Method for screening out fault equipment aiming at truck lithium battery system analysis data information

Abstract

The invention discloses a method for screening out fault equipment aiming at analysis data information of a truck lithium battery system, realizes analysis processing operation on battery data information, screens out corresponding fault results, and relates to the technical field of battery monitoring. According to the method for screening out the fault equipment aiming at the analysis data information of the truck lithium battery system, the data information is preprocessed, compensation of missing data and removal of abnormal data are achieved, a fault analysis model is established, the preprocessed data are introduced into the fault analysis model to be analyzed, and the fault equipment is screened out. Data are processed in parallel through different screening modes to complete extraction of fault data, so that parallel processing operation of multiple screening modes is achieved, the accuracy of data result screening can be improved, the efficiency of screening operation can be improved, meanwhile, screening of fault equipment data and matching of results are effectively completed, and the efficiency of data processing is improved. And rapid positioning and problem feedback and processing are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of battery monitoring, and in particular to a method for analyzing data information of a truck lithium battery system to screen out faulty equipment. Background Art

[0002] At present, truck lithium batteries are gradually replacing lead-acid batteries to become the mainstream truck batteries. With the increasing use of lithium batteries, the failure problems caused by various factors will also increase. If the failure cannot be discovered and handled in time, it will lead to safety problems in truck operation.

[0003] The reference patent name is: A lithium battery fault data screening method constrained by the normalized mutual information criterion (patent publication number: CN110412467A, patent publication date: 2019-11-05). The method includes the following steps: S1: Data collection: Collect real lithium battery fault data through sensors, and use the perception generation network to generate candidate lithium battery fault data; S2: Use fractional Fourier transform to extract the characteristics of lithium battery fault data; S3: Use normalized mutual information as the screening measure for lithium battery fault data, and calculate the normalized mutual information between the real lithium battery fault feature matrix A and the candidate lithium battery fault feature matrix B; S4: Use fault diagnosis experiments to select the screening threshold, which can make the screened lithium battery fault data real and effective, and at the same time improve the screening speed, providing high-quality data guarantee for the deep learning method of fault diagnosis.

[0004] Based on the description in the above-mentioned documents, when the existing lithium battery system implements data analysis operations, it is not only prone to data missing or anomalies when analyzing a large amount of data, but also the processing efficiency is relatively slow. At the same time, the processing process does not combine multiple screening methods to achieve processing, and the accuracy of the data cannot be guaranteed. By the time the value exceeds the threshold, the battery may have failed, and the failure to analyze and predict the failure cannot be played. For this reason, the present invention provides a method for analyzing data information for a truck lithium battery system to screen out faulty equipment. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for analyzing data information of a truck lithium battery system to screen out faulty equipment, which solves the problem that when the existing lithium battery system implements data analysis operations, it analyzes a large amount of data, which is not only prone to data missing or anomalies, but also has a relatively slow processing efficiency. At the same time, the processing process does not combine multiple screening methods to achieve processing, fails to guarantee data accuracy, and fails to play a role in analyzing and predicting faults.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for analyzing data information of a truck lithium battery system to screen out faulty equipment, specifically comprising the following steps:

[0007] A1. Collect relevant information about the battery charging and discharging process and environmental parameter data through the BMS system of the lithium battery equipment, transmit the data information, and store the data information in the database;

[0008] A2. Implement analysis and processing of data information to filter out corresponding fault results. The specific processing operations are as follows:

[0009] a21. Complete the preprocessing operation of data information to compensate for missing data and remove abnormal data;

[0010] a22. Establish a fault analysis model, and introduce the pre-processed data into the fault analysis model for analysis, and process the data in parallel through different screening methods to complete the extraction of fault data;

[0011] a23, then transmit the marked fault data to match the fault result;

[0012] A3. Extract data based on the historical database, and generate solutions to known faults based on the corresponding fault results and the extracted historical data. Unmatched fault results are fed back to after-sales service for timely repairs.

[0013] Preferably, the data preprocessing operation in a21 is:

[0014] B1. Classify the collected data information according to the data categories corresponding to the battery, and sort the data according to the time sequence of collection;

[0015] B2. Identify the classified data, trace and compensate the data in a single category, and mark the abnormal data and trace and replace it;

[0016] B3. After the preprocessing operation is completed, the data is classified to form the required initial data set, which is marked as M.

