs in the target entity system in the preset second time period, sending the second actual operation data of the target entity system collected in the preset second time period to a remote monitoring terminal.

[0007] By adopting the above technical solution, multiple historical operation data groups corresponding to multiple computer room equipment in the target entity system are obtained, and based on the multiple historical operation data groups, the first operation law of the target entity system is obtained, so as to calculate the first predicted operation data of the target entity system in the preset first time period according to the first operation law. After that, the first actual operation data of the target entity system in the preset first time period is obtained, and the calculation parameters in the first operation law are adjusted according to the comparison result of the first predicted operation data and the first actual operation data, and the second operation law of the target entity system is analyzed to obtain the second predicted operation data of the target entity system in the preset second time period, so as to calculate the second predicted operation data of the target entity system in the preset second time period according to the second operation law obtained after the adjustment. At this time, the accuracy of judging whether the target entity system has an abnormality in the preset second time period is improved. When it is determined that the target entity system has an abnormality in the preset second time period, the second actual operation data of the target entity system collected in the preset second time period is sent to the remote monitoring terminal, thereby solving the problem of inaccurate monitoring results when the monitoring data is missed or wrongly detected during the inspection process only through manual inspection.

[0008] Optionally, based on multiple historical operation data groups, the first operation law of the target entity system is obtained, specifically including: obtaining multiple computer room equipment models to be trained, one computer room equipment model to be trained corresponds to one computer room equipment; inputting multiple historical operation data groups into multiple computer room equipment models to be trained, so as to obtain multiple target computer room equipment models corresponding to the multiple computer room equipment through model training, one computer room equipment corresponds to one target computer room equipment model, and the target computer room equipment model is used to simulate the operation behavior of the corresponding computer room equipment; according to the multiple target computer room equipment models, the first operation law of the target entity system is obtained.

[0009] By adopting the above technical solution, multiple computer room equipment models to be trained are obtained, and multiple historical operation data groups are input into the multiple computer room equipment models to be trained, so as to obtain multiple target computer room equipment models corresponding to the multiple computer room equipment through model training. The target computer room equipment model is used to simulate the operation behavior of the corresponding computer room equipment, so as to obtain the first operation law of the target entity system according to the multiple target computer room equipment models.

[0010] Optionally, the multiple historical operation data groups include a historical computer room temperature data group, a historical computer room humidity data group, and a historical computer room energy consumption data group; the first operation law includes the overall temperature change law, the overall humidity change law, and the overall energy consumption change law in the target entity system.

[0011] By adopting the above technical solution, multiple historical operation data groups are used, including but not limited to historical computer room temperature data groups, historical computer room humidity data groups, historical computer room energy consumption data groups, etc., to construct a target computer room equipment model corresponding to the computer room equipment, thereby increasing the prediction range of the target computer room equipment model, so as to obtain a more extensive and comprehensive first operation law, which includes the overall temperature change law, the overall humidity change law and the overall energy consumption change law in the target entity system.

[0012] Optionally, when multiple historical operation data groups are historical computer room temperature data groups, first predicted operation data of the target entity system in a preset first time period is calculated according to the first operation rule, specifically including: according to multiple target computer room equipment models, obtaining predicted equipment temperature data corresponding to multiple computer room equipment respectively, the predicted equipment temperature data including predicted temperature weight coefficient, predicted equipment power, predicted heat dissipation efficiency, predicted ventilation volume and heat dissipation coefficient; according to the predicted equipment temperature data, determining the first operation rule, and calculating the first predicted operation data of the target entity system in the preset first time period, the first operation rule is the overall temperature change rule, and the first predicted operation data is the predicted computer room temperature data.

[0013] By adopting the above technical scheme, the temperature operation behavior of the corresponding computer room equipment is simulated according to multiple target computer room equipment models, and the predicted equipment temperature data corresponding to the multiple computer room equipment are obtained. The predicted equipment temperature data includes the predicted temperature weight coefficient, the predicted equipment power, the predicted heat dissipation efficiency, the predicted ventilation volume and the heat dissipation coefficient, so as to determine the first operation rule according to the predicted equipment temperature data, and calculate the first predicted operation data of the target entity system in the preset first time period. The first operation rule is the overall temperature change rule, and the first predicted operation data is the predicted computer room temperature data. Similarly, according to the multiple target computer room equipment models, the humidity operation behavior of the corresponding computer room equipment is simulated, and the predicted equipment humidity data corresponding to the multiple computer room equipment and the predicted computer room humidity data corresponding to the target entity system can be obtained; according to the multiple target computer room equipment models, the energy consumption operation behavior of the corresponding computer room equipment is simulated, and the predicted equipment energy consumption data corresponding to the multiple computer room equipment and the predicted computer room energy consumption data corresponding to the target entity system can be obtained.

