Machine room environment intelligent regulation and control method fusing multi-source sensing information

Abstract

The invention relates to a machine room environment intelligent regulation and control method fusing multi-source sensing information, and the method comprises the steps: collecting the temperature and humidity data of different regions in a machine room through a temperature and humidity sensor, and collecting the movement track and stay time data of a person in the machine room through a person positioning device, transmitting the collected machine room environment data and personnel activity data to a data fusion module; in the data fusion module, machine room environment data and personnel activity data are aligned according to a time sequence, wrong data and outliers are eliminated, and a data set in a unified format is obtained; a correlation coefficient matrix among the characteristic parameters of the computer room is analyzed through correlation, the characteristic parameters with correlation higher than a preset correlation threshold value are screened out, and the characteristic parameters comprise temperature, humidity, personnel access frequency and staying duration parameters.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a method for intelligently controlling a computer room environment by integrating multi-source sensor information. Background Art

[0002] The environmental management of the computer room faces the contradiction between temperature and humidity control and the entry and exit activities of personnel. Frequent entry and exit of personnel will cause fluctuations in the temperature and humidity of the computer room, and the fluctuations in temperature and humidity will affect the stability of equipment operation. The existing computer room environmental control system often separates temperature and humidity control from personnel management, and each operates independently. When the temperature and humidity sensor detects an abnormality, the air-conditioning system will adjust it, but it cannot predict the impact of personnel activities. Similarly, the access control module only controls the entry and exit of personnel according to preset rules, without considering the impact on the computer room environment. This fragmented management method leads to delayed adjustment of the air-conditioning system, energy waste, and a lack of targeted constraints on personnel activities.

[0003] How to realize the linkage control of air-conditioning system and access control module through data analysis of the correlation between temperature and humidity changes and personnel activities is an urgent problem to be solved. It is necessary to find out the correlation between temperature and humidity and personnel activities through data cleaning, feature extraction, model training and other steps on the basis of data collection, and then guide the coordinated control of air-conditioning and access control modules to realize refined and intelligent personnel management.

[0004] The solution proposed in this scheme to address the above shortcomings is: collect temperature and humidity data, personnel activity trajectories and duration of stay in different areas of the computer room through temperature and humidity sensors and personnel positioning equipment, and transmit these data to the data fusion module for time series alignment and data cleaning to obtain a data set in a unified format. Then, perform feature parameter correlation analysis to screen out parameters closely related to environmental changes, analyze and establish the association pattern between the computer room environment and personnel activities, determine the target structure and threshold, and then deploy the association pattern to the air conditioning and access control modules to monitor and dynamically adjust the equipment operating parameters in real time to achieve environmental control. At the same time, display the control effect through the data visualization module, evaluate the control strategy, and use feedforward operations to optimize the control process to ensure that the environmental parameters return to the target range, thereby achieving intelligent and efficient management of the computer room environment. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the purpose of this application is to provide a method for intelligent control of computer room environment by integrating multi-source sensor information.

[0006] The present application discloses a method for intelligently controlling a computer room environment by integrating multi-source sensor information, comprising:

[0007] S101, collecting temperature and humidity data of different areas in the computer room through temperature and humidity sensors, collecting activity tracks and stay time data of personnel in the computer room through personnel positioning equipment, and transmitting the collected computer room environment data and personnel activity data to the data fusion module;

[0008] S102, in the data fusion module of step S101, align the computer room environment data and the personnel activity data in time series, remove erroneous data and outliers, and obtain a unified format data set;

[0009] S103, calculating the correlation coefficient matrix between the characteristic parameters of the computer room in step S101 through correlation analysis, and screening out characteristic parameters whose correlation is higher than a preset correlation threshold, wherein the characteristic parameters include temperature, humidity, frequency of personnel entry and exit, and length of stay;

[0010] S104, according to the characteristic parameters with correlation higher than the preset threshold selected in step S103, analyzing the association pattern between the computer room environment and the personnel activities, so as to obtain the target structure and target threshold of the association pattern between the computer room environment and the personnel activities, wherein the target threshold includes the target computer room environment threshold and the target personnel activity threshold;

[0011] S105. According to the target threshold in step S104, the computer room environment and personnel activity association mode is deployed to the air conditioning equipment control module, the computer room temperature and humidity data are obtained in real time, and it is determined whether the real-time temperature and humidity data of the computer room exceeds the target computer room environment threshold range. If it exceeds the target computer room environment threshold range, the preset air conditioning equipment adjustment mechanism is triggered to dynamically adjust the air supply temperature, humidity and air volume of the air conditioning equipment for temperature and humidity recovery;

[0012] S106. According to the target threshold in step S104, the computer room environment and personnel activity association mode are deployed to the access control module, and the personnel entry and exit frequency and residence time data are obtained in real time to determine whether the target personnel activity threshold is exceeded. If the target personnel activity threshold is exceeded, the access control authorization time period and number of times are dynamically adjusted to control personnel entry and exit activities;

[0013] S107, according to the control adjustment of the air conditioner and the access control in step S105 and step S106, the environmental data of the computer room after the control adjustment is detected, including the temperature and humidity in different areas of the computer room and the activity tracks and stay time of the personnel in the computer room, and the environmental data is formed into a curve chart through a preset data visualization module to obtain the environmental change trend of the computer room after the control;

[0014] S108. Evaluate the current control strategy and method according to the environmental change trend after the computer room is regulated in step S107. By presetting a target environmental change trend curve, fit the environmental change trend of the computer room after regulation with the pre-set target environmental change trend for use in regulating the computer room environment.

[0015] Preferably, in step S101, the temperature and humidity distribution is calculated by using the inverse distance weighted interpolation method through evenly arranged temperature and humidity sensors and network point coordinates, and a fine distribution map is obtained by increasing the density of sampling points in the hot spot area. At the same time, the positioning base station is used to receive the signal and calculate the position of the positioning tag, and the noise is eliminated by curve smoothing, the movement trajectory and the residence time are calculated, and the data is divided by time window. The temperature and humidity peak values ​​and the correlation values ​​of the activity state of the positioning tag are extracted, and data association analysis is performed to identify anomalies and activity patterns. The sensors are arranged in an area of ​​400 square meters at a spacing of 4 meters to form a 6×6 grid with a sampling frequency of 1 second / time. The high temperature area adopts 1-meter small grid encrypted sampling. The positioning base stations are installed at a spacing of 8 meters, and the signal is sent every 0.1 second. Through time window data alignment and feature extraction, support and confidence threshold analysis, typical abnormal scenes are identified.

[0016] Preferably, in the step S102, the computer room environment data and the positioning signal data are fused through millisecond timestamps, the data points are supplemented within a 10-second sampling interval using a cubic spline curve, and the outliers are processed by a box plot method to obtain an environmental data set after outlier processing. At the same time, according to the computer room building outline and the coverage range of the positioning base station, the positioning signal data is subjected to coordinate sequence extraction and smoothing processing, the environmental data set and the positioning trajectory data are aligned through a 10-second time window, a mapping relationship table is established, and normalization processing is performed to form a unified format data set, synchronize the sensor and base station time, and process data discontinuity, outliers and positioning drift problems.

[0017] Preferably, in step S103, by analyzing the paired data of the temperature and humidity sensors in the computer room at the same grid point, applying median smoothing and time window statistics, the temperature and humidity time series and personnel activity parameters are obtained, the parameters are normalized and the Pearson correlation coefficient is calculated, a correlation coefficient matrix is ​​constructed, and dimensionality reduction is performed through hierarchical clustering and Euclidean distance calculation, and temperature and humidity are selected as characteristic parameters.

[0018] Preferably, in the step S104, by analyzing the time series of the measured data of the computer room environment and the recorded data of personnel activities for 30 consecutive days, a random forest regressor is used to establish a mapping between temperature and humidity and the rules of personnel activities, and the model parameters are optimized through grid search. The data set is divided into training, verification and test sets, and the data is normalized. Based on the predicted deviation distribution, the temperature and humidity threshold intervals and the activity parameter dividing values ​​are divided, and a threshold rule table is constructed. The parameters are adjusted by verifying the data set, the optimal model is determined, and the prediction accuracy of the threshold rule is verified on the test set, and finally the temperature and humidity threshold intervals of the computer room and the threshold intervals of personnel activities are obtained.