[0017] Preferably, the operation of performing parallel processing of data by different screening methods and then completing fault data extraction in a22 is:

[0018] C1. Import the initial data set M into the cloud backend, analyze large amounts of cloud data through Python scripts, and filter out lithium battery equipment data that is faulty or meets the screening conditions;

[0019] C2. At the same time, by using database query statements, filter the data in the cloud backend, export the lithium battery equipment data that meets the query conditions and locate the problem;

[0020] C3. Then the extracted faulty equipment data is extracted and combined for transmission.

[0021] Preferably, the screening step in C1 is:

[0022] c11. Through Python script analysis, set the template table of the required evaluation data. The template table is set with the types of batteries of different devices as the table row titles and the parameter categories corresponding to the batteries as the table column titles.

[0023] c12, and mapping the data information in the initial data set M to the template table for matching, so as to achieve corresponding filling of the parameter values ​​in the template table, and form the required parameter data table marked as L;

[0024] c13. Then, according to the historical data in the database, the limit threshold value of the battery during normal operation is extracted, and the prediction threshold value set is adjusted according to the limit threshold value. The prediction threshold value set is used as the judgment condition to compare with the parameters in the parameter data table L, and the battery data of the corresponding faulty equipment that meets the judgment condition is extracted and marked as P;

[0025] The prediction threshold is within the range of the limit threshold and is used to predict the node value of the battery data that will exceed the limit threshold, and the judgment condition is the data outside the normal range of the prediction threshold.

[0026] Preferably, the corresponding filling operation in c12 to realize the parameter value in the template table is:

[0027] c121, extracting the data of the types of batteries of different devices in the initial data set M, and then matching them with the table row header content of the template table;

[0028] c122. Extract the device battery parameter category information in the initial data set M based on the determined table row header, and pair it with the table column header content of the template table. Then fill the corresponding parameter data in the initial data set M into the parameter column where the paired table row header and table column header intersect, and finally form the parameter data table L;

[0029] c123. Use the recognition window function to check the parameter data table L to process the blank column and abnormal column data.

[0030] Preferably, the identification window function in c123 performs the check to realize the processing operation of the blank column and abnormal column data as follows:

[0031] d1. Set the window function to F(x, y) to identify the contents of the window range. x is the length of the window, y is the width of the window, and F(x, y) starts to identify and move from the parameter corresponding to the first column title of the self-title to the parameter corresponding to the last column title, and then switches to the parameter corresponding to the first column title of the second row title for identification;

[0032] d2. Check the contents of the recognition window range with the data in the initial data set M. If they are consistent, F(x, y) moves horizontally by a distance x.

[0033] When the content of the recognition window range is blank, it is a blank column. Check whether there is data under the corresponding row title and column title in the initial data set M. If so, fill it into the corresponding blank column. Otherwise, it is confirmed that there is no parameter data. F(x, y) moves horizontally by a distance x.

[0034] When the content of the recognition window range is an error code exception, it is an exception column, confirming the data under the corresponding row title and column title in the initial data set M, and calculating the parameter data length to implement the expansion operation of the exception column, and then filling the corresponding data into the expanded exception column, and then F (x, y) moves horizontally by the window distance of the current expansion;

[0035] d3. After all parameter columns have been verified, the required parameter data table L is obtained.

[0036] Preferably, the extended calculation operation of the abnormal column in d2 is:

[0037] The number of characters in the corresponding row and column headers is calculated as k, and the length of each character is recorded as u, and the distance between characters is v, so the length of the parameter data h is calculated as:

[0038] h=k×u+(k-1)×v;

[0039] The expansion of the corresponding abnormal column needs to be adjusted based on the parameter data, and the expansion distance H is:

[0040] H = h - x;

[0041] x is the window length before expansion, and its value is consistent with the initial length of the abnormal column.

[0042] Preferably, the screening step in C2 is:

[0043] c21. By referring to the processed parameter data table L, the required query fields are set, and the query fields are input with the parameter categories corresponding to the battery;

[0044] c22. Then, based on the query field, add the required query conditions, and execute the MySQL query statement to extract the faulty battery parameters;

[0045] c23, and summarize the multiple fault parameter data corresponding to different batteries and mark them as Q.

