Battery Failure Analysis Method, Device, Equipment, Storage Medium and Program Product

Abstract

The present application relates to a battery failure analysis method, device, equipment, storage medium and program product. The method includes: obtaining a failure mode and a first failure feature of a target battery; wherein, the failure feature is used to characterize observable failure phenomena; determining a target failure analysis tree according to the failure mode, and performing factor matching processing in the target failure analysis tree according to the first failure feature to obtain a failure factor set; wherein, the failure factor set includes at least one first failure factor, the failure analysis tree is pre-constructed based on a plurality of second failure factors and a plurality of second failure features, and the failure factor is used to characterize the failure cause; collecting production process data according to the failure factor set; performing statistical processing on the production process data to determine the failure cause of the target battery. Using the present application can improve the failure analysis speed and the failure cause location efficiency.

Description

Technical Field

[0001] This application relates to the technical field of battery failure analysis, and in particular to a battery failure analysis method, device, equipment, storage medium, and program product. Background Art

[0002] Battery production is a complex and delicate process, involving processes such as stirring, coating, slitting, winding / laminating, formation, and assembly. The production process, equipment status, personnel quality, product design, raw material quality, storage conditions, etc. determine the quality of the final product. Battery failures are mainly divided into two categories: performance failures and safety failures; among them, performance failures refer to the situation where the battery performance fails to meet the requirements and indicators, mainly including capacity attenuation or drop, short cycle life, etc.; safety failures refer to the failures where the battery has a failure risk due to improper use, mainly including lithium deposition, liquid leakage, swelling, etc. The production cycle of batteries is long, and abnormalities or combinations in each link may lead to product quality problems. Failure analysis is to find the root cause of the failure from defective products, so as to solve the problem and prevent it from happening again.

[0003] Currently, most of the analysis methods for battery failures are aimed at specific failure phenomena, relying on personal experience to decide whether to disassemble the battery, collecting data of certain process steps for data analysis, and locating the cause of failure. However, the above methods are prone to problems such as slow failure analysis speed and low efficiency in locating the cause of failure due to insufficient experience. Summary of the Invention

[0004] Based on the above problems, this application provides a battery failure analysis method, device, equipment, storage medium, and program product, which can improve the failure analysis speed and the efficiency of locating the cause of failure.

[0005] In a first aspect, this application provides a battery failure analysis method, which includes: obtaining the failure mode and the first failure feature of the target battery; where the failure feature is used to characterize the observable failure phenomenon; determining the target failure analysis tree according to the failure mode, and performing factor matching processing in the target failure analysis tree according to the first failure feature to obtain a set of failure factors; where the set of failure factors includes at least one first failure factor, and the failure analysis tree is pre-constructed based on multiple second failure factors and multiple second failure features, and the failure factor is used to characterize the cause of failure; collecting production process data according to the set of failure factors; and performing statistical processing on the production process data to determine the cause of failure of the target battery.

[0006] In the technical solution of the embodiment of the present application, according to the failure phenomenon of the target battery and the failure analysis tree, the failure factor set, that is, the failure causes to be investigated, can be obtained quickly and accurately. Data collection and statistical analysis are automatically performed according to the failure causes to be investigated, without relying on personal experience. Therefore, the failure analysis speed can be increased, and the positioning efficiency of the failure causes can be improved.

[0007] In some embodiments, the failure analysis tree includes a root node and multiple child nodes; the root node represents the failure mode, and the child nodes include failure factor nodes and failure feature nodes, and the failure feature nodes are set with second failure features; factor matching processing is performed in the target failure analysis tree according to the first failure feature to obtain the failure factor set, including: selecting a failure factor node from the child nodes of the root node of the target failure analysis tree as the target node; when the child nodes of the target node contain failure feature nodes, matching the first failure feature with each second failure feature corresponding to the target node to obtain a matching result; determining the troubleshooting node according to the matching result, and reselecting the target node; after traversing all the child nodes of the target failure analysis tree, determining the failure factor set according to the determined troubleshooting nodes. In the technical solution of the embodiment of the present application, by traversing all the child nodes of the target failure analysis tree, the causes to be investigated can be quickly and accurately recommended, without relying on personal experience, the analysis accuracy of the failure causes can be improved, and moreover, automatic search can also increase the analysis speed of the failure causes.

[0008] In some embodiments, determining the troubleshooting node according to the matching result and reselecting the target node includes: when the matching result meets the preset matching condition, determining the target node as the troubleshooting node. If the troubleshooting node has child nodes, reselecting a failure factor node from the child nodes of the troubleshooting node as the target node, and performing the steps after selecting the target node. If the troubleshooting node has no child nodes, returning to the parent node of the troubleshooting node to reselect the target node; when the matching result does not meet the preset matching condition, if the target node has sibling nodes, reselecting a failure factor node from the sibling nodes of the target node as the target node, and performing the steps after selecting the target node; if the target node has no sibling nodes, returning to the parent node of the target node to reselect the target node. In the technical solution of the embodiment of the present application, by traversing all the child nodes of the target failure analysis tree and according to whether the matching result meets the preset matching condition, the causes to be investigated can be quickly and accurately recommended, without relying on personal experience, the analysis accuracy of the failure causes can be improved, and moreover, automatic search can also increase the analysis speed of the failure causes.

[0009] In some embodiments, the method further includes: when the child nodes of the target node do not contain failure feature nodes, determining the target node as a troubleshooting node; if the target node has sibling nodes, reselecting a failure factor node from the sibling nodes of the target node as the target node, and performing the steps after selecting the target node; if the target node has no sibling nodes, returning to the parent node of the target node to reselect the target node. In the technical solution of the embodiments of the present application, by traversing the child nodes of the target failure analysis tree and based on whether they contain failure feature nodes, the cause to be troubleshot can be quickly and accurately recommended, without relying on personal experience, which can improve the analysis accuracy of the failure cause. Moreover, automatic search can also improve the analysis speed of the failure cause.

[0010] In some embodiments, matching the first failure feature with each second failure feature corresponding to the target node to obtain a matching result includes: obtaining the weights of each second failure feature corresponding to the target node; matching the first failure feature with each second failure feature corresponding to the target node to obtain the probabilities of each second failure feature; calculating the probability of the target node according to the weights and probabilities of each second failure feature, and determining the probability of the target node as the matching result. In the technical solution of the embodiments of the present application, by determining the probabilities of each second failure feature according to the first failure feature, the failure cause can be accurately matched, and the item to be troubleshot can be recommended, providing a basis for subsequent data collection, which can improve the efficiency of failure analysis and the positioning efficiency of the failure cause.

[0011] In some embodiments, performing statistical processing on the production process data to determine the failure cause of the target battery includes: calculating the defect rate of each first failure factor in the failure factor set according to the production process data; performing statistical processing on the defect rates of multiple first failure factors to obtain the failure cause of the target battery. In the technical solution of the embodiments of the present application, automatic data statistics can be performed, which can improve the data analysis efficiency and thus improve the failure analysis efficiency.

