A method and system for monitoring the health of a communication battery for dynamic environment monitoring

Abstract

The present invention discloses a method and system for monitoring the health of a battery for dynamic environmental monitoring and communication. The method comprises obtaining a user-selected measurement point; querying a built-in model based on the user-selected model; obtaining the user's model confirmation result, and if the confirmation result is confirmed, obtaining the user-configured warning rule parameters; saving the user-configured warning rule parameters; training the model based on the saved configured warning rule parameters; and, after model training, applying dynamic thresholds for monitoring the health of a battery for dynamic environmental monitoring and communication. The present invention aims to calculate the upper and lower threshold alarm boundaries for each single indicator of a monitored object. By monitoring whether the parameter exceeds the dynamic alarm threshold boundary, anomalies in the environment can be detected, thereby providing intelligent dynamic warning functions to stabilize the dynamic environmental monitoring system.

Description

Technical Field

[0001] The present invention belongs to the field of power communication technology, and in particular relates to a health monitoring method and system for a dynamic environment monitoring communication battery. Background Art

[0002] Dynamic environment monitoring is also known as power environment monitoring. It mainly refers to the real-time monitoring and management of the power systems and environmental conditions in places such as data centers, computer rooms, and communication base stations. It generally includes the following aspects: Power system monitoring: including power supplies, UPS (uninterruptible power supply), generators, distribution systems, etc., to ensure the stability and reliability of the power supply. Environmental system monitoring: including the monitoring of environmental parameters such as temperature and humidity, smoke, and water leakage to ensure that the equipment operates under suitable environmental conditions. Security system monitoring: including access control, video surveillance, fire protection systems, etc. to ensure the physical safety of the venue. Network system monitoring: including operating status monitoring of IT facilities such as servers, network equipment, and storage devices. Therefore, realizing the health monitoring of communication batteries for dynamic environment monitoring plays an important role in ensuring the reliability of dynamic environment monitoring. Existing methods for monitoring the health of communications batteries using dynamic environmental monitoring rely on a single-variable dynamic threshold warning method. This method sets a fixed warning threshold for each monitored object within the communications room. This results in significant variations in environmental conditions across different sizes, locations, and time periods. Using a single, fixed threshold across all rooms is a crude approach. This inaccuracy in the threshold affects the accuracy of warnings and does not meet the stringent management requirements of current equipment rooms. To address these technical issues, the mainstream approach currently uses the STL (Seasonal-Trend Smoothed Locally Weighted Regression) decomposition algorithm to remove trends and periodicity. Multiple unsupervised anomaly detection algorithms are then used for anomaly detection. The results of these algorithms are then bagged to determine the threshold. This approach requires specialized tuning based on business metrics to detect weekly, monthly, or quarterly periods. While effective identification and elimination of periodicity can be effective, the consistency of periodicity across different sites and data varies. Automatic identification of periodicity using a single variable often yields poor results. Summary of the Invention

[0003] Technical problem to be solved by the present invention: In response to the above-mentioned problems of the prior art, a method and system for monitoring the health of communication batteries for dynamic environment monitoring are provided. The present invention aims to calculate the alarm boundaries of upper and lower thresholds for each single indicator of the monitored object, and discover abnormal conditions in the environment by monitoring whether the parameters exceed the dynamic alarm threshold boundaries, thereby providing an intelligent dynamic early warning function for the stability of the dynamic environment monitoring system.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A method for monitoring the health of a communication battery for dynamic environment monitoring includes the following steps:

[0006] S1, obtaining the measurement points selected by the user for computer room environment monitoring and the algorithm model keywords selected or input by the user;

[0007] S2, querying an algorithm model library according to the algorithm model keyword to obtain a list of matching algorithm models in the algorithm model library, wherein the algorithm model library has multiple built-in algorithm models, and the algorithm models are used to perform prediction on current data within a specified window size in the historical data of the measurement point to obtain a historical predicted value of the battery health;

[0008] S3, displays the list of matching algorithm models in the algorithm model library;

[0009] S4, obtaining the algorithm model selected by the user for confirmation. If the user confirms the selection successfully, jump to step S5, otherwise exit;

[0010] S5, obtain and save the warning rule parameters configured by the user;

[0011] S6, performing algorithm model training based on the saved and configured warning rule parameters to determine the dynamic threshold;

[0012] S7, performing a threshold judgment on the current of the measuring point in combination with the determined dynamic threshold to determine whether the dynamic ring monitoring communication battery is abnormal.

