Machine room dynamic environment monitoring and management method

Abstract

The invention relates to the technical field of environment monitoring, in particular to a machine room dynamic environment monitoring and management method. Comprises: collecting machine room environment parameter data; adding a data label to each piece of parameter data and recording a timestamp; creating an environment monitoring data frame, and filling basic information; adding an abnormal event indication field in the data frame, and comparing with a preset threshold to mark an abnormal condition; identifying a sudden change trend in the time sequence data and recording a result; calculating relevance indexes, and analyzing data relevance among different sensors; the signaling modules are mapped to different data processing units, and data processing and circulation services of corresponding levels are provided; the data frames are interpreted step by step, and corresponding monitoring and management strategies are executed; dynamic weight distribution is implemented, and the data frame processing priority is adjusted according to historical weight changes and the current state; data frame specific processing fields are registered and tracked. According to the method, real-time monitoring, comprehensive analysis and accurate management of the dynamic environment of the machine room are realized.

Description

Technical Field

[0001] This application relates to the technical field of environmental monitoring, and specifically relates to a method for dynamic environmental monitoring and management of computer rooms. Background Art

[0002] In modern data centers and computer rooms, it is crucial to maintain stable environmental conditions. Environmental parameters such as temperature, humidity, air quality, and noise directly affect the operating efficiency and service life of equipment. Traditional environmental monitoring systems usually adopt single sensors or simple data acquisition methods, making it difficult to achieve comprehensive monitoring and management of multiple environmental parameters. At the same time, the lack of effective data analysis means and intelligent processing mechanisms causes the system to lag in response to abnormal situations and unable to provide timely warnings and solutions.

[0003] Therefore, there is an urgent need for a method for dynamic environmental monitoring and management of computer rooms that can perform real-time monitoring, comprehensive analysis, and multi-dimensional management. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] This invention mainly aims at the above problems and proposes a method for dynamic environmental monitoring and management of computer rooms, aiming to solve the problems of comprehensive monitoring of multiple parameters and intelligent analysis and processing of abnormal situations during the environmental monitoring of computer rooms.

[0006] (II) Technical Solutions

[0007] To achieve the above object, this invention provides a method for dynamic environmental monitoring and management of computer rooms, which includes the following steps:

[0008] Real-time collect environmental parameter data in the computer room through multi-type sensor devices;

[0009] Attach a unique data tag to each collected environmental parameter data and record it according to the timestamp;

[0010] Create an environmental monitoring data frame, each environmental monitoring data frame is based on its corresponding unique data tag and fill in the basic information;

[0011] Add an abnormal event indication field to the environmental monitoring data frame, compare the collected environmental parameter values with the preset thresholds, and mark the abnormal situations of the environmental parameters;

[0012] Conduct mutation trend analysis, by analyzing the time series data in the environmental monitoring data frame, identify the mutation trends of environmental parameters and record the analysis results;

[0013] Calculate multi-dimensional correlation indicators, analyze the data correlation between different sensors to assist in judging the mutual influence and possible root causes between environmental parameters;

[0014] Map multiple signaling modules to different data processing units respectively, and each unit provides corresponding levels of data processing and transfer services according to the types and requirements of the connected signaling modules;

[0015] Gradually interpret the environmental monitoring data frame, and execute corresponding environmental monitoring and management strategies based on the aforementioned anomaly indications, mutation trend analysis results, and multi-dimensional correlation indicators;

[0016] Implement dynamic weight allocation. According to the multi-dimensional weight fields in the environmental monitoring data frame, comprehensively consider historical weight changes and the current environmental state, and adjust the processing priority of the data frame in the monitoring system;

[0017] Make a detailed registration and tracking of specific processing fields in the environmental monitoring data frame.

[0018] Furthermore, the multi-type sensor devices include temperature sensors, humidity sensors, smoke sensors, and noise sensors; the environmental parameter data includes temperature, humidity, air quality, noise intensity, and power status.

[0019] Furthermore, when conducting mutation trend analysis, extract the time series data collected by each sensor from the environmental monitoring data frame; divide the extracted time series data into multiple small time segment data fragments by means of a sliding window; apply mathematical models or statistical methods to each data fragment to evaluate the stability and mutation points of the data, and identify whether there are rapid changes or unusual fluctuations in the data; based on the identified data mutation points, determine the abnormal trend of the environmental parameters; record the mutation analysis results of each data fragment, and integrate these results to form a complete mutation trend analysis report; save and display the mutation trend analysis report in the monitoring system.

