Method and system for monitoring running state of battery automatic short circuit antipole detection equipment

Abstract

The invention relates to the field of data processing, in particular to an operation state monitoring method and system for battery automatic short circuit antipole detection equipment, and the method comprises the steps: obtaining the current, voltage and temperature historical data of the battery detection equipment, calculating the data fluctuation degree through employing an HWKS algorithm, setting an initial k'value, and calculating the initial k 'value; clustering the historical operation data of different levels, analyzing the aggregation degree and local density of a cluster, correcting an initial k'value to obtain a corrected significant k value, taking the significant k value as a parameter value in an HWKS algorithm, obtaining the abnormal degree of the data of each level, performing weighted summation on the abnormal degree of the data of each level, and obtaining the abnormal degree of the data of each level. And the data processing module is used for acquiring the comprehensive abnormal degree of the data of each level, judging whether the data point is abnormal data based on the comprehensive abnormal degree, and monitoring the detection equipment. According to the method, the k value is adjusted in a self-adaptive mode, abnormal points in the data are accurately recognized, and therefore the accuracy of operation monitoring is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more specifically to a method and system for monitoring the operating status of a battery automatic short circuit reverse polarity detection device. Background Art

[0002] Batteries play an important role in modern technology and are widely used in various devices, such as electric vehicles, portable electronic devices and energy storage systems. The performance and safety of batteries directly affect the reliability of the equipment and the safety of users. At present, when monitoring the testing equipment, it involves the detection of multi-level data. The detection results of a single data are relatively simple and cannot accurately reflect the operating status of the testing equipment.

[0003] The existing Chinese patent application document with publication number CN118518948A discloses an electromagnetic compatibility detection method, device, equipment and storage medium. The electromagnetic compatibility detection method includes: obtaining multi-dimensional information of the detection equipment, and setting a multi-level test range of electromagnetic environment intensity based on the multi-dimensional information of the detection equipment; collecting the operating status data of the detection equipment according to the set multi-level test range of electromagnetic environment intensity, and obtaining the operating status data of the detection equipment under each level of electromagnetic environment intensity; performing stability calibration on the operating status data to obtain calibrated operating status data, and performing dynamic serialization processing on the calibrated operating status data to obtain dynamic sequence operating status data; the invention can predict potential faults by monitoring and classifying the equipment status, and take maintenance or repair measures in time to extend the service life of the equipment and improve the reliability of the system.

[0004] The above application documents obtain multi-dimensional information of the detection equipment, perform stability calibration and dynamic serialization processing on the operating status data, classify the machine learning algorithm model, and obtain normal or abnormal operating data. Currently, combined with the HWKS algorithm, The value is set manually or empirically. For areas with dense data points, the smaller Values ​​can provide finer detection; in areas with sparse data points, larger The value can provide more context information. Since the fluctuation degree of the detection equipment at different levels varies, the fixed The value cannot adapt to the differences in data at each level, which leads to a certain deviation in the final anomaly detection result of the data point, affecting the accuracy of the result. Summary of the invention

[0005] To solve the fixed The value cannot adapt to the differences in data at each level, which leads to a certain deviation in the final anomaly detection result of the data point. The present invention provides solutions in the following aspects.

[0006] In a first aspect, a method for monitoring the operating status of a battery automatic short circuit reverse polarity detection device comprises: obtaining historical operating data of each level of the battery automatic short circuit reverse polarity detection device, wherein the historical operating data of each level comprises: current, voltage, and temperature; calculating the fluctuation degree of the historical operating data at the level based on the HWKS algorithm by calculating the historical change rate and change frequency of the historical operating data at the level, and using the fluctuation degree as the initial value of the HWKS algorithm; value; cluster the historical operation data of each level to obtain multiple clusters, obtain the degree of aggregation between each cluster, and calculate the local density of the cluster according to the distance difference between the cluster centers and the degree of aggregation between the clusters; The corrected value was obtained. value, will be significant The value is used as the parameter value in the HWKS algorithm to obtain the abnormality degree of the data at each level, and the abnormality degree of the data at each level is weighted and summed to obtain the comprehensive abnormality degree of the data at each level. Based on the comprehensive abnormality degree, it is determined whether the data point is abnormal data, and the detection equipment is monitored.

