Intelligent data center BA automatic control performance evaluation method and system

Abstract

The invention relates to the technical field of performance evaluation, in particular to an intelligent data center BA self-control performance evaluation method and system, and the method comprises the following steps: calculating an environment variable steady-state reference value corresponding to each time window based on environment variable monitoring data of a data center BA system, and obtaining variable steady-state reference information; according to the method, the steady-state reference value of the variable can be calculated based on a large number of data points by monitoring and analyzing the environment variable in a plurality of time windows, the data accuracy is improved and the evaluation reliability is enhanced by screening the data points with a stable fluctuation range, and the evaluation accuracy is improved by analyzing the growth trend of the deviation value. Potential problems can be timely identified and intervened, so that long-term problem accumulation is avoided, and a data center manager can obtain visual performance display and an optimized decision process by analyzing an environment variable deviation trend and calculating an environment self-control stability score, so that high-efficiency and energy-saving operation of a system is ensured.

Description

Technical Field

[0001] The present invention relates to the field of performance evaluation technology, and in particular to a method and system for evaluating BA automatic control performance in a smart data center. Background Art

[0002] The field of performance evaluation technology involves quantitative analysis of the operating status, efficiency and reliability of systems, equipment or software to ensure that they meet design requirements, operating goals or industry standards. This field is widely used in many industries such as computer systems, network architecture, automatic control, energy management, etc. It mainly evaluates key indicators such as system response time, stability, resource utilization, energy consumption, etc. by establishing mathematical models, collecting operating data, building an evaluation index system, and using statistical analysis, simulation, machine learning optimization and other methods. Performance evaluation can not only identify system bottlenecks and optimize resource allocation, but also be used to predict failures, formulate improvement strategies, and improve overall operating efficiency and stability.

[0003] Among them, the BA automatic control performance evaluation method of the smart data center is used to quantitatively analyze the operation of the building automation system, aiming to evaluate the energy efficiency, stability and responsiveness of the building automatic control system. Through data collection, model calculation and intelligent analysis, this method evaluates core parameters such as temperature and humidity adjustment, air quality management, and equipment load balancing, and provides optimization strategies to ensure that the data center meets business needs in an energy-saving, efficient and safe operating state.

[0004] Traditional evaluation methods lack accurate monitoring of long-term trends of environmental variables and real-time performance analysis, and focus on static data analysis and historical performance records, which leads to insufficient response time when dealing with emergencies and rapidly changing environmental conditions, and inability to effectively predict and adjust countermeasures. Data analysis in traditional methods does not make full use of real-time data, resulting in large deviations between evaluation results and actual operating conditions. This limits the optimization of system performance and maximization of resource utilization, and deficiencies in fault prevention and resource allocation may lead to energy waste and system overload, affecting the stability and security of the data center. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for evaluating the BA automatic control performance of a smart data center.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for evaluating BA automatic control performance of a smart data center, comprising the following steps: S1: Based on the environmental variable monitoring data of the data center BA system, the steady-state baseline value of the environmental variable corresponding to each time window is calculated to obtain the variable steady-state baseline information; S2: Based on the steady-state reference information of the variables, the deviation of each environmental variable from the steady-state reference value is calculated, the data points whose fluctuation amplitude exceeds the preset range in a short period of time are screened, the distribution characteristics of the deviation are calculated, and the short-term deviation information of the variables is obtained; S3: calling the short-term deviation information of the variable, accumulating it in multiple time windows, calculating the cumulative deviation value of each variable in each time window, analyzing the growth trend of the deviation value, calculating the cumulative rate of long-term deviation, and obtaining the deviation trend information of the environmental variable; S4: Based on the environmental variable deviation trend coefficient, a scoring weight is set for each environmental variable, and the environmental self-control stability score of the data center BA system is calculated to obtain environmental stability information; S5: calling the environmental stability information, obtaining the unit energy consumption of the cooling system and the total power consumption of the equipment in the computer room, analyzing the power consumption deviation in the load interval, calculating the power consumption automatic control performance score of the equipment, and obtaining the power consumption automatic control performance information.

[0007] As a further scheme of the present invention, the variable steady-state benchmark information specifically includes the computer room temperature benchmark value, the computer room humidity benchmark value, the cooling wind speed benchmark value, the air pressure benchmark value and the air quality benchmark value; the variable short-term deviation information specifically includes the computer room temperature short-term deviation, the computer room humidity short-term deviation, the cooling wind speed short-term deviation, the air pressure short-term deviation and the air quality short-term deviation; the environmental variable deviation trend information includes the computer room temperature long-term deviation coefficient, the computer room humidity long-term deviation coefficient and the cooling wind speed long-term deviation coefficient; the environmental stability information specifically includes the computer room temperature self-control score, the computer room humidity self-control score and the air flow self-control score; the power consumption self-control performance information includes the cooling unit power consumption self-control score, the computer room equipment power consumption self-control score and the load energy consumption ratio score.

[0008] As a further solution of the present invention, the step of obtaining the variable steady-state reference information is specifically as follows: S111: Based on the environmental variable monitoring data of the BA system of the data center, the monitoring data of the computer room temperature, the computer room humidity, the cooling wind speed, the air pressure and the air quality are extracted, and the time window is set. The time window includes 1 hour, 24 hours and 7 days. The mean of each environmental variable is calculated in each time window to obtain the mean data of the environmental variable; S112: calling the mean value data of the environmental variables, calculating the difference between the maximum value and the minimum value, and calculating the change interval of each environmental variable in each time window to obtain the change interval data of the environmental variables; S113: Based on the environmental variable change interval data, filter the data points whose change interval is lower than the set threshold value, using the formula: ; Calculate the variable steady-state benchmark value to obtain the variable steady-state benchmark information; in, represents the steady-state reference value of the variable, Represents the total number of data points in the time window, Representative The value of the environmental variable for each data point, Representative The range of changes in data points, represents the stability threshold, is the indicator function, when Less than the stability threshold The value is 1 when it is true, otherwise it is 0.