[0020] Furthermore, the calculation method of the multi-dimensional correlation index includes:

[0021] Collect environmental parameter data from different types of sensor devices and preprocess it to eliminate noise and outliers;

[0022] Perform standardization processing on the preprocessed environmental parameter data;

[0023] Construct a data matrix, where each row represents the multi-dimensional environmental parameter data at a time point, and each column represents the environmental parameter data of a sensor;

[0024] Calculate the Pearson correlation coefficient between any two columns in the data matrix to quantify the linear correlation between two environmental parameters;

[0025] Statistically analyze the Pearson correlation coefficients between all pairs of sensors to form a correlation matrix;

[0026] Determine the data correlation index between sensors according to the correlation coefficient values in the correlation matrix, and mark the sensor pairs with high correlation.

[0027] Further, the specific steps of mapping multiple signaling modules to different data processing units include:

[0028] Classify each signaling module according to its function and data requirements;

[0029] Configure corresponding data processing units for each class of signaling modules;

[0030] Map and associate the signaling modules with the corresponding data processing units, so that each signaling module sends and receives data that conforms to its processing capabilities and requirements;

[0031] Determine the processing level and priority of each data processing unit according to the actual application scenario;

[0032] Forward the data generated by the signaling modules to the corresponding data processing units through the network communication protocol, and let the data processing units execute the corresponding data processing tasks;

[0033] Monitor the interaction between the data processing units and the signaling modules;

[0034] Adjust the mapping relationship between the signaling modules and the data processing units according to the system operation status and real-time requirements.

[0035] Further, when implementing the environment monitoring and management strategy, it includes two modes: automatic and manual; in the automatic mode, the device operation status and alarm mechanism are automatically adjusted according to the preset rules and real-time analysis results; in the manual mode, the operator is allowed to manually intervene and adjust according to the analysis reports and suggestions provided by the system.

[0036] Further, conduct detailed registration and tracking on specific processing fields of the environment monitoring data frames, including recording the time of each processing, the measures taken for processing, the effect evaluation after processing, and the information of relevant responsible persons.

[0037] Further, the dynamic weight allocation method is based on the weighted moving average method to smooth the historical data to determine the processing priority of the current data frame.

[0038] Further, the unique data tag is composed of the sensor device ID and the timestamp.

[0039] (III) Beneficial effects

[0040] Compared with the prior art, a method for monitoring and managing the dynamic environment of a computer room provided by the present invention collects environmental parameter data through multi-type sensor devices, uses unique data tags and timestamps for recording, and realizes accurate data tracking; by creating an environmental monitoring data frame and adding an abnormal event indication field, it can quickly identify abnormal situations of environmental parameters; combined with mutation trend analysis and multi-dimensional correlation index calculation, it further improves the sensitivity to environmental changes and the judgment accuracy; by adopting the mapping relationship between the signaling module and the data processing unit, as well as the dynamic weight allocation mechanism, it ensures the rationality and timeliness of the data processing priority, thereby comprehensively improving the intelligent level and response efficiency of the computer room environment monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flowchart of a method for monitoring and managing the dynamic environment of a computer room disclosed in the present application.

[0042] Figure 2 It is a flowchart of a mutation trend analysis disclosed in the present application.

[0043] Figure 3 It is a flowchart of a calculation method for multi-dimensional correlation indexes disclosed in the present application.

[0044] Figure 4 It is a flowchart of mapping multiple signaling modules to different data processing units respectively disclosed in the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The following will further describe in detail the specific embodiments of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0046] As Figure 1 shown, the present invention provides a method for monitoring and managing the dynamic environment of a computer room, including the following steps:

[0047] Step S100: Real-time collect the environmental parameter data in the computer room through multi-type sensor devices;

[0048] Real-time collect the environmental parameter data in the computer room through multi-type sensor devices. The multiple sensor devices include but are not limited to temperature sensors, humidity sensors, smoke sensors, noise sensors, etc. Each sensor is installed at different positions in the computer room to ensure comprehensive coverage of the environmental parameter data. For example, temperature sensors can be distributed at the upper, middle, and lower parts of the server rack to obtain temperature data at different heights; humidity sensors can be installed at the four corners and the central position of the computer room to accurately reflect the overall humidity level.

