Battery safety detection method, device and platform and storage medium

Abstract

The invention discloses a battery safety detection method, device and platform and a storage medium, and belongs to the technical field of battery detection. The method comprises the steps of obtaining battery data representing a battery operation condition in response to a triggered battery detection instruction; acquiring a battery type; a target detection sub-algorithm is determined in a preset safety detection algorithm based on the battery type and the battery data, a plurality of detection sub-algorithms are packaged in the preset safety detection algorithm, and the plurality of detection sub-algorithms are used for performing safety detection on different types of batteries; safety detection is performed on the battery data through the target detection sub-algorithm, and the detection sub-algorithm corresponding to each battery type is determined in the preset safety detection algorithms according to different battery types, so that safety detection is performed on various different types of batteries through the target detection sub-algorithm, and the battery detection efficiency is improved. And a detection sub-algorithm in a unified preset safety detection algorithm is used, so that detection of various different types of batteries can be flexibly coped with.

Description

Technical Field

[0001] The present invention relates to the technical field of battery detection, and in particular to a battery safety detection method, device, platform and storage medium. Background Art

[0002] In the field of battery research and development and application, it is necessary to analyze the performance and data presentation of power batteries, but it is impossible to test and analyze multiple different batteries at the same time, and the detection efficiency is low. Summary of the Invention

[0003] The embodiments of the present invention provide a battery safety detection method, device, platform and storage medium, aiming to solve the technical problem that battery display is not intuitive enough.

[0004] In a first aspect, an embodiment of the present application provides a battery safety detection method, the battery safety detection method comprising:

[0005] In response to a triggered battery detection instruction, obtaining battery data representing a battery operating condition;

[0006] Get the battery type;

[0007] Determining a target detection sub-algorithm in a preset safety detection algorithm based on the battery type and the battery data, wherein the preset safety detection algorithm encapsulates multiple detection sub-algorithms, and the multiple detection sub-algorithms are used to perform safety detection on different types of batteries;

[0008] The battery data is safety-checked using the target detection sub-algorithm.

[0009] In this embodiment, by responding to the triggered battery detection instruction, the type of battery that needs to be detected is determined, so that battery data representing the battery operating condition can be obtained. By analyzing the battery data, the battery type is determined, and the target detection algorithm is determined in the preset safety detection algorithm according to the battery type and battery data. The detection sub-algorithm corresponding to each battery type can be determined in the preset safety detection algorithm according to different battery types, so that safety detection of various types of batteries can be performed separately through the target detection sub-algorithm, thereby improving battery detection efficiency. By using the detection sub-algorithm in a unified preset safety detection algorithm, various types of battery detection can be flexibly responded to.

[0010] In some embodiments, determining a target detection sub-algorithm in a preset safety detection algorithm based on the battery type and the battery data includes:

[0011] obtaining a type parameter based on the battery type;

[0012] Obtaining a first detection rule for each detection sub-algorithm in a preset safety detection algorithm, where the first detection rule represents a battery type that can be detected by the detection sub-algorithm;

[0013] Traversing the first detection rule through the type parameter to obtain a first detection rule that matches the type parameter;

[0014] An initial detection sub-algorithm is screened using the battery data to obtain a target detection sub-algorithm, where the initial detection sub-algorithm is a detection sub-algorithm corresponding to a first detection rule that matches the type parameter.

[0015] This embodiment determines the type parameters of the battery according to the battery type, thereby quickly traversing the first detection rule through the type parameters to obtain the first detection rule that matches each type parameter, thereby screening the initial detection sub-algorithm through the battery data, and by matching the input parameters of the battery data, the target detection sub-algorithm of the battery can be quickly obtained.

[0016] In some embodiments, screening the initial detection sub-algorithm using the battery data to obtain a target detection sub-algorithm includes:

[0017] Extracting key fields from the battery data to obtain battery key fields;

[0018] Acquire a second detection rule in the initial detection sub-algorithm, where the second detection rule represents a battery parameter detectable by each of the detection sub-algorithms;

[0019] Traversing the second detection rule through the battery key field to obtain a second detection rule that matches the battery key field;

[0020] A target detection sub-algorithm is obtained based on a second detection rule that matches the battery key field. The target detection sub-algorithm is an initial detection sub-algorithm corresponding to the second detection rule that matches the battery key field.

[0021] In the technical solution of the embodiment of the present application, by extracting key fields from the battery data, the second detection rule that matches can be quickly queried based on the battery key fields in the battery data, and the corresponding target detection sub-algorithm can be obtained based on the second detection rule. The target detection sub-algorithm that can detect the corresponding battery parameters can be quickly queried based on the second detection rule, and the initial detection sub-algorithm can be screened through the battery key fields to improve the accuracy of the query target detection sub-algorithm.

[0022] In some embodiments, before determining the target detection sub-algorithm in the preset safety detection algorithm based on the battery type and the battery data, the method further includes:

[0023] Obtaining detection sub-algorithm sample data and battery sample data representing battery operating conditions;

[0024] Analyzing the detection sub-algorithm sample data based on the battery sample data to determine the battery type and battery parameters that can be detected by each detection sub-algorithm;

[0025] Establishing a first detection rule based on the battery type detectable by each detection sub-algorithm, and establishing a second detection rule based on the battery parameter detectable by each detection sub-algorithm;

[0026] The detection sub-algorithm sample data is encapsulated by the first detection rule and the second detection rule to obtain a preset security detection algorithm.