[0019] Preferably, in the step S105, a 4-20 mA analog signal is collected at 30-second intervals and converted into a digital quantity through a temperature and humidity sensor array and personnel activity monitoring arranged in the machine room, temperature and humidity data are recorded, and the changing trends of hot spots and personnel gathering areas are calculated. The fuzzy controller is used to generate air conditioning adjustment instructions, which are sent to the air conditioning controller through the 485 bus protocol for dynamic adjustment of temperature, humidity and air volume. The sensors are arranged in a 6×6 grid dot matrix, and the hot spot area is determined based on the surrounding temperature difference. The fuzzy controller adjusts the adjustment step according to the deviation level, and updates the control rules in real time to adapt to the temperature and humidity response characteristics, so as to quickly return the environmental parameters to the target threshold range. In actual operation, the controller gives priority to adjusting the air supply volume of the air conditioner according to the temperature increase in the cabinet area and the personnel activity, while the humidity adjustment is performed through the proportion of the humidifier switching time, and the response characteristic curve is used to optimize the control parameters.

[0020] Preferably, in step S106, by analyzing the access control card reader and positioning base station data, combined with temperature and humidity monitoring, the system performs fine management of the entry and exit and stay of personnel in the computer room area. The system counts personnel activities in a 30-minute cycle, calculates environmental load indicators, and sets access control rules accordingly, including entry and exit frequency and stay time limits. Personnel enjoy different entry and exit permissions according to their authorization levels. The system evaluates card swiping applications in real time, locks and issues warnings for violations. During peak hours or when the environmental load is too high, the system automatically adjusts the access control rules to restrict personnel activities, and the authorization rules are combined with operating procedures.

[0021] Preferably, in the step S107, the temperature and humidity sensors and positioning base stations arranged at 6×6 grid points are used to synchronously collect and record the temperature, humidity and personnel position data in the computer room at intervals of 5 minutes, calculate the 10-minute average to eliminate fluctuations, generate an environmental data sequence with a standard timestamp, supplement the data points using linear interpolation, draw temperature and humidity change curves, and mark the time periods exceeding the threshold and the control adjustment moments using a line chart. At the same time, the computer room area is divided into 4m×4m grids, the length of time people stay is accumulated and a regional density distribution map is generated, the temperature and humidity average values ​​and arrow marks indicating the moving path are superimposed, and the sensor uses a two-wire current output.

[0022] Preferably, in the step S108, the computer room system generates an actual environmental curve by analyzing the temperature and humidity data collected every 5 minutes, and compares it with the reference curve of a normal working day, marks the control monitoring points that exceed the threshold, and based on the load forecast, the system generates a control instruction 30 minutes in advance, controls the air supply parameters of the air conditioner through the gear, and records the changes in environmental parameters. If the rate of change exceeds the threshold, access control is performed and the entry and exit of personnel are adjusted. The data on Tuesday without activity is used as a reference. When the temperature and humidity are abnormal, the system uses a graded adjustment mechanism and access control. If the temperature drop rate is insufficient, the entry of personnel is restricted and the stay time is adjusted.

[0023] The intelligent control method for computer room environment integrating multi-source sensor information described in the present application has the advantage that the method collects computer room temperature and humidity and personnel activity data, performs data fusion processing and correlation analysis on the data, analyzes the correlation pattern between the computer room environment and personnel activities, and based on the correlation pattern between the computer room environment and personnel activities, the present invention monitors the computer room environment and personnel activities in real time. When the preset threshold is exceeded, the air-conditioning equipment parameters and access control authorization are automatically adjusted to maintain an ideal computer room environment. At the same time, the present invention also presents the trend of environmental changes through data visualization, and performs fitting analysis with preset target trends, and continuously optimizes the control strategy. This method realizes the coordinated management of the computer room environment and personnel activities, improves the intelligence and accuracy of computer room environment control, and effectively ensures the safe and stable operation of computer room equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flowchart of the steps of a method for intelligently controlling a computer room environment by integrating multi-source sensor information described in the present application;

[0025] Figure 2 This is a flow chart of step S103 of a method for intelligently controlling a computer room environment by integrating multi-source sensor information described in the present application;

[0026] Figure 3 This is a flow chart of step S108 of a method for intelligently controlling a computer room environment by integrating multi-source sensor information described in the present application. DETAILED DESCRIPTION

[0027] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant inventions, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0028] like Figure 1-Figure 3 As shown, the method for intelligently controlling the computer room environment by integrating multi-source sensor information described in the present application includes the following steps:

[0029] S101, collecting temperature and humidity data of different areas in the computer room through temperature and humidity sensors, collecting activity tracks and stay time data of personnel in the computer room through personnel positioning equipment, and transmitting the collected computer room environment data and personnel activity data to the data fusion module;

[0030] S102, in the data fusion module of step S101, align the computer room environment data and the personnel activity data in time series, remove erroneous data and outliers, and obtain a unified format data set;

[0031] S103, calculating the correlation coefficient matrix between the characteristic parameters of the computer room in step S101 through correlation analysis, and screening out characteristic parameters whose correlation is higher than a preset correlation threshold, wherein the characteristic parameters include temperature, humidity, frequency of personnel entry and exit, and length of stay;

[0032] S104, according to the characteristic parameters with correlation higher than the preset threshold selected in step S103, analyzing the association pattern between the computer room environment and the personnel activities, so as to obtain the target structure and target threshold of the association pattern between the computer room environment and the personnel activities, wherein the target threshold includes the target computer room environment threshold and the target personnel activity threshold;

[0033] S105. According to the target threshold in step S104, the computer room environment and personnel activity association mode is deployed to the air conditioning equipment control module, the computer room temperature and humidity data are obtained in real time, and it is determined whether the real-time temperature and humidity data of the computer room exceeds the target computer room environment threshold range. If it exceeds the target computer room environment threshold range, the preset air conditioning equipment adjustment mechanism is triggered to dynamically adjust the air supply temperature, humidity and air volume of the air conditioning equipment for temperature and humidity recovery;

[0034] S106. According to the target threshold in step S104, the computer room environment and personnel activity association mode are deployed to the access control module, and the personnel entry and exit frequency and residence time data are obtained in real time to determine whether the target personnel activity threshold is exceeded. If the target personnel activity threshold is exceeded, the access control authorization time period and number of times are dynamically adjusted to control personnel entry and exit activities;

[0035] S107, according to the control adjustment of the air conditioner and the access control in step S105 and step S106, the environmental data of the computer room after the control adjustment is detected, including the temperature and humidity in different areas of the computer room and the activity tracks and stay time of the personnel in the computer room, and the environmental data is formed into a curve chart through a preset data visualization module to obtain the environmental change trend of the computer room after the control;

[0036] S108. Evaluate the current control strategy and method according to the environmental change trend after the computer room is regulated in step S107. By presetting a target environmental change trend curve, fit the environmental change trend of the computer room after regulation with the pre-set target environmental change trend for use in regulating the computer room environment.

[0037] like Figure 1As shown, in step S101, the temperature and humidity sampling data of the sensor grid points are obtained, and the temperature and humidity spatial distribution data are obtained by using inverse distance weighted interpolation according to the sampling data; the distance from the positioning tag to the base station is determined according to the signal strength received by the positioning base station, and the noise of the positioning tag position coordinates is eliminated by using a curve smoothing function to obtain the moving trajectory points; the temperature and humidity spatial distribution data and the moving trajectory points are segmented according to the time window, and the corresponding data pairs of the temperature and humidity peak points and the positioning tag residence positions are extracted from the segmented data segments; if the temperature and humidity change amount deviates from the regional average value by more than a preset threshold, the positioning tag activity association value is obtained from the corresponding data pair, and the temperature and humidity anomalies and the positioning tag activity rules are obtained by using data association analysis.

[0038] Specifically, in step S101, according to the grid point coordinates of the temperature and humidity sensors evenly arranged in the machine room and the distance between adjacent sensors, the temperature and humidity sampling data is obtained from each sensor node, the spatial distribution of the grid point data is calculated by inverse distance weighted interpolation, and the temperature and humidity distribution map of the hot spot area is obtained by increasing the sampling point density in the fluctuation area;

[0039] By arranging a positioning base station in the computer room to receive the signal transmitted by the positioning tag, the distance from the positioning tag to the base station is converted according to the signal strength attenuation curve, the position coordinates of the positioning tag are established in the computer room coordinate system, the position coordinate data is de-noised based on the curve smoothing function, and the moving trajectory points and residence time of the positioning tag are calculated from the smoothed coordinate sequence;

[0040] The temperature and humidity distribution data and the positioning tag trajectory data are segmented by a time window of 10 seconds, and the corresponding data pairs of the temperature and humidity peak points and the positioning tag residence positions are extracted in the segmented data segments, and the correlation values ​​of the temperature and humidity changes and the positioning tag activity status are obtained from the corresponding data pairs;

[0041] When the temperature and humidity changes deviate from the regional average by more than plus or minus 5 degrees Celsius or the relative humidity is 20%, the location and residence time of the positioning tag in this period are extracted from the associated value. The data association analysis based on the support 0.6 and confidence 0.8 thresholds is used to find the temperature and humidity anomalies and the activity patterns of the positioning tags. The historical data is compared according to the patterns to identify similar scenes.

