Battery production charging and discharging data collection method and system
By adaptively calculating the filter gain in the battery charge and discharge data and combining clustering processing, the problems of low data filtering efficiency and insufficient accuracy in the prior art are solved, and efficient and accurate data acquisition is achieved.
Patent Information
- Application Number
- CN202411561797.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-05
- Publication Date
- 2025-01-03
- Estimated Expiration
- 2044-11-05
AI Technical Summary
In the prior art, the battery charging and discharging data filtering method is low in efficiency, and the filtered data is insufficient, so it cannot adapt to the noise characteristics of different application scenarios, resulting in loss of effective data and large calculation amount.
By obtaining the performance data sequence of the battery during charging and discharging, determining the noise level and change rate of the data segment, calculating the filter gain using exponential function and linear regression, performing adaptive H infinite filtering, and combining clustering processing to improve the accuracy of the filtering result sex.
It improves the efficiency and accuracy of data filtering, ensures the accuracy and response speed of the final collected data, and adapts to the noise characteristics of different scenarios.
Smart Images
Figure CN119064794B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data processing technology, and more specifically, to a method and system for collecting battery production charge and discharge data. Background Art
[0002] In clean energy systems, batteries play a vital role in storing electricity generated by clean energy (such as solar or wind energy) for use when energy production is insufficient or demand peaks. In order to ensure the performance and safety of batteries during charging and discharging, it is usually necessary to collect and analyze relevant data during the charging and discharging process. Therefore, it is particularly important to collect this data accurately and efficiently.
[0003] In the related art, a satellite battery current data denoising method and device are disclosed in a patent application document with publication number CN112732684A. The method includes: obtaining current change characteristic data of n consecutive sampling points based on the satellite battery current data of n consecutive sampling points; and removing n consecutive sampling points, one sampling point before n consecutive sampling points, and one sampling point after n consecutive sampling points from the satellite battery current data based on the current change characteristic data of the sampling points in the n consecutive sampling points and filtering conditions.
[0004] However, the filtering conditions set in the above scheme are fixed, and the noise characteristics of different application scenarios may be different. The same filtering condition may not be able to adapt to all situations, and directly removing the data may cause the loss of valid data, reducing the accuracy of the processed data. In addition, there are many filtering conditions set, and each filtering condition involves multiple parameters. The amount of calculation required for different scenarios is large, and the efficiency is low. Summary of the invention
[0005] In order to solve the problems of low efficiency of data filtering and low accuracy of filtered data, the present invention provides a method and system for collecting battery production charge and discharge data.
[0006] According to a first aspect of the present invention, a method for collecting battery production charge and discharge data is provided, comprising:
[0007] Obtaining a performance data sequence of the battery during the charging and discharging process, determining a change point in the performance data sequence that is closest to the current data, and using a data segment consisting of the current data and the change point as the current data segment, wherein the change point is a data point that is different from the overall change trend of the previous multiple data;
[0008] Calculate the noise level of the current data segment, take the noise level as the power of an exponential function with a preset value as the base, and perform power operation to obtain the filter gain of the current data segment;
[0009] Perform linear regression on the current data segment to obtain a regression line, calculate the reciprocal of the average value of the distance between the data in the current data segment and the regression line, obtain the degree of fit of the regression line, and take the product of the slope of the regression line and the degree of fit as the rate of change of the current data segment;
[0010] Update filter gain, updated filter gain , is the filter gain before updating, is the rate of change, is the default value, The natural constant The exponential function with the base is used to filter the current data segment using the updated filter gain. Infinite filtering is performed to obtain the filtering result of the current data, and the filtering result is used as the final collected data.
[0011] The present invention can adaptively adjust the filter gain when performing H-infinity filtering on the current data segment, and in the process of adjusting the filter gain, the noise level and the change rate of the current data segment are integrated, so that the current data segment can be accurately subjected to H-infinity filtering while improving the response speed, thereby improving the accuracy of the final collected data determined.