[0027] In the technical solution of the embodiment of the present application, by collecting multiple detection sub-algorithms and multiple battery data characterizing the battery operating conditions, different battery data can be matched with each detection sub-algorithm, so that the battery type and battery parameters that each detection sub-algorithm can detect in the battery sample data can be determined, and a first detection rule is established through the battery type that each detection sub-algorithm can detect, that is, the battery type acceptable to each detection sub-algorithm, and a second detection rule is established through the battery parameters that each detection sub-algorithm can detect, so that the battery data that the corresponding detection sub-algorithm can process is identified according to the second detection rule, and a mapping relationship between the battery data and the second detection rule is established, and by establishing the first detection rule and the second detection rule in advance, and encapsulating each detection sub-algorithm in the sample data of each detection sub-algorithm into a preset safety detection algorithm of a unified caliber based on the first detection rule and the second detection rule, various different battery data can be undertaken by the preset safety detection algorithm to form a normalized safety detection algorithm template, thereby improving the efficiency of detection.

[0028] In some embodiments, the object detection sub-algorithm includes multiple detection components;

[0029] Before determining the target detection sub-algorithm in the preset safety detection algorithm based on the battery type and the battery data, the method further includes:

[0030] Obtaining the detection process of the target detection sub-algorithm;

[0031] Creating a corresponding detection component according to the detection process;

[0032] establishing a corresponding relationship between the detection components based on the detection process;

[0033] The detection components are sorted based on the corresponding relationships to generate a target detection sub-algorithm.

[0034] This embodiment analyzes the target detection sub-algorithm to determine the detection process of the target detection sub-algorithm when performing detection, and creates corresponding detection components according to the detection process. It can also establish a correspondence between the detection components according to the detection process, so as to sort the detection components and generate the corresponding target detection sub-algorithm. The arrangement order of the detection components can be continuously adjusted to make the detection components cover a wider range, and the corresponding target detection group algorithm can be continuously optimized to improve the accuracy of detection.

[0035] In some embodiments, sorting the detection components based on the corresponding relationships to generate a target detection sub-algorithm includes:

[0036] Determining a detection order of the detection components based on the corresponding relationship;

[0037] The detection components are sorted according to the detection order, and a target detection sub-algorithm is obtained through the sorted detection components.

[0038] In the technical solution of the embodiment of the present application, after the correspondence between the various detection components is determined, the detection order of the detection components can be obtained, and then the detection components are sorted according to the detection order to obtain the target detection sub-algorithm. The detection process of the target detection sub-algorithm is continuously adjusted according to the detection order of the detection components, thereby improving the detection accuracy.

[0039] In some embodiments, performing safety detection on the battery data using the target detection sub-algorithm includes:

[0040] Calling each detection component in the target detection sub-algorithm;

[0041] The battery data is calculated by each of the detection components to obtain a detection result.

[0042] This embodiment calculates the parameters in the battery data by calling each detection component in the target detection sub-algorithm, thereby obtaining the detection result of the battery safety detection and improving the detection efficiency.

[0043] In some embodiments, the battery safety detection method further includes:

[0044] Generate target visualization reports based on test results;

[0045] The target visualization report is displayed.

[0046] After obtaining the test results, this embodiment generates a visual report of the test results. The visual report can support chart linkage and data drill-down. The test results are displayed through the visual report, thereby visually displaying the test results, allowing users to intuitively and quickly understand the battery test results, thereby improving user experience.

[0047] In some embodiments, generating a target visualization report based on the detection results includes:

[0048] In response to the triggered report selection instruction, determining a target report component in a preset statistical report set;

[0049] Generate a target report through the target report component;

[0050] The detection results are loaded into the target report to generate a target visualization report.

[0051] This embodiment responds to the triggered report selection instruction, thereby determining the report style that the user wants to generate according to the report selection instruction, and determining the corresponding report component in the preset statistical report set, combining the target report components to generate a real statistical report, which can be used to guide battery safety testing business and improve the effect of battery safety testing.

[0052] In some embodiments, generating a target visualization report based on the detection results includes:

[0053] In response to the triggered report creation instruction, obtaining a custom report component;

[0054] Creating a target report using the custom report component;

[0055] The detection results are loaded into the target report to generate a target visualization report.

[0056] This embodiment determines the report mode that the user wants to customize and the corresponding custom report components based on the report creation instruction triggered by the user when the user wants to customize the displayed visual report, so that the user can arbitrarily select the desired components, generate the target report, and load the detection results into the target report for display. The customization capability is flexible and can meet the different needs of different users.

[0057] In some embodiments, the method of responding to the triggered report selection instruction, before determining the target report component in the preset statistical report set, further includes:

[0058] Collect test result samples of batteries under different operating conditions based on preset safety detection algorithms;

[0059] Collect corresponding statistical indicator samples according to the test result samples;

[0060] Creating corresponding report component data according to the statistical indicator sample;

[0061] Generate a preset statistical report set based on the report component data.

[0062] This embodiment collects test result sample data of batteries detected by the detection algorithm in a preset safety detection algorithm under different operating conditions in advance, collects statistical indicators that can be used for the corresponding test results based on the test result samples, pre-prepare a statistical indicator set that has passed industry review, and establishes a preset statistical report set, thereby covering all battery characteristic data performances of battery safety detection, and can intuitively present the statistical results to business personnel, thereby improving the readability of visual reports during battery detection.

[0063] In some embodiments, before obtaining battery data representing the battery operating condition in response to the triggered battery detection instruction, the method further includes:

[0064] Obtain battery sample data of different batteries in different states;

[0065] Performing keyword extraction on the battery sample data to obtain battery keyword samples representing battery operating conditions;

[0066] A physical model is established according to the battery keyword sample, wherein the physical model is used to identify the corresponding battery keyword.

[0067] This embodiment extracts keywords from battery sample data of various batteries in different states, thereby extracting battery keyword samples that characterize the battery operating conditions from massive battery sample data. A physical model is established using the battery keyword samples, and a mapping relationship between the physical model and the battery keywords is established, thereby identifying the battery keywords through the physical model. This can adapt to all device models for battery safety testing and improve the efficiency of testing.