[0012] In some embodiments, performing statistical processing on the defect rates of multiple first failure factors to obtain the failure cause of the target battery includes: selecting m first failure factors in descending order of defect rate; m is a positive integer; when the defect rates of the selected m first failure factors meet the concentration condition, determining the m first failure factors as the failure cause of the target battery. In the technical solution of the embodiments of the present application, by using the element concentration calculation method to locate the failure factors with concentration in the production process data, the positioning efficiency and positioning accuracy of the failure cause can be improved.

[0013] In some embodiments, the method further includes: obtaining the on-site verification result of the failure cause of the target battery; if the on-site verification result fails, excluding the verified failure causes, and returning to execute the step of statistically processing the production process data. In the technical solution of the embodiments of the present application, through on-site verification, the true failure cause of the target battery can be further determined, the failure analysis tree can be improved, and a more accurate basis can be provided for subsequent failure analysis.

[0014] In a second aspect, the present application further provides a battery failure analysis device, which includes:

[0015] A feature acquisition module, configured to acquire the failure mode and the first failure feature of the target battery;

[0016] A cause analysis module, configured to determine a target failure analysis tree according to the failure mode, and perform factor matching processing in the target failure analysis tree according to the first failure feature to obtain a set of failure factors; wherein, the set of failure factors includes at least one first failure factor, and the failure analysis tree is pre-constructed based on a plurality of second failure factors and a plurality of second failure features; the first failure feature and the second failure feature are used to characterize the failure phenomenon, and the first failure factor and the second failure factor are used to characterize the failure cause;

[0017] A data acquisition module, configured to perform data acquisition according to the set of failure factors to obtain production process data;

[0018] A cause determination module, configured to perform statistical processing on the production process data to determine the failure cause of the target battery.

[0019] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the method according to any one of the first aspect is implemented.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method according to any one of the first aspect is implemented.

[0021] In a fifth aspect, the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method according to any one of the first aspect is implemented. Description of the Drawings

[0022] By reading the detailed description of the following alternative embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of illustrating the alternative embodiments and are not considered to be a limitation of the present application. Moreover, in all the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0023] Figure 1 Schematic diagram of the application environment of the battery failure analysis method according to an embodiment of the present application;

[0024] Figure 2 Schematic flowchart of the battery failure analysis method according to an embodiment of the present application;

[0025] Figure 3 Schematic diagram of the failure analysis tree according to an embodiment of the present application;

[0026] Figure 4 One of the schematic flowcharts of the factor matching processing step according to an embodiment of the present application;

[0027] Figure 5 Another schematic flowchart of the factor matching processing step according to an embodiment of the present application;

[0028] Figure 6 Another schematic flowchart of the factor matching processing step according to an embodiment of the present application;

[0029] Figure 7 Schematic diagram of the process of recommending troubleshooting factors according to an embodiment of the present application;

[0030] Figure 8 Schematic flowchart of the failure feature matching processing step according to an embodiment of the present application;

[0031] Figure 9 Schematic flowchart of the process of statistically processing production process data according to an embodiment of the present application;

[0032] Figure 10 Schematic flowchart of the process of statistically processing the defective rate according to an embodiment of the present application;

[0033] Figure 11 Schematic flowchart of the on-site verification processing step according to an embodiment of the present application;

[0034] Figure 12 Schematic block diagram of the battery failure analysis device according to an embodiment of the present application;

[0035] Figure 13 Schematic block diagram of the battery failure analysis device according to another embodiment of the present application;

[0036] Figure 14 Internal structure diagram of a computer device according to an embodiment of the present application. Detailed implementation manners

[0037] Hereinafter, embodiments of the technical solutions of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and thus are only examples and should not be used to limit the protection scope of the present application.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application pertains; the terms used herein are for the purpose of describing specific embodiments only and are not intended to limit this application; the terms "comprising" and "having" and any variations thereof in the specification and claims of this application and the above description of the drawings are intended to cover non-exclusive inclusion.

[0039] In the description of the embodiments of this application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order, or primary-secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "a plurality of" is more than two, unless otherwise specifically defined.

[0040] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0041] In the description of the embodiments of this application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after.

[0042] In the description of the embodiments of this application, the term "a plurality of" refers to more than two (including two). Similarly, "a plurality of groups" refers to more than two groups (including two groups), and "a plurality of pieces" refers to more than two pieces (including two pieces).

[0043] In the description of the embodiments of this application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "coupled", "fixed", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can also be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and can be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of this application can be understood according to specific circumstances.

[0044] Battery production is a complex and delicate process, involving processes such as stirring, coating, slitting, winding / laminating, formation, and assembly. The production process, equipment status, personnel quality, product design, raw material quality, storage conditions, etc. determine the quality of the final product. The battery production cycle is long, and abnormalities or combinations in each link may lead to product quality problems. Failure analysis is to find the root cause of failure from defective products, so as to solve the problems and prevent them from occurring again.

[0045] Currently, most of the battery failure analysis methods are aimed at specific failure phenomena, relying on personal experience to decide whether to disassemble the battery, collect data of certain process steps for data analysis, and locate the failure cause. For example, for battery swelling, personal experience is relied on to decide to disassemble the battery, and formation data and assembly data are collected, so as to locate the failure cause based on the results after battery disassembly, as well as formation data and assembly data; or, for lithium plating, personal experience is relied on to decide to disassemble the battery, and coating data and formation data are collected, so as to locate the failure cause based on the results after battery disassembly, as well as coating data and formation data. It can be seen that the above methods rely too much on personal experience. If the experience is insufficient, it will lead to problems such as slow failure analysis speed and low failure cause location efficiency.

[0046] In view of the above problems, the embodiment of the present application provides a battery failure analysis method, which obtains the failure mode and the first failure feature of the target battery; determines the target failure analysis tree according to the failure mode, and performs factor matching processing in the target failure analysis tree according to the first failure feature to obtain a set of failure factors; collects production process data according to the set of failure factors; and performs statistical processing on the production process data to determine the failure cause of the target battery. In the technical solution of the embodiment of the present application, according to the failure phenomenon of the target battery and the failure analysis tree, a set of failure factors, that is, the failure causes to be investigated, can be obtained quickly and accurately. Data collection and statistical analysis are automatically performed according to the failure causes to be investigated, without relying on personal experience. Therefore, the failure analysis speed can be increased, and the location efficiency of the failure cause can be improved.