[0009] As a further solution of the present invention, the step of obtaining the short-term deviation information of the variable is specifically as follows: S211: Based on the variable steady-state reference information, call the environmental variable monitoring data of the current time period, including the room temperature, room humidity, cooling wind speed, air pressure and air quality, calculate the deviation of each environmental variable from the steady-state reference value, and obtain environmental variable deviation data; S212: calling the environmental variable deviation data, calculating the absolute value and change rate of the deviation, calculating the deviation change amount between data points and normalizing them, and obtaining deviation change rate data; S213: Based on the deviation change rate data, data points whose fluctuation amplitude exceeds a preset range in a short period of time are selected, and distribution characteristics of the deviation are calculated using the formula: ; Calculate the short-term deviation of the variable to obtain the short-term deviation of the variable; in, represents the short-term deviation of the variable, Represents the total number of filtered data points, Representative The deviation value of the data point, Represents the mean deviation value of the filtered data points.

[0010] As a further solution of the present invention, the step of obtaining the environmental variable deviation trend information is specifically as follows: S311: calling the short-term deviation information of the variable, accumulating it in the time window of 1 hour, 24 hours and 7 days, calculating the cumulative deviation value in each time window, calculating the cumulative value by using the time integral of the deviation amount, and obtaining the cumulative deviation value of the environmental variable; S312: calling the accumulated deviation value of the environmental variable, analyzing the growth trend of the deviation value, comparing the accumulated deviation value in each time window, screening the variables with continuously growing deviation values, and obtaining a continuously growing variable set; S313: Based on the continuously growing variable set, the cumulative rate of long-term deviation is calculated for the room temperature, room humidity, and cooling wind speed, using the formula: ; Obtain the deviation trend coefficient and obtain the deviation trend information of the environmental variables; in, represents the deviation trend coefficient of the environmental variable, Represents the total number of time windows, Representative time The accumulated deviation value of the environmental variables at the time, Representative time The accumulated deviation value of the environmental variables at the time, Representative time The weight factor of the moment.

[0011] As a further solution of the present invention, the step of obtaining the environmental stability information is specifically as follows: S411: Based on the environmental variable deviation trend coefficient, a scoring weight is set, and the scoring weight is allocated according to the influence of each environmental variable on the environmental stability of the data center, and a corresponding scoring weight is set for each environmental variable to obtain an environmental variable scoring weight set; S412: calling the environmental variable scoring weight set, performing weighted calculation on the environmental variable deviation trend information, and accumulating the product of the deviation trend coefficient of each variable and the corresponding scoring weight to obtain a weighted score of environmental stability; S413: Based on the weighted score of the environmental stability, the formula is used: ; Calculate the environmental self-control stability score of the data center BA system to obtain environmental stability information; in, Represents the environmental self-control stability score, Represents the number of environment variables, Representative The deviation trend coefficient of each environmental variable is Representative The scoring weight of the environmental variables, Represents the mean of the deviation trend coefficient of the environmental variables.

[0012] As a further solution of the present invention, the step of acquiring the power consumption automatic control performance information is specifically as follows: S511: calling the environmental stability information, obtaining the energy consumption of the cooling system units and the total power consumption of the equipment in the computer room, and obtaining the heat load coefficient of the computer room by using the ratio of the energy consumption of the cooling system units to the total power consumption of the equipment in the computer room; S512: calling the heat load coefficient of the equipment room, using the ratio of the equipment operating power to the equipment rated power to obtain the equipment load rate, calculating the equipment load rate and the unit load energy consumption ratio, using the ratio of the total power consumption of the equipment per unit time to the load rate to obtain the unit load energy consumption ratio, and obtaining the equipment load energy consumption ratio; S513: Based on the equipment load energy consumption ratio, analyze the load interval power consumption deviation, using the formula: ; Calculate the power consumption self-control performance score of the device and obtain the power consumption self-control performance information; in, Indicates the device power consumption self-control performance score. represents the number of load intervals considered, It is Energy efficiency in each load range, It is the average value of energy efficiency in the load range.

[0013] A smart data center BA automatic control performance evaluation system, the smart data center BA automatic control performance evaluation system is used to execute the above-mentioned smart data center BA automatic control performance evaluation method, the system includes: The steady-state benchmark extraction module calculates the steady-state benchmark value of the environmental variable corresponding to each time window based on the environmental variable monitoring data of the data center BA system, and obtains the variable steady-state benchmark information; The short-term deviation analysis module screens data points whose fluctuation amplitude exceeds a preset range in a short period of time based on the steady-state benchmark information of the variable, calculates the distribution characteristics of the deviation, and obtains the short-term deviation information of the variable; The deviation trend analysis module calls the short-term deviation information of the variables, calculates the cumulative deviation value of each variable in each time window, analyzes the growth trend of the deviation value, calculates the cumulative rate of long-term deviation, and obtains the deviation trend information of the environmental variables; The environmental performance evaluation module sets a scoring weight for each environmental variable based on the environmental variable deviation trend coefficient, calculates the environmental self-control stability score of the data center BA system, and obtains environmental stability information; The power consumption performance evaluation module calls the environmental stability information, obtains the unit energy consumption of the cooling system and the total power consumption of the equipment in the computer room, calculates the equipment power consumption automatic control performance score, and obtains the power consumption automatic control performance information.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by monitoring and analyzing environmental variables in multiple time windows, the steady-state baseline value of the variable can be calculated based on a large number of data points. By screening data points with a stable fluctuation range, the accuracy of the data is improved and the reliability of the evaluation is enhanced. By calculating the deviation of the environmental variables from the steady-state baseline value and the distribution characteristics of the deviation, the internal environment of the data center can be monitored and analyzed more carefully. Such processing logic improves the response speed and early warning accuracy. By analyzing the growth trend of the deviation value, potential problems can be identified and intervened in time, thereby avoiding the accumulation of long-term problems. By analyzing the deviation trend of the environmental variables and calculating the environmental self-control stability score, the data center manager can obtain an intuitive performance display and optimize the decision-making process, thereby ensuring efficient and energy-saving operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 A flow chart for obtaining variable steady-state reference information for the present invention; Figure 3 A flow chart of obtaining short-term deviation information of variables in the present invention; Figure 4 A flow chart for obtaining environmental variable deviation trend information for the present invention; Figure 5 A flow chart for obtaining environmental stability information for the present invention; Figure 6 The present invention is a flow chart for obtaining power consumption automatic control performance information. DETAILED DESCRIPTION