[0014] Optionally, calculating the first predicted operation data of the target entity system in a preset first time period specifically includes: calculating the first predicted operation data of the first time period according to the following formula, where the first predicted operation data is predicted computer room temperature data:

[0015] ;

[0016] Among them, T t0is the predicted temperature at the starting time point in the first time period, T t1 is the predicted temperature at the end time point in the first time period, α i is the predicted temperature weight coefficient of the equipment in the i-th computer room, P i is the predicted equipment power of the i-th equipment room, δ i is the predicted heat dissipation efficiency of the equipment in the i-th computer room, V(τ) is the predicted ventilation volume in the target entity system at the τ-th time, C(τ) is the heat dissipation coefficient in the target entity system at the τ-th time, and the τ-th time is any time in the first time period.

[0017] By adopting the above technical solution, the first predicted operating data of the first time period is calculated according to the above formula. The first predicted operating data is the predicted computer room temperature data, so as to obtain the temperature data at any time point in the first time period, thereby determining the overall temperature change law of the target entity system in the first time period.

[0018] Optionally, based on the comparison result between the first predicted operating data and the first actual operating data, the calculation parameters in the first operating law are adjusted, and the second operating law of the target entity system is obtained by analysis, specifically including: obtaining a first predicted operating value based on the first predicted operating data, and obtaining an actual value based on the first actual operating data; obtaining a target value difference between the first predicted operating value and the actual value, the target value difference being the absolute value of the difference between the first predicted operating value and the first actual operating data; determining an adjustment degree of the calculation parameters based on the target value difference; adjusting the calculation parameters based on the adjustment degree, and obtaining the second operating law of the target entity system by analysis, wherein the target value difference is directly proportional to the adjustment degree.

[0019] By adopting the above technical solution, a first predicted operation value is obtained according to the first predicted operation data, an actual value is obtained according to the first actual operation data, and a target value difference between the first predicted operation value and the actual value is obtained, and the target value difference is the absolute value of the difference between the first predicted operation value and the first actual operation data, so as to determine the adjustment degree of the calculation parameters according to the target value difference, and adjust the calculation parameters according to the adjustment degree, and analyze and obtain the second operation law of the target entity system, so that the second predicted operation data obtained through the second operation law is more accurate and reliable.

[0020] Optionally, determining that the target entity system has an abnormality in a preset second time period specifically includes: obtaining a second predicted operation value based on second predicted operation data; determining whether the second predicted operation value is a normal preset value; if the second predicted operation value is not a normal preset value, determining that the target entity system has an abnormality in the preset second time period.

[0021] By adopting the above technical solution, by judging whether the second predicted operation value is a normal preset value, it is possible to confirm that an abnormality occurs in the target entity system in the preset second time period when the second predicted operation value is not a normal preset value.

[0022] In a second aspect of the present application, a computer room intelligent monitoring device is provided, the device comprising an acquisition module and a processing module, wherein:

[0023] An acquisition module is used to acquire multiple historical operation data groups of a target entity system collected by multiple data acquisition devices, where the target entity system includes multiple computer room devices, and one computer room device corresponds to one historical operation data group; based on the multiple historical operation data groups, a first operation law of the target entity system is acquired; and first actual operation data of the target entity system in a preset first time period is acquired.

[0024] A processing module is used to calculate first predicted operating data of a target entity system in a preset first time period according to a first operating rule; adjust the calculation parameters in the first operating rule according to a comparison result between the first predicted operating data and the first actual operating data, and analyze and obtain a second operating rule of the target entity system; calculate second predicted operating data of the target entity system in a preset second time period according to the second operating rule, the preset second time period being after the preset first time period; determine whether an abnormality occurs in the target entity system in the preset second time period according to the second predicted operating data, and if it is determined that an abnormality occurs in the target entity system in the preset second time period, the second actual operating data of the target entity system collected in the preset second time period is sent to a remote monitoring terminal.

[0025] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0026] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform any of the above methods.

[0027] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0028] 1. By acquiring multiple historical operation data groups corresponding to multiple computer room equipment in the target entity system, and based on the multiple historical operation data groups, a first operation law of the target entity system is acquired, so as to calculate the first predicted operation data of the target entity system in a preset first time period according to the first operation law. After that, by acquiring the first actual operation data of the target entity system in the preset first time period, and adjusting the calculation parameters in the first operation law according to the comparison result of the first predicted operation data and the first actual operation data, a second operation law of the target entity system is obtained by analysis, so as to calculate the second predicted operation data of the target entity system in the preset second time period according to the second operation law obtained after adjustment. At this time, the accuracy of judging whether the target entity system has an abnormality in the preset second time period is improved. When it is determined that the target entity system has an abnormality in the preset second time period, the second actual operation data of the target entity system collected in the preset second time period is sent to the remote monitoring terminal, thereby solving the problem of inaccurate monitoring results when the monitoring data is missed or wrongly detected during the inspection process only through manual inspection.