[0013] Optionally, the algorithm model built into the algorithm model library in step S2 is a time series algorithm model, which is used to perform prediction on the current time series data within a specified window size in the historical data of the measurement point to obtain a historical prediction value of the battery health.

[0014] Optionally, the time series algorithm models built into the algorithm model library include part or all of the autoregressive model, the autoregressive and extreme value distribution algorithm model, the exponential smoothing model, and the LSTM model.

[0015] Optionally, the user-configured warning rule parameters acquired in step S5 include an upper boundary threshold and a lower boundary threshold.

[0016] Optionally, performing model training based on the saved configured warning rule parameters in step S6 includes:

[0017] S6.1, obtain historical data of the measurement point selected by the user;

[0018] S6.2, sliding based on the specified window size, obtaining historical measured values ​​of battery health within each window in the historical data, and performing prediction on the time series data within each window using the algorithm model selected by the user to obtain historical predicted values ​​of battery health;

[0019] S6.3. Calculate the difference between the historical measured and predicted battery health values ​​in each window as an anomaly score. If the anomaly score for a window is negative and falls outside the lower boundary threshold, the lower boundary of the window is considered abnormal. If the anomaly score for a window is positive and falls outside the upper boundary threshold, the upper boundary of the window is considered abnormal. Mark any discovered anomalies as abnormal.

[0020] S6.4, the peak over-threshold algorithm POT is used in combination with the abnormal marks of each window to determine the dynamic threshold.

[0021] Optionally, in step S6.4, the function expression for determining the dynamic threshold using the peak over-threshold algorithm in combination with the abnormality marks of each window is:

[0022] ,

[0023] ,

[0024] in, Exceeding the internal threshold The tail distribution function of the excess value of is the residual obtained based on historical data, which refers to the abnormal score score. When the residual data exceeds the internal threshold The difference between the residual data and the selected threshold exceeds The probability of is the residual set, Indicates the internal threshold Approximation constant The tail shape characteristics, is the shape parameter used to control the attenuation speed of the tail distribution, is a scaling function used to control the extent of tail distribution expansion, is the dynamic threshold, is the internal threshold, is the estimated value of the scale parameter of the generalized Pareto distribution GPD, is the estimated value of the shape parameter of the generalized Pareto distribution GPD, Exceeding the dynamic threshold The target probability value, Used to set the tightness of abnormality judgment; is the total number of observations, which represents the number of all samples in the data set; is the number of observations exceeding the threshold t.

[0025] Optionally, in step S7, when a threshold judgment is performed on the current of the measuring point in combination with the determined dynamic threshold to determine whether the dynamic ring monitoring communication battery is abnormal, if the current of the measuring point exceeds the determined dynamic threshold, the dynamic ring monitoring communication battery is judged to be abnormal; otherwise, the dynamic ring monitoring communication battery is judged to be normal.

[0026] In addition, the present invention also provides a health monitoring system for a dynamic environment monitoring communication battery, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the health monitoring method for a dynamic environment monitoring communication battery.

[0027] In addition, the present invention also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the health monitoring method for dynamic environment monitoring communication battery through a processor.

[0028] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the health monitoring method for dynamic environment monitoring communication batteries through a processor.

[0029] Compared with existing technologies, the present invention has the following major advantages: it includes obtaining user-selected measurement points; querying a built-in model based on the user-selected model; obtaining the user's model confirmation result, and if the confirmation result is confirmed, obtaining the user-configured warning rule parameters; saving the user-configured warning rule parameters; training the model based on the saved configured warning rule parameters; and, after model training, applying dynamic thresholds for monitoring the health indicators of dynamic environment monitoring communication batteries. The present invention aims to calculate the upper and lower threshold alarm boundaries for each single indicator of the monitored object. By monitoring whether the parameter exceeds the dynamic alarm threshold boundary, it can detect environmental anomalies and provide intelligent dynamic warning functions to ensure the stability of the dynamic environment monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Schematic diagram of the basic process of the method of the embodiment of the present invention.