[0016] 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.

[0017] 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" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the 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, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] Embodiment 1 See also Figure 1The present invention provides a technical solution: a method for evaluating the BA automatic control performance of a smart data center, comprising the following steps: S1: Based on the environmental variable monitoring data of the data center BA system, the environmental variable monitoring data includes room temperature, room humidity, cooling wind speed, air pressure and air quality. Set multiple time windows, including 1 hour, 24 hours and 7 days. Calculate the mean and variation range of each environmental variable in each time window, select data points with stable fluctuation range as benchmark reference, calculate the steady-state benchmark value of the environmental variable corresponding to each time window, and obtain the steady-state benchmark information of the variable; S2: Based on the variable steady-state benchmark information, call the environmental variable monitoring data of the current time period, calculate the deviation of each environmental variable from the steady-state benchmark value, calculate the absolute value and change rate of the deviation, screen the data points whose fluctuation amplitude exceeds the preset range in a short period of time, calculate the distribution characteristics of the deviation, and obtain the short-term deviation information of the variable; S3: Call the short-term deviation information of the variables, accumulate them in multiple time windows, calculate the cumulative deviation value of each variable in each time window, analyze the growth trend of the deviation value, screen the variables with continuous growth, calculate the cumulative rate of long-term deviation for the long-term changes of room temperature, room humidity, and cooling wind speed, and obtain the deviation trend information of the environmental variables; S4: Based on the environmental variable deviation trend coefficient, set a scoring weight for each environmental variable, call the scoring weight of each variable, perform weighted calculation on the deviation trend coefficient, calculate the environmental self-control stability score of the data center BA system, and obtain environmental stability information; S5: call the environmental stability information, obtain the unit energy consumption of the cooling system and the total power consumption of the equipment in the computer room, calculate the heat load, equipment load rate and unit load energy consumption ratio, analyze the power consumption deviation in the load interval, calculate the power consumption automatic control performance score of the equipment, and obtain the power consumption automatic control performance information; The variable steady-state benchmark information specifically includes the computer room temperature benchmark value, the computer room humidity benchmark value, the cooling wind speed benchmark value, the air pressure benchmark value and the air quality benchmark value; the variable short-term deviation information specifically includes the computer room temperature short-term deviation, the computer room humidity short-term deviation, the cooling wind speed short-term deviation, the air pressure short-term deviation and the air quality short-term deviation; the environmental variable deviation trend information includes the computer room temperature long-term deviation coefficient, the computer room humidity long-term deviation coefficient and the cooling wind speed long-term deviation coefficient; the environmental stability information specifically includes the computer room temperature self-control score, the computer room humidity self-control score and the air flow self-control score; the power consumption self-control performance information includes the cooling unit power consumption self-control score, the computer room equipment power consumption self-control score and the load energy consumption ratio score.

[0019] See also Figure 2 , the specific steps for obtaining the variable steady-state benchmark information are: S111: Based on the environmental variable monitoring data of the BA system of the data center, the monitoring data of the computer room temperature, the computer room humidity, the cooling wind speed, the air pressure and the air quality are extracted, and the time window is set. The time window includes 1 hour, 24 hours and 7 days. The mean of each environmental variable is calculated in each time window to obtain the mean data of the environmental variable; Based on the environmental variable monitoring data of the data center BA system, the monitoring data of the computer room temperature, computer room humidity, cooling wind speed, air pressure and air quality are extracted, and the time window is set. The time window includes 1 hour, 24 hours, and 7 days. The mean of each environmental variable is calculated in each time window to obtain the mean data of the environmental variable. The real-time monitoring data of the environmental variables is obtained from the BA system to ensure the accuracy and completeness of the data. For example, the computer room temperature data can be collected in real time by temperature sensors installed in different locations, the computer room humidity data is obtained by humidity sensors, the cooling wind speed can be measured by an anemometer installed at the air outlet of the air conditioner, and the air pressure and air quality data are obtained by pressure sensors and air quality monitors respectively. Then, different time windows are set, such as 1 hour, 24 hours, and 7 days, in order to analyze the changes of environmental variables on different time scales. In each time window, the data of each environmental variable is statistically analyzed to calculate its mean. For example, in a time window of 1 hour, if the temperature data is collected once a minute, there are 60 data points in the time window. Add these 60 temperature data and divide them by 60 to get the average temperature value in the hour. Similarly, the means of other environmental variables in different time windows can be calculated to obtain the mean data of each environmental variable in different time windows, providing a basis for subsequent analysis.