[0029] 2. By using multiple historical operation data groups, including but not limited to historical computer room temperature data groups, historical computer room humidity data groups, historical computer room energy consumption data groups, etc., to construct a target computer room equipment model corresponding to the computer room equipment, thereby increasing the prediction range of the target computer room equipment model, so as to obtain a more extensive and comprehensive first operation law, which includes the overall temperature change law, the overall humidity change law and the overall energy consumption change law in the target entity system.

[0030] 3. Obtain a first predicted operating value based on the first predicted operating data, obtain an actual value based on the first actual operating data, and obtain a target value difference between the first predicted operating value and the actual value, where the target value difference is the absolute value of the difference between the first predicted operating value and the first actual operating data, so as to determine the adjustment degree of the calculation parameters according to the target value difference, and adjust the calculation parameters according to the adjustment degree, and analyze and obtain a second operating law of the target entity system, so that the second predicted operating data obtained through the second operating law is more accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of a method for intelligent monitoring of a computer room provided in an embodiment of the present application;

[0032] Figure 2 It is a module schematic diagram of a computer room intelligent monitoring device provided in an embodiment of the present application;

[0033] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0034] Explanation of the reference numerals: 21, acquisition module; 22, processing module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0035] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0036] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0037] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0038] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0039] Computer room monitoring refers to the process of real-time monitoring and management of the computer room environment and equipment. Its purpose is to ensure the normal operation of the equipment in the computer room, the stability of the environment, and to promptly discover and solve problems that may affect the operation of the computer room, so as to ensure the stability, security and reliability of the data center.

[0040] Nowadays, computer room monitoring is usually carried out based on manual inspections, that is, specialized operation and maintenance personnel regularly inspect the computer room environment and equipment status to achieve the purpose of real-time monitoring and management of the computer room environment and equipment. However, if only manual inspections are performed, when monitoring data is missed or misdetected during the inspection process, the monitoring results will be inaccurate.

[0041] Therefore, there is an urgent need for an intelligent monitoring method, device and electronic equipment for a computer room.

[0042] Please refer to Figure 1, which shows a flow chart of a method for intelligent monitoring of a computer room provided in an embodiment of the present application. The method is applied to a server. The flow chart mainly includes the following steps: S101 to S107.

[0043] Step S101 , obtaining multiple historical operation data groups of a target entity system collected by multiple data collection devices, the target entity system includes multiple computer room devices, and one computer room device corresponds to one historical operation data group.

[0044] Specifically, the target entity system is a computer room, and there are multiple computer room devices in the target entity system, including but not limited to network equipment, production machinery equipment, generators and UPS equipment, air conditioning equipment, etc. A computer room device is configured with one or more data acquisition devices. For example, the temperature data and humidity data of the same computer room device can be collected by a temperature acquisition device and a humidity acquisition device. The historical data of various different time periods collected in the same computer room device is called a historical operation data group corresponding to a computer room device. The data acquisition device collects the operation data of the computer room equipment in real time and saves the data in the database of the server so that the server can obtain it at any time. The server obtains multiple historical operation data groups of the target entity system in real time through multiple data acquisition devices, that is, the server obtains the historical operation data groups corresponding to all computer room devices in the target entity system through all data acquisition devices in the target entity system.

[0045] Step S102: acquiring a first operating rule of the target entity system based on a plurality of historical operating data groups.

[0046] Specifically, a first operating law of the target entity system is obtained through multiple historical operating data groups. The first operating law is the overall operating law of the computer room, that is, the operating law obtained after comprehensively evaluating the operating characteristics of all operating equipment in the computer room.

[0047] In a possible implementation, step S102 also includes: obtaining multiple computer room equipment models to be trained, one computer room equipment model to be trained corresponds to one computer room equipment; inputting multiple historical operation data groups into multiple computer room equipment models to be trained, so as to obtain multiple target computer room equipment models corresponding to the multiple computer room equipment through model training, one computer room equipment corresponds to one target computer room equipment model, and the target computer room equipment model is used to simulate the operating behavior of the corresponding computer room equipment; according to the multiple target computer room equipment models, obtaining the first operating law of the target entity system.