[0049] Step S200: Attach a unique data label to each collected environmental parameter data and record it according to the timestamp;

[0050] The data label consists of the sensor device ID and the timestamp. In this way, each piece of data is unique, and its source and collection time can be accurately traced. For example, the temperature data collected by the temperature sensor with the sensor device ID TMP01 at 15:30 on October 10, 2023, can have a label expressed as "TMP01_202310101530".

[0051] Step S300: Create an environmental monitoring data frame. Each environmental monitoring data frame is based on its corresponding unique data label and fill in the basic information;

[0052] The basic information includes the sensor device ID, collection time, parameter type (such as temperature, humidity), and parameter value. For example, an environmental monitoring data frame contains the following information:

[0053] Sensor device ID: TMP01;

[0054] Collection time: October 10, 2023, 15:30;

[0055] Parameter type: Temperature;

[0056] Parameter value: 25°C.

[0057] Step S400: Add an abnormal event indication field to the environmental monitoring data frame, compare the collected environmental parameter values with the preset thresholds, and mark the abnormal situations of the environmental parameters;

[0058] It can be understood that, for example, the preset temperature threshold is 20°C to 30°C. When the collected temperature data exceeds this range, "abnormal" is marked in the abnormal event indication field, otherwise "normal" is marked.

[0059] Step S500: Conduct mutation trend analysis. By analyzing the time series data in the environmental monitoring data frame, identify the mutation trends of the environmental parameters and record the analysis results;

[0060] Extract the time series data collected by each sensor from the environmental monitoring data frame, and divide these data into multiple small time segment data fragments by means of a sliding window. For example, taking 5 minutes as a sliding window segment, apply mathematical models or statistical methods to each segment of data to evaluate the stability and mutation points of the data, identify rapid changes or unusual fluctuations, and record these analysis results.

[0061] Step S600: Calculate multi-dimensional correlation indicators, analyze the data correlation between different sensors to assist in judging the mutual influence and possible root causes between environmental parameters;

[0062] First, preprocess the environmental parameter data collected by different types of sensor devices to eliminate noise and outliers. Then, standardize the preprocessed data, construct a data matrix, and calculate the Pearson correlation coefficient between any two columns to quantify the linear correlation between two environmental parameters. Statistically analyze the Pearson correlation coefficients between all pairs of sensors to form a correlation matrix, determine the data correlation index between sensors, and mark the pairs of sensors with high correlation.

[0063] Step S700: Map multiple signaling modules to different data processing units respectively, and each unit provides corresponding levels of data processing and transfer services according to the type and requirements of the connected signaling modules.

[0064] Step S800: Gradually interpret the environmental monitoring data frame, and based on the aforementioned anomaly indication, mutation trend analysis results, and multi-dimensional correlation index, execute corresponding environmental monitoring and management strategies.

[0065] In the automatic mode, the system automatically adjusts the device operation status and alarm mechanism according to the preset rules and real-time analysis results; in the manual mode, the operator performs manual intervention and adjustment according to the analysis report and suggestions provided by the system.

[0066] Step S900: Implement dynamic weight allocation. According to the multi-dimensional weight fields in the environmental monitoring data frame, comprehensively consider the historical weight changes and the current environmental state, and adjust the processing priority of the data frame in the monitoring system.

[0067] Step S1000: Conduct detailed registration and tracking of specific processing fields in the environmental monitoring data frame.

[0068] This includes recording the time of each processing, the measures taken, the effect evaluation after processing, and the information of the relevant responsible person. For example, if a temperature anomaly event occurs at 15:45 on October 10, 2023, and returns to normal after the adjustment of the cooling system, it is necessary to record the adjustment time, specific measures (such as starting the standby air conditioner), effect evaluation (such as the temperature returning to 26°C), and the information of the responsible personnel.

[0069] The present invention provides a method for monitoring and managing the dynamic environment of a computer room, aiming to collect environmental parameter data such as temperature, humidity, air quality, and noise intensity in the computer room in real time through multi-type sensor devices. Each piece of data is appended with a unique data tag and recorded according to the timestamp to ensure the accuracy of tracking. The data is organized into environmental monitoring data frames, which include basic information and an abnormal event indication field, and abnormal situations are marked by comparing with preset thresholds. Subsequently, mutation trend analysis is carried out to identify rapidly changing trends, and combined with the correlation index between sensors, the mutual influence between different environmental parameters is judged. The signaling module is mapped to the corresponding data processing unit to ensure that the data is processed in a timely manner according to requirements. The system automatically or manually executes environmental monitoring and management strategies, and determines the processing priority through dynamic weight allocation to ensure that important data is processed first. The entire process details and tracks the processing fields, recording the processing time, measures, effect evaluation, and responsible person information.