[0031] Figure 2 Schematic diagram of the basic process of model training in an embodiment of the present invention.

[0032] Figure 3 Schematic diagram of a fitting curve of the cumulative distribution function of the extreme value distribution in an embodiment of the present invention.

[0033] Figure 4 Schematic diagram of the distribution of historical measured values, historical predicted values, and abnormal points of current in an embodiment of the present invention, where the black curve is the historical measured value, the blue curve is the historical predicted value, and the red dots are abnormal points.

[0034] Figure 5 2 is a schematic diagram of an example of anomaly score and dynamic threshold curve in an embodiment of the present invention, where the purple solid line is the anomaly score and the dotted line is the dynamic threshold.

[0035] Figure 6 This is a schematic diagram of the first data segment of the MK trend test in an embodiment of the present invention.

[0036] Figure 7 This is a schematic diagram of the second data segment of the MK trend test in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. 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 making creative efforts shall fall within the scope of protection of the present invention.

[0038] like Figure 1 As shown, the health monitoring method for dynamic environment monitoring communication batteries in this embodiment includes the following steps:

[0039] S1, obtaining the measurement points selected by the user for computer room environment monitoring and the algorithm model keywords selected or input by the user;

[0040] S2, querying an algorithm model library according to the algorithm model keyword to obtain a list of matching algorithm models in the algorithm model library, wherein the algorithm model library has multiple built-in algorithm models, and the algorithm models are used to perform prediction on current data within a specified window size in the historical data of the measurement point to obtain a historical predicted value of the battery health;

[0041] S3, displays the list of matching algorithm models in the algorithm model library;

[0042] S4, obtaining the algorithm model selected by the user for confirmation. If the user confirms the selection successfully, jump to step S5, otherwise exit;

[0043] S5, obtain and save the warning rule parameters configured by the user;

[0044] S6, performing algorithm model training based on the saved and configured warning rule parameters to determine the dynamic threshold;

[0045] S7, performing a threshold judgment on the current of the measuring point in combination with the determined dynamic threshold to determine whether the dynamic ring monitoring communication battery is abnormal.

[0046] In step S1 of this embodiment, when the user selects measurement points for monitoring the computer room environment, corresponding measurement points may be set according to different monitored objects.

[0047] The algorithm model built into the algorithm model library in step S2 of this embodiment is a time series algorithm model, which is used to perform prediction on the current time series data within a specified window size in the historical data of the measurement point to obtain a historical prediction value of the battery health.

[0048] The built-in time series algorithm models in the algorithm model library of this embodiment include some or all of the following: autoregressive models, autoregressive and extreme value distribution algorithm models, exponential smoothing models, and LSTM models. For example, as an optional implementation, this embodiment uses an autoregressive model to predict time series for periodic monitoring parameters, uses the absolute value of the residual as the anomaly score (other error calculation methods such as squared error can be used as an alternative), and uses the SPOT algorithm based on the extreme value theorem for modeling to implement dynamic threshold anomaly detection.

[0049] The user-configured warning rule parameters acquired in step S5 of this embodiment include an upper boundary threshold and a lower boundary threshold.

[0050] In this embodiment, an autoregressive model is used to learn past historical data (focusing mainly on cycles and trends). After eliminating cycles and trends through residuals, the SPOT algorithm is used to implement dynamic thresholds. This method eliminates the problem of continuous false positives caused by the sliding window, and at the same time, the autoregressive model learning can better automatically eliminate cycles and trends. And because the algorithm is based on autoregressive prediction, the predicted value of the next time step can be predicted in advance. Figure 2 As shown, in step S6 of this embodiment, model training based on the saved configured warning rule parameters includes:

[0051] S6.1, obtain historical data of the measurement point selected by the user;

[0052] S6.2, sliding based on the specified window size, obtaining historical measured values ​​of battery health within each window in the historical data, and performing prediction on the time series data within each window using the algorithm model selected by the user to obtain historical predicted values ​​of battery health;

[0053] S6.3, calculate the difference between the historical measurement value and the historical prediction value of the battery health in each window as the abnormal score score. If the abnormal score score of a window is negative and falls outside the lower boundary threshold, the lower boundary of the window is determined to be abnormal. If the abnormal score score of a window is positive and falls outside the upper boundary threshold, the upper boundary of the window is determined to be abnormal. The abnormal value found is marked as abnormal. For example, as an optional implementation method, (3) the current measurement point value - the prediction value is obtained. When the score is negative and outside the 98% (configurable) distribution quantile of the extreme value distribution, it is a lower boundary abnormality. When the score is positive and outside the 98% (configurable) distribution quantile of the extreme value distribution, it is an upper boundary abnormality.