[0048] Specifically, for each computer room equipment in the target entity system, a suitable modeling method is selected to establish a corresponding computer room equipment model. The specific steps are as follows: Collect the historical operation data group of all computer room equipment in the target entity system, including but not limited to various sensor data, operation records, fault logs, etc. of each computer room equipment. Clean, process and standardize multiple historical operation data groups. Extract target features from the historical operation data, and the target features should be able to effectively describe the operation behavior of the corresponding computer room equipment. The target features include but are not limited to the power change state, temperature change state, humidity change state, energy consumption change state, etc. of the equipment. According to the target features of different computer room equipment, select a suitable computer room equipment model to be trained. The computer room equipment model to be trained can be any one of a variety of types of models, including but not limited to: linear regression model, decision tree model, random forest model, neural network model, etc. Input the target features into the selected computer room equipment model to be trained, and adjust the model parameters through the optimization algorithm so that the model can better fit the historical operation data group of each computer room equipment. Afterwards, the performance of the model is evaluated using a validation set or cross validation. The indicators used in the validation set or cross validation include mean square error (MSE), mean absolute error (MAE), etc., so as to ensure that the model can accurately simulate the operating behavior of the equipment in the computer room. Through the above steps, a target computer room equipment model is trained for each computer room equipment to simulate the operating behavior of different computer room equipment, that is, a target computer room equipment model is used to simulate the operating mode of a computer room equipment, and the operating behavior of each computer room equipment is predicted by the target computer room equipment model to predict the future operating data of each computer room equipment. The server obtains the operating law after comprehensively evaluating the operating characteristics of all the operating equipment in the computer room based on all the target computer room equipment models in the target entity system. The historical operating data group includes but is not limited to the historical computer room temperature data group, the historical computer room humidity data group, the historical computer room energy consumption data group, etc., wherein the temperature data group can be obtained through a temperature sensor, the humidity data group can be obtained through a humidity sensor, and the source consumption data group can be obtained through a source monitor. By obtaining data groups such as the computer room temperature data group, historical computer room humidity data group, historical computer room energy consumption data group, etc. from each computer room equipment, the target computer room equipment model can simulate the temperature change behavior, humidity change behavior, and body energy consumption change behavior of the corresponding computer room equipment. Afterwards, based on different historical data groups, the server can obtain different first operating laws including the overall temperature change law, the overall humidity change law, and the overall energy consumption change law in the target entity system.

[0049] Step S103: calculating first predicted operation data of the target entity system in a preset first time period according to the first operation rule.

[0050] Specifically, by simulating the operation of each computer room equipment through multiple target computer room equipment models in step S102, the overall operation law of the target entity system, i.e., the first operation law, is obtained, and based on the first operation law, the overall operation data of the target entity system in the first time period, i.e., the first operation data, is obtained.

[0051] In a possible implementation, step S103 also includes: obtaining predicted equipment temperature data corresponding to multiple computer room equipment respectively according to multiple target computer room equipment models, the predicted equipment temperature data including predicted equipment power, predicted heat dissipation efficiency, predicted ventilation volume and heat dissipation coefficient; determining a first operating law according to the predicted equipment temperature data, and calculating the first predicted operating data of the target entity system in a preset first time period, the first operating law being the overall temperature change law, and the first predicted operating data being the predicted computer room temperature data.

[0052] Specifically, according to different historical data groups, the server can obtain different first operating rules in the target entity system. The first operating rule includes but is not limited to: overall temperature change rule, overall humidity change rule and overall energy consumption change rule. For example, taking the temperature change rule as an example, through the simulation of the temperature change behavior of each computer room equipment by multiple target computer room equipment models, the predicted device temperature data of each computer room equipment in the first time period is obtained. The predicted device temperature data includes but is not limited to the predicted temperature weight coefficient, predicted device power, predicted heat dissipation efficiency, predicted ventilation volume, heat dissipation coefficient, etc. of each computer room equipment in the first time period, so as to determine the overall temperature change rule of the computer room as a whole, that is, the first operating rule, and then the overall temperature change rule is calculated according to the first predicted operating data of the target entity system in the preset first time period, that is, the predicted computer room temperature data, and the predicted computer room temperature data is the overall temperature data of the computer room. It should be noted that this embodiment does not limit the number of predicted device temperature data required to determine the overall temperature change rule of the computer room as a whole. Other predicted device temperature data also include predicted heat transfer data, predicted external environment temperature data, etc. The more predicted device temperature data used, the more accurate the first predicted operating data of the preset first time period calculated by the first operating rule. For other computer room operation rules such as the overall humidity change rule and the overall energy consumption change rule, it is only necessary to change the prediction device data and obtain them in the same way. This embodiment will not repeat the examples.

[0053] In a possible implementation, step S103 further includes: calculating first predicted operation data for the first time period according to the following formula, where the first predicted operation data is predicted computer room temperature data:

[0054] ;

[0055] Among them, T t0 is the predicted temperature at the starting time point in the first time period, T t1 is the predicted temperature at the end time point in the first time period, α i is the predicted temperature weight coefficient of the i-th equipment in the computer room. The larger the temperature weight coefficient of the equipment in the computer room, the greater its impact on the overall temperature of the computer room. i is the predicted equipment power of the i-th equipment room, δ i is the predicted heat dissipation efficiency of the equipment in the i-th computer room, V(τ) is the predicted ventilation volume in the target entity system at the τ-th time, C(τ) is the heat dissipation coefficient in the target entity system at the τ-th time, and the τ-th time is any time in the first time period.