[0042] When arranging temperature and humidity sensors in the computer room, they are arranged according to a grid point arrangement with a spacing of 4 meters. The area of ​​a single area is 400 square meters. 36 sensor nodes are arranged to form a 6×6 grid array. The sensor sampling frequency is 1 second to collect data. The temperature measurement accuracy is 0.5 degrees Celsius and the relative humidity measurement accuracy is 2%.

[0043] For high temperature areas, small grid sampling points are added to the standard grid with a small grid spacing of 1 meter to construct a detailed temperature and humidity distribution of the hot spot area;

[0044] When inverse distance weighting is used, the distance attenuation exponent is set to 2, and the closer the sampling point is, the greater the weight is, so as to achieve continuous and smooth interpolation of temperature and humidity distribution;

[0045] The positioning base stations are installed on the walls around the room at intervals of 8 meters. The 2.4G frequency band signal is used, and the positioning tag sends a signal every 0.1 seconds. The signal strength attenuation curve adopts a logarithmic attenuation model. The signal strength is -40dBm at a distance of 1 meter, and the attenuation factor is 3. When the base station receives a signal strength of -70dBm, the ranging error is less than 0.5 meters. The curve smoothing function adopts a 5-point average value. For the residence judgment, staying within a range of 2 meters for more than 30 seconds is counted as one residence.

[0046] When aligning the time window data, the sampling interval of temperature and humidity distribution data is 1 second, and the sampling interval of positioning trajectory data is 0.1 second. The data is divided into 10-second time windows, and the maximum and minimum value points of temperature and humidity data and the positioning trajectory residence points are extracted in each time window;

[0047] If the temperature change in the split window exceeds 5 degrees Celsius or the relative humidity change exceeds 20%, extract the activity trajectory features of the positioning tag in the window;

[0048] In the data association analysis, the support threshold of 0.6 means that similar temperature and humidity anomalies and human activity patterns appear in at least 60% of all time windows, and the confidence threshold of 0.8 means that when temperature and humidity anomalies occur, there is an 80% probability that they correspond to specific human activity patterns;

[0049] Typical abnormal scenarios in the equipment room include a sudden drop of 5 degrees Celsius in temperature near the air outlet of the air conditioner, a local temperature rise of 2 degrees Celsius caused by a person staying in front of the equipment for more than 3 minutes, and a temperature fluctuation of 4 degrees Celsius caused by maintenance work in the rear area of ​​the cabinet;

[0050] Similar scene judgment is based on the temperature change range, humidity change range, personnel residence position deviation less than 1 meter, and residence time deviation less than 30 seconds;

[0051] In actual applications, temperature and humidity grid monitoring covers all areas of the computer room, including the equipment area, aisle area, and air-conditioning area, with a focus on monitoring equipment-intensive areas; personnel positioning covers the passages inside and outside the computer room and the equipment maintenance passages, realizing the correlation monitoring of personnel activities and environmental changes. Temperature and humidity anomalies and personnel activity patterns include short-term temperature rise caused by maintenance work, sudden temperature changes caused by equipment switching on and off, and airflow disturbances caused by maintenance operations.

[0052] like Figure 1As shown, in step S102, according to the sampling timestamp of the computer room environment data and the sampling timestamp of the positioning signal data, a cubic spline curve is used to supplement the data points to obtain the environment positioning fusion data; for the environment positioning fusion data, the quartiles and interquartile range of the environment parameters are calculated through a box plot, the abnormal values ​​beyond the quartile range are truncated and the environment data set after abnormal processing is obtained through forward data filling; according to the positioning signal data, the excessive coordinate points are limited according to the adjacent coordinate spacing threshold and smoothed based on the distance threshold to obtain the positioning trajectory state mark; for the environment data set after abnormal processing and the positioning trajectory state mark, they are segmented and aligned through a time window and a mapping relationship table of environment parameters and trajectory states is established, and the environment parameter values ​​and trajectory state values ​​in the mapping relationship are normalized to obtain a unified format data set.

[0053] Specifically, in step S102, according to the sampling timestamps of the computer room environment data and the positioning signal data, a timestamp normalization format with a time accuracy of milliseconds is adopted, data points are supplemented through a cubic spline curve within 10 seconds of the computer room environment data sampling interval, and the original data are merged according to the timestamps to obtain environmental positioning fusion data under a unified time scale;

[0054] From the environmental positioning fusion data under the unified time scale, the quartiles and interquartile ranges of environmental parameters are calculated through box plots. Data exceeding 1.5 times the interquartile range of the upper and lower quartiles are marked as outliers. The outliers are truncated according to the upper and lower quartiles, and the missing points are filled with forward data to obtain the environmental data set after outlier processing.

[0055] According to the coordinate range of the machine room building outline and the coverage range of the positioning base station, the coordinate sequence is extracted from the positioning signal data, the excessive coordinate points are limited according to the adjacent coordinate spacing threshold, the coordinate points are smoothed based on the 4-meter distance threshold, and the corresponding state mark of the positioning trajectory is obtained. The trajectory points that exceed the building outline or base station coverage are bounded, and the environmental data set and the positioning trajectory data are segmented and aligned through a 10-second time window. A mapping relationship table between environmental parameters and trajectory status is established based on the status mark within the time window, and the environmental parameter values ​​and trajectory status values ​​in the mapping relationship are normalized. A unified format data set of environmental parameters and positioning trajectory is obtained from the normalized data;

[0056] The time synchronization between the temperature and humidity sensor in the equipment room and the positioning base station adopts the network time protocol. The timestamp accuracy reaches the millisecond level. The temperature and humidity data are collected every 10 seconds, and the positioning signal is collected every 1 second. In the alignment process, cubic spline interpolation is used to supplement the temperature and humidity data points. The interpolation interval is 10 seconds. 9 interpolation data points are generated within this interval. The timestamp uses a 13-bit length format, accurate to milliseconds. For example, 163888888888 represents 20:21:28:888 on December 7, 2021.

[0057] In the detection of abnormal values ​​of environmental data, the quartile Q1 of temperature data is 22 degrees Celsius, Q3 is 26 degrees Celsius, the interquartile range IQR is 4 degrees Celsius, and the abnormal value judgment range is 16-32 degrees Celsius. The Q1 of humidity data is 45%, Q3 is 55%, IQR is 10%, and the abnormal value judgment range is 30%-70%. Data outside the judgment range is truncated according to the boundary value. For temperatures below 16 degrees Celsius, the value is 16 degrees Celsius, and for temperatures above 32 degrees Celsius, the value is 32 degrees Celsius.

[0058] When processing positioning signals, the building outline of the equipment room adopts a rectangular coordinate system, which is 40 meters long and 25 meters wide. Four positioning base stations are installed at the four corners. The transmission power of the positioning tag is 4dBm, and the receiving sensitivity of the base station is -90dBm. The ranging accuracy is better than 1 meter in an open environment. If the distance between adjacent coordinate points exceeds 4 meters, it is judged as an abnormal jump and processed by distance limiting to maintain the continuity of the motion trajectory. For coordinate points beyond the building outline, the nearest boundary point is used instead. In a 10-second time window, the temperature and humidity data contains 10 data points, and the positioning data contains 10 coordinate points;

[0059] In the environmental parameter mapping, the temperature value range of 16-32 degrees Celsius is normalized to the interval of 0-1, and the humidity value range of 30%-70% is normalized to the interval of 0-1;

[0060] In the trajectory state value, the coordinate point is normalized to the interval 0-1 on the x-axis and the y-axis.

[0061] For boundary coordinate points, the x-axis takes a value of 0 or 1, and the y-axis takes a value of 0 or 1;

[0062] In actual application scenarios, the temperature and humidity data alignment processing eliminates the data discontinuity problem caused by differences in sampling frequencies of different sensors. The environmental data outlier processing avoids data deviations caused by sensor failures, line interference and other reasons. The positioning signal trajectory smoothing solves the positioning drift caused by multipath effects and signal obstruction. Data normalization realizes unified measurement between different physical quantities, which facilitates the subsequent mining of the correlation between environmental parameters and the patterns of human activities.