[0068] In some embodiments, obtaining battery data representing the battery operating condition includes:

[0069] Get the initial battery data of the battery;

[0070] The initial battery data is input into the physical model to obtain battery data representing the battery operating condition. The physical model is used to identify battery keywords in the initial battery data.

[0071] This embodiment obtains the initial data of the battery and uses the physical model to perform keyword recognition on the initial data to obtain battery data representing the battery operating conditions. This can quickly identify the data representing the battery operating conditions in the initial battery data and improve the accuracy of detection.

[0072] In a second aspect, an embodiment of the present invention further provides a battery safety detection device, comprising:

[0073] An acquisition module, configured to acquire battery data representing a battery operating condition in response to a triggered battery detection instruction;

[0074] Determine the module for obtaining the battery type;

[0075] The determination module is further configured to determine a target detection sub-algorithm in a preset safety detection algorithm based on the battery type and the battery data, wherein the preset safety detection algorithm encapsulates a plurality of detection sub-algorithms, and the plurality of detection sub-algorithms are used to perform safety detection on different types of batteries;

[0076] A detection module is used to perform safety detection on the battery data through the target detection sub-algorithm.

[0077] In a third aspect, an embodiment of the present invention further provides a battery safety detection platform, on which the battery safety detection method as described above is executed.

[0078] In a fourth aspect, an embodiment of the present invention further provides a storage medium, on which a battery safety detection program is stored. When the battery safety detection program is executed by a processor, the battery safety detection method as described above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0080] Figure 1 This is a structural diagram of an embodiment of the battery safety testing platform proposed in this application;

[0081] Figure 2 A flowchart of an embodiment of a battery safety detection method proposed in an embodiment of the present invention;

[0082] Figure 3 This is another flowchart of an embodiment of the battery safety detection method proposed in an embodiment of the present invention;

[0083] Figure 4 This is another flowchart of an embodiment of the battery safety detection method proposed in an embodiment of the present invention;

[0084] Figure 5 This is another flowchart of an embodiment of the battery safety detection method proposed in an embodiment of the present invention;

[0085] Figure 6a-6b A schematic diagram showing the test results of an embodiment of a battery safety test method according to an embodiment of the present invention;

[0086] Figure 7 A schematic diagram of a custom generated target visualization report according to an embodiment of a battery safety detection method according to an embodiment of the present invention;

[0087] Figure 8 A schematic diagram of the overall process of the battery safety detection method proposed in an embodiment of the present invention;

[0088] Figure 9 This is another overall flow chart of the battery safety detection method proposed in an embodiment of the present invention;

[0089] Figure 10 This is a structural diagram of an embodiment of a battery safety detection device proposed in an embodiment of the present invention.

[0090] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0091] The following embodiments of the technical solution of the present invention will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are therefore only examples and are not intended to limit the scope of protection of the present invention.

[0092] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which the present invention belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions.

[0093] In the description of the embodiments of the present invention, technical terms such as "first" and "second" are used solely to distinguish between different objects and should not be understood to indicate or imply relative importance or to implicitly specify the quantity, specific order, or primary and secondary relationship of the technical features indicated. In the description of the embodiments of the present invention, "plurality" means more than two, unless otherwise specifically defined.

[0094] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0095] In the description of the embodiments of the present invention, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exists simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0096] In the description of the embodiments of the present invention, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0097] In the description of the embodiments of the present invention, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the embodiments of the present invention.

[0098] In the description of the embodiments of the present invention, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connect," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and can refer to internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on specific circumstances.

[0099] At present, in the field of battery research and development and application, it is necessary to analyze the performance and data performance of power batteries.

[0100] When the performance and data of a large number of power batteries need to be quickly analyzed, the batteries can only be tested one by one, and it is impossible to test and analyze multiple different batteries at the same time. If you want to change the type of battery test and flexibly select the indicator set for statistical battery performance, you need to first test the battery data of different battery types separately and compile statistics on the test results separately. The detection efficiency is low and cannot meet the user's usage needs.

[0101] In order to solve the above-mentioned defects, the invention of this application is as follows:

[0102] By integrating the routine needs of batteries during safety testing, a comprehensive solution is formed, which can simultaneously perform battery safety testing on various types of battery data and display subsequent test results, guide business decisions in real time, and realize battery safety management quickly and conveniently.

[0103] Based on the above considerations, in order to improve the efficiency of battery detection, after in-depth research, a battery safety detection method, device, platform and storage medium were proposed, which are suitable for various scenarios of battery safety detection during actual battery use, for example, in the specific application scenarios of safety monitoring of batteries of electric vehicles, ships or aircraft during driving or charging.

[0104] In order to better understand the embodiments of the present application, the battery safety detection method, device, platform and storage medium provided according to the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0105] The execution subject of the battery safety detection method provided in the embodiment of the present application can be a battery safety detection platform. The battery safety detection platform can be used to perform safety detection on the batteries of electric vehicles, energy storage cabinets, battery swap stations, and mobile power banks. It can also perform safety detection on the batteries of other terminals or devices. This embodiment does not limit this. The battery safety detection platform is a low-code platform. By using low-code development technology, the detection sub-algorithms used to detect the safety of various types of batteries are encapsulated into preset safety detection algorithms. Other detection sub-algorithms used to detect various types of battery failures, battery life, etc. can also be encapsulated into corresponding detection algorithms, so that batch calculations can be performed directly for actual business needs. Low-code development technology greatly improves the efficiency of algorithm development and reduces labor and time costs.