[0007] The effect is: through effective monitoring and timely fault handling, the damage to the battery can be reduced, thereby extending the battery life, and being able to adapt to different operating conditions and environmental changes, using the HWKS algorithm to dynamically calculate the initial Value and Significance The value enables the detection system to adapt to changes in data and improves the flexibility and robustness of the detection system.

[0008] Preferably, the initial Values, including steps: Select historical data of a preset period of time at the data point collection time in the historical data, draw a line graph for the selected historical data, obtain extreme value points in the line graph, calculate the slope between adjacent extreme value points, and form a slope set; The square of the difference between the sum of the absolute values ​​of the positive slope and the sum of the absolute values ​​of the negative slope is taken as the slope difference, and the square of the probability difference between the positive slope and the negative slope is taken as the probability difference. The square root of the sum of the slope difference and the probability difference is taken to evaluate the fluctuation degree of the data at each level, and the fluctuation degree is taken as the initial value.

[0009] The effect is that by calculating the slope difference and probability difference, the volatility of the data within the preset period can be quantified, providing a more appropriate k-NN algorithm in the HWKS algorithm. The direction of data change is considered by distinguishing between positive and negative slopes, which helps to identify anomalies in the data and adjust the data when the data fluctuates greatly. The value can improve the sensitivity of the detection system and better capture potential anomalies.

[0010] Preferably, obtaining the degree of aggregation between the clusters includes: Select historical data of a preset period before the data point collection time in the historical data, cluster the selected local data, obtain several clusters, take any cluster as the target cluster, and obtain the mean distance between each data point in the target cluster and the cluster center of the target cluster as the degree of clustering.

[0011] The effect is that by calculating the mean distance between the data points in the cluster and the cluster center, the smaller the mean, the more closely the data points are around the cluster center, and the degree of data aggregation in the local area can be captured.

[0012] Preferably, the local density of the clusters includes: Taking any cluster as the target cluster, obtaining the inverse of the distance between the cluster center of the target cluster and the cluster center of the adjacent cluster, and the inverse of the difference between the aggregation degree of the target cluster and the aggregation degree of the adjacent cluster; The inverse of the distance between cluster centers is taken as the weight of the inverse of the difference between the clustering degrees, and the weighted sum is taken as the local density of the cluster.

[0013] The effect is that by using the inverse of the distance between cluster centers as weights, the influence of neighboring clusters on local density is emphasized, so that the calculation of local density pays more attention to data points in neighboring areas. By considering local density, the anomaly detection capability can be enhanced to improve the accuracy of anomaly detection.

[0014] Preferably, the significant The values ​​satisfy the following relationship: ; In the formula, Indicates the significance of each level of data value, represents the preset hyperparameters, represents the local density of the target cluster, Indicates the initial state of each level of data value, Indicates An exponential function with base .

[0015] The effect is: through significant Dynamic adjustment of the value helps improve the accuracy of anomaly detection and can be optimized according to the local characteristics of the data. Dynamic adjustment of values ​​can reduce the impact of outliers and noise, thereby enhancing the robustness of the algorithm on different data sets.

[0016] Preferably, the comprehensive abnormality degree satisfies the following relationship: ; In the formula, Indicates the overall abnormality level. Indicates the degree of voltage level abnormality. Indicates the abnormality of the current level. Indicates the degree of abnormality of the temperature level. They represent voltage level weight, current level weight, and temperature level weight respectively.

[0017] Preferably, judging whether a data point is abnormal data based on the comprehensive abnormality degree and monitoring the detection equipment includes: If the comprehensive abnormality level is greater than the abnormal threshold, it is abnormal data, otherwise it is normal data; after obtaining the abnormal data, the early warning device is triggered to monitor the detection equipment in real time.

[0018] In the second aspect, a battery automatic short circuit and reverse polarity detection device operation status monitoring system includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned battery automatic short circuit and reverse polarity detection device operation status monitoring method is implemented.

[0019] The present invention has the following effects: 1. The present invention adjusts the The algorithm can dynamically adjust according to the local density and level fluctuation of data points, thereby improving the accuracy of anomaly detection, more accurately identifying anomalies in the data, and reducing false detections and missed detections.