[0063] like Figure 1 and Figure 2As shown, in step S103, the paired sampling point data of the temperature and humidity sensors are obtained, and the sampling point data are processed by the median smoothing function to obtain the temperature and humidity time series; the timestamp data is obtained according to the computer room access control record, the entry and exit frequency sequence is counted by the timestamp data, and the residence time sequence is obtained by accumulating the discrete sampling point time intervals; the temperature and humidity time series, the entry and exit frequency sequence and the residence time sequence are normalized, and the Pearson coefficient is calculated to obtain a correlation coefficient matrix; for the parameter pairs whose absolute values ​​of the correlation coefficients in the correlation coefficient matrix exceed the correlation coefficient threshold, a parameter group is obtained based on Euclidean distance clustering, and the parameter with the smallest correlation is selected from the parameter group as the characteristic parameter.

[0064] Specifically, in step S103, according to the spatial pairing relationship of the temperature and humidity sensors in the computer room at the same grid point, paired data points with a sampling interval of 5 minutes are extracted from the original temperature and humidity data set, and the temperature and humidity data are processed using the median smoothing function to obtain a continuous and stable temperature and humidity time series. For the temperature mutation point, the average value of the data before and after is used instead of the mutation value;

[0065] Extract the entry and exit timestamps of personnel from the access control record data of the computer room, count the number of entries and exits in a 30-minute time window to obtain the entry and exit frequency sequence, calculate the personnel position coordinates in each time window based on the positioning signal sampling points, and obtain the stay time sequence by accumulating the time intervals of discrete sampling points;

[0066] The temperature series, humidity series, entry and exit frequency series, and residence time series were normalized at a confidence level of 0.95. The Pearson correlation coefficient was used to calculate the correlation between the two pairs of each series. The significance of the correlation coefficient was determined by the T test to obtain a 4×4 dimensional correlation coefficient matrix. For the parameter pairs with an absolute value of correlation coefficient greater than 0.7 in the correlation coefficient matrix, the similarity between parameter groups was calculated based on the Euclidean distance. Hierarchical clustering was used to group similar parameters. The parameters with the smallest correlation with the parameters outside the group were selected from each group as feature parameters to obtain the feature parameter combination after dimensionality reduction.

[0067]

[0068] , N represents the total number of parameter groups, d represents the dimension of each parameter group, x ik represents the k-th dimension value of the i-th parameter group, x jk represents the k-th dimension value of the j-th parameter group, σ j represents some standard deviation or coefficient of variation of the jth parameter group. This formula measures the similarity between parameter groups by calculating the Euclidean distance between all parameter groups and normalizing them;

[0069] The temperature and humidity sensors in the equipment room are arranged at a 4-meter grid spacing to form a 6×6 layout matrix. Each grid point is equipped with both a temperature sensor and a humidity sensor. The sampling time is once every 5 minutes. The temperature measurement range is 16-32 degrees Celsius, and the humidity measurement range is 30%-70%;

[0070] Median smoothing uses 5 data points to calculate the median. For data points with a sudden change in temperature exceeding 5 degrees Celsius or a sudden change in humidity exceeding 10%, the average of the two previous and next data points is used to replace them.

[0071] Two-way access control card readers are installed at the entrances and exits of the computer room to record the entry and exit timestamps of personnel accurately to the second, and count the number of entries and exits within a 30-minute time window. For example, within the time window of 8:00-8:30, 3 people are recorded to enter and 2 people leave;

[0072] The sampling interval of the positioning signal is 1 second. The position coordinates of the personnel are calculated by the signal strength received by the base station. The time intervals between adjacent sampling points are accumulated to obtain the length of stay in the area. For example, the area in front of the cabinet stays for a total of 800 seconds in the time window of 9:00-9:30.

[0073] During normalization, the temperature data is subtracted from the minimum value of 16 degrees Celsius and divided by the range of 16 degrees Celsius. The humidity data is subtracted from the minimum value of 30% and divided by the range of 40%. The frequency of entry and exit is divided by the maximum frequency of 10 times / 30 minutes. The length of stay is divided by the time window of 1800 seconds.

[0074] The Pearson correlation coefficient was calculated using a confidence level of 0.95, a degree of freedom of 58, and a critical correlation coefficient of 0.254, which was considered significant when greater than this value;

[0075] In the correlation coefficient matrix, the correlation coefficient between temperature and residence time is 0.82, the correlation coefficient between temperature and entry and exit frequency is 0.75, the correlation coefficient between humidity and temperature is 0.71, and the correlation coefficient between residence time and entry and exit frequency is 0.68;

[0076] The Euclidean distance was used to calculate the parameter similarity, and the distance threshold was set to 0.3. The temperature and the length of stay were grouped together, and the temperature and the frequency of entry and exit were grouped together. Finally, the temperature and humidity were selected as the characteristic parameters. The length of stay and the frequency of entry and exit can be derived from the temperature.

[0077] In actual applications, the temperature of the computer room operation area is strongly positively correlated with the length of time people stay there, and the temperature of the cabinet area is also positively correlated with the frequency of people entering and leaving. However, the correlation between humidity and other parameters is relatively weak.

[0078] Through parameter clustering and dimensionality reduction, the most representative temperature and humidity parameters are retained, which not only reflect the environmental status, but also indirectly reflect the patterns of personnel activities. The correlation analysis results show that there is an obvious correlation between the computer room environmental parameters and personnel activities, and there are differences in the correlation characteristics in different areas.

[0079] like Figure 1 As shown, in step S104, based on the time series of the measured data of the computer room environment and the recorded data of personnel activities, the temperature data, humidity data, personnel entry and exit frequency data, and residence time data are normalized according to the time series to obtain a normalized data set; a random forest regressor is established according to the normalized data set, and the number of decision trees and tree depth parameters of the random forest regressor are obtained through grid search; if the prediction deviation of the random forest regressor for the validation data set exceeds the standard deviation threshold, the temperature and humidity values ​​of the corresponding sample points are recorded, and the candidate environmental threshold values ​​are obtained according to the probability distribution of the temperature and humidity values; kernel density estimation is used to calculate the probability density curve of the personnel entry and exit frequency and the residence time, the activity parameter cutoff value is extracted from the inflection point of the probability density curve, and a threshold rule table is constructed according to the candidate environmental threshold value and the activity parameter cutoff value.

[0080] Specifically, in step S104, according to the time series of the measured data of the computer room environment and the recorded data of personnel activities, the data of 30 consecutive days are divided into the first 20 days as the training data set, the middle 5 days as the verification data set, and the last 5 days as the test data set, and the temperature data, humidity data, personnel entry and exit frequency data, and residence time data are normalized respectively;

[0081] A random forest regressor was used to establish a mapping between temperature and humidity and the regularity of human activities. The number and depth of decision trees were determined through grid search. The root mean square error was calculated on the validation data set. The parameters were adjusted through multiple rounds of iterations. The optimal parameter combination of random forest was determined from the minimum root mean square error. The standard values ​​of the association between temperature and humidity and human activities were predicted based on random forest regression. Sample points in the validation data set whose prediction deviation exceeded 2 times the standard deviation were recorded. The temperature interval and humidity interval were divided based on the probability distribution of the temperature and humidity values ​​of the recorded points, and the interval boundaries were obtained as candidate values ​​for the environmental threshold.

[0082] Kernel density estimation is used to calculate the probability density curve of personnel entry and exit frequency and length of stay. The activity parameter cutoff value is extracted from the curve inflection point. The temperature and humidity thresholds are combined with the activity parameter cutoff value to construct a threshold rule table. The prediction accuracy of the threshold rule is verified based on the test data set, and the temperature and humidity threshold intervals and personnel activity threshold intervals of the computer room are obtained.

[0083] The measured data collection of the computer room environment is divided into two categories: normal working days and rest days. Each type of data contains 720 hours of sampling points. The temperature range on working days is 20-28 degrees Celsius, and the temperature range on rest days is 18-25 degrees Celsius. The relative humidity on working days is 45%-65%, and the relative humidity on rest days is 40%-60%. The personnel activity records include the frequency of entry and exit of 0-8 times / hour and the length of stay of 0-45 minutes / hour.