[0046] Preferably, the extracted faulty device data is extracted in C3 and combined with the operation as follows:

[0047] c31. Combine the faulty equipment battery data P and the fault parameter data Q;

[0048] c32. Match the battery types in the faulty device battery data P and the fault parameter data Q, and merge the fault parameters of the same batteries;

[0049] c33. The overlapping fault parameters are cleared and the single data are retained, while the non-overlapping ones are retained, and finally the fault parameters of the same type of batteries are formed.

[0050] Preferably, the operation of generating a known fault solution according to matching the corresponding fault result with the extracted historical data in A3 is:

[0051] a31. Perform data tracing operation on the similar battery fault parameters formed in c33 and the historical data in the historical database, and locate the location where the fault equipment parameters are taken based on the initial data;

[0052] a32. Then, based on the traceability, the corresponding matching of parameter data is realized to find the historical fault results, and then the solution is directly fed back according to the fault results for timely processing operations.

[0053] The present invention provides a method for analyzing data information of a truck lithium battery system to screen out faulty equipment. Compared with the prior art, it has the following beneficial effects:

[0054] (1) The method for analyzing data information of a truck lithium battery system to screen out faulty equipment achieves compensation for missing data and removal of abnormal data by completing preprocessing operations on data information, and establishes a fault analysis model. The data after preprocessing operations are introduced into the fault analysis model for analysis, and the data is processed in parallel through different screening methods to complete the extraction of fault data, thereby realizing parallel processing operations of multiple screening methods, which can not only improve the accuracy of data result screening, but also improve the efficiency of screening operations, and effectively complete the screening of faulty equipment data and the matching of results, thereby achieving rapid positioning and problem feedback and processing.

[0055] (2) The method for analyzing data information of a truck lithium battery system to screen out faulty equipment uses a python script to analyze large amounts of cloud data, screen out data of lithium battery equipment that is faulty or meets the screening conditions, and maps the data information in the initial data set to a template table for matching, so that the corresponding parameter values ​​in the template table are filled in to form the required parameter data table, thereby achieving early data screening, ensuring smooth subsequent data processing, and completing data verification operations, confirming or compensating for blank columns, expanding abnormal columns and data compensation, effectively improving data accuracy, and facilitating the application of parallel screening by other methods.

[0056] (3) The method for analyzing data information of a truck lithium battery system to screen out faulty equipment is to perform data tracing operations on the fault parameters of similar batteries and historical data in a historical database, and then to achieve corresponding matching of parameter data based on tracing, to find the fault results generated in history, and then to directly feedback solutions based on the fault results for timely processing operations, thereby combining the processed data to improve the accuracy of the data, and then through tracing, not only can the faulty equipment be quickly located, but also a solution can be quickly formed through historical data for timely processing.

[0057] (4) The method for analyzing data information of a truck lithium battery system to screen out faulty equipment extracts the limit threshold of the battery during normal operation from historical data in a database, and then adjusts the limit threshold to obtain a predicted threshold set. The predicted threshold set is used as a judgment condition to compare with the parameters in a parameter data table, and the battery data of the corresponding faulty equipment that meets the judgment condition is extracted. In this way, adjustments can be made based on the limit threshold, effectively predicting possible risks in advance and giving personnel time to handle the situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is an operation flow chart of the method for screening faulty equipment of the present invention;

[0059] Figure 2 is an operational flow chart of the data processing operation of the present invention;

[0060] Figure 3 The operation flow chart of data preprocessing of the present invention;

[0061] Figure 4 This is an operational flow chart of implementing fault data extraction using different screening methods of the present invention. DETAILED DESCRIPTION

[0062] 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 only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] See also Figure 1-Figure 4 , the present invention provides a technical solution:

[0064] The method for analyzing data information of a truck lithium battery system to screen out faulty equipment specifically includes the following steps:

[0065] A1. Collect relevant information about the battery charging and discharging process and environmental parameter data through the BMS system of the lithium battery equipment, transmit the data information, and store the data information in the database;

[0066] A2. Implement analysis and processing of data information to filter out corresponding fault results. The specific processing operations are as follows:

[0067] a21. Complete the preprocessing operation of data information to compensate for missing data and remove abnormal data;

[0068] a22. Establish a fault analysis model, and introduce the pre-processed data into the fault analysis model for analysis, and process the data in parallel through different screening methods to complete the extraction of fault data;

[0069] a23, then transmit the marked fault data to match the fault result;

[0070] A3. Extract data based on the historical database, and generate solutions to known faults based on the corresponding fault results and the extracted historical data. Unmatched fault results are fed back to after-sales service for timely repairs.