[0020] 2. The present invention can significantly improve the monitoring effect of the HWKS algorithm on the operating status of the battery automatic short circuit reverse polarity detection device based on the fluctuation degree and local density of data points at different levels. It not only improves the accuracy and robustness of abnormal detection, but also enhances the sensitivity and adaptability to changes in device status. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a method flow chart of steps S1 to S4 in the method for monitoring the operating status of a battery automatic short-circuit reverse polarity detection device according to an embodiment of the present invention.

[0022] Figure 2 It is a structural block diagram of the operating status monitoring system of the battery automatic short circuit reverse polarity detection device according to the embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] Reference Figure 1 The method for monitoring the operating status of a battery automatic short circuit reverse polarity detection device includes steps S1 to S4, which are as follows: S1: Acquire historical operation data of each level of the battery automatic short circuit reverse polarity detection device, wherein the historical operation data of each level includes: current, voltage, and temperature.

[0026] Further explanation, in this embodiment, a sensor is installed in a battery automatic short circuit reverse polarity detection device (hereinafter referred to as: detection device) to collect the current, voltage, temperature, etc. of the device. The historical operation data includes but is not limited to: current ,Voltage ,temperature Etc., where due to the different dimensions of historical operating parameters, the historical operating data are layered, with the current dimension data being the current layer, the voltage dimension data being the voltage layer, and the temperature dimension data being the temperature layer, and anomaly detection is performed on the data at each layer.

[0027] It should be noted that in the process of monitoring the operating status of the detection equipment, the operating parameters of the detection equipment belong to multi-dimensional data, and then the HWKS algorithm can be used to perform anomaly detection at different levels. The HWKS algorithm refers to a method based on hierarchical structure and weighted The K-Nearest Neighbors (K-NN) algorithm is used for anomaly detection or classification tasks. The algorithm mainly uses data points at different levels to The similarity of data points within the neighborhood is used to obtain the abnormal performance of the data point in this level.

[0028] S2: Based on the HWKS algorithm, the historical change rate and change frequency of the historical operation data at the level are used to calculate the fluctuation degree of the historical operation data at the level, and the fluctuation degree is used as the initial value of the HWKS algorithm. value.

[0029] Select historical data of a preset period of time at the data point collection time in the historical data, draw a line graph for the selected historical data, obtain extreme value points in the line graph, calculate the slope between adjacent extreme value points, and form a slope set; Specifically, the slope set satisfies the following relationship: ; In the formula, represents the current layer slope set, Indicates the first extreme point of the current layer line graph in the historical data. Indicates the second extreme point of the current layer line graph in the historical data, Indicates the third extreme point of the current layer line graph in the historical data, The first line graph of the current layer in the historical data An extreme point, The first line graph of the current layer in the historical data An extreme point, It represents the slope of the straight line between the second extreme point and the first extreme point. It represents the slope of the straight line between the third extreme point and the second extreme point. Indicates The extreme point and The slope of the straight line where the extreme point lies.

[0030] To further explain, in this embodiment, the preset time period is 1 hour before the data point collection time, and the extreme points in the line graph are local maximum and minimum values. Specifically, the slope between adjacent extreme points is the slope of the straight line formed by the two extreme points.

[0031] The square of the difference between the sum of the absolute values ​​of the positive slope and the sum of the absolute values ​​of the negative slope is taken as the slope difference, the square of the probability difference between the positive slope and the negative slope is taken as the probability difference, and the square root of the sum of the slope difference and the probability difference is taken to evaluate the volatility of the data at each level, and the volatility is used as the initial value.

[0032] Specifically, the initial The values ​​satisfy the following relationship: ; In the formula, Indicates the degree of fluctuation of data at each level. represents the cumulative sum of the positive slopes of the current layer slope concentration, represents the cumulative sum of the negative slopes of the current layer slope concentration, represents the probability of positive slope concentration in the current layer slope, It indicates the probability of negative slope in the current layer slope concentration.