[0012] Preferably, calculating the noise level of the current data segment includes:
[0013] Determine the optimal number of clusters when clustering the current data segment, use a positive integer between 1 and the optimal number of clusters as a reference value, use each reference value as the number of clusters when clustering the current data segment, perform clustering processing on the current data segment multiple times, and obtain a clustering result for each clustering;
[0014] Calculate the average value of the distance between the cluster center and the nearest neighbor cluster center after any clustering is completed, and obtain the separation degree of the clustering result of any clustering;
[0015] The first-order difference of the separation degree of the clustering result of each clustering is performed to obtain a first-order difference sequence, the cumulative sum of each differential data in the first-order difference sequence is calculated, and the opposite number of the cumulative sum is normalized to obtain the noise level of the current data segment.
[0016] The present invention utilizes the fact that when there is less noise data, the clustering process can capture the characteristics of clear local structures and patterns, thereby ensuring the accuracy of determining the noise level.
[0017] Preferably, the distance between the cluster center and the nearest neighbor cluster center is a comprehensive distance, and a method for obtaining the comprehensive distance includes:
[0018] Get the nearest neighbor cluster of any cluster after any clustering is completed, calculate the average value of the distance between the data points in the nearest neighbor cluster and the cluster center of the nearest neighbor cluster, and take the reciprocal to obtain the clustering goodness of the nearest neighbor cluster;
[0019] The clustering goodness and the distance between the cluster center of any cluster and the cluster center of the nearest neighbor cluster are multiplied to obtain the comprehensive distance between the cluster center of any cluster and the cluster center of the nearest neighbor cluster.
[0020] The comprehensive distance determined by the present invention can accurately measure the separation degree of the clustering results of each clustering.
[0021] Preferably, the separation degree of the clustering results of any clustering satisfies the following relationship:
[0022] ;
[0023] In the formula, For the The separation degree of the clustering results of the sub-clustering; For the After the clustering is completed, the The cluster center of the cluster is After the clustering is completed, the The distance between the cluster centers of the nearest neighbor clusters of a cluster; For the After the clustering is completed, the The clustering goodness of the nearest neighbor cluster of each cluster; for the summation symbol; For the The number of clusters obtained after the clustering is completed.
[0024] Preferably, the noise level of the current data segment satisfies the following relationship:
[0025] ;
[0026] In the formula, is the noise level of the current data segment; is the cumulative sum of each difference data in the first-order difference sequence; The natural constant An exponential function with base .
[0027] The present invention adopts a normalization method, which can limit the noise level of the current data segment to within 0-1, thereby reducing the calculation difficulty when determining the filter gain based on the noise level.
[0028] Preferably, determining the change point in the performance data sequence that is closest to the current data includes:
[0029] Perform change point detection on the performance data sequence to obtain all change points in the performance data sequence, and take the change point with the shortest time interval between the corresponding sampling moment and the current moment as the change point in the performance data sequence closest to the current data.
[0030] The present invention can ensure that the change trend of the data in the determined current data segment is close to linear, so that the current data segment can be filtered by using the H-infinity filtering method to obtain the filtered current data.
[0031] Preferably, performing linear regression on the current data segment to obtain a regression line includes:
[0032] The least square method is used to perform linear regression on the current data segment to obtain the fitting line of the current data segment, and the fitting line is used as the regression line.
[0033] According to a second aspect of the present invention, a battery production charge and discharge data acquisition system is provided. The battery production charge and discharge data acquisition system includes a memory and a processor. The memory stores a computer program. The processor executes the computer program to implement the steps of the first aspect of the present invention.
[0034] The present invention has the following effects:
[0035] The change trend of the data in the current data segment determined by the present invention is close to linear, so that the current data segment can be filtered using the H-infinity filtering method with strong robustness, thereby ensuring the accuracy of the determined filtered data; and in combination with the noise level and the change rate of the current data segment, the filter gain when the H-infinity filtering is performed on the current data segment is adaptively adjusted, so that the efficiency of filtering the data can be improved while further improving the accuracy of the obtained filtered data, that is, the accuracy of the final collected data. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] 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:
[0037] Figure 1 It is a schematic diagram of the steps of the method for collecting battery production charge and discharge data according to an embodiment of the present invention. DETAILED DESCRIPTION
[0038] 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.
[0039] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.