[0020] S112: calling the mean value data of the environmental variables, calculating the difference between the maximum value and the minimum value, calculating the change interval of each environmental variable in each time window, and obtaining the change interval data of the environmental variables; Call the mean data of environmental variables, calculate the difference between the maximum and minimum values, calculate the change interval of each environmental variable in each time window, and obtain the environmental variable change interval data. Specifically, in each time window, determine the maximum and minimum values ​​of each environmental variable. For example, in a 24-hour time window, assuming that temperature data is recorded once an hour, there are 24 temperature data points, from which the highest and lowest temperature values ​​are found. Calculate the difference between these two values, which is the temperature change interval in the time window. The same method can be applied to other environmental variables. By calculating the change interval of each environmental variable in different time windows, the environmental variable change interval data is obtained, which provides a basis for evaluating environmental stability.

[0021] S113: Based on the environmental variable change interval data, filter the data points whose change interval is lower than the set threshold value, using the formula: ; Calculate the variable steady-state benchmark value to obtain the variable steady-state benchmark information; in, represents the steady-state reference value of the variable, Represents the total number of data points in the time window, Representative The value of the environmental variable for each data point, Representative The range of changes in data points, represents the stability threshold, is the indicator function, when Less than the stability threshold The value is 1 when it is true, otherwise it is 0.

[0022] Based on the environmental variable change interval data, filter the data points whose change interval is lower than the set threshold, use the formula to calculate the variable steady-state benchmark value, and obtain the variable steady-state benchmark information. Set a stable threshold , which is used to determine whether the change of environmental variables is in a stable state. The selection of this threshold is usually based on the statistical characteristics of long-term monitoring data. For example, it can be set by calculating the standard deviation of each variable in historical data. . Set a stable threshold for the room temperature , that is, when the temperature change within a certain time window is less than When , the data point is considered to be within the steady-state range. Similarly, the thresholds of humidity, cooling wind speed, air pressure, and air quality can be set as , m / s, Pa, AQI. In each time window, filter out the change interval Less than If a data point If this condition is met, then its indicator function The value is 1, otherwise the value is 0. For example, in a 24-hour time window, the following data points of the room temperature are collected (unit: °C): ; Calculate the difference between the maximum and minimum values ​​to get the variation range: °C; Because the range of this change °C is greater than the threshold , so this time window is not included in the steady-state calculation range. In another time window, the data is as follows (unit: °C): ; The variation interval is calculated as: °C; The window changes range °C is less than , so the data points in this window can be used for steady-state calculations.

[0023] The steady-state reference value is calculated using the formula: ; in, represents the steady-state reference value of the variable, Represents the total number of data points in the time window, Representative The value of the environmental variable for each data point, Representative The range of changes in data points, represents the stability threshold, is the indicator function, when Less than the stability threshold The value is 1 when it is true, otherwise it is 0.

[0024] The data point values ​​brought into the above stable time window are: ; Calculate the steady-state reference value of the room temperature within the time window °C. Similarly, the corresponding calculations can be made for humidity, cooling air velocity, air pressure and air quality. The formula is useful in that by using the indicator function The stability of fluctuations can be automatically screened to ensure that only data points that meet the stability conditions are included in the benchmark calculation. At the same time, the influence of short-term fluctuations on the benchmark is eliminated by averaging, making the calculation results more reliable and thus can be used for subsequent deviation analysis and environmental self-control optimization. It reflects the stability level of environmental variables during long-term monitoring. The closer its value is to the real trend of the monitoring data, the more accurate the subsequent analysis of abnormal fluctuations will be. If the steady-state baseline value deviates from the normal working range, it means that the data center environment may have long-term slow changes and needs to be optimized and adjusted.

[0025] See also Figure 3 , the steps for obtaining the short-term deviation information of the variable are as follows: S211: Based on the variable steady-state benchmark information, call the environmental variable monitoring data of the current time period, including the room temperature, room humidity, cooling wind speed, air pressure and air quality, calculate the deviation of each environmental variable from the steady-state benchmark value, and obtain the environmental variable deviation data; Based on the variable steady-state benchmark information, the environmental variable monitoring data of the current time period is called, including the room temperature, room humidity, cooling wind speed, air pressure and air quality, and the deviation of each environmental variable from the steady-state benchmark value is calculated to obtain the environmental variable deviation data. The data center management system collects the latest environmental data from its sensor network, including but not limited to the room temperature, humidity, wind speed, pressure and air quality in the current time period, and calls the previously calculated steady-state benchmark value of the environmental variable, and calculates the difference between each monitored environmental data and the corresponding steady-state benchmark value. For example, if the current room temperature is , and the steady-state reference temperature is , then the temperature deviation is , the deviations of other environmental parameters are calculated in a similar way. These deviation values ​​reflect the difference between the actual environmental state and the expected steady state, which is crucial for data center environmental management. It allows technicians to quickly identify any environmental parameters that need to be adjusted to maintain the optimal operating state of the equipment. The obtained environmental variable deviation data provides an immediate adjustment basis for the system.

[0026] S212: calling environmental variable deviation data, calculating the absolute value and change rate of the deviation, calculating the deviation change between data points and normalizing them, and obtaining deviation change rate data; Call the environmental variable deviation data, calculate the absolute value and change rate of the deviation, calculate the deviation change between data points and normalize them to obtain the deviation change rate data. The system first processes the acquired environmental variable deviation data, and provides an intuitive quantification of the deviation size by calculating the absolute value of the deviation of each data point, evaluates the deviation change between adjacent data points, and obtains the deviation change of each data point by differential calculation. For example, if the temperature deviations are respectively and , then the change is In order to make the data more comparable, normalization processing is performed to convert all deviation changes into a unified scale. The normalized change rate data of the calculation results can enable the data center management system to monitor the environmental stability more accurately and respond quickly to changes in excessive deviations. The obtained deviation change rate data provides a dynamic environmental monitoring tool for the data center.