[0033] Further explanation: Taking the current layer data as an example, the slope set of the current layer data is obtained. The larger the sum of the squares of the two differences in the slope set, the greater the fluctuation of the current layer slope set, and the greater the fluctuation of the current layer of the historical data corresponding to the data point at the time of acquisition. At this time, the initial The smaller the value, the smaller the impact of this fluctuation on the accuracy of the results of abnormal performance of data points in the current layer. In other words, the difference between the sum of the absolute values ​​of the positive slope and the negative slope is large, and the probability difference is also large, indicating that the volatility of the current layer slope is very high, and the amplitude and frequency of the data between rising and falling are uneven, resulting in a greater degree of fluctuation.

[0034] S3: Cluster the historical operation data of each level to obtain multiple clusters, obtain the degree of aggregation between each cluster, and calculate the local density of the cluster according to the distance difference between the cluster centers and the degree of aggregation between the clusters.

[0035] Select historical data of a preset period before the data point collection time in the historical data, cluster the selected local data to obtain several clusters, take any cluster as the target cluster, and obtain the mean distance between each data point in the target cluster and the cluster center of the target cluster as the degree of clustering; Further explanation: the preset time period is the historical data in the first minute before the data point is collected. For example, in the clustering result, the degree of clustering and the distance between clusters reflect the local density of the data point in the layer. The higher the degree of clustering and the closer the distance between clusters, the higher the density of this part of the data. Taking the local density acquisition process of data points in the current layer as an example, the acquisition method of the voltage layer and the temperature layer is the same.

[0036] Specifically, the degree of aggregation satisfies the following relationship: ; In the formula, Indicates The degree of clustering of clusters, Indicates The first Data and cluster centers The distance Indicates The number of data in a cluster.

[0037] Taking any cluster as the target cluster, obtaining the inverse of the distance between the cluster center of the target cluster and the cluster center of the adjacent cluster, and the inverse of the difference between the aggregation degree of the target cluster and the aggregation degree of the adjacent cluster; The inverse of the distance between cluster centers is taken as the weight of the inverse of the difference between the clustering degrees, and the weighted sum is taken as the local density of the cluster.

[0038] Specifically, the local density satisfies the following relationship: ; In the formula, represents the local density of the target cluster, Indicates The cluster centers of the clusters With The cluster centers of the nearest clusters of clusters The distance between Indicates The degree of clustering of clusters, Indicates The degree of clustering of the nearest neighbor clusters of clusters, Indicates the total number of clusters.

[0039] That is to say, the smaller the difference in the degree of aggregation between each cluster and its nearest neighbor cluster, the higher the local density of the data point. The closer the distance between the cluster and the cluster center of its nearest neighbor cluster, the greater the corresponding weight of the cluster. For example, taking the current layer data as an example, the higher the local density of the data point in the current layer, the smaller the weight required. The value better captures local anomalies, so the initial The value is corrected.

[0040] S4: Initialization based on local density The corrected value was obtained. value, will be significant The value is used as the parameter value in the HWKS algorithm to obtain the abnormality degree of the data at each level, and the abnormality degree of the data at each level is weighted and summed to obtain the comprehensive abnormality degree of the data at each level. Based on the comprehensive abnormality degree, it is determined whether the data point is abnormal data, and the detection equipment is monitored.

[0041] Further explanation: In this embodiment, the HWKS algorithm is well known to those skilled in the art, and the specific implementation steps are not described in detail again.

[0042] Significant The values ​​satisfy the following relationship: ; In the formula, Indicates the significance of each level of data value, represents the preset hyperparameters, represents the local density of the target cluster, Indicates the initial state of each level of data value, Indicates An exponential function with base .

[0043] That is to say, in this embodiment, , which can be adjusted according to the actual scenario.

[0044] The comprehensive abnormality degree satisfies the following relationship: ; In the formula, Indicates the overall abnormality level. Indicates the degree of voltage level abnormality. Indicates the abnormality of the current level. Indicates the degree of abnormality of the temperature level. They represent voltage level weight, current level weight, and temperature level weight respectively.

[0045] That is to say, in this embodiment, .

[0046] If the comprehensive abnormality level is greater than the abnormal threshold, it is abnormal data, otherwise it is normal data; after obtaining the abnormal data, the early warning device is triggered to monitor the detection equipment in real time.

[0047] In this embodiment, the abnormal threshold is 1, which can be adjusted according to those skilled in the art.

[0048] The present invention also provides a battery automatic short circuit reverse polarity detection device operation status monitoring system. Figure 2 As shown, the system includes a processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method for monitoring the operating status of the battery automatic short circuit reverse polarity detection device according to the first aspect of the present invention is implemented.