[0054] S6.4, the peak over-threshold algorithm POT is used in combination with the abnormal marks of each window to determine the dynamic threshold.

[0055] In step S6.4 of this embodiment, the function expression for determining the dynamic threshold using the peak over-threshold algorithm in combination with the abnormality marks of each window is:

[0056] ,

[0057] ,

[0058] in, Exceeding the internal threshold The tail distribution function of the excess value of is the residual obtained based on historical data, which refers to the abnormal score score. When the residual data exceeds the internal threshold The difference between the residual data and the selected threshold exceeds The probability of is the residual set, Indicates the internal threshold Approximation constant The tail shape characteristics, is the shape parameter used to control the attenuation speed of the tail distribution, is a scaling function used to control the extent of tail distribution expansion, is the dynamic threshold, is the internal threshold, is the estimated value of the scale parameter of the generalized Pareto distribution GPD, is the estimated value of the shape parameter of the generalized Pareto distribution GPD, Exceeding the dynamic threshold The target probability value, Used to set the tightness of abnormality judgment; is the total number of observations, which represents the number of all samples in the data set; is the number of observations exceeding the threshold t.

[0059] Extreme Value Distribution (EVD) is a statistical model used to describe the distribution of the maximum or minimum values ​​of a random variable. The extreme value distribution (EVD) of the difference between the historical measured values ​​and the historical predicted values ​​of battery health within each window can be expressed as:

[0060] ,

[0061] in, Indicates that the parameter is The cumulative distribution function of the extreme value distribution is, is the residual obtained based on historical data. The residual refers to the abnormal score. is the parameter of the extreme value distribution, represents a real number, Define the domain condition for the function and ensure that the expression within the parentheses is positive. Figure 3 Schematic diagram of the fitting curve of the cumulative distribution function of the extreme value distribution in the embodiment of the present invention, wherein the parameter of the extreme value distribution is The values ​​are -0.8, 0 and 1 respectively, combined Figure 3 , represents the tail shape feature, then the tail shape feature , parameters of the extreme value distribution The relationship is as follows: (1) The tail shape has a heavy tail phenomenon: , , the extreme value distribution at this time is: Frechet distribution. (2) The tail shape feature has an e exponential tail: , , the extreme value distribution at this time is: γ distribution. (3) The tail shape feature is bounded: , , the extreme value distribution is uniform. By fitting the extreme value distribution (EVD) to the tail of the unknown input, we can find the probability of these low-probability events. However, directly estimating the parameters of the distribution is very difficult, so we use the Peaks-Over-Threshold (POT) method, where the threshold follows the generalized Pareto distribution (GPT):

[0062] ,

[0063] in, Exceeding the internal threshold The tail distribution function of the excess value of is the residual obtained based on historical data, which refers to the abnormal score score. When the residual data exceeds the internal threshold The difference between the residual data and the selected threshold exceeds The probability of is the residual set, Indicates the internal threshold Approximation constant The tail shape characteristics, is the shape parameter used to control the attenuation speed of the tail distribution, is a scaling function used to control the extent of the tail distribution. The quantile can be calculated using the following formula:

