A smart data center ba self-control performance evaluation method and system

Abstract

The present application relates to the technical field of performance evaluation, in particular to a kind of intelligent data center BA automatic control performance evaluation method and system, comprising the following steps, based on the environmental variable monitoring data of data center BA system, the steady-state reference value corresponding to each time window is calculated, and the variable steady-state reference information is obtained.In the present application, through the monitoring and analysis of environmental variables in multiple time windows, the steady-state reference value of the variable can be calculated based on a large number of data points, the accuracy of the data is improved by selecting data points with stable fluctuation range, the reliability of the evaluation is enhanced, through the analysis of the growth trend of the deviation value, potential problems can be identified and intervened in time, thereby avoiding the accumulation of long-term problems, through the analysis of the deviation trend of the environmental variable, the environmental automatic control stability score is calculated, so that the data center manager can get intuitive performance display, optimize the decision-making process, so as to ensure the efficient and energy-saving operation of the system.

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 the BA automatic control performance of a smart data center. Background Art

[0002] The field of performance evaluation technology involves quantitatively analyzing the operating status, efficiency, and reliability of systems, equipment, or software to ensure they meet design requirements, operational objectives, or industry standards. This field is widely used in various industries, including computer systems, network architecture, automated control, and energy management. It primarily measures key indicators such as system response time, stability, resource utilization, and energy consumption by establishing mathematical models, collecting operational data, and constructing evaluation metrics. It also employs statistical analysis, simulation, and machine learning optimization methods. Performance evaluation not only identifies system bottlenecks and optimizes resource allocation, but can also be used to predict failures, develop improvement strategies, and enhance overall operational efficiency and stability.

[0003] The Smart Data Center BA Automation Performance Evaluation Method quantitatively analyzes the operation of building automation systems, aiming to assess their energy efficiency, stability, and responsiveness. Through data collection, model calculation, and intelligent analysis, this method evaluates core parameters such as temperature and humidity regulation, air quality management, and equipment load balancing, and provides optimization strategies to ensure that data centers meet business needs while operating in an energy-efficient, efficient, and secure manner.

[0004] Traditional assessment methods lack accurate monitoring of long-term trends in environmental variables and real-time performance analysis. Instead, they focus on static data analysis and historical performance records. This results in inadequate response to emergencies and rapidly changing environmental conditions, making it difficult to effectively predict and adjust countermeasures. Traditional data analysis methods fail to fully utilize real-time data, resulting in significant deviations from actual operating conditions. This limits optimal system performance and maximized resource utilization. Furthermore, deficiencies in fault prevention and resource allocation can lead to energy waste and system overload, impacting data center stability and security. 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 an intelligent data center.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for evaluating the BA automatic control performance of a smart data center, comprising the following steps:

[0007] 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;

[0008] 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, data points whose fluctuation amplitude exceeds a preset range in a short period of time are screened, and the distribution characteristics of the deviation are calculated to obtain the short-term deviation information of the variable;

[0009] 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;

[0010] S4: Based on the environmental variable deviation trend coefficient, a scoring weight is set for each environmental variable, and an environmental automatic control stability score of the data center BA system is calculated to obtain environmental stability information;

[0011] S5: Call the environmental stability information, obtain the energy consumption of the cooling system units and the total power consumption of the equipment in the computer room, 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.

[0012] As a further solution of the present invention, the variable steady-state benchmark information is specifically 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 is specifically 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 is specifically 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.

[0013] As a further solution of the present invention, the step of obtaining the variable steady-state reference information is specifically as follows:

[0014] S111: Based on the environmental variable monitoring data of the data center's BA system, the monitoring data of the computer room temperature, computer room humidity, cooling air speed, air pressure, and air quality are extracted. Time windows are set, including 1 hour, 24 hours, and 7 days. The mean value of each environmental variable in each time window is calculated to obtain the environmental variable mean data;

[0015] S112: calling the environmental variable mean data, 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 environmental variable change interval data;

[0016] 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:

[0017] ;

[0018] Calculate the variable steady-state benchmark value to obtain the variable steady-state benchmark information;

[0019] in, represents the steady-state reference value of the variable, Represents the total number of data points in the time window, Representative The environmental variable value of each data point, Representative The variation range of the data points, represents the stability threshold, is the indicator function, when Less than the stability threshold The value is 1 when it is set, otherwise it is 0.