[0106] For the sake of convenience, here we use Figure 1 The battery safety detection platform shown serves as a specific application environment of the battery safety detection method provided in this embodiment. The battery safety detection platform is only a possible example, and does not limit the specific application environment of the battery safety detection method provided in the embodiment of the present application. The battery safety detection method provided in the embodiment of the present application can also be applied to other battery safety detection platforms.

[0107] like Figure 1As shown, the battery safety testing platform can include four major modules: algorithm library, component library, data set and my application. More modules can be added as needed. The algorithm library stores a large number of algorithms for battery testing, the component library includes corresponding algorithm components, and the data set includes a large amount of collected battery data. In my application, you can select the corresponding application or component for testing. Components include general components, special components and user-developed components. General components are components that are frequently used when using battery testing, such as process components, form components, text components and image components, etc., and can also include other types of components. Special components refer to components used for specific tests, such as battery safety testing components, battery fault diagnosis components, battery life prediction components and battery charge and discharge control components. User-developed components refer to user-defined components, such as algorithm components or other types of components.

[0108] When users click "My Applications" in the battery safety testing platform and select the corresponding application, the corresponding algorithm components will be displayed on the screen, allowing them to establish a detection sub-algorithm or a preset detection algorithm. For example, if they need to detect abnormal self-discharge outliers in battery cells, they can click the "Battery Self-Discharge Outlier Monitoring" application in "My Applications" and establish the corresponding components. For example, the battery self-discharge outlier monitoring detection includes components such as the fusion component, the characteristic indicator calculation component, the battery self-discharge outlier monitoring component, the data storage component, and the output report component. Based on the battery self-discharge outlier monitoring component, the battery self-discharge outlier monitoring algorithm is generated.

[0109] The low-code battery safety testing platform also displays detection sub-algorithm information and corresponding application information, such as the name of the detection sub-algorithm, creation time, last update time, and an introduction to the detection sub-algorithm. The information can also be updated as needed.

[0110] If you need to quickly calculate the safety failures that may occur in the next three days for a batch of 1,000 power batteries consisting of different battery models, different battery materials, different battery manufacturers, and different energy densities, you can collect battery data for power batteries of different battery models, different battery materials, different battery manufacturers, and different energy densities, and then use a normalized preset battery safety detection algorithm to perform the calculations based on the corresponding battery data. In the low-code platform, business personnel do not have visibility into the components that make up the preset safety detection algorithm and the corresponding algorithm parameter configurations. Business personnel only need to import the data to be tested according to the low-code platform's data import requirements. The battery safety detection platform will adaptively identify which batteries participate in the calculation of which type of detection sub-algorithm. After the concurrent calculations are completed, the algorithm execution results are uniformly output.

[0111] As an example, power batteries of different models, materials, and energy densities require different fault prediction algorithms. For example, complex battery materials, such as oxide batteries, exhibit slow particle flow, and failures often manifest as low battery charge and discharge efficiency. Common faults in lithium-ion batteries include abnormal voltage trends, abnormal internal resistance, and excessive current; while lead-acid batteries often experience disc cracking and reduced charge and discharge efficiency. Therefore, the fault detection algorithms for batteries with different materials and configuration parameters rely on different battery detection algorithms. Therefore, the detection sub-algorithms for different batteries should be unified into a pre-set safety detection algorithm to accommodate the diverse range of products in the power battery market and rapidly complete battery safety testing.

[0112] In some embodiments of the present invention, reference Figure 2 , an embodiment of the present invention provides a battery safety detection method, which may include:

[0113] Step S10: In response to the triggered battery detection instruction, battery data representing the battery operating condition is acquired.

[0114] It should be noted that the battery test instruction is user-triggered information for battery testing, and may specifically include the battery type, battery quantity, battery parameters, etc. The battery test instruction can be triggered by a user clicking directly on the battery safety testing platform, or by a user sending an instruction to the battery safety testing platform using a terminal. Other triggering methods are also possible, and this embodiment does not limit this.

[0115] It should be understood that the battery operating conditions may include battery charging and discharging conditions or other conditions, and the battery data representing the battery operating conditions may include battery configuration data, battery voltage and current data, etc., and may also include other data.

[0116] Step S20: Obtain the battery type.

[0117] It is understood that the battery type can be directly obtained, or the battery data can be analyzed to determine how many types of batteries are present in the battery data, and the battery type of each battery can be obtained. The battery type can then be determined based on the battery data. For example, battery types include oxide batteries, lithium-ion batteries, sodium-ion batteries, etc.

[0118] Step S30: determining a target detection sub-algorithm in a preset safety detection algorithm based on the battery type and battery data, wherein the preset safety detection algorithm encapsulates multiple detection sub-algorithms, and the multiple detection sub-algorithms are used to perform safety detection on different types of batteries.

[0119] The preset safety detection algorithm is a unified detection algorithm template. The preset safety detection algorithm encapsulates multiple detection sub-algorithms, and the multiple detection sub-algorithms can be used to perform safety detection on different types of batteries.

[0120] The detection sub-algorithm is a mature battery safety detection algorithm that can be directly obtained or independently developed. It can effectively guide developers of low-code development platforms to build more accurate battery detection algorithms, thereby greatly improving battery management efficiency.

[0121] The preset safety detection algorithm can identify battery data and battery type, thereby determining the detection sub-algorithm that can be used for the corresponding battery data and battery type. The battery safety detection is performed using the detection sub-algorithm. Therefore, the target detection sub-algorithm can be determined in the preset safety detection algorithm based on the battery type and battery data. The target detection sub-algorithm is the algorithm that can perform safety detection on the current battery.

[0122] By querying the detection sub-algorithm corresponding to each battery type in the preset safety detection algorithm, various types of battery safety detection can be met.