[0071] By completing the preprocessing operation of data information, missing data compensation and abnormal data removal are achieved, and a fault analysis model is established. The data after preprocessing operation is introduced into the fault analysis model for analysis. The data is processed in parallel through different screening methods to complete the extraction of fault data. In this way, parallel processing operations of multiple screening methods are realized, which can not only improve the accuracy of data result screening, but also improve the efficiency of screening operations. At the same time, the screening of faulty equipment data and the matching of results are effectively completed, and rapid positioning, problem feedback and processing are achieved;

[0072] It can effectively improve the safety and reliability of truck lithium battery systems, reduce potential risks caused by battery failures, ensure the safety and efficiency of truck operations, and quickly locate and filter out battery equipment that meets the problem from a large amount of equipment data, thereby improving after-sales efficiency and accuracy.

[0073] The fault analysis model is established by extracting relevant equipment parameters based on the operation of lithium battery equipment to form a system model that can simulate the actual operation of lithium batteries. The fault analysis model realizes dynamic demonstration by collecting real-time data and combines the analysis operation of the system to screen and extract fault data in advance, and perform corresponding tracing operations based on the fault data, and then generate solutions for timely processing.

[0074] In the embodiment of the present invention, the data preprocessing operation in a21 is:

[0075] B1. Classify the collected data information according to the data categories corresponding to the battery, and sort the data according to the time sequence of collection;

[0076] B2. Identify the classified data, trace and compensate the data in a single category, and mark the abnormal data and trace and replace it;

[0077] B3. After the preprocessing operation is completed, the data is classified to form the required initial data set, which is marked as M.

[0078] In the embodiment of the present invention, the operation of performing parallel processing of data by different screening methods and then completing fault data extraction in a22 is:

[0079] C1. Import the initial data set M into the cloud backend, analyze large amounts of cloud data through Python scripts, and filter out lithium battery equipment data that is faulty or meets the screening conditions;

[0080] C2. At the same time, by using database query statements, filter the data in the cloud backend, export the lithium battery equipment data that meets the query conditions and locate the problem;

[0081] C3. Then the extracted faulty equipment data is extracted and combined for transmission.

[0082] Among them, Python script is a mature existing technology, and is mainly used to set the required table parameters and ensure the regularity of the data.

[0083] Large amounts of cloud data are analyzed through Python scripts to screen out lithium battery equipment data that is faulty or meets the screening conditions. The data information in the initial data set is mapped to the template table for matching, and the corresponding parameter values ​​in the template table are filled in to form the required parameter data table. This achieves early data screening, ensures smooth subsequent data processing, and completes data verification operations, confirmation or compensation of blank columns, expansion of abnormal columns and data compensation, effectively improving data accuracy and facilitating the application of parallel screening in other ways.

[0084] In the embodiment of the present invention, the screening step in C1 is:

[0085] c11. Through Python script analysis, set the template table of the required evaluation data. The template table is set with the types of batteries of different devices as the table row titles and the parameter categories corresponding to the batteries as the table column titles.

[0086] c12, and mapping the data information in the initial data set M to the template table for matching, so as to achieve corresponding filling of the parameter values ​​in the template table, and form the required parameter data table marked as L;

[0087] c13. Then, according to the historical data in the database, the limit threshold value of the battery during normal operation is extracted, and the prediction threshold value set is adjusted according to the limit threshold value. The prediction threshold value set is used as the judgment condition to compare with the parameters in the parameter data table L, and the battery data of the corresponding faulty equipment that meets the judgment condition is extracted and marked as P;

[0088] The prediction threshold is within the range of the limit threshold and is used to predict the node value of the battery data that will exceed the limit threshold, and the judgment condition is the data outside the normal range of the prediction threshold.