[0056] Specifically, the above formula is the overall temperature change law of the target entity system in the first time period when the predicted device temperature data is the predicted device power, predicted heat dissipation efficiency, predicted ventilation volume, and heat dissipation coefficient, where the first time period is t0-t1, T t0 is the predicted temperature at the starting time point in the first time period, T t1 is the predicted temperature at the end time point of the first time period, δ i is the predicted heat dissipation efficiency of the equipment in the i-th computer room, V(τ) is the predicted ventilation volume in the target entity system at the τ-th time, C(τ) is the heat dissipation coefficient in the target entity system at the τ-th time, and the τ-th time is any time in the first time period. By determining the τ-th time, the overall temperature of the target entity system corresponding to the τ-th time can be obtained.

[0057] Step S104: obtaining first actual operation data of the target entity system in a preset first time period.

[0058] Specifically, the first actual operation data of the target entity system in the preset first time period is also acquired through multiple data acquisition devices. The actual operation data includes actual temperature data, actual humidity data, actual energy consumption data, etc. of each equipment in the computer room.

[0059] Step S105 , according to the comparison result between the first predicted operation data and the first actual operation data, the calculation parameters are adjusted to analyze and obtain the second operation rule of the target entity system.

[0060] Specifically, the server compares and analyzes the first predicted operation data and the first actual operation data, and adjusts the calculation parameters in the first operation rule according to the analysis result, so as to obtain the second operation rule.

[0061] In a possible implementation, step S105 further includes: obtaining a first predicted operation value according to the first predicted operation data, and obtaining an actual value according to the first actual operation data; obtaining a target value difference between the first predicted operation value and the actual value, the target value difference being the absolute value of the difference between the first predicted operation value and the first actual operation data; determining an adjustment degree of a calculation parameter according to the target value difference; adjusting the calculation parameter according to the adjustment degree, and analyzing to obtain a second operation law of the target entity system. The target value difference is proportional to the adjustment degree.

[0062] Specifically, the first predicted operation value is a specific parameter value of the first predicted operation data, including but not limited to the predicted temperature value, predicted humidity value, predicted energy consumption value, etc. in the first time period. Similarly, the actual value is a specific parameter value in the first actual operation data, including but not limited to the actual temperature value, actual humidity value, actual energy consumption value, etc. in the first time period. The server obtains the target value difference between the first predicted operation value and the actual value by calculation, and the target value difference is the absolute value of the difference between the first predicted operation value and the first actual operation data, thereby determining the adjustment degree of the calculation parameter in the first operation rule, wherein the larger the target value difference is, the larger the adjustment degree of the calculation parameter is. The above calculation parameters are adjusted according to the determined adjustment degree to obtain the second operation rule.

[0063] For example, taking the overall humidity change law in the first operating law as an example, the server can calculate the variance of the predicted temperature in the first time period and the variance of the actual temperature in the first time period, and then calculate the target numerical difference between the predicted temperature variance and the actual temperature variance. When the target numerical difference is larger, the server adjusts the calculation parameters corresponding to the overall humidity change law more. The adjustment can be specifically manifested as increasing the number of predicted device temperature data, such as heat transfer data, predicted external environment temperature data, etc. The greater the server adjusts the calculation parameters corresponding to the overall humidity change law, the more the number of predicted device temperature data increases; changing the predicted temperature weight coefficient of different computer room equipment in the target entity system; adjusting the step size of parameter adjustment to make the adjustment process slower or more aggressive. If the target numerical difference is small, a smaller step size can be selected to avoid over-adjustment. Otherwise, a larger step size can be selected to make the prediction result closer to the actual situation more quickly.

[0064] Step S106 , calculating second predicted operating data of the target entity system in a preset second time period according to the second operating rule, where the preset second time period is after the preset first time period.

[0065] Specifically, the second predicted operating data of the target entity system in the preset second time period is calculated according to the second operating rule. The preset second time period is any time period after the preset first time period. The time period can be set according to the user's monitoring needs. For example, on March 14, the user can set the server to predict the operating data of each computer room device in the target entity system on March 15; the server can also monitor automatically. For example, every day, the server can automatically predict the operating data of each computer room device in the target entity system on the next day. This embodiment does not limit the setting of the second time period. The second operating data also includes but is not limited to the predicted computer room temperature data, predicted humidity data, predicted energy consumption data, etc. of the target entity system in the second time period.

[0066] Step S107, based on the second predicted operation data, determine whether the target entity system has an abnormality in the preset second time period. If it is determined that the target entity system has an abnormality in the preset second time period, the second actual operation data of the target entity system collected in the preset second time period is sent to the remote monitoring terminal.