[0047] The battery failure analysis method provided by the embodiment of the present application can be applied to, for example Figure 1In the application environment shown. The application environment includes a terminal 102 and a server 104. Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. The terminal 102 can obtain the failure mode and the first failure feature of the target battery; determine the target failure analysis tree according to the failure mode, and determine the failure cause of the target battery according to the first failure feature and the target failure analysis tree. The terminal 102 can also, after obtaining the failure mode and the first failure feature of the target battery, send the failure mode and the first failure feature to the server 104, and the server determines the target failure analysis tree according to the failure mode, and determines the failure cause of the target battery according to the first failure feature and the target failure analysis tree. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0048] According to some embodiments of the present application, referring to Figure 2 , a battery failure analysis method is provided. Taking the terminal in Figure 1 as an example for description, it may include the following steps:

[0049] Step 201, obtain the failure mode and the first failure feature of the target battery.

[0050] Among them, the failure mode is used to represent the general classification of the failure cause, including poor K value, lithium plating, cell swelling, etc. The failure feature is used to represent the observable failure phenomenon, including appearance damage, element composition exceeding the standard, monitoring index abnormality, etc. The failure feature can be obtained by means of observation, disassembly, experiment, etc. And, the failure feature can be obtained multiple times. For example, the observable failure feature is extracted first, and then extracted after disassembly and experiment. The first failure feature is used to represent the failure phenomenon of the target battery.

[0051] The terminal can provide a failure analysis program. After the failure analysis program is started, the terminal displays a failure analysis interface. The failure analysis interface can include a failure mode input area and a failure feature input area. The staff can input the failure mode of the target battery in the failure mode input area and input the first failure feature of the target battery in the failure feature input area. Or, the staff selects the failure mode of the target battery based on the failure mode input area and selects the first failure feature of the target battery based on the failure feature input area. The terminal can obtain the failure mode and the first failure feature of the target battery according to the input operation or selection operation of the staff.

[0052] In some other embodiments, the terminal displays a failure analysis interface in the form of a web page, and obtains the failure mode and the first failure feature of the target battery based on the failure analysis interface.

[0053] It should be noted that the method for the terminal to obtain the failure mode and the first failure feature is not limited to the above examples. In practical applications, other methods can also be adopted.

[0054] Step 202: Determine the target failure analysis tree according to the failure mode, and perform factor matching processing in the target failure analysis tree according to the first failure feature to obtain a set of failure factors.

[0055] Among them, the failure factor is used to characterize the failure cause, and the failure cause is hierarchical. For example, the failure cause includes the first-level cause and the second-level cause. Among them, the second-level cause is the specific cause of the first-level cause, and the higher the cause level, the more accurate. There are many kinds of failure causes, such as equipment abnormality, operation abnormality, raw material abnormality, design structure, etc.

[0056] The set of failure factors includes at least one first failure factor, that is, the set of failure factors includes at least one failure cause of the target battery.

[0057] The failure analysis tree is pre-constructed based on multiple second failure factors and multiple second failure features, that is, the failure analysis tree includes multiple levels of failure causes and the failure phenomena corresponding to each failure cause. For example, the failure analysis tree includes failure factors A and B. Among them, failure factor A corresponds to failure features t1 and t2, the sub-factors of failure factor A include A-1, A-2, A-3, and failure factor A-1 corresponds to failure feature t1; failure factor B corresponds to failure features t1, t4, and t5, and the sub-factors of failure factor B include B-1, B-2, B-3.

[0058] After the terminal obtains the failure mode of the target battery, according to the correspondence between the failure analysis tree and the failure mode, it searches for the target failure analysis tree corresponding to the failure mode of the target battery from multiple pre-created failure analysis trees. For example, a failure analysis tree 1 corresponding to the K value being bad, a failure analysis tree 2 corresponding to lithium plating, and a failure analysis tree 3 corresponding to the cell swelling are pre-established. If the failure mode of the target battery is the K value being bad, then according to the correspondence between the failure analysis tree and the failure mode, it can be determined that the target failure analysis tree corresponding to the target battery is failure analysis tree 1.

[0059] After determining the target failure analysis tree corresponding to the target battery, match the first failure characteristics of the target battery with the second failure characteristics corresponding to each second failure factor in the target failure analysis tree to obtain a set of failure factors. For example, the first failure characteristics of the target battery include t1 and t4. The second failure characteristics t1 and t4 are found in the target failure analysis tree. According to the second failure characteristic t1, the set of failure factors is determined to include the failure factor A-1. According to the second failure characteristic t4, the set of failure factors is determined to include the failure factor B.

[0060] Step 203: Perform data collection based on the set of failure factors to obtain production process data.

[0061] Among them, the production process data includes data such as the equipment, raw materials, processes, and workstations that the product passes through. The production process data can be divided into two types: structured data and unstructured data. Structured data is the standard data generated by each software system, including data from the MES system, ERP system, Scada system, etc. Unstructured data includes process files, experimental files, etc.

[0062] The terminal can create an analysis task according to the set of failure factors. The analysis task includes the data collection parameters defined by each first failure factor in the set of failure factors. Automatically start the analysis task, or start the analysis task based on the start operation input by the staff. The terminal performs data collection based on the analysis task to obtain production process data.

[0063] For example, the terminal obtains data from the MES system according to the analysis task, and obtains process files, experimental files, etc. from a preset folder.

[0064] In some embodiments, when determining the set of failure factors, the terminal can obtain the failure matching degree between each first failure factor and the second failure factor in the target failure analysis tree. When performing data collection, in the order from high to low of the failure matching degree, data collection is performed for each first failure factor in turn. It can be understood that by performing data collection in the above order, data collection can be preferentially performed on the failure causes with high failure matching degrees, so as to preferentially perform statistical analysis on these data, thereby improving the failure analysis speed and failure analysis efficiency, improving product quality, and reducing production costs.

[0065] Step 204: Perform statistical processing on the production process data to determine the failure cause of the target battery.

[0066] Perform statistical processing on the production process data of each first failure factor to obtain the probability corresponding to each first failure factor; determine the failure cause of the target battery according to the probability corresponding to each first failure factor.

[0067] For example, statistical processing is performed on the production process data, and the probability corresponding to the first failure factor A-1 is obtained as a%, and the probability corresponding to the first failure factor B is obtained as b%. Since a% > b%, A-1 is determined as the failure cause of the target battery.

[0068] In the above embodiment, the failure mode and the first failure feature of the target battery are obtained; a target failure analysis tree is determined according to the failure mode, and factor matching processing is performed in the target failure analysis tree according to the first failure feature to obtain a failure factor set; data collection is performed according to the failure factor set to obtain production process data; statistical processing is performed on the production process data to determine the failure cause of the target battery. In the technical solution of the embodiment of the present application, according to the failure phenomenon of the target battery and the target failure analysis tree, the failure factor set, that is, the failure cause to be investigated, can be quickly and accurately obtained, and data collection and statistical analysis are automatically performed according to the failure cause to be investigated, without relying on personal experience. Therefore, the failure analysis speed can be improved, and the positioning efficiency of the failure cause can be improved.