[0020] As a further solution of the present invention, the step of obtaining the short-term deviation information of the variable is specifically as follows:

[0021] S211: Based on the variable steady-state benchmark information, call the environmental variable monitoring data of the current time period, including the computer room temperature, computer 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 environmental variable deviation data;

[0022] S212: calling the environmental variable deviation data, calculating the absolute value and change rate of the deviation, calculating the deviation change between data points and normalizing the deviation to obtain deviation change rate data;

[0023] S213: Based on the deviation change rate data, data points with fluctuations exceeding a preset range within a short period of time are screened, and distribution characteristics of the deviations are calculated using the formula:

[0024] ;

[0025] Calculate the short-term deviation of the variable to obtain the short-term deviation of the variable;

[0026] 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.

[0027] As a further solution of the present invention, the step of obtaining the environmental variable deviation trend information is specifically as follows:

[0028] 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;

[0029] S312: Calling the cumulative deviation value of the environmental variable, analyzing the growth trend of the deviation value, comparing the cumulative deviation value in each time window, screening variables with continuously growing deviation values, and obtaining a continuously growing variable set;

[0030] S313: Based on the continuously growing variable set, the cumulative rate of long-term deviation is calculated for the computer room temperature, computer room humidity, and cooling wind speed using the formula:

[0031] ;

[0032] Obtain the deviation trend coefficient and obtain the deviation trend information of the environmental variables;

[0033] in, represents the deviation trend coefficient of the environmental variable, Represents the total number of time windows, Representative time The cumulative deviation value of the environmental variables at the moment, Representative time The cumulative deviation value of the environmental variables at the moment, Representative time The weight factor of the moment.

[0034] As a further solution of the present invention, the step of obtaining the environmental stability information is specifically as follows:

[0035] S411: Setting a scoring weight based on the environmental variable deviation trend coefficient. 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.

[0036] 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 environmental stability score;

[0037] S413: Based on the weighted score of the environmental stability, the formula is used:

[0038] ;

[0039] Calculate the environmental self-control stability score of the data center BA system to obtain environmental stability information;

[0040] in, Represents the environmental self-control stability score, Represents the number of environment variables, Representative The deviation trend coefficient of each environmental variable, Representative The scoring weight of each environmental variable, Represents the mean of the deviation trend coefficient of the environmental variable.

[0041] As a further solution of the present invention, the step of acquiring the power consumption automatic control performance information is specifically as follows:

[0042] S511: Calling environmental stability information to obtain the energy consumption of the cooling system units and the total power consumption of the equipment in the computer room. Using the ratio of the energy consumption of the cooling system units to the total power consumption of the equipment in the computer room, the heat load coefficient of the computer room is obtained.

[0043] S512: Call the heat load coefficient of the equipment 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 the unit load energy consumption ratio, use the ratio of the total equipment power consumption per unit time to the load rate to obtain the unit load energy consumption ratio, and obtain the equipment load energy consumption ratio;

[0044] S513: Based on the device load energy consumption ratio, analyze the load interval power consumption deviation using the formula:

[0045] ;

[0046] Calculate the power consumption self-control performance score of the device and obtain power consumption self-control performance information;

[0047] in, Indicates the device power consumption automatic 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.

[0048] A smart data center BA automatic control performance evaluation system, which is used to execute the above-mentioned smart data center BA automatic control performance evaluation method, includes:

[0049] 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;

[0050] The short-term deviation analysis module screens data points whose fluctuation amplitude exceeds a preset range within 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;

[0051] 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;

[0052] The environmental performance evaluation module sets a scoring weight for each environmental variable based on the environmental variable deviation trend coefficient, calculates the environmental automatic control stability score of the data center BA system, and obtains environmental stability information;

[0053] The power consumption performance evaluation module calls the environmental stability information, obtains the energy consumption of the cooling system units 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.