[0040] Reference Figure 1 The method for collecting battery production charge and discharge data includes steps S1 to S4, which are as follows:
[0041] S1: Obtain the performance data sequence of the battery during the charging and discharging process, determine the change point in the performance data sequence that is closest to the current data, and use the data segment consisting of the current data and the change point as the current data segment. The change point is a data point that is different from the overall change trend of the previous multiple data.
[0042] In an exemplary embodiment of the present invention, the performance data sequence refers to a sequence of any relevant data collected during the charging and discharging process of the battery, which is used to reflect the performance and state of the battery. The performance data sequence may be a current data sequence, a temperature data sequence, a voltage data sequence, a resistance data sequence, etc. This embodiment does not specifically limit the data type of the performance data sequence.
[0043] Optionally, a current sensor may be used to collect current data generated during the charging and discharging process of the battery within a certain period of time and at a certain collection frequency, such as 1 Hz, to obtain a current data sequence.
[0044] A change point refers to a data point in a performance data sequence that is inconsistent with the change trend of multiple previous data, and is used to divide the performance data sequence into multiple data segments with similar or close change trends. It should be noted that during the charging and discharging process of the battery, it will be affected by multiple factors, such as internal pressure, electrolyte concentration, external charging current or voltage, ambient temperature, etc., and the interaction between multiple influencing factors may cause the collected performance data sequence to appear nonlinear, but the change of data is close to linear in a short time or within a small operating range. Therefore, this embodiment uses change points to determine the data segment in the performance data sequence whose overall change trend is close to linear.
[0045] In an exemplary embodiment of the present invention, the following steps may be used to determine the change point closest to the current data in the performance data sequence:
[0046] Perform change point detection on the performance data sequence to obtain all change points in the performance data sequence, and take the change point with the shortest time interval between the corresponding sampling moment and the current moment as the change point in the performance data sequence closest to the current data.
[0047] Further, after determining the change point closest to the current data in the performance data sequence, the data segment between the change point and the current data can be used as the current data segment. It should be noted that the specific process of detecting the change point of the data segment is a prior art and will not be described in detail in this embodiment.
[0048] S2: Calculate the noise level of the current data segment, and use the noise level as the power of an exponential function with a preset value as the base, and perform a power operation to obtain the filter gain of the current data segment.
[0049] In an exemplary embodiment of the present invention, the noise level refers to data used to reflect the amount of noise data in the current data segment. For example, if the noise data in the current data segment is relatively large, the noise level of the current data segment is relatively large.
[0050] The filter gain refers to the parameter used to determine the filter's response strength to the measured data and noise data when the H-infinity filter is used for filtering. When the filter gain is high, the filter is relatively more sensitive to the measured data and can respond to system changes more quickly, but it may also be more susceptible to noise; on the contrary, when the filter gain is low, the sensitivity to noise data can be reduced, but the response to dynamic changes in the system is also relatively slow.
[0051] It should be noted that H-infinity filtering is a method for filtering a linearly changing data stream. Although it has high robustness for various types of data during battery charging and discharging, it is not suitable for nonlinear changes of various types of data during battery charging and discharging. However, the method for determining the current data segment in the present invention can ensure that the change of data in the current data segment is close to linear, so that the current data segment can be filtered using the H-infinity filtering method.
[0052] Optionally, the preset value can be set to 10 based on empirical values, when determining the noise level of the current data segment After that, the filter gain of the current data segment is , this embodiment does not specifically limit the setting of the preset value.
[0053] In an exemplary embodiment of the present invention, the determination of the noise level of the current data segment may be achieved by the following steps:
[0054] Step 1: Determine the optimal number of clusters when clustering the current data segment, use a positive integer between 1 and the optimal number of clusters as a reference value, use each reference value as the number of clusters when clustering the current data segment, perform multiple clustering processes on the current data segment, and obtain the clustering result of each clustering;
[0055] It should be noted that in the process of clustering the data set, as the number of clusters increases, the clustering algorithm will try to find more cluster centers in the data set to divide the data points more finely, thereby capturing more local structures and patterns in the data set. If there is less noise data in the data set, the local structures and patterns captured by the clustering algorithm will be clearer. Therefore, this feature can be used to perform multiple clustering processes on the current data segment, so that the noise level of the current data segment can be evaluated based on the clustering results of each clustering.