[0027] S213: Based on the deviation change rate data, filter the data points whose fluctuation amplitude exceeds the preset range in a short period of time, calculate the distribution characteristics of the deviation, and use the formula: ; Calculate the short-term deviation of the variable to obtain the short-term deviation of the variable; in, represents the short-term deviation of the variable, Represents the total number of filtered data points, Representative The deviation value of the data point, Represents the mean deviation value of the filtered data points.

[0028] Based on the deviation change rate data, filter out data points whose fluctuation range exceeds the preset range in a short period of time, calculate the distribution characteristics of the deviation, and use the formula to calculate the short-term deviation of the variable to obtain the short-term deviation of the variable. For example, set the preset range of the fluctuation range to the deviation change rate. , filter out the data points that exceed this threshold, and set the deviation change rates of the five data points recorded within one hour to be The selected data points have a change rate exceeding point, that is , use the following formula to calculate the deviation distribution characteristics of these points: ; in, represents the short-term deviation of the variable, is the total number of filtered data points, They are The deviation value of is the average deviation of the data points, and these values ​​are substituted into the formula for calculation: ; What you get The standard deviation of the deviation value of the data point provides a quantitative indicator of short-term volatility for the environmental management of the data center, helping technicians understand the degree of fluctuation of the deviation and adjust the environmental control system accordingly to maintain the stability of the data center environment. This calculation method provides a dynamic monitoring and response mechanism for data center environmental management, making environmental adjustments more accurate and timely. The short-term deviation information obtained is the key basis for dynamic adjustments, helping managers to evaluate and respond to environmental changes in real time.

[0029] See also Figure 4 ,The specific steps for obtaining the environmental variable deviation trend information are: S311: Call the short-term deviation information of the variable, accumulate it in the time window of 1 hour, 24 hours, and 7 days, calculate the cumulative deviation value in each time window, calculate the cumulative value by using the time integral of the deviation amount, and obtain the cumulative deviation value of the environmental variable; The short-term deviation information of the variable is called, and accumulated within the time window of 1 hour, 24 hours, and 7 days. The cumulative deviation value within each time window is calculated, and the cumulative value is calculated using the time integral of the deviation amount to obtain the cumulative deviation value of the environmental variable. The deviation data of each specified time window is extracted from the stored short-term deviation information. For example, a 1-hour window is selected, and all deviation records within the period are summarized. The time integral of the deviation amount is realized by adding up the deviation values. The calculation process provides a total deviation value within the specified time window for each environmental variable. For the time windows of 24 hours and 7 days, the same accumulation steps are repeated. Such cumulative calculations provide a long-term perspective on the changes in environmental variables for data center management, allowing managers to observe environmental stability or abnormal fluctuations over a longer period of time. The obtained cumulative deviation values ​​of environmental variables can be used as a basis for adjusting the environmental control system or early warning mechanism.

[0030] S312: calling the accumulated deviation value of the environment variable, analyzing the growth trend of the deviation value, comparing the accumulated deviation value in each time window, screening the variables with continuously growing deviation values, and obtaining a continuously growing variable set; Call the cumulative deviation value of the environmental variable, analyze the growth trend of the deviation value, compare the cumulative deviation value in each time window, screen the variables with continuously growing deviation values, and obtain a continuously growing variable set. By analyzing the cumulative deviation value, it is possible to identify which environmental variables have deviation values ​​that show a continuously growing trend in continuous time windows. For example, if the cumulative deviation value of the computer room temperature gradually increases in the 1-hour, 24-hour, and 7-day time windows, this indicates a potential problem with the computer room cooling system. By comparing the cumulative deviation values ​​of continuous time windows, such continuously growing variables can be effectively screened out. The variables are then classified into a continuously growing variable set, which provides key data points for data center managers to conduct diagnosis and response measures. The continuously growing variable set provides decision support for subsequent environmental control strategy adjustments and fault prevention.

[0031] S313: Based on the continuously growing variable set, the cumulative rate of long-term deviation is calculated for the room temperature, room humidity, and cooling wind speed, using the formula: ; Obtain the deviation trend coefficient and obtain the deviation trend information of the environmental variables; in, represents the deviation trend coefficient of the environmental variable, Represents the total number of time windows, Representative time The accumulated deviation value of the environmental variables at the time, Representative time The accumulated deviation value of the environmental variables at the time, Representative time The weight factor of the moment.

[0032] Based on the continuously growing variable set, the cumulative rate of long-term deviation is calculated for the room temperature, room humidity, and cooling wind speed. The deviation trend coefficient is calculated using the formula to obtain the environmental variable deviation trend information. The calculation formula is: ; in, represents the deviation trend coefficient of the environmental variable, Represents the total number of time windows, Representative time The accumulated deviation value of the environmental variables at the time, Representative time The accumulated deviation value of the environmental variables at the time, Representative time The weight factor at each moment. For example, in a 7-day monitoring period, the accumulated deviation value and weight for each day are as follows: Day 1: , ; Day 2: , ; Day 3: , ; Day 4: , ; Day 5: , ; Day 6: , ; Day 7: , ; And set , ; Substitute into the formula and calculate: ; Calculation results It means that the cumulative deviation value of the environmental variable increases by 2 units on average every day. This trend coefficient provides an important indicator of long-term environmental changes for data center management, allowing the technical team to make corresponding environmental adjustments or early warnings to maintain the stable operation of the computer room.