[0054] Compared with the prior art, the advantages and positive effects of the present invention are:

[0055] 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 stable fluctuation ranges, the accuracy of the data is improved and the reliability of the evaluation is enhanced. By calculating the deviation between the environmental variables and the steady-state baseline value, as well as 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, data center managers 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

[0056] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0057] Figure 2 A flow chart for obtaining variable steady-state benchmark information for the present invention;

[0058] Figure 3 A flow chart of obtaining short-term deviation information of variables according to the present invention;

[0059] Figure 4 A flow chart for obtaining environmental variable deviation trend information for the present invention;

[0060] Figure 5 A flow chart for obtaining environmental stability information for the present invention;

[0061] Figure 6 This is a flow chart of the present invention for obtaining power consumption automatic control performance information. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0063] 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, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0064] Example 1

[0065] See also Figure 1 The present invention provides a technical solution: a method for evaluating the BA automatic control performance of a smart data center, comprising the following steps:

[0066] S1: Based on the environmental variable monitoring data of the data center's BA system, which includes computer room temperature, humidity, cooling air speed, air pressure, and air quality, multiple time windows are set, including 1 hour, 24 hours, and 7 days. The mean and variation range of each environmental variable are calculated within each time window. Data points with stable fluctuation ranges are selected as benchmark references. The steady-state baseline value of the environmental variable corresponding to each time window is calculated to obtain the variable steady-state benchmark information.

[0067] S2: Based on the variable steady-state baseline information, call the environmental variable monitoring data of the current time period, calculate the deviation of each environmental variable from the steady-state baseline value, calculate the absolute value and change rate of the deviation, filter out 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;

[0068] S3: Call the short-term deviation information of the variables and accumulate it 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 out variables with continuous growth, and calculate the cumulative rate of long-term deviation for the long-term changes in the computer room temperature, computer room humidity, and cooling air speed to obtain the deviation trend information of the environmental variables.

[0069] 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 automatic control stability score of the data center BA system, and obtain environmental stability information;

[0070] S5: Call environmental stability information to obtain the cooling system unit energy consumption 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 within the load range. Calculate the equipment power consumption automatic control performance score to obtain power consumption automatic control performance information.

[0071] 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.

[0072] See also Figure 2 , the specific steps for obtaining the steady-state benchmark information of the variables are:

[0073] S111: Based on the environmental variable monitoring data of the data center's BA system, the monitoring data of the computer room temperature, computer room humidity, cooling air speed, air pressure, and air quality are extracted. Time windows are set, including 1 hour, 24 hours, and 7 days. The mean value of each environmental variable in each time window is calculated to obtain the environmental variable mean data;

[0074] Based on the environmental variable monitoring data from the data center's BA system, we extract monitoring data for computer room temperature, humidity, cooling air speed, air pressure, and air quality. We then set time windows, including 1 hour, 24 hours, and 7 days. Within each time window, we calculate the mean of each environmental variable to obtain the mean data. We then obtain real-time monitoring data of environmental variables from the BA system to ensure data accuracy and completeness. For example, computer room temperature data can be collected in real time using temperature sensors installed in various locations, humidity data can be obtained using humidity sensors, cooling air speed can be measured using an anemometer installed at the air conditioning outlet, and air pressure and air quality data can be obtained using pressure sensors and air quality monitors, respectively. Next, we set different time windows, such as 1 hour, 24 hours, and 7 days, to analyze how environmental variables vary over different timescales. Within each time window, we perform statistical analysis on the data for each environmental variable and calculate its mean. For example, within a 1-hour time window, if temperature data is collected every minute, there will be 60 data points within that time window. By adding these 60 temperature data points and dividing by 60, we can obtain the average temperature value for that hour. Similarly, the mean values ​​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.