[0123] The low-code development platform does not need to maintain multiple homogeneous detection algorithms. It only needs to maintain a preset security detection algorithm for devices under one application type, and adjust the algorithm's detection rules according to needs to improve detection efficiency.

[0124] Step S40: Perform safety detection on the battery data using the target detection sub-algorithm.

[0125] As an example, after the target detection sub-algorithm is determined, the target detection sub-algorithm can be directly called in the battery safety detection platform to perform safety detection on the battery data, thereby obtaining the detection results.

[0126] This embodiment obtains battery data characterizing the battery operating condition in response to a triggered battery detection instruction; obtains the battery type; determines a target detection sub-algorithm in a preset safety detection algorithm based on the battery type and battery data, wherein the preset safety detection algorithm encapsulates multiple detection sub-algorithms, and the multiple detection sub-algorithms are used to perform safety detection on different types of batteries; performs safety detection on the battery data through the target detection sub-algorithm, and determines the detection sub-algorithm corresponding to each battery type in the preset safety detection algorithm according to different battery types, thereby performing safety detection on different types of batteries respectively through the target detection sub-algorithm, thereby improving battery detection efficiency, and using a unified detection sub-algorithm in the preset safety detection algorithm, it can flexibly respond to different types of battery detection.

[0127] In some embodiments, each detection sub-algorithm in the preset safety detection algorithm has a corresponding detection rule, so the matching detection rule can be determined by the battery type and the remaining battery data to obtain the corresponding detection sub-algorithm. Therefore, referring to Figure 3 The steps of determining the target detection sub-algorithm in the preset safety detection algorithm based on the battery type and battery data specifically include:

[0128] Step S301: Obtain type parameters based on the battery type.

[0129] It should be noted that different battery types have different corresponding parameters. For example, if the battery type is a lithium battery, the type parameter is A, and if the battery type is a sodium battery, the corresponding type parameter is B. Therefore, the corresponding type parameter can be obtained according to the battery type.

[0130] Step S302: obtaining a first detection rule of each detection sub-algorithm in a preset safety detection algorithm, where the first detection rule represents a battery type that can be detected by the detection sub-algorithm.

[0131] It can be understood that the preset safety detection algorithm is provided with detection rules of the detection sub-algorithm, so that the battery data that can be detected is determined by the detection rules. Therefore, the first detection rule of each detection sub-algorithm in the preset safety algorithm can be obtained. The first detection rule is a rule that can identify the battery type and is used to determine the acceptable battery type.

[0132] Step S303: traverse the first detection rule by the type parameter to obtain the first detection rule that matches the type parameter.

[0133] It should be noted that the type parameter represents the type of the battery. Based on the type parameter, a first detection rule matching the type parameter can be determined in each first detection rule, so that the corresponding battery type can be detected through the first detection rule.

[0134] Step S304: screening the initial detection sub-algorithm through the battery data to obtain a target detection sub-algorithm, where the initial detection sub-algorithm is a detection sub-algorithm corresponding to the first detection rule that matches the type parameter.

[0135] As an example, there are corresponding battery key fields in the battery data, which can reflect the characteristics of the battery. Therefore, the initial detection sub-algorithm can be screened through the battery data to obtain the target detection sub-algorithm.

[0136] The initial detection sub-algorithm is a detection sub-algorithm corresponding to the first detection rule that matches the type parameter. By screening the first detection rule, a first detection rule that can be used to detect the battery type is obtained, thereby obtaining the corresponding initial detection sub-algorithm. The initial detection sub-algorithm can be used to judge the current battery type.

[0137] It should be noted that after the initial detection sub-algorithm is obtained, the initial detection sub-algorithm may be screened a second time to obtain the target detection sub-algorithm.

[0138] In this embodiment, the type parameters of the battery are determined according to the battery type, so that the first detection rule is quickly traversed through the type parameters to obtain the first detection rule that matches each type parameter, so that the initial detection sub-algorithm is screened through the battery data, and the target detection sub-algorithm of the battery can be quickly obtained by matching the input parameters of the battery data.

[0139] It should be noted that the initial detection sub-algorithm can identify the battery type. After identifying the corresponding battery type, the initial detection sub-algorithm can detect the battery data corresponding to the battery type. Some initial detection sub-algorithms can identify multiple battery types, but can only detect the battery data corresponding to a certain battery type. Therefore, it is necessary to perform a secondary screening of the initial detection sub-algorithm to determine the target detection sub-algorithm that can detect the battery data.

[0140] In some embodiments, the steps of screening the initial detection sub-algorithm using battery data to obtain the target detection sub-algorithm include:

[0141] Extract key fields from battery data to obtain battery key fields;

[0142] Obtaining a second detection rule in the initial detection sub-algorithm, where the second detection rule represents a battery parameter detectable by each detection sub-algorithm;

[0143] Traversing the second detection rule through the battery key field to obtain a second detection rule that matches the battery key field;

[0144] A target detection sub-algorithm is obtained based on the second detection rule that matches the battery key field. The target detection sub-algorithm is an initial detection sub-algorithm corresponding to the second detection rule that matches the battery key field.

[0145] It should be noted that keyword extraction algorithms can be used to extract keywords from battery data. These algorithms can be embedded in the model or the battery safety testing platform. Specifically, text keywords can be extracted from battery data to obtain battery key fields. Battery key fields represent battery characteristics, such as battery model, capacity, and other battery parameter information.

[0146] The initial detection sub-algorithm also has a second detection rule, which can identify battery parameters that can be processed. Therefore, the second detection rule of the initial detection sub-algorithm can be obtained. The second detection rule can be established by analyzing the initial detection sub-algorithm in advance.

[0147] As an example, all second detection rules may be traversed through the extracted battery key field to obtain a second detection rule matching the battery key field, and the initial detection sub-algorithm corresponding to the second detection rule may be used as the target detection sub-algorithm.