[0056] The optimal number of clusters refers to the optimal k value when clustering the current data segment. The optimal number of clusters can be determined using the elbow method or the silhouette coefficient method. This embodiment does not specifically limit the method for determining the optimal number of clusters.
[0057] Optionally, the current data segment can be clustered using a K-means clustering algorithm, or a DBSCAN clustering algorithm. Of course, a suitable clustering algorithm can be selected for clustering according to specific circumstances. This embodiment does not specifically limit the selection of clustering algorithms.
[0058] Further, after determining the optimal k value for clustering the current data segment, a positive integer between 1 and k can be used as a reference value, wherein the reference value includes 1 and the optimal k value, that is, the reference value is a sequence {1, 2, ..., k}.
[0059] Furthermore, when it is determined that the optimal k value for clustering the current data segment is 5, each value in the sequence {1,2,3,4,5} is used as the number of clusters for clustering the current data segment, that is, the current data segment is clustered with cluster numbers 1, 2, 3, 4 and 5, respectively, to obtain the clustering results of each clustering.
[0060] Step 2: Calculate the average value of the distance between the cluster center and the nearest neighbor cluster center after any clustering is completed, and obtain the separation degree of the clustering result of any clustering;
[0061] The separation degree refers to the data that can reflect the dispersion of the clusters obtained after clustering is completed, and is used to evaluate the impact of noise data in the current data segment. For example, when the distance between the center of each cluster and the center of the corresponding nearest neighbor cluster is large after any clustering is completed, it means that the clusters obtained after the clustering is completed are relatively dispersed, which can indicate that the local structure and pattern captured in the clustering process are relatively clear, and further, it can be explained that there is less noise data in the current data set.
[0062] Optionally, the distance between any cluster center after any clustering is completed and the corresponding nearest neighbor cluster center can be Euclidean distance, Manhattan distance, Chebyshev distance, etc. This embodiment does not specifically limit the type of distance.
[0063] In an exemplary embodiment of the present invention, the distance between the cluster center and the nearest neighbor cluster center is a comprehensive distance, and the comprehensive distance can be determined by the following steps:
[0064] Obtain the nearest neighbor cluster of any cluster after any clustering is completed, calculate the average value of the distance between the data points in the nearest neighbor cluster and the cluster center of the nearest neighbor cluster, and take the reciprocal to obtain the clustering goodness of the nearest neighbor cluster; multiply the clustering goodness and the distance between the cluster center of any cluster and the cluster center of the nearest neighbor cluster to obtain the comprehensive distance between the cluster center of any cluster and the cluster center of the nearest neighbor cluster.
[0065] Specifically, After the clustering is completed, the The cluster center of the cluster is After the clustering is completed, the The comprehensive distance between the cluster centers of the nearest neighbor clusters of a cluster ,in, For the After the clustering is completed, the The cluster center of the cluster is After the clustering is completed, the The distance between the cluster centers of the nearest neighbor clusters of a cluster, For the After the clustering is completed, the The clustering goodness of the nearest neighbor cluster of a cluster.
[0066] Furthermore, after determining the comprehensive distance between the cluster center of any cluster after any clustering is completed and the nearest neighbor cluster center of the cluster, the separation degree of the clustering result of this clustering can be calculated. Specifically, the separation degree of the clustering result of any clustering satisfies the following relationship:
[0067] ;
[0068] In the formula, For the The separation degree of the clustering results of the sub-clustering; For the After the clustering is completed, the The cluster center of the cluster is After the clustering is completed, the The distance between the cluster centers of the nearest neighbor clusters of a cluster; For the After the clustering is completed, the The clustering goodness of the nearest neighbor cluster of each cluster; for the summation symbol; For the The number of clusters obtained after the clustering is completed.
[0069] Step 3: Perform first-order difference on the separation degree of the clustering results of each clustering to obtain a first-order difference sequence, calculate the cumulative sum of each differential data in the first-order difference sequence, normalize the opposite of the cumulative sum, and obtain the noise level of the current data segment.