[0075] S112: calling the mean value data of the environmental variables, calculating the difference between the maximum value and the minimum value, and calculating the variation interval of each environmental variable in each time window to obtain the variation interval data of the environmental variables;

[0076] The mean value data of the environmental variables is called, and by calculating the difference between the maximum and minimum values, the variation range of each environmental variable within each time window is calculated to obtain the environmental variable variation range data. Specifically, within each time window, the maximum and minimum values ​​of each environmental variable are determined. For example, within 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. The difference between these two values ​​is calculated as the temperature variation range within the time window. The same method can be applied to other environmental variables. By calculating the variation range of each environmental variable within different time windows, the environmental variable variation range data is obtained, providing a basis for evaluating environmental stability.

[0077] 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:

[0078] ;

[0079] Calculate the variable steady-state benchmark value to obtain the variable steady-state benchmark information;

[0080] in, represents the steady-state reference value of the variable, Represents the total number of data points in the time window, Representative The environmental variable value of each data point, Representative The variation range of the data points, represents the stability threshold, is the indicator function, when Less than the stability threshold The value is 1 when it is set, otherwise it is 0.

[0081] 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 changes in environmental variables are 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 in the steady-state range. Similarly, the thresholds of humidity, cooling wind speed, air pressure and air quality variables can be set as 、 m / s, Pa, AQI. In each time window, filter out the change range Less than If a data point If the condition is met, then its indicator function The value is 1, otherwise the value is 0. For example, within a 24-hour time window, the following data points of the equipment room temperature are collected (unit: °C):

[0082] ;

[0083] Calculate the difference between the maximum and minimum values ​​to get the variation range:

[0084] °C;

[0085] Because this variation range °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 are as follows (unit: °C):

[0086] ;

[0087] The variation interval is calculated as:

[0088] °C;

[0089] The window change range °C less than , so the data points in this window can be used for steady-state calculations.

[0090] The steady-state reference value is calculated using the formula:

[0091] ;

[0092] in, represents the steady-state reference value of the variable, Represents the total number of data points in the time window, Representative The environmental variable value of each data point, Representative The variation range of the data points, represents the stability threshold, is the indicator function, when Less than the stability threshold The value is 1 when it is set, otherwise it is 0.

[0093] Bring in the data point values ​​for the above stable time window:

[0094]

[0095] ;

[0096] 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 speed, air pressure and air quality. The formula is useful in that by using the indicator function Automatic screening of fluctuation stability is achieved to ensure that only data points that meet the stability conditions are included in the benchmark value calculation. At the same time, the influence of short-term fluctuations on the benchmark value is eliminated by averaging, making the calculation results more reliable and thus useful for subsequent deviation analysis and environmental self-control optimization. This value reflects the stability of environmental variables over the long term. The closer the value is to the true trend of the monitored data, the more accurate the subsequent analysis of abnormal fluctuations. If the steady-state baseline value deviates from the normal operating range, it indicates that the data center environment may be experiencing slow, long-term changes and requires optimization and adjustment.

[0097] See also Figure 3 , the steps for obtaining the short-term deviation information of the variable are as follows:

[0098] S211: Based on the variable steady-state baseline information, call the environmental variable monitoring data of the current time period, including the computer room temperature, computer room humidity, cooling air speed, air pressure, and air quality, calculate the deviation of each environmental variable from the steady-state baseline value, and obtain environmental variable deviation data;

[0099] Based on the variable steady-state benchmark information, the environmental variable monitoring data of the current time period is called, including the computer room temperature, computer 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. These data include but are not limited to the computer 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. The difference between each monitored environmental data and the corresponding steady-state benchmark value is calculated. For example, if the current computer 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 the system with an immediate adjustment basis.

[0100] 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 it to obtain deviation change rate data;

[0101] 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 it 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 deviations that are too large. The obtained deviation change rate data provides a dynamic environmental monitoring tool for the data center.

[0102] 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, and calculate the distribution characteristics of the deviation using the formula:

[0103] ;

[0104] Calculate the short-term deviation of the variable to obtain the short-term deviation of the variable;

[0105] 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.