[0069] According to some embodiments of the present application, referring to Figure 3 , the failure analysis tree includes a root node and a plurality of child nodes; the root node represents the failure mode, and the child nodes include failure factor nodes and failure feature nodes, and the second failure feature is set on the failure feature nodes.

[0070] The terminal displays a failure analysis interface, and the failure analysis interface further includes an analysis tree configuration area. The staff can input the failure mode, a plurality of failure causes, and the failure phenomena corresponding to each failure cause based on the analysis tree configuration area. The terminal obtains the content input by the staff, creates a failure analysis tree according to the content input by the staff, and configures the failure analysis tree.

[0071] For example, the terminal configures the failure mode input by the staff as the root node of the failure analysis tree, configures the failure cause as the failure factor node, and configures the failure phenomenon as the failure feature node.

[0072] It can be understood that for different failure modes, different failure analysis trees can be created.

[0073] Referring to Figure 4 , in the above embodiment, "performing factor matching processing in the target failure analysis tree according to the first failure feature to obtain a failure factor set" may include the following steps:

[0074] Step 301, select a failure factor node from the child nodes of the root node of the target failure analysis tree as the target node.

[0075] Referring to Figure 3, the child nodes of the root node include failure factor nodes A, B, C, and D, and one of the failure factor nodes is taken as the target node. For example, the failure factor node A is taken as the target node, or the failure factor node B is taken as the target node.

[0076] Step 302, when the child nodes of the target node contain failure feature nodes, match the first failure feature with each second failure feature corresponding to the target node to obtain a matching result.

[0077] Suppose the failure factor node A is the target node and the child nodes of the failure factor node A contain failure feature nodes, then match the first failure feature of the target battery with the second failure features t1 and t2 corresponding to the failure factor node A to obtain a matching result.

[0078] Suppose the failure factor node B is the target node and the child nodes of the failure factor node B contain failure feature nodes, then match the first failure feature of the target battery with the second failure features t1, t4, and t5 corresponding to the failure factor node B to obtain a matching result.

[0079] The matching for other failure factor nodes will not be elaborated here.

[0080] Step 303, determine the troubleshooting node according to the matching result, and reselect the target node.

[0081] When the matching result meets the preset matching condition, determine the target node as the troubleshooting node. If the troubleshooting node has child nodes, reselect a failure factor node from the child nodes of the troubleshooting node as the target node, and execute the steps after selecting the target node. If the troubleshooting node has no child nodes, return to the parent node of the troubleshooting node to reselect the target node.

[0082] When the matching result does not meet the preset matching condition, if the target node has sibling nodes, reselect a failure factor node from the sibling nodes of the target node as the target node, and execute the steps after selecting the target node. If the target node has no sibling nodes, return to the parent node of the target node to reselect the target node.

[0083] Step 304, after traversing all the child nodes of the target failure analysis tree, determine the failure factor set according to the determined troubleshooting nodes.

[0084] Traverse all the child nodes of the target failure analysis tree according to the above steps. After traversing, at least one determined troubleshooting node can be obtained. The troubleshooting nodes correspond to the failure factor nodes one by one. Summarize the troubleshooting nodes to obtain the failure factor set.

[0085] During the summarization process, screening processing can be performed on the failure factor nodes. For example, both the failure factor nodes A and A-1 are nodes to be investigated. Since the failure factor node A-1 is a child node of the failure factor node A, the failure factor node A can be excluded, and the failure factor node A-1 can be summarized into the failure factor set.

[0086] In the above embodiment, a failure factor node is selected from the child nodes of the root node of the target failure analysis tree as the target node; when the child nodes of the target node contain failure feature nodes, the first failure feature is matched with each second failure feature corresponding to the target node to obtain a matching result; when the matching result meets the preset matching condition, the target node is determined as the node to be investigated. If the node to be investigated has child nodes, a failure factor node is reselected from the child nodes of the node to be investigated as the target node, and the steps after selecting the target node are executed. If the node to be investigated has no child nodes, the parent node of the node to be investigated is returned to reselect the target node; after traversing all the child nodes of the target failure analysis tree, the failure factor set is determined according to the determined nodes to be investigated. In the technical solution of the embodiment of the present application, by traversing all the child nodes of the target failure analysis tree, the reasons to be investigated can be quickly and accurately recommended, without relying on personal experience, which can improve the accuracy of analyzing the failure reasons. Moreover, automatic search can also improve the analysis speed of the failure reasons.

[0087] According to some embodiments of the present application, with reference to Figure 5 the above embodiment, "determining the node to be investigated according to the matching result and reselecting the target node" may include the following steps:

[0088] Step 3031, when the matching result meets the preset matching condition, the target node is determined as the node to be investigated. If the node to be investigated has child nodes, a failure factor node is reselected from the child nodes of the node to be investigated as the target node, and the steps after selecting the target node are executed. If the node to be investigated has no child nodes, the parent node of the node to be investigated is returned to reselect the target node.

[0089] Among them, the matching result may include a failure matching degree, and the failure matching degree is expressed as a percentage or a decimal. For example, the failure matching degree is 0.8, or the failure matching degree is 80%.

[0090] The preset matching condition may include that the failure matching degree is greater than or equal to the preset matching degree threshold. For example, the preset matching degree threshold is 0.8. The first failure feature of the target battery is matched with the second failure features t1 and t2 corresponding to the failure factor node A, and the obtained failure matching degree is 0.8. This failure matching degree is equal to the preset matching degree threshold, indicating that the matching result meets the preset matching condition, and the failure factor node A is determined as the node to be investigated.

[0091] It should be noted that the preset matching degree threshold can be modified according to different scenarios and failure modes to meet different requirements.

[0092] If the troubleshooting node is the failure factor node A and A has child nodes, then select one of the failure factor nodes A-1, A-2, A-3 of the child nodes of the failure factor node A as the target node. For example, select the failure factor node A-1 as the target node. The child nodes of the failure factor node A-1 contain failure feature nodes, and match the first failure feature of the target battery with the second failure feature corresponding to the failure factor node A-1 to obtain a matching result.

[0093] If the troubleshooting node has no child nodes, then return the parent node of the troubleshooting node. For example, the failure factor node A-2 is the troubleshooting node, and the child nodes of the failure factor node A-2 have no failure feature nodes, then return its parent node A. Since the parent node A is not the root node, select one of the sibling nodes B, C, D of the parent node A as the target node again. If the parent node is the root node after returning the parent node, it is determined that the traversal is complete.

[0094] Step 3032, in the case where the matching result does not meet the preset matching condition, if the target node has sibling nodes, then select one of the sibling nodes of the target node as the target node again and execute the steps after selecting the target node. If the target node has no sibling nodes, then return the parent node of the target node to select the target node again.