[0070] First, the separation degree sequence of the clustering results from the first clustering to the last clustering can be recorded as ,in, , as well as The separation degree of the clustering results obtained when the current data is clustered with the number of clusters being 1, the number of clusters being 2, and the number of clusters being the optimal k value; then, the first-order difference sequence of the separation degree sequence is calculated ,in, , , , finally, the first-order difference sequence is summed to obtain the cumulative sum of the first-order difference sequence.
[0071] Furthermore, after determining the cumulative sum of the first-order difference sequence of the separation degree of the clustering results of each clustering, the noise level of the current data segment can be calculated. Specifically, the noise level of the current data segment satisfies the following relationship:
[0072] ;
[0073] In the formula, is the noise level of the current data segment; is the cumulative sum of each difference data in the first-order difference sequence; The natural constant An exponential function with base .
[0074] Alternatively, other normalization methods can be used, such as using , the inverse number of the cumulative sum of the first-order difference sequence is normalized. This embodiment does not specifically limit the normalization method.
[0075] S3: Perform linear regression on the current data segment to obtain a regression line, calculate the inverse of the average value of the distance between the data in the current data segment and the regression line, obtain the degree of fit of the regression line, and take the product of the slope of the regression line and the degree of fit as the rate of change of the current data segment.
[0076] In an exemplary embodiment of the present invention, the regression line of the current data segment can be determined by the following steps:
[0077] The least square method is used to perform linear regression on the current data segment to obtain the fitting line of the current data segment, and the fitting line is used as the regression line.
[0078] Optionally, a gradient descent method may be used to perform linear regression on the current data segment. This embodiment does not specifically limit the selected linear regression method.
[0079] Furthermore, after determining the regression line of the current data segment, the slope of the regression line can be used as the rate of change of the data in the current data segment. It should be noted that since the change trend of the data in the current data segment is close to linear, the slope of the determined regression line can characterize the rate of change of the data in the current data segment.
[0080] Furthermore, since there is a certain fitting error when performing linear regression on the current data segment, the fitting degree of the regression line of the current data segment is used as the credibility of the slope of the regression line, which can further ensure the accuracy of the determined change rate of the current data segment.
[0081] S4: Update the filter gain and use the updated filter gain to filter the current data segment. Infinite filtering is performed to obtain the filtering result of the current data, and the filtering result is used as the final collected data.
[0082] Specifically, the updated filter gain satisfies the following relationship:
[0083] ;
[0084] In the formula, is the updated filter gain of the current data segment; is the filter gain before updating; is the rate of change of the current data segment; is a preset value. , The natural constant An exponential function with base .
[0085] Among them, if the change rate of the current data segment is positive, it means that the data in the current data segment is positively increasing, and a larger filter gain needs to be set to respond to the changing trend of the data in the current data segment. On the contrary, if the change rate of the current data segment is negative, it means that the data in the current data segment is gradually decreasing, and a smaller filter gain needs to be set, so as to save computing costs. The larger the absolute value of the change rate of the current data segment, the greater the correction amplitude of the filter gain before updating.
[0086] Further, after determining the updated filter gain, the filter gain can be used as the filter gain when performing H-infinity filtering on the current data segment to obtain the filtering result of the current data, and the filtering result is used as the final collected data. It should be noted that the process of performing H-infinity filtering on the data segment to obtain the filtering result of the current data is a prior art, and this embodiment will not be described in detail here. In addition, the present invention only changes the determination of the filter gain when performing H-infinity filtering on the current data segment, and does not change the determination method of other parameters in the filtering method.
[0087] Furthermore, the final collected data with higher accuracy can be applied to actual scenarios, such as monitoring the status of the battery, or performing performance analysis on the battery, or optimizing the control strategy of battery charging and discharging.
[0088] The present invention also provides a battery production charge and discharge data acquisition system, the system includes a memory and a processor, and a computer program is stored in the memory, the computer program integrates the functions of the battery production charge and discharge data acquisition method, when the computer program is executed, the accuracy of the collected data can be improved through the battery production charge and discharge data acquisition method.