[0084] Data normalization uses the maximum and minimum method to uniformly map temperature, humidity, and personnel activity data to the 0-1 range;

[0085] The random forest regressor setting includes 100 decision trees, the maximum tree depth is 8 layers, the feature random selection ratio is 0.8, the sample random selection ratio is 0.7, and the root mean square error is calculated on the validation data set. The temperature prediction error is 0.8 degrees Celsius, the humidity prediction error is 3%, the entry and exit frequency prediction error is 1 time / hour, and the stay time prediction error is 5 minutes / hour. After multiple rounds of parameter adjustment, the optimal tree depth is finally determined to be 6 layers;

[0086] The temperature and humidity threshold intervals are divided based on the distribution of prediction deviations. The sample points with a temperature prediction deviation exceeding 1.6 degrees Celsius account for 5%, and the sample points with a humidity prediction deviation exceeding 6% account for 8%;

[0087] According to the peak point of the deviation distribution curve, the temperature is divided into three intervals: 16-22 degrees Celsius, 22-28 degrees Celsius, and 28-32 degrees Celsius. The humidity is divided into three intervals: 30%-45%, 45%-60%, and 60%-70%. The kernel density curve of the personnel activity parameters presents a bimodal distribution. The inflection point of the entry and exit frequency is at 4 times / hour, corresponding to the normal inspection frequency, 8 times / hour corresponds to the equipment maintenance frequency, and the inflection point of the stay time is at 15 minutes / hour, corresponding to the inspection stop, and 30 minutes / hour corresponds to the maintenance operation stop. In the threshold rule table, the temperature of 22-28 degrees Celsius and the humidity of 45%-60% correspond to the normal inspection mode with an entry and exit frequency of 4 times / hour and a stay time of 15 minutes / hour.

[0088] In practical applications, the data set division takes into account the periodicity of the computer room's operating rules to ensure that the training data covers the complete working cycle. The temperature and humidity threshold ranges are set in combination with the computer room's air-conditioning control parameters, and the personnel activity threshold ranges are based on standard operating procedures. The threshold rule table reflects the standard operating mode under different environmental conditions, including daily inspections, equipment maintenance, and emergency response scenarios. Each scenario corresponds to a specific temperature and humidity range and personnel activity parameters.

[0089] like Figure 1As shown, in step S105, sampling data of the temperature and humidity sensor array is obtained, and the sampling data is recorded by the data collector according to the coordinates of the sensor arrangement position; the temperature and humidity change trends of the hot spot area and the personnel gathering area are calculated according to the sampling data, and the fuzzy controller is used to generate air conditioning adjustment instructions from the change trend and the target threshold; an adjustment sequence is generated through the air conditioning adjustment instruction, and the adjustment sequence sends temperature adjustment instructions, humidity adjustment instructions and air volume adjustment instructions to the air conditioning controller through the bus protocol; a temperature and humidity response characteristic curve is obtained from the adjustment sequence by piecewise linear fitting, and the input and output membership function parameters of the fuzzy controller are adjusted online according to the slope of the response characteristic curve.

[0090] Specifically, in step S105, according to the temperature and humidity sensor array in the computer room and the personnel activity monitoring data, the data is sampled at intervals of 30 seconds through the data collector to obtain a 4-20 mA analog signal, the analog quantity is converted into a standard digital quantity by a signal converter, the temperature and humidity sampling data are recorded according to the sensor layout position coordinates, the temperature and humidity change trends of the hot spot area and the personnel gathering area are calculated from the sampling data, the environmental parameter deviation value is calculated from the temperature and humidity change trend and the target threshold, and the air conditioning adjustment instruction is generated by a fuzzy controller. The air supply temperature adjustment step, the humidifier adjustment step, and the variable frequency fan speed adjustment step are calculated based on the temperature deviation, the humidity deviation, and the hot spot area position. An adjustment sequence is generated according to each adjustment amplitude not exceeding the set threshold. According to the adjustment sequence, the temperature adjustment instruction, the humidity adjustment instruction, and the air volume adjustment instruction are sent to the air conditioning controller through the 485 bus protocol. The temperature adjustment step is 0.5 degrees Celsius, the relative humidity adjustment step is 2%, and the fan speed adjustment step is 5% of the rated speed. It is judged from the environmental parameter sampling value whether the parameter returns to the target threshold interval;

[0091] The temperature and humidity response characteristic curve is calculated by piecewise linear fitting, and the temperature adjustment rate and humidity adjustment rate are obtained from the slope of the curve. The fuzzy control rules are adjusted online based on the response characteristic curve, and the input and output membership function parameters of the fuzzy controller are updated to achieve dynamic temperature and humidity adjustment.

[0092] The temperature and humidity sensors in the computer room are arranged in a 6×6 grid matrix. One temperature sensor and one humidity sensor are installed at each grid point. The sensors output a 4-20 mA standard current signal.

[0093] 4 mA corresponds to a temperature of 16 degrees Celsius or a relative humidity of 30%;

[0094] 20 mA corresponds to a temperature of 32 degrees Celsius or a relative humidity of 70%;

[0095] The sampling period is set to 30 seconds, during which the temperature and humidity data of 36 measuring points are collected and the coordinates of the personnel's location are recorded. The hot spot area determination standard is that the average temperature of the surrounding 8 measuring points is 3 degrees Celsius higher;

[0096] The fuzzy controller sets 7 temperature deviation levels, from -3 to +3 degrees Celsius, and 5 humidity deviation levels, from -10% to +10%. The adjustment step is determined according to the deviation level:

[0097] The temperature deviation is within 1 degree Celsius, and the step size is 0.5 degrees Celsius;

[0098] The temperature deviation is 1-2 degrees Celsius with a step size of 1 degree Celsius;

[0099] If the temperature deviation is more than 2 degrees Celsius, the step size is 2 degrees Celsius;

[0100] Humidity regulation uses a similar classification:

[0101] Use 2% step size within 5% deviation;

[0102] 5%-10% with 5% step size;

[0103] The fan speed is adjusted within the range of 50%-100% of the rated speed, with a step size of 5%;

[0104] The air conditioning controller receives adjustment instructions through the 485 bus, with a communication rate of 9600 bits / second and using the standard Modbus protocol. Each instruction contains the device address, function code, data length, and check code.

[0105] The temperature adjustment command is written into register 40001, the humidity adjustment command is written into register 40002, and the fan adjustment command is written into register 40003. After receiving the command, the controller responds and executes the adjustment action within 100 milliseconds;

[0106] The temperature and humidity response characteristic curve records the parameter change process within 300 seconds, records a data point every 30 seconds, and obtains 10 data points to form the response curve. The slope of the curve is calculated by piecewise linear fitting. The typical value of the temperature response rate is 0.1 degrees Celsius / second, and the humidity response rate is 0.2% / second;

[0107] Based on the response characteristics, the fuzzy rules are updated, and the temperature adjustment fast response interval is 1-2 degrees Celsius deviation, and the humidity adjustment fast response interval is 3%-8% deviation;

[0108] In actual operation, when it is detected that the temperature in the cabinet area has risen and someone is staying in this area, the controller will give priority to increasing the air supply volume of the air conditioner near this area. When the temperature rises rapidly, the controller will simultaneously reduce the air supply temperature and increase the air volume. The humidity adjustment is relatively slow, mainly adjusted by the proportion of the humidifier switching time. The response characteristic curve reflects the dynamic process of air conditioning adjustment, which is used to optimize the control parameters to avoid oscillation caused by too fast adjustment or environmental recovery affected by too slow adjustment.

[0109] like Figure 1 As shown, in step S106, the card swiping record collected by the access control card reader and the location coordinates collected by the positioning base station are received, and the area entry and exit records and the area residence time are obtained according to the card swiping record and the location coordinates according to the fixed time period; for the area entry and exit records and the area residence time, the regional temperature and humidity data are used to calculate the environmental load index, and if the environmental load index exceeds the preset threshold range, the regional access control rules are obtained according to the rule-based threshold judgment; the authorization level corresponding to the identity identification is obtained, if the authorization level is a high-level authorization, entry and exit at any time period and unlimited stay are allowed, if the authorization level is a low-level authorization, entry and exit during peak hours and continuous stay are restricted; the identity identification is obtained by real-time card swiping application, the authorization level is queried according to the identity identification, and the authorization level and the area access control rules are used to determine whether the entry and exit restrictions are met, and if the entry and exit restrictions are not met, the access control actuator is triggered to lock.