[0095] If the failure matching degree is less than the preset matching threshold, it indicates that the matching result does not meet the preset matching condition. In this case, if the target node has sibling nodes, then select one of the sibling nodes of the target node as the target node again.

[0096] For example, take the failure factor node A as the target node. If the matching result of the first failure feature of the target battery and the second failure feature of the failure factor node A does not meet the preset matching condition, then select one of the sibling nodes B, C, D of the failure factor node A as the target node again. Another example is to take the failure factor node A-1 as the target node. If the matching result of the first failure feature of the target battery and the second failure feature of the failure factor node A-1 does not meet the preset matching condition, then select one of the sibling nodes A-2, A-3 of the failure factor node A-1 as the target node again.

[0097] After reselecting the target node, determine whether the child nodes of the target node contain failure feature nodes. If the child nodes of the target node contain failure feature nodes, then match the first failure feature with each second failure feature corresponding to the target node to obtain a matching result.

[0098] If the target node has no sibling nodes, return the parent node of the target node; if the parent node is the root node, it is determined that the traversal is complete; if the parent node is not the root node, reselect a failure factor node from the sibling nodes of the parent node as the target node.

[0099] In the above embodiments, when the matching result meets the preset matching condition, the target node is determined as the troubleshooting node. If the troubleshooting node has child nodes, reselect a failure factor node from the child nodes of the troubleshooting node as the target node, and execute the steps after selecting the target node. If the troubleshooting node has no child nodes, return the parent node of the troubleshooting node to reselect the target node. When the matching result does not meet the preset matching condition, if the target node has sibling nodes, reselect a failure factor node from the sibling nodes of the target node as the target node, and execute the steps after selecting the target node; if the target node has no sibling nodes, return the parent node of the target node to reselect the target node. In the technical solution of the embodiments of the present application, by traversing each child node of the target failure analysis tree, according to whether the matching result meets the preset matching condition, the cause to be troubleshot can be quickly and accurately recommended, without relying on personal experience, which can improve the accuracy of analyzing the failure cause. Moreover, automatic search can also improve the speed of analyzing the failure cause.

[0100] According to some embodiments of the present application, referring to Figure 6 , the following steps may further be included:

[0101] Step 305, when the child nodes of the target node do not contain failure feature nodes, determine the target node as the troubleshooting node.

[0102] If the child nodes of the target node contain failure feature nodes, the first failure feature of the target battery may be matched with the second failure feature in the failure feature nodes to obtain a matching result. If the child nodes of the target node do not contain failure feature nodes, directly determine the target node as the troubleshooting node.

[0103] Referring to Figure 3 , assuming that the failure factor node A-1 is the target node and the child nodes of the failure factor node A-1 contain failure feature nodes, the first failure feature of the target battery is matched with the second failure feature in the failure feature nodes to obtain a matching result. Assuming that the failure factor node A-2 is the target node and the child nodes of the failure factor node A-2 do not contain failure feature nodes, determine the failure factor node A-2 as the troubleshooting node.

[0104] Step 306, if the target node has sibling nodes, reselect a failure factor node from the sibling nodes of the target node as the target node, and execute the steps after selecting the target node. If the target node has no sibling nodes, return the parent node of the target node to reselect the target node.

[0105] Referring to Figure 3 After determining the failed factor node A-2 as the troubleshooting node, select a failed factor node from the sibling nodes of the failed factor node A-2 as the target node. For example, re-select the failed factor node A-3 as the target node. The failed factor node A-3 does not contain a failure feature node, and the failed factor node A-3 is determined as the troubleshooting node. Since the failed factor node A-3 has no sibling nodes, return to the parent node A of the failed factor node and re-select the target node from the sibling nodes B, C, and D of the parent node A.

[0106] And so on, each child node in the target failure analysis tree can be traversed. Referring to Figure 7 which shows the process of recommending troubleshooting factors.

[0107] In the above embodiment, when the child node of the target node does not contain a failure feature node, the target node is determined as the troubleshooting node; if the target node has sibling nodes, re-select a failed factor node from the sibling nodes of the target node as the target node and execute the steps after selecting the target node; if the target node has no sibling nodes, return to the parent node of the target node to re-select the target node. In the technical solution of the embodiment of the present application, by traversing each child node of the target failure analysis tree, according to whether it contains a failure feature node, the cause to be troubleshot can be quickly and accurately recommended, without relying on personal experience, which can improve the accuracy of failure cause analysis. Moreover, automatic search can also improve the analysis speed of failure causes.

[0108] According to some embodiments of the present application, referring to Figure 8 in the above embodiment, "matching the first failure feature with each second failure feature corresponding to the target node to obtain a matching result" may include the following steps:

[0109] Step 401, obtain the weights of each second failure feature corresponding to the target node.

[0110] When configuring the failure analysis tree, the weights of each second failure feature in the failure analysis tree can be configured. After selecting the target node, obtain the weights of each second failure feature corresponding to the target node from the target failure analysis tree.

[0111] Referring to Figure 3 take the failed factor node A as the target node. The weight of the second failure feature t1 corresponding to the failed factor node A is W1, and the weight of the second failure feature t2 is W2.

[0112] Step 402, match the first failure feature with each second failure feature corresponding to the target node to obtain the probabilities of each second failure feature.

[0113] Assume that the first failure feature includes t1. When matching the first failure feature t1 with each second failure feature corresponding to the target node, the probability of the second failure feature t1 can be obtained as 1. Assume that the first failure feature does not include t1. When matching the first failure feature with each second failure feature corresponding to the target node, the probability of the second failure feature t1 can be obtained as 0. And so on. According to the first failure feature, the probabilities of each second failure feature can be determined.

[0114] Step 403: Calculate the probability of the target node according to the weights and probabilities of each second failure feature, and determine the probability of the target node as the matching result.

[0115] The probability of the target node can be calculated using formula (1):

[0116] Z = (a1 * W1 + a2 * W2 + … + an * Wn) / SumW -------------- (1)

[0117] Where a1 is the probability of the second failure feature t1, W1 is the weight of the second failure feature t1, and so on. an is the probability of the second failure feature tn, Wn is the weight of the second failure feature tn, and SumW is the total weight.

[0118] After determining the weights and probabilities of each second failure feature corresponding to the target node, substitute the weights and probabilities of each second failure feature into the above formula for calculation, then the probability of the target node can be obtained, and the probability of the target node is used as the matching result.

[0119] In the above embodiment, obtain the weights of each second failure feature corresponding to the target node; match the first failure feature with each second failure feature corresponding to the target node to obtain the probabilities of each second failure feature; calculate the probability of the target node according to the weights and probabilities of each second failure feature, and determine the probability of the target node as the matching result. In the technical solution of the embodiment of the present application, determining the probabilities of each second failure feature according to the first failure feature can accurately match the failure cause, recommend items to be checked, provide a basis for subsequent data collection, improve the efficiency of failure analysis, and improve the positioning efficiency of the failure cause.