[0106] 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 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 amplitude 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 are those with a change rate exceeding point, that is , use the following formula to calculate the deviation distribution characteristics of these points:

[0107] ;

[0108] 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:

[0109] ;

[0110] Obtained The standard deviation, representing the deviation of data points, provides a quantitative indicator of short-term volatility for data center environmental management. This helps technical personnel understand the degree of deviation fluctuation and adjust environmental control systems accordingly to maintain data center environmental stability. This calculation method provides a dynamic monitoring and response mechanism for data center environmental management, enabling more accurate and timely environmental adjustments. The resulting short-term deviation information is a key basis for dynamic adjustments, helping managers assess and respond to environmental changes in real time.

[0111] See also Figure 4 ,The steps for obtaining the environmental variable deviation trend information are as follows:

[0112] 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;

[0113] The short-term deviation information of the variable is called, accumulated within the time window of 1 hour, 24 hours, and 7 days, the cumulative deviation value within each time window is calculated, the time integral of the deviation amount is used to calculate the cumulative value, the cumulative deviation value of the environmental variable is obtained, and 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 24-hour and 7-day time windows, the same accumulation steps are repeated. Such cumulative calculations provide data center management with a long-term perspective on the changes in environmental variables, 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.

[0114] S312: Calling the cumulative deviation value of the environmental variable, analyzing the growth trend of the deviation value, comparing the cumulative deviation value in each time window, screening the variables with continuously growing deviation values, and obtaining a continuously growing variable set;

[0115] 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 time windows of 1 hour, 24 hours and 7 days, 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, providing data center managers with key data points for diagnosis and response measures. The continuously growing variable set provides decision support for subsequent environmental control strategy adjustments and fault prevention.

[0116] S313: Based on the continuously growing variable set, calculate the cumulative rate of long-term deviation for the computer room temperature, computer room humidity, and cooling air speed using the formula:

[0117] ;

[0118] Obtain the deviation trend coefficient and obtain the deviation trend information of the environmental variables;

[0119] in, represents the deviation trend coefficient of the environmental variable, Represents the total number of time windows, Representative time The cumulative deviation value of the environmental variables at the moment, Representative time The cumulative deviation value of the environmental variables at the moment, Representative time The weight factor of the moment.

[0120] Based on the continuously growing variable set, the cumulative rate of long-term deviation is calculated for the computer room temperature, computer 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:

[0121] ;

[0122] in, represents the deviation trend coefficient of the environmental variable, Represents the total number of time windows, Representative time The cumulative deviation value of the environmental variables at the moment, Representative time The cumulative deviation value of the environmental variables at the moment, 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:

[0123] Day 1: , ;

[0124] Day 2: , ;

[0125] Day 3: , ;

[0126] Day 4: , ;

[0127] Day 5: , ;

[0128] Day 6: , ;

[0129] Day 7: , ;

[0130] And set , ;

[0131] Substitute into the formula and calculate:

[0132] ;

[0133] Calculation results This indicates that the cumulative deviation of the environmental variable increases by an average of 2 units per day. This trend coefficient provides data center management with an important indicator of long-term environmental changes, allowing the technical team to make corresponding environmental adjustments or early warnings to maintain the stable operation of the computer room.

[0134] See also Figure 5 , the specific steps for obtaining environmental stability information are:

[0135] 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.

[0136] 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 assigned 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 it has a 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 the basis for subsequent weighted scoring calculations.

[0137] 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 environmental stability score;

[0138] Call the environmental variable scoring weight set to perform a weighted calculation on the environmental variable deviation trend information. The product of the deviation trend coefficient of each variable and the corresponding scoring weight is accumulated to obtain the weighted environmental stability score. The deviation trend coefficient of each environmental variable is multiplied by its corresponding scoring weight, and then the results of all environmental variables are accumulated. For example, if the deviation trend coefficient of the computer room temperature is 2, the humidity is 1, the wind speed is 1.5, and the air quality is 0.5, and using the previously assigned weights, the weighted score is calculated 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.