[0110] Specifically, in step S106, based on the 1-second interval card swiping records collected by the access control card reader and the location coordinates collected by the positioning base station, the entry and exit records and location data of each identity identification are counted according to a fixed time period of 30 minutes, the entry time, exit time, and identification number are extracted from the card swiping records, and the length of time spent in the area is obtained from the positioning data;

[0111] Calculate the environmental load index based on regional temperature and humidity data. For areas that exceed the temperature and humidity threshold range, use rule-based threshold judgment to calculate the upper limit of the frequency of entry and exit and the upper limit of the length of stay in the area. Generate regional access control rules from the calculation results, including the limit value of the number of entry and exit per hour and the limit value of the single stay length;

[0112] There are 4 authorization levels based on personnel identification:

[0113] Advanced authorization allows entry and exit at any time and unlimited stay;

[0114] Intermediate authorization restricts entry and exit during non-working hours and continuous stay of more than 4 hours;

[0115] Low-level authorization restricts entry and exit during peak hours and continuous stays of more than 2 hours;

[0116] Temporary authorization only allows one-time entry and exit during working hours and stay within 1 hour;

[0117] Obtain authorization level based on real-time card swipe application and identity identification, query access control rule table according to the time period and regional environmental status of the card swipe time, determine whether the entry and exit frequency limit and stay time limit are met, trigger access control actuator lock for card swipe application exceeding the limit, and indicate authorization restriction through flashing red light and buzzing;

[0118] The computer room access control system uses a standard Wiegand protocol card reader with a card reading distance of 5 cm, a response time of 0.1 seconds, and a card number length of 32 bits;

[0119] The positioning base station uses 2.4G active radio frequency positioning, with a signal coverage radius of 20 meters and a positioning accuracy of 0.5 meters;

[0120] During the 8:00-9:00 peak period, a typical area recorded 12 entries and 10 exits, with an average stay of 25 minutes per person, and the peak area temperature rose by 2.5 degrees Celsius and the relative humidity rose by 8%;

[0121] The environmental load index is calculated based on the temperature and humidity change rate. When the temperature change rate exceeds 1 degree Celsius / hour or the humidity change rate exceeds 5% / hour, the environmental load is judged to be too high;

[0122] In overloaded areas, the upper limit of entry and exit frequency is set at 6 times / hour, and the upper limit of stay time is set at 30 minutes / time;

[0123] Dynamic adjustment of regional access control rules. For example, if the temperature change rate is detected to be 1.2 degrees Celsius per hour during the period of 9:00-10:00, the upper limit of the entry and exit frequency will be automatically reduced to 4 times per hour.

[0124] Identity authorization levels correspond to different job types:

[0125] Level 0 authorization corresponds to operation and maintenance management personnel, who have 24-hour access rights;

[0126] Level 1 authorization corresponds to patrol personnel, and entry and exit are restricted from 23:00 to 7:00;

[0127] Level 2 authorization corresponds to maintenance personnel, and entry and exit are restricted from 8:00-10:00 and 14:00-16:00;

[0128] Level 3 authorization corresponds to temporary personnel, and entry and exit are only allowed from 9:00-11:00 and 15:00-17:00;

[0129] When the temperature in a high temperature area exceeds 28 degrees Celsius, the authorized stay time for level 2 and below will be automatically reduced by 50%;

[0130] The access control is implemented by magnetic lock control, with a normally closed torque of 300 Newtons and an unlocking time of 3 seconds;

[0131] When the card swipe application is rejected, the red light on the card reader flashes 3 times and the buzzer sounds 3 times;

[0132] Records show that between 9:00 and 10:00 a.m. on weekdays, Level 2 authorized personnel swiped their cards 100 times, of which 15 were rejected for exceeding the frequency of entry and exit limit and 8 were rejected for exceeding the length of stay limit;

[0133] Authorization rule records show that the regional temperature rose from 24 degrees Celsius to 26.5 degrees Celsius during that period, causing the system to automatically tighten entry and exit restrictions;

[0134] In actual applications, access control and environmental monitoring are linked. When the temperature in the cabinet area rises rapidly, the system automatically limits the number of people staying in the area. The authorization rules are combined with the operating procedures to ensure that only necessary operation and maintenance activities are allowed during the peak period of environmental load. The access control data records detailed entry and exit information, including card swiping time, location, authorization level, and access results, providing a basis for subsequent optimization of authorization rules.

[0135] like Figure 1 As shown, in step S107, the temperature and humidity data and personnel location data sent by the acquisition device are received, and the acquisition device records data at fixed time intervals after synchronizing the clock through the network time protocol; the average value of the fixed time period is calculated according to the temperature and humidity data to obtain an environmental data sequence, and the environmental data sequence includes a standard timestamp; the temperature and humidity comparison data are extracted from the environmental data sequence, and the change in the number of people in the area is calculated according to the personnel location data, and a continuous change curve is obtained by linear interpolation; a line graph is generated for the continuous change curve, and if the temperature and humidity values ​​exceed the target threshold, the corresponding data segment is marked; the regional density distribution map is obtained by grid division, and the regional density distribution map maps the color depth according to the length of time the personnel stay, and the regional temperature and humidity average values ​​are superimposed and displayed in the regional density distribution map.

[0136] Specifically, in step S107, according to the temperature and humidity sensor array and the positioning base station arranged at the 6×6 grid points in the machine room, the clocks of all acquisition devices are synchronized through the network time protocol, the temperature and humidity data and personnel location data are recorded at 5-minute intervals, and the temperature and humidity data are averaged for a fixed time period of 10 minutes to eliminate data fluctuations and generate an environmental data sequence with a standard timestamp;

[0137] Extract temperature and humidity comparison data before and after air conditioning control from the environmental data sequence, calculate the change in the number of people in the area based on the personnel location data before and after access control, generate related data pairs based on the temperature and humidity changes and the number of people changes, and use linear interpolation to supplement the data points within a 5-minute interval to obtain a continuous change curve;

[0138] Draw the temperature curve and humidity curve in the line chart. The left axis shows the temperature value of 16 to 32 degrees Celsius, the right axis shows the humidity value of 30% to 70%, and the time axis shows the data of the last 4 hours. The data segment exceeding the target threshold is marked in red, and the time mark is added to the control adjustment moment;

[0139] The computer room area is divided into 4m×4m grids, the length of time people stay in each grid is accumulated, and the color depth is mapped according to the length of stay per hour to generate a regional density distribution map. The average temperature and humidity values ​​of the area are superimposed on the map, and arrow marks are generated from the personnel position sequence to indicate the movement path.

[0140] The temperature and humidity sensor in the equipment room adopts a two-wire standard current output. The temperature range of 16-32 degrees Celsius corresponds to 4-20 mA, and the humidity range of 30%-70% corresponds to 4-20 mA.

[0141] In the 6×6 grid, one temperature sensor and one humidity sensor are installed at each grid point, and the distance between adjacent grid points is 4 meters. All sensors are synchronized through a network time server, and the clock accuracy is better than 1 millisecond. 36 temperature data points and 36 humidity data points are collected within a 5-minute sampling interval, and the 10-minute average calculation eliminates the impact of air supply fluctuations of air conditioning.

[0142] During the air conditioning process, the records show that at 9:00, the average temperature of the cabinet area was 26.5 degrees Celsius, the relative humidity was 58%, and 4 people stayed in the area at the same time; after the air conditioning was adjusted to cool down, the average temperature dropped to 24.2 degrees Celsius at 9:30, the relative humidity dropped to 52%, and the number of people in the area was reduced to 2; through linear interpolation of 5-minute interval data points, the temperature drop rate was 0.15 degrees Celsius / minute, and the humidity drop rate was 0.4% / minute;

[0143] The line chart adopts a double Y-axis structure. The temperature curve corresponds to the left Y-axis with a scale interval of 2 degrees Celsius, and the humidity curve corresponds to the right Y-axis with a scale interval of 5%; the time axis displays the data of the last 4 hours with a scale interval of 15 minutes. When the temperature exceeds 28 degrees Celsius or the humidity exceeds 65%, the curve segment is marked in red; vertical marking lines are added at 9:00 and 9:30 to mark the air conditioning adjustment time. The computer room is divided into 36 areas according to a 4m×4m grid, and the accumulated stay time of personnel in each area; the color depth mapping rule is that the stay time of 0-15 minutes is displayed in light blue, 15-30 minutes is displayed in blue, 30-45 minutes is displayed in dark blue, and more than 45 minutes is displayed in dark purple; the arrow mark indicates the direction of personnel movement, and the arrow length is proportional to the movement speed. The typical inspection speed corresponds to an arrow length of 2 meters;

[0144] In actual applications, temperature and humidity data show that the temperature in the cabinet area changes dramatically, which is positively correlated with the length of time people stay. Through the personnel density distribution map, it is found that high-temperature areas often correspond to hot spots where people stay. The temperature and humidity trend curve reflects the effect of air conditioning regulation. Combined with personnel activity data, it intuitively shows the impact of personnel activities on the environment. The area division and arrow markings reveal the patterns of personnel activities, providing a basis for optimizing air conditioning control and personnel management.