[0148] like Figure 4 As shown, Figure 4 This is a schematic diagram of using a battery safety testing platform to perform battery safety testing. For example, battery data of 1,000 power batteries consisting of different battery models, different battery materials, different battery manufacturers, and different energy densities are required to calculate the safety failure warning results within the next three days. The corresponding battery data is input into the preset safety detection algorithm, and the battery data is identified through the detection rules. The corresponding target detection sub-algorithm is determined and the target detection sub-algorithm is triggered to use. The target detection sub-algorithm is then used to calculate the safety failure warning results that may occur within the next three days.

[0149] In the technical solution of the embodiment of the present application, by extracting key fields from the battery data, the second detection rule that matches can be quickly queried based on the battery key fields in the battery data, and the corresponding target detection sub-algorithm can be obtained based on the second detection rule. The target detection sub-algorithm that can detect the corresponding battery parameters can be quickly queried based on the second detection rule, and the initial detection sub-algorithm can be screened through the battery key fields to improve the accuracy of the query target detection sub-algorithm.

[0150] In some embodiments, the preset safety detection algorithm encapsulates multiple detection sub-algorithms. To improve the detection efficiency during battery safety detection, the detection sub-algorithms may be encapsulated into the preset safety detection algorithm in advance. Therefore, before step S30, the following steps may be further included:

[0151] Step S21: Acquire detection sub-algorithm sample data and battery sample data representing battery operating conditions.

[0152] It should be noted that the detection sub-algorithm sample data is various sub-algorithm data for battery detection. The detection sub-algorithm sample data can detect different types of batteries. The battery sample data that characterizes the battery operating conditions can be obtained through the data set on the platform, and can also be directly collected through the external interface.

[0153] The battery sample data is battery data corresponding to different types of batteries.

[0154] Step S22: Analyze the detection sub-algorithm sample data based on the battery sample data to determine the battery type and battery parameters that can be detected by each detection sub-algorithm.

[0155] In a specific implementation, a mapping relationship between battery sample data and detection sub-algorithm sample data can be established, so as to analyze the detection sub-algorithm sample data based on the battery sample data, thereby determining the battery type and battery parameters that can be detected by each detection sub-algorithm.

[0156] Step S23: establishing a first detection rule based on the battery types detectable by each detection sub-algorithm, and establishing a second detection rule based on the battery parameters detectable by each detection sub-algorithm.

[0157] It is understood that after determining the battery types that can be detected by each detection sub-algorithm, a first detection rule can be established based on the battery type, thereby determining the battery type that can be detected by the detection sub-algorithm using the first detection rule. A second detection rule can also be established based on the battery parameters, thereby mapping the second detection rule to the electromagnetic parameters, and determining the battery parameters that can be detected by the detection sub-algorithm using the second detection rule.

[0158] Step S24: Encapsulate the detection sub-algorithm sample data through the first detection rule and the second detection rule to obtain a preset security detection algorithm.

[0159] As an example, all detection sub-algorithms are established with first detection rules and second detection rules, so that each detection sub-algorithm in the detection sub-algorithm sample data is encapsulated through the first detection rules and the second detection rules to obtain a preset safety detection algorithm, forming a normalized algorithm that can perform safety detection on various types of batteries.

[0160] In the technical solution of the embodiment of the present application, different battery data can be matched with each detection sub-algorithm by collecting multiple detection sub-algorithms and multiple battery data characterizing the battery operating conditions, so that the battery types and battery parameters that each detection sub-algorithm can detect in the battery sample data can be determined, and a first detection rule is established through the battery types that each detection sub-algorithm can detect, that is, the battery types acceptable to each detection sub-algorithm, and a second detection rule is established through the battery parameters that each detection sub-algorithm can detect, so that the battery data that the corresponding detection sub-algorithm can process is identified according to the second detection rule, and a mapping relationship between the battery data and the second detection pair is established, and by establishing the first detection rule and the second detection rule in advance, and encapsulating each detection sub-algorithm in the sample data of each detection sub-algorithm into a preset safety detection algorithm of a unified caliber based on the first detection rule and the second detection rule, various different battery data can be undertaken by the preset safety detection algorithm to form a normalized safety detection algorithm template, thereby improving the efficiency of detection.

[0161] In some embodiments, the object detection sub-algorithm includes multiple detection components.

[0162] When the target detection sub-algorithm performs detection, the battery data can be pre-processed first, and then feature extraction and anomaly calculation are performed. Therefore, the target detection sub-algorithm includes multiple detection components, and the battery data is calculated through each detection component.

[0163] Optionally, before determining the target detection sub-algorithm in the preset safety detection algorithm based on the battery type and battery data, the method further includes:

[0164] Get the detection process of the target detection sub-algorithm;

[0165] Create corresponding detection components according to the detection process;

[0166] Establishing the corresponding relationship between detection components based on the detection process;

[0167] The detection components are sorted based on the correspondence and the target detection sub-algorithm is generated.

[0168] The detection process of the target detection sub-algorithm can be obtained by analyzing the target detection algorithm. Figure 1 As shown, for example, the detection process of the target detection sub-algorithm includes fusion, feature index calculation, outlier monitoring, data storage and output report, then the corresponding detection components can be created according to the detection process, and the correspondence between the components can be established according to the detection process. The correspondence between the components is the order between the components, so the detection components are sorted according to the correspondence between the components to obtain the sorted detection sub-algorithm, that is, the target detection sub-algorithm.

[0169] For example, the fusion component is placed in the initial execution position, and its output is the input of the second component, the characteristic indicator calculation component. The output of the second component is the input of the battery cell self-discharge abnormal outlier monitoring component. After the execution is completed, the data storage component is called, and the stored data is presented using the output report component. The sequential execution of the five detection components forms a target detection sub-algorithm.