[0067] Specifically, according to the second predicted operation data, the second predicted operation value of the target entity system in the preset second time period is obtained, and the second predicted operation value includes, but is not limited to, the predicted temperature value, predicted humidity value, predicted energy consumption value, etc. in the second time period. The server determines whether there is any second predicted operation value among the second predicted operation values ​​such as the predicted temperature value, the predicted humidity value, the predicted energy consumption value, etc., which is not within the range of the normal preset value. If there is any second predicted operation value that is not within the range of the normal preset value, the server determines that the target entity system has an abnormality in the preset second time period, and sends the second actual operation data of the target entity system collected in the preset second time period to the remote monitoring terminal, so that the user can perform corresponding processing; if there is no second predicted operation value, which is within the range of the normal preset value, it means that in the preset second time period, the target entity system has not produced an abnormality in the preset second time period. The normal preset value is a manually set range threshold of the target entity system in normal operation, which can be set according to the actual operation of the target entity system. The setting range of the normal preset value is not limited in this embodiment.

[0068] The present application obtains multiple historical operation data groups corresponding to multiple computer room equipment in the target entity system by adopting the above method, and obtains the first operation law of the target entity system based on the multiple historical operation data groups, so as to calculate the first predicted operation data of the target entity system in the preset first time period according to the first operation law. After that, the first actual operation data of the target entity system in the preset first time period is obtained, and the calculation parameters in the first operation law are adjusted according to the comparison result of the first predicted operation data and the first actual operation data, and the second operation law of the target entity system is obtained by analysis, so as to calculate the second predicted operation data of the target entity system in the preset second time period according to the second operation law obtained after adjustment. At this time, the accuracy of judging whether the target entity system has an abnormality in the preset second time period is improved. When it is determined that the target entity system has an abnormality in the preset second time period, the second actual operation data of the target entity system collected in the preset second time period is sent to the remote monitoring terminal, thereby solving the problem of inaccurate monitoring results when the monitoring data is missed or wrongly detected during the inspection process only through manual inspection.

[0069] Please refer to Figure 2 , which shows a module schematic diagram of an intelligent monitoring device for a computer room provided in an embodiment of the present application, the device includes an acquisition module 21 and a processing module 22, wherein:

[0070] The acquisition module 21 is used to acquire multiple historical operation data groups of the target entity system collected by multiple data acquisition devices, where the target entity system includes multiple computer room devices, and one computer room device corresponds to one historical operation data group; based on the multiple historical operation data groups, the first operation law of the target entity system is acquired; and the first actual operation data of the target entity system in a preset first time period is acquired.

[0071] The processing module 22 is used to calculate the first predicted operating data of the target entity system in a preset first time period according to the first operating rule; adjust the calculation parameters in the first operating rule according to the comparison result of the first predicted operating data and the first actual operating data, and analyze and obtain the second operating rule of the target entity system; calculate the second predicted operating data of the target entity system in a preset second time period according to the second operating rule, and the preset second time period is after the preset first time period; determine whether the target entity system has an abnormality in the preset second time period according to the second predicted operating data, and if it is determined that the target entity system has an abnormality in the preset second time period, the second actual operating data of the target entity system collected in the preset second time period is sent to the remote monitoring terminal.

[0072] In a possible implementation, the acquisition module 21 is used to acquire the first operating law of the target entity system based on multiple historical operating data groups, specifically including: acquiring multiple computer room equipment models to be trained, one computer room equipment model to be trained corresponds to one computer room equipment; inputting multiple historical operating data groups into multiple computer room equipment models to be trained, so as to obtain multiple target computer room equipment models corresponding to multiple computer room equipment through model training, one computer room equipment corresponds to one target computer room equipment model, and the target computer room equipment model is used to simulate the operating behavior of the corresponding computer room equipment; according to multiple target computer room equipment models, the first operating law of the target entity system is acquired.

[0073] In one possible implementation, the multiple historical operation data groups include a historical computer room temperature data group, a historical computer room humidity data group, and a historical computer room energy consumption data group; the first operation law includes the overall temperature change law, the overall humidity change law, and the overall energy consumption change law in the target entity system.

[0074] In a possible implementation, when multiple historical operation data groups are historical computer room temperature data groups, the processing module 22 is used to calculate the first predicted operation data of the target entity system in a preset first time period according to the first operation law, specifically including: according to multiple target computer room equipment models, obtaining the predicted equipment temperature data corresponding to multiple computer room equipment respectively, the predicted equipment temperature data including the predicted temperature weight coefficient, predicted equipment power, predicted heat dissipation efficiency, predicted ventilation volume and heat dissipation coefficient; according to the predicted equipment temperature data, determining the first operation law, and calculating the first predicted operation data of the target entity system in the preset first time period, the first operation law is the overall temperature change law, and the first predicted operation data is the predicted computer room temperature data.

[0075] In a possible implementation, the processing module 22 is used to calculate the first predicted operation data of the target entity system in a preset first time period, specifically including: calculating the first predicted operation data of the first time period according to the following formula, where the first predicted operation data is predicted computer room temperature data: ;

[0076] Among them, T t0 is the predicted temperature at the starting time point in the first time period, T t1 is the predicted temperature at the end time point in the first time period, α i is the predicted temperature weight coefficient of the equipment in the i-th computer room, P i is the predicted equipment power of the i-th equipment room, δ i is the predicted heat dissipation efficiency of the equipment in the i-th computer room, V(τ) is the predicted ventilation volume in the target entity system at the τ-th time, C(τ) is the heat dissipation coefficient in the target entity system at the τ-th time, and the τ-th time is any time in the first time period.