[0145] like Figure 1 and Figure 3 As shown, in step S108, an environmental parameter data sequence collected by a temperature and humidity sensor is received, an actual environmental curve is generated according to the environmental parameter data sequence, and a temperature deviation value and a humidity deviation value are calculated by comparing the actual environmental curve with a target reference curve; for a monitoring period in which the temperature deviation value and the humidity deviation value exceed a preset deviation threshold, a load predictor is used to perform a feedforward operation, and a control target value and an advance control time are determined according to the feedforward operation result; an air conditioning supply air parameter control instruction sequence is generated according to the control target value, and the supply air temperature and the supply air humidity are adjusted in gears through the control instruction sequence, and environmental parameter change data during the gear adjustment process are collected; the temperature change rate and the humidity change rate are extracted from the environmental parameter change data, and if the temperature change rate and the humidity change rate exceed the preset threshold, access control is performed on the corresponding area until the environmental parameters return to the target reference curve range.

[0146] Specifically, in step S108, the actual environment curve is generated according to the data sequence collected by the temperature and humidity sensor in the computer room every 5 minutes, the temperature and humidity curve of a normal working day is selected from the operation data of the date before the regulation as the target reference curve, the temperature deviation value and the humidity deviation value are calculated after aligning the sampling point time, and the period when the deviation exceeds 1 degree Celsius for temperature or 5% for humidity is marked as a regulation monitoring point;

[0147] Based on the load forecaster, the environmental parameters of the monitoring period are fed forward, and the control target value is calculated according to the temperature and humidity change trend. The advance control time is set 30 minutes ahead from the control monitoring point, and the control instruction sequence is generated according to the temperature and humidity status at the advance control time.

[0148] The air supply parameters of the air conditioner are controlled according to the control target values ​​in the control instruction sequence:

[0149] The temperature high-level adjustment step is 2 degrees Celsius;

[0150] The temperature adjustment step is 1 degree Celsius in the middle position;

[0151] The temperature low gear adjustment step is 0.5 degrees Celsius;

[0152] The humidity high level adjustment step is 10%;

[0153] The humidity mid-range adjustment step is 5%;

[0154] The humidity low gear adjustment step is 2%;

[0155] Record the change curve of environmental parameters in each control cycle;

[0156] The air conditioning controller collects environmental response data in each control cycle, extracts the temperature change rate and humidity change rate from the response data, performs access control on areas where the change rate exceeds the preset threshold, and adjusts the entry and exit restriction rules until the environmental parameters return to the target curve range;

[0157] The temperature and humidity monitoring of the computer room selects the data of the previous Tuesday of a normal working day as the target reference. There was no equipment maintenance and personnel maintenance activities on this date. The temperature was stable at 24 degrees Celsius and the relative humidity was stable at 55%. The current environmental curve shows that during the period of 9:00-10:00, the temperature rose to 26 degrees Celsius and the relative humidity rose to 63%. The deviation value exceeded the preset range, and the system marked this period as a control monitoring point. The load forecast calculation shows that during the period of 8:30-9:00, the power of the cabinet in area A increased by 20%, the personnel stay time increased by 15 minutes, the temperature rise rate was 0.1 degrees Celsius / minute, and the humidity rise rate was 0.3% / minute.

[0158] Based on these parameters, the system generates control instructions at 8:30 a.m., with the goal of reducing the temperature by 2 degrees Celsius and the humidity by 8% within 30 minutes;

[0159] The air conditioning gear control adopts a 3-level adjustment mechanism:

[0160] When the temperature deviation is greater than 2 degrees Celsius, the high-speed adjustment is started and the air supply temperature is reduced by 2 degrees Celsius at one time;

[0161] When the temperature deviation is between 1-2 degrees Celsius, the mid-range adjustment is adopted, and the supply air temperature is reduced by 1 degree Celsius twice;

[0162] When the temperature deviation is less than 1 degree Celsius, low-level adjustment is adopted, and the supply air temperature is reduced by 0.5 degrees Celsius in 4 times, and the humidity adjustment adopts a similar graded method;

[0163] During the control period of 8:30-9:00, the record shows that the temperature dropped from 26 degrees Celsius to 24.5 degrees Celsius, with a drop rate of 0.05 degrees Celsius / minute, and the humidity dropped from 63% to 58%, with a drop rate of 0.17% / minute; because the temperature drop rate was lower than the expected 0.067 degrees Celsius / minute, the system issued an access control command to restrict the entry of people in the area, adjusting the original limit of 4 people to 2 people, and shortening the stay time from 30 minutes to 15 minutes;

[0164] In actual operation, different areas exhibit different environmental characteristics. The cabinet area is sensitive to human activities, the temperature rises quickly, and a larger adjustment step is required. The temperature in the aisle area is relatively stable and suitable for small step adjustment. Access control is given priority to control in high-density areas to reduce heat load by restricting human activities. Air conditioning is given priority to control in the air supply area to quickly cool down by increasing the air supply volume. Appropriate control measures are selected according to the characteristics of each area to achieve precise control. Historical data shows that using this multi-level linkage control method can shorten the regression time of environmental parameter deviation by an average of 50%.

[0165] In the description of this application, the technical solution of the present invention will be clearly and completely described in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Claims

1. A method for intelligently controlling a computer room environment by integrating multi-source sensor information, characterized in that: The following steps are involved: S101, collecting temperature and humidity data of different areas in the computer room through temperature and humidity sensors, collecting activity tracks and stay time data of personnel in the computer room through personnel positioning equipment, and transmitting the collected computer room environment data and personnel activity data to the data fusion module; S102, in the data fusion module of step S101, align the computer room environment data and the personnel activity data in time series, remove erroneous data and outliers, and obtain a unified format data set; S103, calculating the correlation coefficient matrix between the characteristic parameters of the computer room in step S101 through correlation analysis, and screening out characteristic parameters whose correlation is higher than a preset correlation threshold, wherein the characteristic parameters include temperature, humidity, frequency of personnel entry and exit, and length of stay; S104, according to the characteristic parameters with correlation higher than the preset threshold selected in step S103, analyzing the association pattern between the computer room environment and the personnel activities, so as to obtain the target structure and target threshold of the association pattern between the computer room environment and the personnel activities, wherein the target threshold includes the target computer room environment threshold and the target personnel activity threshold; S105. According to the target threshold in step S104, the computer room environment and personnel activity association mode is deployed to the air conditioning equipment control module, the computer room temperature and humidity data are obtained in real time, and it is determined whether the real-time temperature and humidity data of the computer room exceeds the target computer room environment threshold range. If it exceeds the target computer room environment threshold range, the preset air conditioning equipment adjustment mechanism is triggered to dynamically adjust the air supply temperature, humidity and air volume of the air conditioning equipment for temperature and humidity recovery; S106. According to the target threshold in step S104, the computer room environment and personnel activity association mode are deployed to the access control module, and the personnel entry and exit frequency and residence time data are obtained in real time to determine whether the target personnel activity threshold is exceeded. If the target personnel activity threshold is exceeded, the access control authorization time period and number of times are dynamically adjusted to control personnel entry and exit activities; S107, according to the control adjustment of the air conditioner and the access control in step S105 and step S106, the environmental data of the computer room after the control adjustment is detected, including the temperature and humidity in different areas of the computer room and the activity tracks and stay time of the personnel in the computer room, and the environmental data is formed into a curve chart through a preset data visualization module to obtain the environmental change trend of the computer room after the control; S108. Evaluate the current control strategy and method according to the environmental change trend after the computer room is regulated in step S107. By presetting a target environmental change trend curve, fit the environmental change trend of the computer room after regulation with the pre-set target environmental change trend for use in regulating the computer room environment.

2. According to claim 1, a method for intelligently controlling a computer room environment by integrating multi-source sensor information, characterized in that: In step S101, the temperature and humidity data of different areas in the computer room are collected by the temperature and humidity sensor, the activity trajectory and stay time data of the personnel in the computer room are collected by the personnel positioning device, and the collected computer room environment data and personnel activity data are transmitted to the data fusion module, including: Acquire temperature and humidity sampling data of sensor grid points, and obtain temperature and humidity spatial distribution data by inverse distance weighted interpolation based on the sampling data; The distance between the positioning tag and the base station is determined according to the signal strength received by the positioning base station, and the curve smoothing function is used to eliminate the noise of the positioning tag position coordinates to obtain the moving trajectory point; The temperature and humidity spatial distribution data and the movement trajectory points are segmented according to time windows, and corresponding data pairs of temperature and humidity peak points and positioning tag residence positions are extracted from the segmented data segments; If the temperature and humidity variation deviates from the regional average value by more than a preset threshold, the positioning tag activity association value is obtained from the corresponding data pair, and the temperature and humidity anomaly and the positioning tag activity pattern are obtained by data association analysis.