[0033] See also Figure 5 , the specific steps for obtaining environmental stability information are: S411: Based on the environmental variable deviation trend coefficient, a scoring weight is set. The scoring weight is allocated according to the degree of influence of each environmental variable on the environmental stability of the data center. Each environmental variable is assigned a corresponding scoring weight to obtain an environmental variable scoring weight set. Based on the environmental variable deviation trend coefficient, the scoring weight is set. The scoring weight is allocated according to the degree of influence of each environmental variable on the environmental stability of the data center. Each environmental variable is set with a corresponding scoring weight to obtain an environmental variable scoring weight set. The data center management system classifies the environmental variables and identifies the degree of influence of each variable on the environmental stability of the data center. For example, the temperature of the computer room may be given a higher weight because of its significant impact on the operation of the equipment. Humidity, wind speed and air quality are assigned weights according to their degree of influence. For example, the weight of temperature is 0.4, humidity is 0.3, wind speed is 0.2, and air quality is 0.1. These weights reflect the importance of each variable to the environmental control strategy. The overall environmental stability score is calculated by weight, providing a quantitative tool to monitor and adjust the environmental control strategy. The obtained scoring weight set provides a basis for subsequent weighted scoring calculations.

[0034] S412: calling the environmental variable scoring weight set, performing weighted calculation on the environmental variable deviation trend information, and accumulating the product of the deviation trend coefficient of each variable and the corresponding scoring weight to obtain a weighted score of environmental stability; Call the environmental variable scoring weight set, perform weighted calculation on the environmental variable deviation trend information, and accumulate the product of the deviation trend coefficient of each variable and the corresponding scoring weight to obtain the weighted score of environmental stability. Multiply the deviation trend coefficient of each environmental variable by its corresponding scoring weight, and then accumulate the results of all environmental variables. For example, if the deviation trend coefficient of the room temperature is 2, the humidity is 1, the wind speed is 1.5, and the air quality is 0.5, and use the previously assigned weights, calculate the weighted score as follows: This weighted score reflects the stability of the entire data center environment. A high score indicates a relatively stable environment, while a low score indicates that environmental parameters need to be adjusted. This calculation process provides a dynamic and comprehensive evaluation tool for data center management, helping to optimize environmental control strategies and prevent potential environmental risks.

[0035] S413: Weighted scoring based on environmental stability using the formula: ; Calculate the environmental self-control stability score of the data center BA system to obtain environmental stability information; in, Represents the environmental self-control stability score, Represents the number of environment variables, Representative The deviation trend coefficient of the environmental variables is Representative The scoring weights of the environmental variables, represents the mean of the deviation trend coefficient of the environmental variables; Based on the weighted score of environmental stability, the environmental self-control stability score of the data center BA system is calculated using the formula to obtain environmental stability information. The formula is: ; in, Represents the environmental self-control stability score, Represents the number of environment variables, Representative The deviation trend coefficient of the environmental variables is Representative The scoring weights of the environmental variables, represents the mean of the deviation trend coefficients of the environmental variables. To calculate this score, the deviation trend coefficient and score weight of each variable are first determined, then multiplied and added together. The mean of all deviation trend coefficients and the square root of the sum of the squares of the differences between each coefficient and the mean are calculated at the same time. Finally, these values ​​are used to calculate For example, there are four environmental variables, whose deviation trend coefficients are 2, 1, 1.5, and 0.5, respectively, and whose weights are 0.4, 0.3, 0.2, and 0.1, respectively, and whose mean is 1.25, and are calculated as follows: ; ; ; ; This value reflects the overall stability of the data center environmental control system. The score indicates that environmental conditions are relatively stable, providing valuable information to data center management to support the optimization and adjustment of environmental control strategies.

[0036] See also Figure 6 , the specific steps for obtaining power consumption automatic control performance information are: S511: calling the environmental stability information, obtaining the energy consumption of the cooling system units and the total power consumption of the equipment in the computer room, and obtaining the heat load coefficient of the computer room by using the ratio of the energy consumption of the cooling system units to the total power consumption of the equipment in the computer room; Call the environmental stability information to obtain the energy consumption of the cooling system unit and the total power consumption of the equipment in the computer room. Use the ratio of the energy consumption of the cooling system unit to the total power consumption of the equipment in the computer room to obtain the heat load coefficient of the computer room. The energy consumption data of the cooling system unit and the total power consumption data of all equipment in the computer room will be extracted from its monitoring system. For example, if the energy consumption of the cooling system unit is 2000 kWh, and the total power consumption of the equipment in the computer room is 5000 kWh, the heat load coefficient of the computer room is This ratio reflects the relationship between cooling efficiency and equipment power consumption. A lower ratio usually indicates efficient energy use. This ratio is crucial for evaluating the energy efficiency of data centers and optimizing energy management strategies. The obtained computer room heat load coefficient provides basic data for energy consumption analysis and equipment configuration optimization.

[0037] S512: calling the heat load coefficient of the equipment room, using the ratio of the equipment operating power to the equipment rated power to obtain the equipment load rate, calculating the equipment load rate and the unit load energy consumption ratio, using the ratio of the total power consumption of the equipment per unit time to the load rate to obtain the unit load energy consumption ratio, and obtaining the equipment load energy consumption ratio; Call the heat load coefficient of the computer room, use the ratio of the equipment operating power to the equipment rated power to obtain the equipment load rate, calculate the equipment load rate and unit load energy consumption ratio, use the ratio of the total power consumption of the equipment per unit time to the load rate to obtain the unit load energy consumption ratio, and obtain the equipment load energy consumption ratio. For example, if the actual operating power of the equipment is 3000 kilowatts and the rated power of the equipment is 4000 kilowatts, the equipment load rate is , combined with the previously calculated heat load coefficient of the computer room, the unit load energy consumption ratio can be calculated. Assuming the total power consumption per unit time is 5000 kWh, the unit load energy consumption ratio is Kilowatt-hour, this ratio is a key indicator to measure the energy efficiency of data centers, helping managers understand the energy consumed per unit load, thereby evaluating equipment operating efficiency and the economy of energy use. The equipment load energy consumption ratio obtained is an important reference for energy consumption optimization and cost control.