[0089] For example, when the battery is operating normally, the limit threshold of the battery temperature is 0 degrees-60 degrees. Under normal circumstances, when the operating temperature of the battery is less than 0 degrees or greater than 60 degrees, there is a high probability that the battery will have problems. Therefore, the prediction threshold is obtained through adjustment, and the normal range of the prediction threshold is within the range of the limit threshold, which is set to 5 degrees-55 degrees. Then 5 degrees and 55 degrees are the node values ​​of the judgment condition. The judgment condition at this time is: whether the battery temperature is less than or equal to 5 degrees or greater than or equal to 55 degrees. If the data parameters meet the judgment condition, it corresponds to the battery data of the faulty device.

[0090] In the embodiment of the present invention, the corresponding filling operation in c12 to realize the parameter value in the template table is:

[0091] c121, extracting the data of the types of batteries of different devices in the initial data set M, and then matching them with the table row header content of the template table;

[0092] c122. Extract the device battery parameter category information in the initial data set M based on the determined table row header, and pair it with the table column header content of the template table. Then fill the corresponding parameter data in the initial data set M into the parameter column where the paired table row header and table column header intersect, and finally form the parameter data table L;

[0093] c123. Use the recognition window function to check the parameter data table L to process the blank column and abnormal column data.

[0094] By extracting the limit threshold of normal battery operation from the historical data in the database, the prediction threshold set is adjusted according to the limit threshold, and the prediction threshold set is used as the judgment condition to compare with the parameters in the parameter data table, and the battery data of the corresponding faulty equipment that meets the judgment condition is extracted. In this way, adjustments can be made based on the limit threshold, effectively predicting possible risks in advance and giving personnel time to deal with them.

[0095] In the embodiment of the present invention, the identification window function in c123 performs the following operations to check and process the blank column and abnormal column data:

[0096] d1. Set the window function to F(x, y) to identify the contents of the window range. x is the length of the window, y is the width of the window, and F(x, y) starts to identify and move from the parameter corresponding to the first column title of the self-title to the parameter corresponding to the last column title, and then switches to the parameter corresponding to the first column title of the second row title for identification;

[0097] d2. Check the contents of the recognition window range with the data in the initial data set M. If they are consistent, F(x, y) moves horizontally by a distance x.

[0098] When the content of the recognition window range is blank, it is a blank column. Check whether there is data under the corresponding row title and column title in the initial data set M. If so, fill it into the corresponding blank column. Otherwise, it is confirmed that there is no parameter data. F(x, y) moves horizontally by a distance x.

[0099] When the content of the recognition window range is an error code exception, it is an exception column, confirming the data under the corresponding row title and column title in the initial data set M, and calculating the parameter data length to implement the expansion operation of the exception column, and then filling the corresponding data into the expanded exception column, and then F (x, y) moves horizontally by the window distance of the current expansion;

[0100] d3. After all parameter columns have been verified, the required parameter data table L is obtained.

[0101] In the embodiment of the present invention, the extended calculation operation of the abnormal column in d2 is:

[0102] The number of characters in the corresponding row and column headers is calculated as k, and the length of each character is recorded as u, and the distance between characters is v, so the length of the parameter data h is calculated as:

[0103] h=k×u+(k-1)×v;

[0104] The expansion of the corresponding abnormal column needs to be adjusted based on the parameter data, and the expansion distance H is:

[0105] H = h - x;

[0106] x is the window length before expansion, and its value is consistent with the initial length of the abnormal column.

[0107] In the embodiment of the present invention, the screening step in C2 is:

[0108] c21. By referring to the processed parameter data table L, the required query fields are set, and the query fields are input with the parameter categories corresponding to the battery;

[0109] c22. Then, based on the query field, add the required query conditions, and execute the MySQL query statement to extract the faulty battery parameters;

[0110] c23, and summarize the multiple fault parameter data corresponding to different batteries and mark them as Q.

[0111] Among them, the MySQL query statement is a mature existing technology, which is mainly used to retrieve the corresponding category and battery type data from the parameter data table, so as to achieve fast data extraction;

[0112] For example, the query field is set to the content entered in the corresponding MySQL query statement, the query condition is set to the judgmental condition content entered in the corresponding MySQL query statement, the query field is defined as the battery cell temperature, and the query condition temperature is higher than 60°C, and the temperature difference between the data is greater than 5°C. At this time, the above MySQL query statement is executed to obtain the battery equipment data that meets the conditions. When the battery equipment in the data meets the corresponding battery cell temperature higher than 60°C, it is the corresponding fault parameter data.