[0139] S413: Based on the weighted score of environmental stability, the formula is:

[0140] ;

[0141] Calculate the environmental self-control stability score of the data center BA system to obtain environmental stability information;

[0142] in, Represents the environmental self-control stability score, Represents the number of environment variables, Representative The deviation trend coefficient of each environmental variable, Representative The scoring weight of each environmental variable, represents the mean of the deviation trend coefficient of the environmental variable;

[0143] Based on the weighted environmental stability score, 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:

[0144] ;

[0145] in, Represents the environmental self-control stability score, Represents the number of environment variables, Representative The deviation trend coefficient of each environmental variable, Representative The scoring weight of each environmental variable, Represents the mean of the deviation trend coefficients of the environmental variables. To calculate this score, first determine the deviation trend coefficient and score weight of each variable, then multiply and accumulate them, and calculate 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. Finally, use these values ​​to calculate For example, there are four environmental variables with deviation trend coefficients of 2, 1, 1.5, and 0.5, weights of 0.4, 0.3, 0.2, and 0.1, and a mean of 1.25. The calculation is as follows:

[0146] ;

[0147] ;

[0148] ;

[0149] ;

[0150] This value reflects the overall stability of the data center environmental control system. The score indicates that the environmental conditions are relatively stable, providing valuable information to data center management to support the optimization and adjustment of environmental control strategies.

[0151] See also Figure 6 ,The specific steps for obtaining power consumption automatic control performance information are:

[0152] S511: Calling environmental stability information to obtain the energy consumption of the cooling system units and the total power consumption of the equipment in the computer room. Using the ratio of the energy consumption of the cooling system units to the total power consumption of the equipment in the computer room, the heat load coefficient of the computer room is obtained.

[0153] Call the environmental stability information to obtain the energy consumption of the cooling system units and the total power consumption of the equipment in the computer room. Use the ratio of the energy consumption of the cooling system units 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 units 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 units 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.

[0154] S512: The heat load coefficient of the equipment room is called, and the equipment load rate is obtained by using the ratio of the equipment operating power to the equipment rated power. The equipment load rate and the unit load energy consumption ratio are calculated. The unit load energy consumption ratio is obtained by using the ratio of the total equipment power consumption per unit time to the load rate, and the equipment load energy consumption ratio is obtained.

[0155] 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. If 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 for measuring 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 obtained equipment load energy consumption ratio is an important reference for energy consumption optimization and cost control.

[0156] S513: Based on the equipment load energy consumption ratio, analyze the load interval power consumption deviation using the formula:

[0157] ;

[0158] Calculate the power consumption self-control performance score of the device and obtain power consumption self-control performance information;

[0159] in, Indicates the device power consumption automatic 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.

[0160] Based on the equipment load energy consumption ratio, the power consumption deviation in the load interval is analyzed, and the power consumption automatic control performance score of the equipment is calculated using the formula to obtain the power consumption automatic control performance information. The formula is:

[0161] ;

[0162] in, Indicates the device power consumption automatic 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:

[0163] ;

[0164] Rating , which indicates the data center's ability 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.

[0165] A smart data center BA automatic control performance evaluation system is used to implement the above-mentioned smart data center BA automatic control performance evaluation method. The system includes:

[0166] 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;

[0167] The short-term deviation analysis module, based on the variable steady-state benchmark information, screens data points whose fluctuation amplitude exceeds the preset range in a short period of time, calculates the distribution characteristics of the deviation, and obtains the short-term deviation information of the variable;

[0168] 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;

[0169] 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;

[0170] The power consumption performance evaluation module calls the environmental stability information, obtains the energy consumption of the cooling system units and the total power consumption of the equipment in the computer room, calculates the power consumption automatic control performance score of the equipment, and obtains the power consumption automatic control performance information.