[0070] For example, multiple sensors are installed in a certain computer room, including a temperature sensor (TMP01), a humidity sensor (HMD01), and a smoke sensor (SMK01). These sensors collect data once every minute, and the generated data frames contain sensor IDs, collection times, parameter types, and parameter values (such as TMP01_202310101530_temperature_25°C). The system detects that the collected data of the temperature sensor TMP01 exceeds the set range of 20°C to 30°C and immediately marks "abnormal" in the data frame. At the same time, sliding window analysis finds that the temperature has been rising continuously in the past ten minutes, forming a mutation trend report. By calculating the Pearson correlation coefficient, it is found that the temperature and humidity data are highly correlated. The system maps this abnormal situation to a high-priority data processing unit, triggering cooling measures such as turning on the standby air conditioner. All processing steps and results are recorded in detail to form a complete monitoring log. This all-round and dynamic monitoring method effectively guarantees the stable operation of the computer room environment.

[0071] In this embodiment, the multi-type sensor devices include temperature sensors, humidity sensors, smoke sensors, and noise sensors; the environmental parameter data includes temperature, humidity, air quality, noise intensity, and power status.

[0072] Such as Figure 2, when performing mutation trend analysis, extract the time series data collected by each sensor from the environmental monitoring data frame; divide the extracted time series data into data segments of multiple small time periods by means of a sliding window; apply a mathematical model or statistical method to each data segment to evaluate the data stability and mutation points, and identify whether there are rapid changes or unusual fluctuations in the data; determine the abnormal trend of environmental parameters based on the identified data mutation points; record the mutation analysis results of each data segment, and integrate these results to form a complete mutation trend analysis report; save and display the mutation trend analysis report in the monitoring system.

[0073] In this embodiment, by extracting the time series data of each sensor from the environmental monitoring data frame and dividing it into multiple small time periods. Then, apply a mathematical model or statistical method to analyze the data of each time period to evaluate its stability and identify mutation points, so as to judge whether there are rapid changes or abnormal fluctuations. These mutation points are recorded and integrated to form a complete mutation trend analysis report, which is finally saved and displayed in the monitoring system to timely discover and respond to potential environmental problems.

[0074] In step S600, as Figure 3 shown, the calculation method of the multi-dimensional correlation index includes:

[0075] S601. Collect environmental parameter data from different types of sensor devices and preprocess it to eliminate noise and outliers;

[0076] The sensor devices include temperature sensors, humidity sensors, smoke sensors, etc. Clean the collected data, use filtering methods to eliminate noise, and use interpolation methods or methods of deleting outliers to process missing data and outliers. Suppose the temperature collected by the temperature sensor is: [22.5, 23.0, 100.0, 23.5], where 100.0 is an outlier. After correction, the data becomes: [22.5, 23.0, 23.2, 23.5].

[0077] S602. Perform standardization processing on the preprocessed environmental parameter data;

[0078] Standardize the preprocessed data so that its mean is 0 and its variance is 1 to eliminate the influence between different dimensions. After standardization processing, the temperature data may become: [-0.5, 0.0, 0.5, 1.0].

[0079] S603. Construct a data matrix, where each row represents the multi-dimensional environmental parameter data at a time point, and each column represents the environmental parameter data of a type of sensor;

[0080] Construct a data matrix, where each row represents the environmental parameter data at a time point, and each column represents the environmental parameter data of a sensor. For example:

[0081] Time point Temperature Humidity Smoke concentration T1 22.5 50 0.1 T2 23.0 52 0.2 T3 23.2 55 0.1 T4 23.5 53 0.3

[0082] S604. Calculate the Pearson correlation coefficient between any two columns in the data matrix to quantify the linear correlation between two environmental parameters;

[0083] S605. Statistically analyze the Pearson correlation coefficients between all pairs of sensors to form a correlation matrix;

[0084] For example, the following table:

[0085] Temperature Humidity Smoke concentration Temperature 1 0.8 0.3 Humidity 0.8 1 0.5 Smoke concentration 0.3 0.5 1

[0086] S606. Determine the data correlation index between sensors according to the correlation coefficient values in the correlation matrix, and mark the pairs of sensors with high correlation.