[0038] S513: Based on the equipment load energy consumption ratio, analyze the load interval power consumption deviation using the formula: ; Calculate the power consumption self-control performance score of the device and obtain the power consumption self-control performance information; in, Indicates the device power consumption self-control performance score. represents the number of load intervals considered, It is Energy efficiency in each load range, It is the average value of energy efficiency in the load range.

[0039] Based on the equipment load energy consumption ratio, the load interval power consumption deviation is analyzed, and the equipment power consumption automatic control performance score is calculated using the formula to obtain the power consumption automatic control performance information. The formula is: ; in, Indicates the device power consumption self-control performance score. represents the number of load intervals considered, It is Energy efficiency in each load range, is the average value of the energy efficiency in the load interval. For example, assuming there are three load intervals, the energy efficiency is , ,and kWh, the average energy efficiency is Kilowatt-hours, plug these values ​​into the formula: ; Rating , which indicates the ability of a data center to effectively manage energy consumption under an automatic control system. A higher score indicates excellent energy consumption automatic control performance. This information is crucial for data center management and provides a quantitative tool to evaluate and optimize energy usage strategies.

[0040] A smart data center BA automatic control performance evaluation system, the smart data center BA automatic control performance evaluation system is used to execute the above-mentioned smart data center BA automatic control performance evaluation method, the system includes: The steady-state benchmark extraction module calculates the steady-state benchmark value of the environmental variable corresponding to each time window based on the environmental variable monitoring data of the data center BA system, and obtains the variable steady-state benchmark information; The short-term deviation analysis module screens the data points whose fluctuation amplitude exceeds the preset range in a short period of time based on the steady-state benchmark information of the variable, calculates the distribution characteristics of the deviation, and obtains the short-term deviation information of the variable; The deviation trend analysis module calls the short-term deviation information of the variables, calculates the cumulative deviation value of each variable in each time window, analyzes the growth trend of the deviation value, calculates the cumulative rate of long-term deviation, and obtains the deviation trend information of the environmental variables; The environmental performance evaluation module sets a scoring weight for each environmental variable based on the environmental variable deviation trend coefficient, calculates the environmental self-control stability score of the data center BA system, and obtains environmental stability information; The power consumption performance evaluation module calls the environmental stability information, obtains the unit energy consumption of the cooling system and the total power consumption of the equipment in the computer room, calculates the equipment power consumption automatic control performance score, and obtains the power consumption automatic control performance information.

[0041] 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 method for evaluating BA automatic control performance in a smart data center, characterized in that: The following steps are involved: S1: Based on the environmental variable monitoring data of the data center BA system, the steady-state baseline value of the environmental variable corresponding to each time window is calculated to obtain the variable steady-state baseline information; S2: Based on the steady-state reference information of the variables, the deviation of each environmental variable from the steady-state reference value is calculated, the data points whose fluctuation amplitude exceeds the preset range in a short period of time are screened, the distribution characteristics of the deviation are calculated, and the short-term deviation information of the variables is obtained; S3: calling the short-term deviation information of the variable, accumulating it in multiple time windows, calculating the cumulative deviation value of each variable in each time window, analyzing the growth trend of the deviation value, calculating the cumulative rate of long-term deviation, and obtaining the deviation trend information of the environmental variable; S4: Based on the environmental variable deviation trend coefficient, a scoring weight is set for each environmental variable, and the environmental self-control stability score of the data center BA system is calculated to obtain environmental stability information; S5: calling the environmental stability information, obtaining the unit energy consumption of the cooling system and the total power consumption of the equipment in the computer room, analyzing the power consumption deviation in the load interval, calculating the power consumption automatic control performance score of the equipment, and obtaining the power consumption automatic control performance information.

2. The method for evaluating BA automatic control performance of a smart data center according to claim 1 is characterized in that: The variable steady-state benchmark information specifically includes the computer room temperature benchmark value, the computer room humidity benchmark value, the cooling wind speed benchmark value, the air pressure benchmark value and the air quality benchmark value; the variable short-term deviation information specifically includes the computer room temperature short-term deviation, the computer room humidity short-term deviation, the cooling wind speed short-term deviation, the air pressure short-term deviation and the air quality short-term deviation; the environmental variable deviation trend information includes the computer room temperature long-term deviation coefficient, the computer room humidity long-term deviation coefficient and the cooling wind speed long-term deviation coefficient; the environmental stability information specifically includes the computer room temperature self-control score, the computer room humidity self-control score and the air flow self-control score; the power consumption self-control performance information includes the cooling unit power consumption self-control score, the computer room equipment power consumption self-control score and the load energy consumption ratio score.

3. The method for evaluating BA automatic control performance of a smart data center according to claim 1 is characterized in that: The steps for obtaining the variable steady-state benchmark information are specifically as follows: S111: Based on the environmental variable monitoring data of the BA system of the data center, the monitoring data of the computer room temperature, the computer room humidity, the cooling wind speed, the air pressure and the air quality are extracted, and the time window is set. The time window includes 1 hour, 24 hours and 7 days. The mean of each environmental variable is calculated in each time window to obtain the mean data of the environmental variable; S112: calling the mean value data of the environmental variables, calculating the difference between the maximum value and the minimum value, and calculating the change interval of each environmental variable in each time window to obtain the environmental variable change interval data; S113: Based on the environmental variable change interval data, filter the data points whose change interval is lower than the set threshold value, using the formula: ； Calculate the variable steady-state benchmark value to obtain the variable steady-state benchmark information; in, represents the steady-state reference value of the variable, Represents the total number of data points in the time window, Representative The value of the environmental variable for each data point, Representative The range of changes in data points, represents the stability threshold, is the indicator function, when Less than the stability threshold The value is 1 when it is true, otherwise it is 0.