3. According to claim 1, a method for intelligently controlling a computer room environment by integrating multi-source sensor information, characterized in that: In step S102, in the data fusion module of step S101, the computer room environment data and the personnel activity data are aligned in time series, and erroneous data and outliers are removed to obtain a unified format data set, including: According to the sampling timestamp of the computer room environment data and the sampling timestamp of the positioning signal data, the cubic spline curve is used to supplement the data points to obtain the environmental positioning fusion data; For the environmental positioning fusion data, the quartiles and interquartile ranges of environmental parameters are calculated through box plots, abnormal values ​​beyond the quartile range are truncated, and the abnormally processed environmental data set is obtained through forward data filling; According to the positioning signal data, the excessive coordinate points are limited according to the adjacent coordinate spacing threshold and smoothed based on the distance threshold to obtain the positioning trajectory status mark; The environmental data set after the abnormal processing and the positioning trajectory status mark are segmented and aligned through a time window, and a mapping relationship table between environmental parameters and trajectory status is established. The environmental parameter values ​​and trajectory status values ​​in the mapping relationship are normalized to obtain a unified format data set.

4. According to claim 1, a method for intelligently controlling a computer room environment by integrating multi-source sensor information, characterized in that: In step S103, the correlation coefficient matrix between the characteristic parameters of the computer room in step S101 is calculated by correlation analysis, and characteristic parameters with correlation higher than a preset correlation threshold are screened out, and the characteristic parameters include temperature, humidity, frequency of personnel entry and exit, and length of stay parameters, including: Acquire paired sampling point data of temperature and humidity sensors, and process the sampling point data using a median smoothing function to obtain a temperature and humidity time series; Obtaining timestamp data based on the computer room access control record, using the timestamp data to count the entry and exit frequency sequence, and obtaining the stay duration sequence by accumulating the time intervals of discrete sampling points; Normalizing the temperature and humidity time series, the entry and exit frequency series, and the residence time series, and using the Pearson coefficient to calculate a correlation coefficient matrix; For the parameter pairs whose absolute values ​​of correlation coefficients in the correlation coefficient matrix exceed the correlation coefficient threshold, a parameter group is obtained based on Euclidean distance clustering, and a parameter with the minimum correlation is selected from the parameter group as a characteristic parameter.

5. According to claim 1, a method for intelligently controlling a computer room environment by integrating multi-source sensor information, characterized in that: In step S104, according to the feature parameters with correlation higher than the preset threshold selected in step S103, the computer room environment and personnel activity association pattern are analyzed to obtain the target structure and target threshold of the computer room environment and personnel activity association pattern, wherein the target threshold includes the target computer room environment threshold and the target personnel activity threshold, including: Based on the time series of the measured data of the computer room environment and the recorded data of personnel activities, the temperature data, humidity data, personnel entry and exit frequency data and residence time data are normalized according to the time series to obtain a normalized data set; Establishing a random forest regressor according to the normalized data set, and obtaining the number of decision trees and tree depth parameters of the random forest regressor through grid search; If the prediction deviation of the random forest regressor for the validation data set exceeds the standard deviation threshold, the temperature and humidity values ​​of the corresponding sample points are recorded, and the candidate environmental threshold values ​​are obtained according to the probability distribution of the temperature and humidity values; Kernel density estimation is used to calculate the probability density curve of personnel entry and exit frequency and residence time, activity parameter cutoff values ​​are extracted from the inflection points of the probability density curve, and a threshold rule table is constructed according to the environment threshold candidate values ​​and the activity parameter cutoff values.

6. According to claim 1, a method for intelligently controlling a computer room environment by integrating multi-source sensor information, characterized in that: In step S105, according to the target threshold in step S104, the computer room environment and personnel activity association mode is deployed to the air conditioning equipment control module, the computer room temperature and humidity data is obtained in real time, and it is determined whether the real-time temperature and humidity data of the computer room exceeds the target computer room environment threshold range. If it exceeds the target computer room environment threshold range, the preset air conditioning equipment adjustment mechanism is triggered to dynamically adjust the air supply temperature, humidity and air volume of the air conditioning equipment for temperature and humidity recovery, including: Acquire sampling data of the temperature and humidity sensor array, wherein the sampling data is recorded by a data collector according to the sensor arrangement position coordinates; Calculate the temperature and humidity change trends of hot spots and areas where people gather based on the sampled data, and use a fuzzy controller to generate air conditioning adjustment instructions based on the change trends and target thresholds; Generate an adjustment sequence through the air conditioning adjustment instruction, and send the temperature adjustment instruction, humidity adjustment instruction and air volume adjustment instruction to the air conditioning controller through the bus protocol; A temperature and humidity response characteristic curve is obtained from the adjustment sequence by piecewise linear fitting, and input and output membership function parameters of the fuzzy controller are adjusted online according to the slope of the response characteristic curve.

7. According to claim 1, a method for intelligently controlling a computer room environment by integrating multi-source sensor information, characterized in that: In step S106, according to the target threshold in step S104, the computer room environment and personnel activity association mode are deployed to the access control module, and the personnel entry and exit frequency and residence time data are obtained in real time to determine whether it exceeds the target personnel activity threshold. If it exceeds the target personnel activity threshold, the access control authorization time period and number of times are dynamically adjusted to control the personnel entry and exit activities, including: Receive the card swiping record collected by the access control card reader and the location coordinates collected by the positioning base station, and obtain the area entry and exit record and the area residence time according to the card swiping record and the location coordinates according to the fixed time period; Based on the entry and exit records of the area and the length of time spent in the area, the environmental load index is calculated using the regional temperature and humidity data. If the environmental load index exceeds the preset threshold range, the regional access control rule is obtained based on the rule-based threshold judgment; Obtain the authorization level corresponding to the identity identifier. If the authorization level is high-level authorization, entry and exit at any time and unlimited length of stay are allowed. If the authorization level is low-level authorization, entry and exit during peak hours and continuous stay are restricted. The identity is obtained by real-time card swiping application, the authorization level is queried according to the identity, and the authorization level and the regional access control rules are used to determine whether the entry and exit restrictions are met. If the entry and exit restrictions are not met, the access control actuator is triggered to lock.

8. According to claim 1, a method for intelligently controlling a computer room environment by integrating multi-source sensor information, characterized in that: In step S107, the control and adjustment of the air conditioner and the access control in step S105 and step S106 are used to detect the environmental data of the computer room after the control and adjustment, including the temperature and humidity in different areas of the computer room and the activity tracks and the length of stay of the personnel in the computer room, and the environmental data is formed into a curve chart through a preset data visualization module to obtain the environmental change trend of the computer room after the control, including: Receiving temperature and humidity data and personnel location data sent by a collection device, the collection device synchronizes the clock through a network time protocol and records the data at fixed time intervals; Performing fixed time period mean calculation on the temperature and humidity data to obtain an environmental data sequence, wherein the environmental data sequence includes a standard timestamp; Extracting temperature and humidity comparison data from the environmental data sequence, and calculating the change in the number of people in the area according to the personnel position data, and using linear interpolation to obtain a continuous change curve; Generate a line graph for the continuous change curve, and mark the corresponding data segment if the temperature and humidity values ​​exceed the target threshold; A regional density distribution map is obtained through grid division. The regional density distribution map maps color depth according to the length of time people stay, and the average values ​​of regional temperature and humidity are superimposed and displayed in the regional density distribution map.

9. The method for intelligently controlling the computer room environment by integrating multi-source sensor information according to claim 1, characterized in that: In step S108, the current control strategy and method are evaluated according to the environmental change trend of the computer room after control in step S107, and the environmental change trend of the computer room after control is fitted with the preset target environmental change trend by presetting the target environmental change trend curve, which is used for the control of the computer room environment, including: An environmental parameter data sequence collected by a temperature and humidity sensor is received, an actual environmental curve is generated according to the environmental parameter data sequence, and a temperature deviation value and a humidity deviation value are calculated by comparing the actual environmental curve with a target reference curve.

10. The method for intelligently controlling the computer room environment by integrating multi-source sensor information according to claim 9, characterized in that: During the monitoring period when the temperature deviation value and the humidity deviation value exceed the preset deviation threshold, a load predictor is used to perform a feedforward operation, and a control target value and an advance control time are determined according to the feedforward operation result; Generate an air conditioning air supply parameter control instruction sequence according to the control target value, adjust the supply air temperature and supply air humidity through the control instruction sequence, and collect environmental parameter change data during the gear adjustment process; The temperature change rate and humidity change rate are extracted from the environmental parameter change data. If the temperature change rate and humidity change rate exceed the preset threshold, access control is performed on the corresponding area until the environmental parameters return to the target reference curve range.

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