[0113] In the embodiment of the present invention, C3 extracts the faulty device data and combines the operations as follows:

[0114] c31. Combine the faulty equipment battery data P and the fault parameter data Q;

[0115] c32. Match the battery types in the faulty device battery data P and the fault parameter data Q, and merge the fault parameters of the same batteries;

[0116] c33. The overlapping fault parameters are cleared and the single data are retained, while the non-overlapping ones are retained, and finally the fault parameters of the same type of batteries are formed.

[0117] In the embodiment of the present invention, the operation of generating a known fault solution according to matching the corresponding fault result with the extracted historical data in A3 is:

[0118] a31. Perform data tracing operation on the similar battery fault parameters formed in c33 and the historical data in the historical database, and locate the location where the fault equipment parameters are taken based on the initial data;

[0119] a32. Then, based on the traceability, the corresponding matching of parameter data is realized to find the historical fault results, and then the solution is directly fed back according to the fault results for timely processing operations.

[0120] By tracing the fault parameters of similar batteries with the historical data in the historical database, and then matching the parameter data based on the traceability, the historical fault results are found, and then the solutions are directly fed back according to the fault results for timely processing operations, so as to combine the processed data to improve the accuracy of the data. Then, through tracing, not only can the faulty equipment be located quickly, but also the solution can be quickly formed through historical data for timely processing.

[0121] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0122] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0123] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing data information of a truck lithium battery system to screen out faulty equipment, characterized in that: The specific steps include: A1. Collect relevant information about the battery operation process and environmental parameter data through the BMS system of the lithium battery equipment, transmit the data information, and store the data information in the database; A2. Implement analysis and processing of data information to filter out corresponding fault results. The specific processing operations are as follows: a21. Complete the preprocessing operation of data information to compensate for missing data and remove abnormal data; a22. Establish a fault analysis model, and introduce the pre-processed data into the fault analysis model for analysis, and process the data in parallel through different screening methods to complete the extraction of fault data; a23, then transmit the marked fault data to match the fault result; A3. Extract data based on the historical database, and generate solutions to known faults based on the corresponding fault results and the extracted historical data. Unmatched fault results are fed back to after-sales service for timely repairs.

2. The method for analyzing data information of a truck lithium battery system to screen out faulty equipment according to claim 1, characterized in that: The data preprocessing operation in a21 is as follows: B1. Classify the collected data information according to the data categories corresponding to the battery, and sort the data according to the time sequence of collection; B2. Identify the classified data, trace and compensate the data in a single category, and mark the abnormal data and trace and replace it; B3. After the preprocessing operation is completed, the data is classified to form the required initial data set, which is marked as M.

3. The method for analyzing data information of a truck lithium battery system to screen out faulty equipment according to claim 2, characterized in that: The operation of performing parallel processing of data by different screening methods and then completing fault data extraction in a22 is: C1. Import the initial data set M into the cloud backend, analyze large amounts of cloud data through Python scripts, and filter out lithium battery equipment data that is faulty or meets the screening conditions; C2. At the same time, by using database query statements, filter the data in the cloud backend, export the lithium battery equipment data that meets the query conditions and locate the problem; C3. Then the extracted faulty equipment data is extracted and combined for transmission.

4. The method for analyzing data information for a truck lithium battery system to screen out faulty equipment according to claim 3, characterized in that: The screening steps in C1 are: c11. Through Python script analysis, set the template table of the required evaluation data. The template table is set with the types of batteries of different devices as the table row titles and the parameter categories corresponding to the batteries as the table column titles. c12, and mapping the data information in the initial data set M to the template table for matching, so as to achieve corresponding filling of the parameter values ​​in the template table, and form the required parameter data table marked as L; c13. Then, according to the historical data in the database, the limit threshold value of the battery during normal operation is extracted, and the prediction threshold value set is adjusted according to the limit threshold value. The prediction threshold value set is used as the judgment condition to compare with the parameters in the parameter data table L, and the battery data of the corresponding faulty equipment that meets the judgment condition is extracted and marked as P; The prediction threshold is within the range of the limit threshold and is used to predict the node value of the battery data that will exceed the limit threshold, and the judgment condition is the data outside the normal range of the prediction threshold.