4. The method for evaluating BA automatic control performance of a smart data center according to claim 1 is characterized in that: The steps for obtaining the short-term deviation information of the variable are specifically as follows: S211: Based on the variable steady-state reference information, call the environmental variable monitoring data of the current time period, including the room temperature, room humidity, cooling wind speed, air pressure and air quality, calculate the deviation of each environmental variable from the steady-state reference value, and obtain environmental variable deviation data; S212: calling the environmental variable deviation data, calculating the absolute value and change rate of the deviation, calculating the deviation change amount between data points and normalizing them, and obtaining deviation change rate data; S213: Based on the deviation change rate data, data points whose fluctuation amplitude exceeds a preset range in a short period of time are selected, and distribution characteristics of the deviation are calculated using the formula: ； Calculate the short-term deviation of the variable to obtain the short-term deviation of the variable; in, represents the short-term deviation of the variable, Represents the total number of filtered data points, Representative The deviation value of the data point, Represents the mean deviation value of the filtered data points.

5. The method for evaluating BA automatic control performance of a smart data center according to claim 1 is characterized in that: The steps for obtaining the environmental variable deviation trend information are specifically as follows: S311: calling the short-term deviation information of the variable, accumulating it in the time window of 1 hour, 24 hours and 7 days, calculating the cumulative deviation value in each time window, calculating the cumulative value by using the time integral of the deviation amount, and obtaining the cumulative deviation value of the environmental variable; S312: calling the accumulated deviation value of the environmental variable, analyzing the growth trend of the deviation value, comparing the accumulated deviation value in each time window, screening the variables with continuously growing deviation values, and obtaining a continuously growing variable set; S313: Based on the continuously growing variable set, the cumulative rate of long-term deviation is calculated for the room temperature, room humidity, and cooling wind speed, using the formula: ； Obtain the deviation trend coefficient and obtain the deviation trend information of the environmental variables; in, represents the deviation trend coefficient of the environmental variable, Represents the total number of time windows, Representative time The accumulated deviation value of the environmental variables at the time, Representative time The accumulated deviation value of the environmental variables at the time, Representative time The weight factor of the moment.

6. The method for evaluating BA automatic control performance of a smart data center according to claim 1 is characterized in that: The steps for obtaining the environmental stability information are specifically as follows: S411: Based on the environmental variable deviation trend coefficient, a scoring weight is set, and the scoring weight is allocated according to the influence of each environmental variable on the environmental stability of the data center, and a corresponding scoring weight is set for each environmental variable to obtain an environmental variable scoring weight set; S412: calling the environmental variable scoring weight set, performing weighted calculation on the environmental variable deviation trend information, and accumulating the product of the deviation trend coefficient of each variable and the corresponding scoring weight to obtain a weighted score of environmental stability; S413: Based on the weighted score of the environmental stability, the formula is used: ； Calculate the environmental self-control stability score of the data center BA system to obtain environmental stability information; in, Represents the environmental self-control stability score, Represents the number of environment variables, Representative The deviation trend coefficient of the environmental variables is Representative The scoring weights of the environmental variables, Represents the mean of the deviation trend coefficient of the environmental variables.

7. The method for evaluating BA automatic control performance of a smart data center according to claim 1 is characterized in that: The steps for obtaining the power consumption automatic control performance information are specifically as follows: S511: calling the environmental stability information, obtaining the energy consumption of the cooling system units and the total power consumption of the equipment in the computer room, and obtaining the heat load coefficient of the computer room by using the ratio of the energy consumption of the cooling system units to the total power consumption of the equipment in the computer room; S512: calling the heat load coefficient of the equipment room, using the ratio of the equipment operating power to the equipment rated power to obtain the equipment load rate, calculating the equipment load rate and the unit load energy consumption ratio, using the ratio of the total power consumption of the equipment per unit time to the load rate to obtain the unit load energy consumption ratio, and obtaining the equipment load energy consumption ratio; S513: Based on the equipment load energy consumption ratio, analyze the load interval power consumption deviation, using the formula: ； Calculate the power consumption self-control performance score of the device and obtain the power consumption self-control performance information; in, Indicates the device power consumption self-control performance score. represents the number of load intervals considered, It is Energy efficiency in each load range, It is the average value of energy efficiency in the load range.

8. A smart data center BA automatic control performance evaluation system, characterized in that: According to any one of claims 1 to 7, the method for evaluating BA automatic control performance of a smart data center comprises: The steady-state benchmark extraction module calculates the steady-state benchmark value of the environmental variable corresponding to each time window based on the environmental variable monitoring data of the data center BA system, and obtains the variable steady-state benchmark information; The short-term deviation analysis module screens data points whose fluctuation amplitude exceeds a preset range in a short period of time based on the steady-state benchmark information of the variable, calculates the distribution characteristics of the deviation, and obtains the short-term deviation information of the variable; The deviation trend analysis module calls the short-term deviation information of the variables, calculates the cumulative deviation value of each variable in each time window, analyzes the growth trend of the deviation value, calculates the cumulative rate of long-term deviation, and obtains the deviation trend information of the environmental variables; The environmental performance evaluation module sets a scoring weight for each environmental variable based on the environmental variable deviation trend coefficient, calculates the environmental self-control stability score of the data center BA system, and obtains environmental stability information; The power consumption performance evaluation module calls the environmental stability information, obtains the unit energy consumption of the cooling system and the total power consumption of the equipment in the computer room, calculates the equipment power consumption automatic control performance score, and obtains the power consumption automatic control performance information.

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