[0087] It can be understood that in the above matrix, it is marked that there is a high correlation between temperature and humidity.

[0088] In the above method for calculating the multi-dimensional correlation index, the formula for the Pearson correlation coefficient is as follows:

[0089]

[0090] where r xy is the Pearson correlation coefficient between two variables x and y, and its value ranges from -1 to +1, where +1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation; x i x i and y i respectively represent the values of two environmental parameters at the i-th observation point; and are the average values of parameters x and y over all observation points, respectively; n represents the total number of observation points or data points.

[0091] In this way, the correlation between any two columns (representing environmental parameters measured by different sensors) in the data matrix can be calculated. After calculating all the correlation coefficients, a correlation matrix can be constructed, where each element represents the degree of linear association between a pair of sensors.

[0092] In step S700, as Figure 4 shown, the specific steps of mapping multiple signaling modules to different data processing units include:

[0093] Step S701: Classify each signaling module according to its function and data requirements;

[0094] For example, environmental monitoring signaling modules, alarm signaling modules, etc. For example, classify the signaling module of the temperature sensor as an environmental monitoring signaling module, and classify the signaling module of the smoke sensor as an alarm signaling module.

[0095] Step S702: Configure corresponding data processing units for each type of signaling module;

[0096] For example, environmental monitoring processing units, alarm processing units, etc.; configure an environmental monitoring processing unit dedicated to processing temperature and humidity data, and an alarm processing unit for processing smoke concentration data.

[0097] Step S703: Map and associate the signaling modules with the corresponding data processing units so that each signaling module sends and receives data that conforms to its processing capabilities and requirements;

[0098] It can be understood that map the signaling module of the temperature sensor to the environmental monitoring processing unit, and map the signaling module of the smoke sensor to the alarm processing unit.

[0099] Step S704: Determine the processing level and priority of each data processing unit according to the actual application scenario;

[0100] Step S705: Forward the data generated by the signaling module to the corresponding data processing unit through the network communication protocol, and the data processing unit executes the corresponding data processing tasks;

[0101] Step S706: Monitor the interaction between the data processing unit and the signaling module;

[0102] Step S707: Adjust the mapping relationship between the signaling module and the data processing unit according to the system operation status and real-time requirements.

[0103] If the environmental monitoring processing unit is overloaded, some temperature data processing tasks can be transferred to another standby processing unit.

[0104] In this embodiment, there are two modes for executing the monitoring and management strategies in the computer room dynamic environment monitoring and management method: automatic mode and manual mode. In the automatic mode, the system automatically adjusts the operating state of the device and activates the alarm mechanism according to the preset rules and the real-time analysis results obtained from the environmental monitoring data frame to cope with possible environmental problems. In the manual mode, the operator can perform manual intervention and adjustment according to the analysis report and suggestions provided by the system to finely control the environmental conditions or solve specific problems. This dual-mode strategy enables the system to respond automatically and quickly while retaining the flexibility of manual adjustment.

[0105] In this embodiment, detailed records and tracking are carried out for each environmental parameter processing activity. This includes recording the specific time when the processing occurs, the specific measures taken, the effect evaluation after processing, and the information of the person responsible for the processing. Such detailed records enable the monitoring system to track the effects of each intervention, ensure quality control and responsibility traceability for environmental management activities, and at the same time provide data support for optimizing and adjusting management strategies.

[0106] In this embodiment, the dynamic weight allocation method is based on the weighted moving average method to smooth the historical data to determine the processing priority of the current data frame.

[0107] In this embodiment, the unique data label consists of the sensor device ID and the timestamp.