[0171] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection 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, data points whose fluctuation amplitude exceeds a preset range in a short period of time are screened, and the distribution characteristics of the deviation are calculated to obtain the short-term deviation information of the variable; 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, calculating the deviation trend coefficient of the environmental variable, and obtaining the deviation trend information of the environmental variable; S4: Based on the environmental variable deviation trend information, a scoring weight is set for each environmental variable, and an environmental automatic control stability score of the data center BA system is calculated to obtain environmental stability information; The steps for obtaining the environmental stability information are specifically as follows: S411: Based on the environmental variable deviation trend information, 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. S412: calling the environmental variable scoring weight set, performing weighted calculation on the deviation trend coefficients of the environmental variables, and accumulating the product of the deviation trend coefficient of each environmental variable and the corresponding scoring weight to obtain a weighted environmental stability score; S413: Based on the weighted score of the environmental stability, the formula Calculate the environmental self-control stability score of the data center BA system , obtain environmental stability information; in, Represents the number of environment variables, Representative The deviation trend coefficient of each environmental variable, Representative The scoring weight of each environmental variable, represents the mean of the deviation trend coefficient of the environmental variable; S5: Calling the environmental stability information, obtaining the energy consumption of the cooling system units and the total energy consumption of the equipment in the computer room, analyzing the energy consumption deviation in the load interval, calculating the equipment energy consumption automatic control performance score, and obtaining energy consumption automatic control performance information; The steps for obtaining the energy consumption automatic control performance information are specifically as follows: S511: Calling environmental stability information to obtain the energy consumption of the cooling system units and the total energy consumption of the equipment in the computer room. Using the ratio of the energy consumption of the cooling system units to the total energy consumption of the equipment in the computer room, the heat load coefficient of the computer room is obtained. S512: 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, use the ratio of the total energy consumption of the equipment in the computer room per unit time to the load rate to obtain the unit load energy consumption ratio, and obtain the equipment load energy consumption ratio; S513: Based on the equipment load energy consumption ratio, analyze the load interval energy consumption deviation, using the formula Computing equipment energy consumption automatic control performance score , obtain energy consumption automatic control performance information; in, 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.

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 energy consumption self-control performance information includes the cooling unit energy consumption self-control score and the computer room equipment energy consumption self-control 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 data center's BA system, the monitoring data of the computer room temperature, computer room humidity, cooling air speed, air pressure, and air quality are extracted. Time windows are set, including 1 hour, 24 hours, and 7 days. The mean value of each environmental variable in each time window is calculated to obtain the environmental variable mean data; S112: calling the environmental variable mean data, 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 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 stability threshold, and use the formula Calculate the steady-state reference value of the variable , obtain the steady-state benchmark information of the variables; in, Represents the total number of data points in the time window, Representative The environmental variable value of each data point, Representative The variation range of the data points, represents the stability threshold, is the indicator function, when Less than the stability threshold The value is 1 when it is set, otherwise it is 0.

4. The method for evaluating BA automatic control performance of a smart data center according to claim 1, 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 benchmark information, call the environmental variable monitoring data of the current time period, including the computer room temperature, computer 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 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 between data points and normalizing the deviation to obtain deviation change rate data; 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 a variable , get the short-term deviation information of the variable; in, 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: 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; S312: Calling the cumulative deviation value of the environmental variable, analyzing the growth trend of the deviation value, comparing the cumulative deviation value in each time window, screening 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 Get the deviation trend coefficient of environmental variables , obtain the deviation trend information of environmental variables; in, Represents the total number of time windows, Representative time The cumulative deviation value of the environmental variables at the moment, Representative time The cumulative deviation value of the environmental variables at the moment, Representative time The weight factor of the moment.

6. A smart data center BA automatic control performance evaluation system, characterized by: The system is used to implement the smart data center BA automatic control performance evaluation method according to any one of claims 1 to 5, and 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 within 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 information, calculates the environmental automatic control stability score of the data center BA system, and obtains environmental stability information; The energy consumption performance evaluation module calls the environmental stability information, obtains the energy consumption of the cooling system units and the total energy consumption of the equipment in the computer room, calculates the energy consumption automatic control performance score of the equipment, and obtains the energy consumption automatic control performance information.

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