[0120] According to some embodiments of the present application, referring to Figure 9 , in the above embodiment, "statistically process the production process data to determine the failure cause of the target battery" may include the following steps:

[0121] Step 501: Calculate the defect rate of each first failure factor in the failure factor set according to the production process data.

[0122] Among them, the defective rate refers to the proportion of the number of products that do not meet the specified standards or have defects in a certain number of products to the total sample size, usually expressed as a percentage. The defective rate is calculated using formula (2):

[0123] Defective rate = Number of defective products / (Number of qualified products + Number of defective products) * 100%-----------(2)

[0124] For each first failure factor in the set of failure factors, the terminal obtains the production process data corresponding to the first failure factor, determines the number of defective products and the number of qualified products based on the production process data, and calculates the defective rate of the first failure factor using formula (2).

[0125] For example, if the set of failure factors includes first failure factors x1, x2... xn, the defective rates y1, y2... yn of each first failure factor are calculated respectively.

[0126] Step 502: Perform statistical processing on the defective rates of multiple first failure factors to obtain the failure cause of the target battery.

[0127] When performing statistical processing on the defective rates of multiple first failure factors, the first failure factor with the highest defective rate can be determined as the failure cause of the target battery; alternatively, m first failure factors with relatively high defective rates can be determined as the failure cause of the target battery.

[0128] In some embodiments, a data chart can be drawn after statistical processing. Among them, the chart styles include bar charts, line charts, box plots, etc.

[0129] In the above embodiments, according to the production process data, the defective rate of each first failure factor in the set of failure factors is calculated; statistical processing is performed on the defective rates of multiple first failure factors to obtain the failure cause of the target battery. In the technical solution of the embodiments of the present application, data statistics are automatically performed, which can improve the data analysis efficiency and thus improve the failure analysis efficiency.

[0130] According to some embodiments of the present application, referring to Figure 10 In the above embodiments, "performing statistical processing on the defective rates of multiple first failure factors to obtain the failure cause of the target battery" may include the following steps:

[0131] Step 601: Select m first failure factors in descending order of defective rate; m is a positive integer.

[0132] Arrange the multiple first failure factors in ascending order of defective rate to obtain a data set of failure factors and defective rates {x1:y1, x2:y2... xn:yn}. Among them, x is the first failure factor, y is the defective rate corresponding to the first failure factor, y1 is the lowest, and yn is the highest.

[0133] Assume that m is 1, then select one first failure factor with the highest defect rate; assume that m is 2, then select the first failure factor with the highest and the second highest defect rate.

[0134] Step 602, when the defect rates of the selected m first failure factors satisfy the concentration condition, determine the m first failure factors as the failure causes of the target battery.

[0135] Among them, the concentration condition includes that the total defect rate of the m first failure factors is greater than the total defect rate of all other first failure factors.

[0136] After selecting m first failure factors, determine whether the total defect rate of the m first failure factors is greater than the total defect rate of all other first failure factors; if so, determine that the defect rates of the m first failure factors satisfy the concentration condition. If not, determine that the defect rates of the m first failure factors do not satisfy the concentration condition, and reselect the first failure factors.

[0137] For example, select the first failure factor xn with the highest defect rate. If yn > (y1 + y2 + … + yn-1), then determine that the defect rate of the first failure factor xn satisfies the concentration condition, the first failure factor xn has concentration, and determine the first failure factor xn as the failure cause of the target battery. If yn ≤ (y1 + y2 + … + yn-1), then determine that the defect rate of the first failure factor xn does not satisfy the concentration condition. In this case, select the first failure factor xn and xn-1 with the highest and the second highest defect rate. If (yn + yn-1) > (y1 + y2 + … + yn-2), then determine that the defect rates of the first failure factor xn and xn-1 satisfy the concentration condition, the first failure factor xn and xn-1 have concentration, and determine the first failure factor xn and xn-1 as the failure cause of the target battery. And so on. If the defect rates of the first failure factor xn and xn-1 do not satisfy the concentration condition, then select xn, xn-1 and xn-2 with higher defect rates. If (yn + yn-1 + yn-2) > (y1 + y2 + … + yn-3), then determine that the defect rates of the first failure factor xn, xn-1 and xn-2 satisfy the concentration condition, the first failure factor xn, xn-1 and xn-2 have concentration, and determine the first failure factor xn, xn-1 and xn-2 as the failure cause of the target battery.

[0138] In the above embodiments, m first failure factors are selected in the order of the defect rate from high to low; when the defect rates of the selected m first failure factors satisfy the concentration condition, the m first failure factors are determined as the failure causes of the target battery. In the technical solution of the embodiment of the present application, according to the element concentration calculation method, the failure factors with concentration in the production process data are located, which can improve the location efficiency and location accuracy of the failure causes.

[0139] According to some embodiments of the present application, with reference to Figure 11 , after determining the failure cause of the target battery, the embodiments of the present application may further include the following steps:

[0140] Step 701, obtain the on-site verification result of the failure cause of the target battery.

[0141] The staff of the battery production line can conduct on-site verification according to the failure cause of the target battery. For example, if the failure mode of the target battery is poor K value and the determined failure cause of the target battery is F1, the staff can disassemble the target battery and conduct on-site verification by means of simulation experiments according to the failure cause F1 to confirm whether the true failure cause of the target battery is the failure cause F1.

[0142] After on-site verification, the staff can input the on-site verification result into the terminal. For example, the terminal displays a failure analysis interface, and the failure analysis interface includes a verification result input area; the terminal obtains the on-site verification result according to the input content of the staff in the verification result input area.

[0143] Step 702, if the on-site verification result fails, exclude the verified failure cause and return to execute the step of statistically processing the production process data.

[0144] If the on-site verification result fails, exclude the already verified failure cause, statistically process the production process data of other failure causes, recalculate the failure cause of the target battery, and conduct on-site verification again.

[0145] If the on-site verification result passes, end the failure analysis process.

[0146] In some embodiments, the terminal can also adjust the parameters in the failure analysis tree according to the on-site verification result. For example, adjust the failure factor node and / or failure feature node of the failure analysis tree, and adjust the weight of the second failure feature in the failure analysis tree.

[0147] It should be noted that the adjustment of the failure analysis tree is not limited to the above examples and can be determined according to the actual situation.

[0148] In the above embodiments, obtain the on-site verification result of the failure cause of the target battery; if the on-site verification result fails, exclude the verified failure cause and return to execute the step of statistically processing the production process data. In the technical solution of the embodiments of the present application, through on-site verification, the true failure cause of the target battery can be further determined, the failure analysis tree can be improved, and a more accurate basis can be provided for subsequent failure analysis.