[0049] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0050] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0051] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0052] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for monitoring the operating status of a battery automatic short circuit reverse polarity detection device, characterized in that: include: Acquire historical operation data of each level of the battery automatic short circuit reverse polarity detection device, wherein the historical operation data of each level includes: current, voltage, and temperature; Based on the HWKS algorithm, the historical change rate and change frequency of the historical operation data at the level are used to calculate the fluctuation degree of the historical operation data at the level, and the fluctuation degree is used as the initial value of the HWKS algorithm. value; Cluster the historical operation data of each level to obtain multiple clusters, obtain the degree of aggregation between each cluster, and calculate the local density of the cluster according to the distance difference between the cluster centers and the degree of aggregation between the clusters; According to the local density The corrected value was obtained. value, will be significant The value is used as the parameter value in the HWKS algorithm to obtain the abnormality degree of the data at each level, and the abnormality degree of the data at each level is weighted and summed to obtain the comprehensive abnormality degree of the data at each level. Based on the comprehensive abnormality degree, it is determined whether the data point is abnormal data, and the detection equipment is monitored.

2. The method for monitoring the operating status of a battery automatic short circuit reverse polarity detection device according to claim 1, characterized in that: The initial Values, including steps: Select historical data of a preset period of time at the data point collection time in the historical data, draw a line graph for the selected historical data, obtain extreme value points in the line graph, calculate the slope between adjacent extreme value points, and form a slope set; The square of the difference between the sum of the absolute values ​​of the positive slope and the sum of the absolute values ​​of the negative slope is taken as the slope difference, and the square of the probability difference between the positive slope and the negative slope is taken as the probability difference. The square root of the sum of the slope difference and the probability difference is taken to evaluate the fluctuation degree of the data at each level, and the fluctuation degree is taken as the initial value.

3. The method for monitoring the operating status of a battery automatic short circuit reverse polarity detection device according to claim 1, characterized in that: The step of obtaining the degree of aggregation between the clusters includes: Select historical data of a preset period before the data point collection time in the historical data, cluster the selected local data, obtain several clusters, take any cluster as the target cluster, and obtain the mean distance between each data point in the target cluster and the cluster center of the target cluster as the degree of clustering.

4. The method for monitoring the operating status of a battery automatic short circuit reverse polarity detection device according to claim 1, characterized in that: The local density of the clusters includes: Taking any cluster as the target cluster, obtaining the inverse of the distance between the cluster center of the target cluster and the cluster center of the adjacent cluster, and the inverse of the difference between the aggregation degree of the target cluster and the aggregation degree of the adjacent cluster; The inverse of the distance between cluster centers is taken as the weight of the inverse of the difference between the clustering degrees, and the weighted sum is taken as the local density of the cluster.

5. The method for monitoring the operating status of a battery automatic short circuit reverse polarity detection device according to claim 1, characterized in that: The significant The values ​​satisfy the following relationship: ； In the formula, Indicates the significance of each level of data value, represents the preset hyperparameters, represents the local density of the target cluster, Indicates the initial state of each level of data value, Indicates An exponential function with base .

6. The method for monitoring the operating status of a battery automatic short circuit reverse polarity detection device according to claim 1, characterized in that: The comprehensive abnormality degree satisfies the following relationship: ； In the formula, Indicates the overall abnormality level. Indicates the degree of voltage level abnormality. Indicates the abnormality of the current level. Indicates the degree of abnormality of the temperature level. They represent voltage level weight, current level weight, and temperature level weight respectively.

7. The method for monitoring the operating status of a battery automatic short circuit reverse polarity detection device according to claim 1, characterized in that: Determine whether a data point is abnormal data based on the comprehensive abnormality level, and monitor the detection equipment, including: If the comprehensive abnormality level is greater than the abnormal threshold, it is abnormal data, otherwise it is normal data; after obtaining the abnormal data, the early warning device is triggered to monitor the detection equipment in real time.

8. Battery automatic short circuit reverse polarity detection equipment operation status monitoring system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method for monitoring the operating status of a battery automatic short-circuit reverse polarity detection device according to any one of claims 1 to 7 is implemented.

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