[0077] In a possible implementation, the processing module 22 is used to adjust the calculation parameters in the first operating law according to the comparison result between the first predicted operating data and the first actual operating data, and analyze to obtain the second operating law of the target entity system, specifically including: obtaining a first predicted operating value according to the first predicted operating data, and obtaining an actual value according to the first actual operating data; obtaining a target value difference between the first predicted operating value and the actual value, the target value difference being the absolute value of the difference between the first predicted operating value and the first actual operating data; determining the adjustment degree of the calculation parameters according to the target value difference; adjusting the calculation parameters according to the adjustment degree, and analyzing to obtain the second operating law of the target entity system, wherein the target value difference is directly proportional to the adjustment degree.

[0078] In one possible implementation, the processing module 22 is used to determine whether an abnormality occurs in the target entity system during a preset second time period, specifically including: obtaining a second predicted operation value based on second predicted operation data; determining whether the second predicted operation value is a normal preset value; if the second predicted operation value is not a normal preset value, determining that an abnormality occurs in the target entity system during the preset second time period.

[0079] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0080] The present application also provides an electronic device. Figure 3 , Figure 3 30 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0081] The communication bus 302 is used to realize the connection and communication between these components.

[0082] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0083] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0084] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0085] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and a computer room intelligent monitoring application program.

[0086] exist Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the computer room intelligent monitoring application stored in the memory 305. When executed by one or more processors 301, the electronic device executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0087] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0088] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0089] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0090] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0092] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0093] The above are only exemplary embodiments disclosed in this application, and the scope of the disclosure of this application cannot be limited by this. That is, any equivalent changes and modifications made according to the teachings disclosed in this application are still within the scope of the disclosure of this application. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other implementation schemes disclosed in this application.

[0094] This application is intended to cover any modifications, uses or adaptive changes disclosed in this application, which follow the general principles disclosed in this application and include common knowledge or customary technical means in the technical field not recorded in this application.

Claims

1. A computer room intelligent monitoring method, characterized in that: The method comprises: Acquire multiple historical operation data groups of a target entity system collected by multiple data collection devices, wherein the target entity system includes multiple computer room devices, and one computer room device corresponds to one historical operation data group; Based on the plurality of historical operation data groups, obtaining a first operation rule of the target entity system; Calculate first predicted operation data of the target entity system in a preset first time period according to the first operation rule; Acquire first actual operation data of the target entity system in the preset first time period; According to the comparison result of the first predicted operation data and the first actual operation data, the calculation parameters in the first operation law are adjusted to obtain the second operation law of the target entity system through analysis; Calculating second predicted operation data of the target entity system in a preset second time period according to the second operation rule, the preset second time period being after the preset first time period; According to the second predicted operation data, determining whether the target entity system has an abnormality in the preset second time period, and if it is determined that the target entity system has an abnormality in the preset second time period, sending the second actual operation data of the target entity system collected in the preset second time period to the remote monitoring terminal; The plurality of historical operation data groups include a historical computer room temperature data group, a historical computer room humidity data group, and a historical computer room energy consumption data group; the first operation law includes an overall temperature change law, an overall humidity change law, and an overall energy consumption change law in the target entity system; When the plurality of historical operation data groups are historical computer room temperature data groups, calculating the first predicted operation data of the target entity system in a preset first time period according to the first operation rule specifically includes: According to multiple target computer room equipment models, obtain predicted equipment temperature data corresponding to the multiple computer room equipment respectively, wherein the predicted equipment temperature data includes predicted temperature weight coefficient, predicted equipment power, predicted heat dissipation efficiency, predicted ventilation volume and heat dissipation coefficient; Determine the first operating law according to the predicted device temperature data, and calculate the first predicted operating data of the target entity system in a preset first time period, wherein the first operating law is the overall temperature change law, and the first predicted operating data is the predicted computer room temperature data; The calculating the first predicted operation data of the target entity system in a preset first time period specifically includes: The first predicted operation data for the first time period is calculated according to the following formula, where the first predicted operation data is the predicted computer room temperature data: Among them, T t0 is the predicted temperature at the starting time point in the first time period, T t1 is the predicted temperature at the end time point in the first time period, α i is the predicted temperature weight coefficient of the i-th equipment in the computer room, P i is the predicted equipment power of the i-th equipment in the computer room, δ i is the heat dissipation efficiency of the i-th equipment in the computer room, V(τ) is the predicted ventilation volume in the target entity system at the τ-th time, C(τ) is the heat dissipation coefficient in the target entity system at the τ-th time, and the τ-th time is any time in the first time period; The adjusting the calculation parameters in the first operation rule according to the comparison result between the first predicted operation data and the first actual operation data, and analyzing and obtaining the second operation rule of the target entity system specifically includes: Obtain a first predicted operation value according to the first predicted operation data, and obtain a first actual value according to the first actual operation data; Obtaining a target numerical difference between the first predicted operating value and the first actual operating data, wherein the target numerical difference is an absolute value of a difference between the first predicted operating value and the first actual operating data; Determining the degree of adjustment of the calculation parameter according to the target numerical difference; The calculation parameters are adjusted according to the adjustment degree, and a second operation rule of the target entity system is obtained through analysis.