[0064] ,

[0065] in, is the dynamic threshold (quantile), is the internal threshold, is the estimated value of the scale parameter of the generalized Pareto distribution GPD, is the estimated value of the shape parameter of the generalized Pareto distribution GPD, Exceeding the dynamic threshold The target probability value (such as 95%), Used to set the tightness of abnormality judgment; is the total number of observations, which represents the number of all samples in the data set; is the number of observations exceeding the threshold t, i.e., the number of peaks. Finally, the results obtained in this embodiment are as follows Figure 4 and Figure 5 As shown, Figure 4 Schematic diagram of the distribution of historical measured values, historical predicted values, and abnormal points of current in an embodiment of the present invention, where the black curve is the historical measured value, the blue curve is the historical predicted value, and the red dots are abnormal points. Figure 5 This is a schematic diagram of an example anomaly score and dynamic threshold curve in an embodiment of the present invention, where the purple solid line represents the anomaly score, and the dashed line represents the dynamic threshold. In step S7 of this embodiment, the current at the measuring point is combined with the determined dynamic threshold to determine whether the dynamic ring monitoring communication battery is abnormal. If the current at the measuring point exceeds the determined dynamic threshold, the dynamic ring monitoring communication battery is determined to be abnormal; otherwise, the dynamic ring monitoring communication battery is determined to be normal.

[0066] In order to verify the health monitoring method of the communication battery for dynamic environment monitoring in this embodiment, the Mann-Kendall (MK) trend test is used in this embodiment. The Mann-Kendall (MK) trend test is a non-parametric statistical method used to detect trends in time series data. It is widely used in fields such as meteorology, hydrology, and environmental science to analyze trend changes in long-term data sets. The basic principle of the MK trend test is to determine whether there is a monotonic trend (i.e., a continuous upward or downward trend) in the time series data. The advantage of this method is that it does not require the data to conform to a specific distribution (such as a normal distribution) and is robust to missing data and irregular time intervals. The specific steps include: (1) Calculating the MK statistics within the data window, including the S statistic and the variance of the S statistic. (2) Calculating the Z statistic. (3) Setting a threshold and calculating the MK statistic that exceeds the threshold. Among them, calculating the S statistic includes: For a given time series data Calculate for each pair of data points and The difference , its calculation function expression is:

[0067] ,

[0068] in, is the sign function; then the S statistic is calculated according to the following formula:

[0069] ,

[0070] in, is the S statistic, is the number of data points. When the amount of data is large (usually (n>10)), the S statistic approximately follows a normal distribution, and its variance for:

[0071] ,

[0072] in, is the size of the group with the same value in the data set. The function expression for calculating the Z statistic is:

[0073] ,

[0074] in, is the Z statistic, is the S statistic, is the variance of the S statistic. Figure 6 and Figure 7 Schematic diagrams of two data segments of the MK trend test in an embodiment of the present invention, wherein the red ones are identified abnormal points.

[0075] In summary, the health monitoring method for dynamic environment monitoring communication batteries in this embodiment has the following advantages: (1) Automatic identification of warning threshold cycles and improved alarm threshold configuration efficiency. This embodiment uses time series analysis to automatically identify the warning threshold cycle, uses an autoregressive model for time series prediction, and an adaptive learning algorithm to develop an adaptive algorithm that can learn and adjust the threshold in real time. The threshold is dynamically adjusted according to the latest data to adapt to the ever-changing environment, and the cycle of the warning threshold is automatically identified, without the need for personnel to periodically maintain the data. By using intelligent algorithm tools, multi-level alarm strategies, and configuration methods combined with business scenarios, the accuracy and efficiency of alarms can be effectively improved. (2) This embodiment can realize multi-dimensional identification of abnormalities, improve monitoring coverage, and enhance business availability; the monitoring parameters are divided into two types according to data characteristics: periodicity and monotonicity for dynamic threshold monitoring. For abnormal situations that occur, the periodicity, overall trend, and fluctuation size of the monitoring indicators are monitored in a timely manner, and abnormal behaviors are monitored from multiple dimensions to ensure the stable operation of the system.

[0076] In addition, this embodiment also provides a health monitoring system for a dynamic environment monitoring communication battery, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the health monitoring method for a dynamic environment monitoring communication battery.

[0077] In addition, this embodiment also provides a computer-readable storage medium, which stores a computer program or instruction. The computer program or instruction is programmed or configured to execute the health monitoring method for dynamic environment monitoring communication battery through a processor.

[0078] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the health monitoring method for dynamic environment monitoring communication batteries through a processor.