[0170] For the generated target detection sub-algorithm, the arrangement order of the detection components can also be adjusted according to the battery data and detection requirements, so as to continuously adjust the coverage of the components and improve the detection accuracy of the target detection components.

[0171] This embodiment analyzes the target detection sub-algorithm to determine the detection process of the target detection sub-algorithm when performing detection, and creates corresponding detection components according to the detection process. It can also establish a correspondence between the detection components according to the detection process, so as to sort the detection components and generate the corresponding target detection sub-algorithm. The arrangement order of the detection components can be continuously adjusted to make the detection components cover a wider range, and the corresponding target detection group algorithm can be continuously optimized to improve the accuracy of detection.

[0172] In some embodiments, the detection components are sorted based on the correspondence relationship to generate an object detection sub-algorithm, including:

[0173] Determining a detection order of the detection components based on the corresponding relationship;

[0174] The detection components are sorted according to the detection order, and the target detection sub-algorithm is obtained through the sorted detection components.

[0175] It should be noted that there is a detection order of detection components in the correspondence between each detection component. Therefore, the detection order of detection components can be determined according to the correspondence between the components, so that the detection components can be sorted by the detection order of detection components, and the overall process implementation of the sorted detection components can be used as the overall process implementation of the target detection sub-algorithm.

[0176] For example, the detection order of the detection component is fusion component-feature index calculation component-outlier monitoring component, then the target detection sub-algorithm includes a fusion component, a feature index calculation component and an outlier monitoring component, and the overall detection process of the target detection sub-algorithm is fusion-feature index calculation-outlier monitoring.

[0177] In the technical solution of the embodiment of the present application, after the correspondence between the various detection components is determined, the detection order of the detection components can be obtained, and then the detection components are sorted according to the detection order to obtain the target detection sub-algorithm. The detection process of the target detection sub-algorithm is continuously adjusted according to the detection order of the detection components, thereby improving the detection accuracy.

[0178] In some embodiments, after determining the target detection sub-algorithm, the battery data can be security detected through the target detection sub-algorithm to obtain the detection results. Since the target detection sub-algorithm includes multiple detection components, the battery data can be calculated accordingly through each detection component to obtain the detection results.

[0179] Further, if Figure 5 As shown, step S40 specifically includes:

[0180] Step S401: calling each detection component in the target detection sub-algorithm.

[0181] It should be noted that, since there are multiple detection components in the target detection sub-algorithm, each detection component in the target detection sub-algorithm can be called to process the battery data.

[0182] Step S402: Calculate the battery data through various detection components to obtain a detection result.

[0183] In a specific implementation, for example, the detection component includes a fusion component, a feature index calculation component, and an outlier monitoring component. The battery data can be calculated by the fusion component to obtain fused battery data, and the feature index of the fused battery data can be calculated to obtain feature data, and then the feature data can be monitored for outliers to obtain the final detection result.

[0184] This embodiment calculates the parameters in the battery data by calling each detection component in the target detection sub-algorithm, thereby obtaining the detection result of the battery safety detection and improving the detection efficiency.

[0185] In some embodiments, after obtaining the battery test results, in order to enable the user to understand the battery condition more intuitively, the test results can be visualized through visualization tools, such as directly displaying them on a visualization interface, or using visualization reports to display them, thereby improving the readability of the test results.

[0186] Optionally, the battery safety detection method further includes:

[0187] Generate target visualization reports based on test results;

[0188] Display the target visualization report.

[0189] It should be understood that the detection results can be presented on a target visualization report, so that the target visualization report can be displayed through a display device.

[0190] like Figure 6a-6b As shown, Figure 6a-6b A schematic diagram showing the test results. Figure 6a This is a graph showing the distribution of warnings of batteries affected by different factors in the test results, including temperature, cumulative mileage, total current, total voltage, and SOC. Figure 6b This diagram shows the distribution of warnings within different total battery voltage ranges in the test results. When the total voltage is less than 300, the number of warnings is 10; when the total voltage is 300-350, the number of warnings is 6; when the total voltage is 350-400, the number of warnings is 7; and when the total voltage is greater than 400, the number of warnings is 16. By displaying the test results on the target visualization report, a realistic statistical report is generated. Based on the target visualization report, the real-time and historical data analysis of individual battery cells can be drilled down to implement safety warning troubleshooting and repair work.

[0191] After obtaining the test results, this embodiment generates a visual report of the test results. The visual report can support chart linkage and data drill-down. The test results are displayed through the visual report, thereby visually displaying the test results, allowing users to intuitively and quickly understand the battery test results, thereby improving user experience.

[0192] In some embodiments, generating a target visualization report based on the detection results includes:

[0193] In response to the triggered report selection instruction, determining a target report component in a preset statistical report set;

[0194] Generate target reports through target report components;

[0195] Load the detection results into the target report and generate a target visualization report.

[0196] It should be noted that the report selection instruction is an instruction generated by the user by clicking the corresponding report generation component. The corresponding report component is determined by the report selection instruction. The preset statistical report set is a set of reports collected in advance that can display the test results. A preset statistical report set can be generated by pre-preparing a large number of statistical indicators in advance.

[0197] According to the user's report selection instructions, the preset statistical report set is screened to determine the report component required by the user, that is, the target report component.

[0198] In specific implementation, the target report components can be moved, merged, etc. to generate the target report.

[0199] The target report is a display framework composed of multiple report components. After obtaining the test results, the test results can be loaded into the target report to generate a target visualization report.