5. The method for analyzing data information of a truck lithium battery system to screen out faulty equipment according to claim 4, characterized in that: The corresponding filling operation in c12 to realize the parameter value in the template table is: c121, extracting the data of the types of batteries of different devices in the initial data set M, and then matching them with the table row header content of the template table; c122. Extract the device battery parameter category information in the initial data set M based on the determined table row header, and pair it with the table column header content of the template table. Then fill the corresponding parameter data in the initial data set M into the parameter column where the paired table row header and table column header intersect, and finally form the parameter data table L; c123. Use the recognition window function to check the parameter data table L to process the blank column and abnormal column data.

6. The method for analyzing data information of a truck lithium battery system to screen out faulty equipment according to claim 5, characterized in that: The identification window function in c123 performs the following operations to check the blank column and abnormal column data: d1. Set the window function to F(x, y) to identify the contents of the window range. x is the length of the window, y is the width of the window, and F(x, y) starts to identify and move from the parameter corresponding to the first column title of the self-title to the parameter corresponding to the last column title, and then switches to the parameter corresponding to the first column title of the second row title for identification; d2. Check the contents of the recognition window range with the data in the initial data set M. If they are consistent, F(x, y) moves horizontally by a distance x. When the content of the recognition window range is blank, it is a blank column. Check whether there is data under the corresponding row title and column title in the initial data set M. If so, fill it into the corresponding blank column. Otherwise, it is confirmed that there is no parameter data. F(x, y) moves horizontally by a distance x. When the content of the recognition window range is an error code exception, it is an exception column, confirming the data under the corresponding row title and column title in the initial data set M, and calculating the parameter data length to implement the expansion operation of the exception column, and then filling the corresponding data into the expanded exception column, and then F (x, y) moves horizontally by the window distance of the current expansion; d3. After all parameter columns have been verified, the required parameter data table L is obtained.

7. The method for analyzing data information of a truck lithium battery system to screen out faulty equipment according to claim 6, characterized in that: The extended calculation operation of the abnormal column in d2 is: The number of characters in the corresponding row and column headers is calculated as k, and the length of each character is recorded as u, and the distance between characters is v, so the length of the parameter data h is calculated as: h=k×u+(k-1)×v; The expansion of the corresponding abnormal column needs to be adjusted based on the parameter data, and the expansion distance H is: H = h - x; x is the window length before expansion, and its value is consistent with the initial length of the abnormal column.

8. The method for analyzing data information of a truck lithium battery system to screen out faulty equipment according to claim 4, characterized in that: The screening steps in C2 are: c21. By referring to the processed parameter data table L, the required query fields are set, and the query fields are input with the parameter categories corresponding to the battery; c22. Then, based on the query field, add the required query conditions, and execute the MySQL query statement to extract the faulty battery parameters; c23, and summarize the multiple fault parameter data corresponding to different batteries and mark them as Q.

9. The method for analyzing data information of a truck lithium battery system to screen out faulty equipment according to claim 8, characterized in that: In C3, the extracted faulty device data is extracted and combined into the following operations: c31. Combine the faulty equipment battery data P and the fault parameter data Q; c32. Match the battery types in the faulty device battery data P and the fault parameter data Q, and merge the fault parameters of the same batteries; c33. The overlapping fault parameters are cleared and the single data are retained, while the non-overlapping ones are retained, and finally the fault parameters of the same type of batteries are formed.

10. The method for analyzing data information of a truck lithium battery system to screen out faulty equipment according to claim 9, characterized in that: The operation of generating a known fault solution by matching the corresponding fault result with the extracted historical data in A3 is: a31. Perform data tracing operation on the similar battery fault parameters formed in c33 and the historical data in the historical database, and locate the location where the fault equipment parameters are taken based on the initial data; a32. Then, based on the traceability, the corresponding matching of parameter data is realized to find the historical fault results, and then the solution is directly fed back according to the fault results for timely processing operations.

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