[0108] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for dynamic environment monitoring and management of a computer room, characterized in that: The following steps are involved: Real-time collection of environmental parameter data in the computer room through multiple types of sensor equipment; Attach a unique data tag to each collected environmental parameter data and record it according to the timestamp; Create an environmental monitoring data frame, each of which is based on its corresponding unique data tag and fills in basic information; An abnormal event indication field is added to the environmental monitoring data frame, and the collected environmental parameter values ​​are compared with the preset thresholds to indicate abnormal conditions of the environmental parameters; Conduct mutation trend analysis, identify mutation trends of environmental parameters by analyzing time series data in environmental monitoring data frames, and record analysis results; Calculate multi-dimensional correlation indicators and analyze the data correlation between different sensors to help determine the mutual influence and possible root causes between environmental parameters; Multiple signaling modules are mapped to different data processing units, each of which provides corresponding levels of data processing and flow services according to the type and requirements of the connected signaling module; The environmental monitoring data frames are gradually interpreted, and corresponding environmental monitoring and management strategies are implemented based on the aforementioned abnormal indications, mutation trend analysis results and multi-dimensional correlation indicators; Implement dynamic weight allocation, adjust the processing priority of data frames in the monitoring system based on the multi-dimensional weight fields in the environmental monitoring data frames, comprehensively consider historical weight changes and current environmental status; Detailed registration and tracking of specific processing fields in environmental monitoring data frames.

2. A method for dynamic environment monitoring and management of a computer room as claimed in claim 1, characterized in that: The multi-type sensor devices include a temperature sensor, a humidity sensor, a smoke sensor and a noise sensor; the environmental parameter data include temperature, humidity, air quality, noise intensity and power status.

3. A method for dynamic environment monitoring and management of a computer room as claimed in claim 1, characterized in that: When performing mutation trend analysis, extract the time series data collected by each sensor from the environmental monitoring data frame; divide the extracted time series data into multiple small time period data segments by means of sliding windows; apply mathematical models or statistical methods to each data segment to evaluate the stability and mutation points of the data, and identify whether there are rapid changes or unusual fluctuations in the data; determine the abnormal trends of environmental parameters based on the identified data mutation points; record the mutation analysis results of each data segment, and integrate these results to form a complete mutation trend analysis report; save and display the mutation trend analysis report in the monitoring system.

4. A method for dynamic environment monitoring and management of a computer room as claimed in claim 1, characterized in that: The calculation method of multi-dimensional correlation index includes: Collect environmental parameter data from different types of sensor devices and pre-process them to eliminate noise and outliers; Standardize the preprocessed environmental parameter data; Construct a data matrix, where each row represents the multidimensional environmental parameter data at a time point, and each column represents the environmental parameter data of a sensor; The Pearson correlation coefficient between any two columns in the data matrix was calculated to quantify the linear correlation between two environmental parameters; The Pearson correlation coefficients between all sensor pairs are counted to form a correlation matrix; According to the correlation coefficient values ​​in the correlation matrix, the data correlation index between sensors is determined, and the sensor pairs with high correlation are marked.

5. A method for dynamic environment monitoring and management of a computer room as claimed in claim 1, characterized in that: The specific steps of mapping multiple signaling modules to different data processing units include: Classify each signaling module according to its function and data requirements; Configure a corresponding data processing unit for each type of signaling module; Map and associate the signaling modules with the corresponding data processing units so that each signaling module sends and receives data that meets its processing capabilities and requirements; Determine the processing level and priority of each data processing unit based on the actual application scenario; Through the network communication protocol, the data generated by the signaling module is forwarded to the corresponding data processing unit, and the data processing unit performs the corresponding data processing tasks; Monitor the interaction between the data processing unit and the signaling module; Adjust the mapping relationship between the signaling module and the data processing unit according to the system operation status and real-time requirements.

6. A method for dynamic environment monitoring and management of a computer room as claimed in claim 1, characterized in that: When executing environmental monitoring and management strategies, there are two modes: automatic and manual. In automatic mode, the equipment operating status and alarm mechanism are automatically adjusted according to preset rules and real-time analysis results. In manual mode, operators are allowed to manually intervene and adjust according to the analysis reports and suggestions provided by the system.

7. A method for dynamic environment monitoring and management of a computer room as claimed in claim 1, characterized in that: Detailed registration and tracking of specific processing fields of environmental monitoring data frames, including recording the time of each processing, the measures taken for processing, the evaluation of the effects after processing, and the information of relevant responsible persons.

8. A method for dynamic environment monitoring and management of a computer room as claimed in claim 1, characterized in that: The dynamic weight allocation method is based on the weighted moving average method to smooth the historical data to determine the processing priority of the current data frame.

9. A method for dynamic environment monitoring and management of a computer room as claimed in claim 1, characterized in that: The unique data tag consists of the sensor device ID and a timestamp.

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