[0089] 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.
[0090] 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 collecting battery production charge and discharge data, characterized in that: include: Acquire a performance data sequence of the battery during the charging and discharging process, determine a change point in the performance data sequence that is closest to the current data, and use a data segment consisting of the current data and the change point as the current data segment, wherein the change point is a data point that is different from the overall change trend of the previous multiple data; Calculating the noise level of the current data segment, taking the noise level as the power of an exponential function with a preset value as the base, and performing a power operation to obtain a filter gain of the current data segment; Performing linear regression on the current data segment to obtain a regression line, calculating the reciprocal of the average value of the distance between the data in the current data segment and the regression line to obtain the degree of fit of the regression line, and taking the product of the slope of the regression line and the degree of fit as the rate of change of the current data segment; Update the filter gain, the updated filter gain , is the filter gain before updating, is the rate of change, is the preset value, The natural constant The exponential function with the base is used to filter the current data segment using the updated filter gain. Infinite filtering is performed to obtain a filtering result of the current data, and the filtering result is used as the final collected data.
2. The battery production charge and discharge data acquisition method according to claim 1, characterized in that: The calculating the noise level of the current data segment comprises: Determine the optimal number of clusters when clustering the current data segment, use a positive integer between 1 and the optimal number of clusters as a reference value, use each reference value as the number of clusters when clustering the current data segment, perform clustering processing on the current data segment multiple times, and obtain a clustering result of each clustering; Calculate the average value of the distance between the cluster center and the nearest neighbor cluster center after any clustering is completed, and obtain the separation degree of the clustering result of any clustering; The first-order difference is performed on the separation degree of the clustering result of each clustering to obtain a first-order difference sequence, the cumulative sum of each differential data in the first-order difference sequence is calculated, and the inverse of the cumulative sum is normalized to obtain the noise level of the current data segment.
3. The battery production charge and discharge data acquisition method according to claim 2, characterized in that: The distance between the cluster center and the nearest neighbor cluster center is a comprehensive distance, and the method for obtaining the comprehensive distance includes: Obtain the nearest neighbor cluster of any cluster after any clustering is completed, calculate the average value of the distance between the data points in the nearest neighbor cluster and the cluster center of the nearest neighbor cluster, and take the reciprocal to obtain the clustering goodness of the nearest neighbor cluster; The clustering goodness and the distance between the cluster center of any cluster and the cluster center of the nearest neighbor cluster are multiplied to obtain the comprehensive distance between the cluster center of any cluster and the cluster center of the nearest neighbor cluster.
4. The battery production charge and discharge data acquisition method according to claim 3 is characterized in that: The separation degree of the clustering results of any clustering satisfies the following relationship: ； In the formula, For the The separation degree of the clustering results of the sub-clustering; For the After the clustering is completed, the The cluster center of the cluster is After the clustering is completed, the The distance between the cluster centers of the nearest neighbor clusters of a cluster; For the After the clustering is completed, the The clustering goodness of the nearest neighbor cluster of each cluster; for the summation symbol; For the The number of clusters obtained after the clustering is completed.
5. The battery production charge and discharge data acquisition method according to claim 2, characterized in that: The noise level of the current data segment satisfies the following relationship: ； In the formula, is the noise level of the current data segment; is the cumulative sum of each difference data in the first-order difference sequence; The natural constant An exponential function with base .
6. The battery production charge and discharge data collection method according to claim 1, characterized in that: The determining of the change point in the performance data sequence that is closest to the current data includes: Perform change point detection on the performance data sequence to obtain all change points in the performance data sequence, and take the change point with the shortest time interval between the corresponding sampling moment and the current moment as the change point in the performance data sequence closest to the current data.
7. The method for collecting battery production charge and discharge data according to claim 1, characterized in that: The performing linear regression on the current data segment to obtain a regression line includes: The current data segment is subjected to linear regression using the least square method to obtain a fitting straight line of the current data segment, and the fitting straight line is used as the regression straight line.
8. Battery production charge and discharge data acquisition system, characterized by: The battery production charge and discharge data acquisition system includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the battery production charge and discharge data acquisition method as described in any one of claims 1-7.