[0079] Those skilled in the art should understand that the technical solutions provided by the embodiments of the present application may be in the form of methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0080] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring the health of a communication battery for dynamic environment monitoring, characterized in that: The steps include: S1, obtaining the measurement points selected by the user for computer room environment monitoring and the algorithm model keywords selected or input by the user; S2, querying an algorithm model library according to the algorithm model keyword to obtain a list of matching algorithm models in the algorithm model library, wherein the algorithm model library has multiple built-in algorithm models, and the algorithm models are used to perform prediction on current data within a specified window size in the historical data of the measurement point to obtain a historical predicted value of the battery health; S3, displays the list of matching algorithm models in the algorithm model library; S4, obtaining the algorithm model selected by the user for confirmation. If the user confirms the selection successfully, jump to step S5, otherwise exit; S5, obtain and save the warning rule parameters configured by the user; S6, performing algorithm model training based on the saved and configured warning rule parameters to determine the dynamic threshold; S7, performing a threshold judgment on the current of the measuring point in combination with the determined dynamic threshold to determine whether the dynamic ring monitoring communication battery is abnormal; The user-configured warning rule parameters obtained in step S5 include an upper boundary threshold and a lower boundary threshold; The model training based on the saved configured warning rule parameters in step S6 includes: S6.1, obtain historical data of the measurement point selected by the user; S6.2, sliding based on the specified window size, obtaining historical measured values ​​of battery health within each window in the historical data, and performing prediction on the time series data within each window using the algorithm model selected by the user to obtain historical predicted values ​​of battery health; S6.

3. Calculate the difference between the historical measured and predicted battery health values ​​in each window as an anomaly score. If the anomaly score for a window is negative and falls outside the lower boundary threshold, the lower boundary of the window is considered abnormal. If the anomaly score for a window is positive and falls outside the upper boundary threshold, the upper boundary of the window is considered abnormal. Mark any discovered anomalies as abnormal. S6.4, the peak over-threshold algorithm POT is used in combination with the abnormal marks of each window to determine the dynamic threshold.

2. The method for monitoring the health of a battery for dynamic environment monitoring communication according to claim 1, characterized in that: The algorithm model built into the algorithm model library in step S2 is a time series algorithm model, which is used to perform prediction on the current time series data within a specified window size in the historical data of the measurement point to obtain a historical prediction value of the battery health.

3. The method for monitoring the health of a battery for dynamic environment monitoring communication according to claim 2, characterized in that: The time series algorithm models built into the algorithm model library include part or all of the autoregressive model, the autoregressive and extreme value distribution algorithm model, and the exponential smoothing model.

4. The method for monitoring the health of a battery for dynamic environment monitoring communication according to claim 1, characterized in that: In step S6.4, the function expression for determining the dynamic threshold using the peak threshold algorithm in combination with the abnormality marks of each window is: ， ， in, Exceeding the internal threshold The tail distribution function of the excess value of is the residual obtained based on historical data, which refers to the abnormal score score. When the residual data exceeds the internal threshold The difference between the residual data and the selected threshold exceeds The probability of is the residual set, Indicates the internal threshold Approximation constant The tail shape characteristics, is the shape parameter used to control the attenuation speed of the tail distribution, is a scaling function used to control the extent of tail distribution expansion, is the dynamic threshold, is the internal threshold, is the estimated value of the scale parameter of the generalized Pareto distribution GPD, is the estimated value of the shape parameter of the generalized Pareto distribution GPD, Exceeding the dynamic threshold The target probability value, Used to set the tightness of abnormality judgment; is the total number of observations, which represents the number of all samples in the data set; is the number of observations exceeding the threshold t.

5. The method for monitoring the health of a battery for dynamic environment monitoring communication according to claim 1, characterized in that: In step S7, when the current of the measuring point is judged by the determined dynamic threshold value to determine whether the dynamic ring monitoring communication battery is abnormal, if the current of the measuring point exceeds the determined dynamic threshold value, the dynamic ring monitoring communication battery is judged to be abnormal; otherwise, the dynamic ring monitoring communication battery is judged to be normal.

6. A health monitoring system for a dynamic environment monitoring communication battery, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the health monitoring method for dynamic environment monitoring communication battery as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute the health monitoring method for dynamic environment monitoring communication battery described in any one of claims 1 to 5 through a processor.

8. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute the health monitoring method for dynamic environment monitoring communication battery described in any one of claims 1 to 5 through a processor.

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