2. The method according to claim 1, characterized in that: The acquiring the first operation rule of the target entity system based on the plurality of historical operation data groups specifically includes: Acquire multiple computer room equipment models to be trained, where one computer room equipment model to be trained corresponds to one computer room equipment; Inputting the plurality of historical operation data groups into the plurality of computer room equipment models to be trained, so as to obtain a plurality of target computer room equipment models corresponding to the plurality of computer room equipment through model training, wherein one computer room equipment corresponds to one target computer room equipment model, and the target computer room equipment model is used to simulate the operation behavior of the corresponding computer room equipment; The first operating rule of the target entity system is obtained according to the multiple target computer room equipment models.

3. The method according to claim 1, characterized in that The target value difference is directly proportional to the adjustment degree.

4. An intelligent monitoring device for a computer room, characterized in that: The device comprises an acquisition module (21) and a processing module (22), wherein: The acquisition module (21) is used to acquire multiple historical operation data groups of a target entity system acquired by multiple data acquisition devices, wherein the target entity system includes multiple computer room devices, and each computer room device corresponds to one historical operation data group; based on the multiple historical operation data groups, acquire a first operation law of the target entity system; and acquire first actual operation data of the target entity system in a preset first time period; The processing module (22) is used to calculate first predicted operation data of the target entity system in a preset first time period according to the first operation rule; adjust the calculation parameters in the first operation rule according to the comparison result between the first predicted operation data and the first actual operation data, and analyze and obtain a second operation rule of the target entity system; calculate second predicted operation data of the target entity system in a preset second time period according to the second operation rule, and the preset second time period is after the preset first time period; determine whether the target entity system has an abnormality in the preset second time period according to the second predicted operation data, and if it is determined that the target entity system has an abnormality in the preset second time period, send the second actual operation data of the target entity system collected in the preset second time period to the remote monitoring terminal; The plurality of historical operation data groups include a historical computer room temperature data group, a historical computer room humidity data group, and a historical computer room energy consumption data group; the first operation law includes an overall temperature change law, an overall humidity change law, and an overall energy consumption change law in the target entity system; When the plurality of historical operation data groups are historical computer room temperature data groups, calculating the first predicted operation data of the target entity system in a preset first time period according to the first operation rule specifically includes: According to multiple target computer room equipment models, obtain predicted equipment temperature data corresponding to the multiple computer room equipment respectively, wherein the predicted equipment temperature data includes predicted temperature weight coefficient, predicted equipment power, predicted heat dissipation efficiency, predicted ventilation volume and heat dissipation coefficient; Determine the first operating law according to the predicted device temperature data, and calculate the first predicted operating data of the target entity system in a preset first time period, wherein the first operating law is the overall temperature change law, and the first predicted operating data is the predicted computer room temperature data; The calculating the first predicted operation data of the target entity system in a preset first time period specifically includes: The first predicted operation data for the first time period is calculated according to the following formula, where the first predicted operation data is the predicted computer room temperature data: Among them, T t0 is the predicted temperature at the starting time point in the first time period, T t1 is the predicted temperature at the end time point in the first time period, α i is the predicted temperature weight coefficient of the i-th equipment in the computer room, P i is the predicted equipment power of the i-th equipment in the computer room, δ i is the heat dissipation efficiency of the i-th equipment in the computer room, V(τ) is the predicted ventilation volume in the target entity system at the τ-th time, C(τ) is the heat dissipation coefficient in the target entity system at the τ-th time, and the τ-th time is any time in the first time period; The adjusting the calculation parameters in the first operation rule according to the comparison result between the first predicted operation data and the first actual operation data, and analyzing and obtaining the second operation rule of the target entity system specifically includes: Obtain a first predicted operation value according to the first predicted operation data, and obtain a first actual value according to the first actual operation data; Obtaining a target numerical difference between the first predicted operating value and the first actual operating data, wherein the target numerical difference is an absolute value of a difference between the first predicted operating value and the first actual operating data; Determining the degree of adjustment of the calculation parameter according to the target numerical difference; The calculation parameters are adjusted according to the adjustment degree, and a second operation rule of the target entity system is obtained through analysis.

5. An electronic device, characterized in that: The electronic device comprises a processor (301), a communication bus (302), a user interface (303), a network interface (304) and a memory (305), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device executes the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 3 is performed.

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