[0149] According to some embodiments of the present application, a method for analyzing battery failure is provided. Taking the terminal in Figure 1 as an example, the method may include the following steps:

[0150] Step 1: Obtain the failure mode and the first failure feature of the target battery.

[0151] The failure feature is used to characterize the observable failure phenomenon.

[0152] Step 2: Determine the target failure analysis tree according to the failure mode.

[0153] The failure factor set includes at least one first failure factor. The failure analysis tree is pre-constructed based on a plurality of second failure factors and a plurality of second failure features. The failure factor is used to characterize the failure cause.

[0154] The failure analysis tree includes a root node and a plurality of sub-nodes; the root node represents the failure mode, and the sub-nodes include failure factor nodes and failure feature nodes. The failure feature nodes are provided with the second failure features.

[0155] Step 3: Select a failure factor node from the sub-nodes of the root node of the target failure analysis tree as the target node.

[0156] According to whether the sub-nodes of the target node contain failure feature nodes, execute Step 4 or Step 7.

[0157] Step 4: When the sub-nodes of the target node contain failure feature nodes, match the first failure feature with each second failure feature corresponding to the target node to obtain a matching result.

[0158] Matching the first failure feature with each second failure feature corresponding to the target node to obtain a matching result includes: obtaining the weights of each second failure feature corresponding to the target node; matching the first failure feature with each second failure feature corresponding to the target node to obtain the probabilities of each second failure feature; calculating the probability of the target node according to the weights and probabilities of each second failure feature, and determining the probability of the target node as the matching result.

[0159] After obtaining the matching result, execute Step 5 or Step 6 according to whether the matching result meets the preset matching condition.

[0160] Step 5: When the matching result meets the preset matching condition, determine the target node as the troubleshooting node. If the troubleshooting node has sub-nodes, select a failure factor node from the sub-nodes of the troubleshooting node as the target node again, and execute the steps after selecting the target node. If the troubleshooting node has no sub-nodes, return to the parent node of the troubleshooting node to select the target node again.

[0161] Step 6. In the case where the matching result does not meet the preset matching condition, if the target node has sibling nodes, select a new failure factor node from the sibling nodes of the target node as the target node, and execute the steps after selecting the target node; if the target node has no sibling nodes, return to the parent node of the target node to re-select the target node.

[0162] Step 7. In the case where the child nodes of the target node do not contain failure feature nodes, determine the target node as the troubleshooting node. If the target node has sibling nodes, select a new failure factor node from the sibling nodes of the target node as the target node, and execute the steps after selecting the target node; if the target node has no sibling nodes, return to the parent node of the target node to re-select the target node.

[0163] Step 8. After traversing all the child nodes of the target failure analysis tree, determine the failure factor set according to the determined troubleshooting nodes.

[0164] Step 9. Perform data collection according to the failure factor set to obtain production process data.

[0165] Step 10. According to the production process data, calculate the defect rate of each first failure factor in the failure factor set.

[0166] Step 11. Select m first failure factors in descending order of defect rate; m is a positive integer.

[0167] Step 12. In the case where the defect rates of the selected m first failure factors meet the concentration condition, determine the m first failure factors as the failure causes of the target battery.

[0168] Step 13. Obtain the on-site verification result of the failure cause of the target battery.

[0169] Step 14. If the on-site verification result fails, exclude the verified failure causes, and return to execute the steps of statistically processing the production process data.

[0170] In the above embodiments, according to the failure phenomenon of the target battery and the target failure analysis tree, the failure factor set, that is, the failure causes to be investigated, can be quickly and accurately obtained. Automatic data collection and statistical analysis are performed according to the failure causes to be investigated, without relying on personal experience. Therefore, the failure analysis speed can be increased, and the positioning efficiency of the failure causes can be improved.

[0171] It should be understood that although the steps in the above flowcharts are shown sequentially in the direction of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless otherwise clearly stated in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the above flowcharts may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0172] Based on the same inventive concept, an embodiment of the present application further provides a battery failure analysis device for implementing the battery failure analysis method involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the battery failure analysis device provided below can refer to the limitations on the battery failure analysis method in the above text, and will not be repeated here.

[0173] According to some embodiments of the present application, with reference to Figure 12 , a battery failure analysis device is provided, and the device includes:

[0174] A feature acquisition module 801, configured to acquire a failure mode and a first failure feature of a target battery;

[0175] A cause analysis module 802, configured to determine a target failure analysis tree according to the failure mode, and perform factor matching processing in the target failure analysis tree according to the first failure feature to obtain a failure factor set; wherein, the failure factor set includes at least one first failure factor, and the failure analysis tree is pre-constructed based on a plurality of second failure factors and a plurality of second failure features; the first failure feature and the second failure feature are used to characterize the failure phenomenon, and the first failure factor and the second failure factor are used to characterize the failure cause;

[0176] A data acquisition module 803, configured to perform data acquisition according to the failure factor set to obtain production process data;

[0177] A cause determination module 804, configured to perform statistical processing on the production process data to determine the failure cause of the target battery.

[0178] In some embodiments, the failure analysis tree includes a root node and multiple child nodes; the root node represents a failure mode, the child nodes include failure factor nodes and failure feature nodes, and the failure feature nodes are provided with second failure features; the cause analysis module 802 is specifically configured to select a failure factor node from the child nodes of the root node of the target failure analysis tree as the target node; when the child nodes of the target node contain failure feature nodes, match the first failure feature with each second failure feature corresponding to the target node to obtain a matching result; when the matching result meets the preset matching condition, determine the target node as the troubleshooting node. If the troubleshooting node has child nodes, select a failure factor node from the child nodes of the troubleshooting node as the target node again, and execute the steps after selecting the target node. If the troubleshooting node has no child nodes, return to the parent node of the troubleshooting node to select the target node again; after traversing all the child nodes of the target failure analysis tree, determine the failure factor set according to the determined troubleshooting nodes.

[0179] In some embodiments, the cause analysis module 802 is further configured to, when the matching result does not meet the preset matching condition, if the target node has sibling nodes, select a failure factor node from the sibling nodes of the target node as the target node again, and execute the steps after selecting the target node; if the target node has no sibling nodes, return to the parent node of the target node to select the target node again.

[0180] In some embodiments, the cause analysis module 802 is further configured to, when the child nodes of the target node do not contain failure feature nodes, determine the target node as the troubleshooting node; if the target node has sibling nodes, select a failure factor node from the sibling nodes of the target node as the target node again, and execute the steps after selecting the target node; if the target node has no sibling nodes, return to the parent node of the target node to select the target node again.

[0181] In some embodiments, the cause analysis module 802 is specifically configured to obtain the weights of each second failure feature corresponding to the target node; match the first failure feature with each second failure feature corresponding to the target node to obtain the probabilities of each second failure feature; calculate the probability of the target node according to the weights and probabilities of each second failure feature, and determine the probability of the target node as the matching result.

[0182] In some embodiments, the cause determination module 804 is specifically configured to calculate the defect rate of each first failure factor in the failure factor set according to the production process data; perform statistical processing on the defect rates of multiple first failure factors to obtain the failure cause of the target battery.

[0183] In some embodiments, the cause determination module 804 is specifically configured to select m first failure factors in descending order of defect rate; m is a positive integer; when the defect rates of the selected m first failure factors meet the concentration condition, determine the m first failure factors as the failure causes of the target battery.

[0184] In some embodiments, referring to Figure 13 , the device further includes:

[0185] The verification result acquisition module 805 is configured to acquire the on-site verification result of the failure cause of the target battery;

[0186] The re-statistics module 806 is configured to, if the on-site verification result fails, exclude the verified failure causes and return to execute the step of statistically processing the production process data.

[0187] Each module in the above battery failure analysis device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or stored in the memory of the electronic device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0188] According to some embodiments of the present application, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as Figure 14 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. The computer program, when executed by the processor, implements a battery failure analysis method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0189] Those skilled in the art can understand that Figure 14 the structure shown in Figure 14 is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component arrangement.

[0190] According to some embodiments of the present application, there is also provided a non-transitory computer-readable storage medium including instructions, such as a memory including instructions, and the above instructions can be executed by a processor of an electronic device to complete the above method. For example, the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0191] According to some embodiments of the present application, there is also provided a computer program product. When the computer program is executed by a processor, the above method can be implemented. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, part or all of the above method can be implemented in accordance with the process or function described in the embodiments of the present application.

[0192] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0193] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0194] The above-described embodiments merely represent several implementation manners of the present application, facilitating a specific and detailed understanding of the technical solution of the present application. However, it should not be construed as a limitation on the protection scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. It should be understood that the technical solutions obtained by those skilled in the art through logical analysis, reasoning or limited experiments based on the technical solution provided by the present application are all within the protection scope of the appended claims of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the content of the appended claims, and the description and drawings can be used to explain the content of the claims.

Claims

1. A method for analyzing battery failure, characterized in that, The method includes: Obtaining the failure mode and the first failure feature of the target battery; wherein, the failure feature is used to characterize the observable failure phenomenon; Searching for the target failure analysis tree corresponding to the failure mode of the target battery from multiple pre-created failure analysis trees, and selecting a failure factor node as the target node from the child nodes of the root node of the target failure analysis tree; when the child nodes of the target node contain failure feature nodes, matching the first failure feature with each second failure feature corresponding to the target node to obtain a matching result; determining the troubleshooting node according to the matching result, and re-selecting the target node; after traversing all the child nodes of the target failure analysis tree, determining the failure factor set according to the determined troubleshooting nodes; wherein, the failure factor set includes at least one first failure factor, the failure analysis tree is pre-constructed based on multiple second failure factors and multiple second failure features, the failure factor is used to characterize the failure cause, the failure analysis tree includes a root node and multiple child nodes; the root node represents the failure mode, the child nodes include failure factor nodes and failure feature nodes, and each level of child nodes includes the failure factor nodes, and the failure feature nodes are set with the second failure features; Collecting data according to the failure factor set to obtain production process data; Calculating the defect rate of each of the first failure factors in the failure factor set according to the production process data; Performing statistical processing on the defect rates of multiple first failure factors to obtain the failure cause of the target battery.

2. The method according to claim 1, characterized in that The determining the troubleshooting node according to the matching result and re-selecting the target node includes: When the matching result meets the preset matching condition, determining the target node as the troubleshooting node. If the troubleshooting node has child nodes, re-selecting a failure factor node from the child nodes of the troubleshooting node as the target node, and executing the steps after selecting the target node. If the troubleshooting node has no child nodes, returning to the parent node of the troubleshooting node to re-select the target node; When the matching result does not meet the preset matching condition, if the target node has sibling nodes, re-selecting a failure factor node from the sibling nodes of the target node as the target node, and executing the steps after selecting the target node; If the target node has no sibling nodes, returning to the parent node of the target node to re-select the target node.

3. The method according to claim 1, characterized in that, The method further includes: When the child nodes of the target node do not contain failure feature nodes, determining the target node as the troubleshooting node; If the target node has sibling nodes, re-selecting a failure factor node from the sibling nodes of the target node as the target node, and executing the steps after selecting the target node; If the target node has no sibling nodes, returning to the parent node of the target node to re-select the target node.

4. The method according to any one of claims 1 to 3, characterized in that The matching the first failure feature with each second failure feature corresponding to the target node to obtain a matching result includes: Obtain the weights of the respective second failure features corresponding to the target node; Match the first failure feature with the respective second failure features corresponding to the target node to obtain the probabilities of the respective second failure features; Calculate the probability of the target node based on the weights and probabilities of the respective second failure features, and determine the probability of the target node as the matching result.

5. The method according to claim 1, wherein The statistical processing of the defect rates of the multiple first failure factors to obtain the failure cause of the target battery includes: Select m of the first failure factors in descending order of defect rate; m is a positive integer; When the defect rates of the selected m first failure factors satisfy the concentration condition, determine the m first failure factors as the failure cause of the target battery.

6. The method according to claim 1, characterized in that The method further includes: Obtain the on-site verification result of the failure cause of the target battery; If the on-site verification result fails, exclude the verified failure causes and return to execute the step of statistically processing the production process data.

7. A battery failure analysis device, characterized in that, The device includes: A feature acquisition module, configured to acquire the failure mode and the first failure feature of the target battery; A cause analysis module, configured to find a target failure analysis tree corresponding to the failure mode of the target battery from a plurality of pre-created failure analysis trees, and select a failure factor node as the target node from the child nodes of the root node of the target failure analysis tree; when the child nodes of the target node contain failure feature nodes, match the first failure feature with the respective second failure features corresponding to the target node to obtain a matching result; determine a troubleshooting node according to the matching result, and re-select the target node; after traversing all the child nodes of the target failure analysis tree, determine the failure factor set according to the determined troubleshooting node; wherein, the failure factor set includes at least one first failure factor, and the failure analysis tree is pre-constructed based on a plurality of second failure factors and a plurality of second failure features; the first failure feature and the second failure feature are used to characterize the failure phenomenon, the first failure factor and the second failure factor are used to characterize the failure cause, the failure analysis tree includes a root node and a plurality of child nodes; the root node represents the failure mode, the child nodes include failure factor nodes and failure feature nodes, and each level of child nodes includes the failure factor nodes, and the failure feature nodes are set with the second failure features; A data acquisition module, configured to perform data acquisition according to the failure factor set to obtain production process data; A cause determination module, configured to calculate the defect rate of each of the first failure factors in the failure factor set according to the production process data; perform statistical processing on the defect rates of the multiple first failure factors to obtain the failure cause of the target battery.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method according to any one of claims 1 to 6.

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