[0200] This embodiment responds to the triggered report selection instruction, thereby determining the report style that the user wants to generate according to the report selection instruction, and determining the corresponding report component in the preset statistical report set, combining the target report components to generate a real statistical report, which can be used to guide battery safety testing business and improve the effect of battery safety testing.

[0201] In some embodiments, generating a target visualization report based on the detection results includes:

[0202] In response to the triggered report creation instruction, obtaining a custom report component;

[0203] Create target reports through custom report components;

[0204] Load the detection results into the target report and generate a target visualization report.

[0205] The report creation instruction is an instruction that the user needs to customize the report. The user can click the customized button component on the battery safety testing platform to generate the report creation instruction.

[0206] The battery safety testing platform responds to the report creation instruction, collects the custom report components created by the user, combines the custom report components, and creates a target report.

[0207] After establishing the target report, the battery safety testing platform can load the test results calculated by the detection sub-algorithm into the target report to form a target visualization report for display. Users can arbitrarily select and form a target package, and the customization capability is flexible and changeable.

[0208] like Figure 7 As shown, Figure 7 To customize the target visualization report diagram, you can generate the target report by customizing the target report name, type, and corresponding report components in the target report.

[0209] This embodiment determines the report mode that the user wants to customize and the corresponding custom report components based on the report creation instruction triggered by the user when the user wants to customize the displayed visual report, so that the user can arbitrarily select the desired components, generate the target report, and load the detection results into the target report for display. The customization capability is flexible and can meet the different needs of different users.

[0210] In some embodiments, in response to the triggered report selection instruction, before determining the target report component in the preset statistical report set, the method further includes:

[0211] Collect test result samples of batteries under different operating conditions based on preset safety detection algorithms;

[0212] Collect corresponding statistical indicator samples based on the test result samples;

[0213] Create corresponding report component data based on statistical indicator samples;

[0214] Generates a set of preset statistical reports based on report component data.

[0215] It should be understood that the platform can collect in advance test result samples of different types of batteries under different operating conditions based on preset safety detection algorithms, so as to formulate multiple statistical reports based on the test result samples, and collect corresponding statistical indicators to form statistical indicator samples.

[0216] The statistical indicator sample is a set of indicators commonly used in the power battery industry and can be used to guide battery safety testing algorithm business.

[0217] After obtaining the statistical indicator samples, corresponding report component data can be created based on the statistical indicator samples. For example, if the statistical indicators include temperature, report component data such as histogram components, pie chart components, area chart components, and bar chart components can be created.

[0218] In specific implementations, after creating report component data, a preset statistical report set can be generated based on the report component data, which can fully cover the battery characteristic data performance of battery safety testing, thereby improving user experience.

[0219] This embodiment collects test result sample data of batteries detected by the detection algorithm in a preset safety detection algorithm under different operating conditions in advance, collects statistical indicators that can be used for the corresponding test results based on the test result samples, pre-prepare a statistical indicator set that has passed industry review, and establishes a preset statistical report set, thereby covering all battery characteristic data performances of battery safety detection, and can intuitively present the statistical results to business personnel, thereby improving the readability of visual reports during battery detection.

[0220] In some embodiments, before obtaining battery data representing the battery operating condition in response to the triggered battery detection instruction, the method further includes:

[0221] Obtain battery sample data of different batteries in different states;

[0222] Perform keyword extraction on the battery sample data to obtain battery keyword samples that characterize the battery operating conditions;

[0223] A physical model is established according to the battery keyword sample, wherein the physical model is used to identify the corresponding battery keyword.

[0224] It should be noted that the battery sample data of the battery in different states may include battery sample data such as battery charging and battery discharging. In order to quickly and conveniently determine the key features of the battery, the battery sample data can be keyword extracted to obtain battery keyword samples that characterize the battery operating conditions. For example, the battery keyword is battery charging, voltage 200V, current 10A, etc.

[0225] After obtaining the battery keywords, corresponding physical models can be established for different battery types, such as commercial vehicle batteries, passenger car batteries, energy storage station batteries, and battery swap station batteries. This allows the battery data to be synchronously and concurrently involved in the corresponding battery testing when the corresponding battery types are imported in batches.

[0226] A physical model is established through battery keyword samples, so that battery keywords in battery data can be identified through the physical model, thereby improving detection efficiency.

[0227] This embodiment extracts keywords from battery sample data of various batteries in different states, thereby extracting battery keyword samples that characterize the battery operating conditions from massive battery sample data. A physical model is established using the battery keyword samples, and a mapping relationship between the physical model and the battery keywords is established, thereby identifying the battery keywords through the physical model. This can adapt to all device models for battery safety testing and improve the efficiency of testing.

[0228] In some embodiments, obtaining battery data representing battery operating conditions includes:

[0229] Get the initial battery data of the battery;

[0230] The initial battery data is input into the physical model to obtain battery data representing the battery operating conditions. The physical model is used to identify battery keywords in the initial battery data.

[0231] Initial battery data refers to unidentified battery data, including some useless data when performing battery safety testing. Therefore, the battery data can be keyword-identified through the established physical model to identify useful battery data.

[0232] This embodiment obtains the initial data of the battery and uses the physical model to perform keyword recognition on the initial data to obtain battery data representing the battery operating conditions. This can quickly identify the data representing the battery operating conditions in the initial battery data and improve the accuracy of detection.

[0233] like Figure 8 As shown, Figure 8 This is a schematic diagram of the overall process of the battery safety testing method, which specifically includes the battery detection sub-algorithm calculation link, the platform preparation of the battery safety testing statistical indicator set link, the platform providing the custom report link, and the target visualization report link.

[0234] like Figure 9 As shown, Figure 9This is another overall flow diagram of the battery safety testing method. When a user logs in to the battery safety testing platform, they create an algorithm application, for example, to determine whether the batteries of 1,000 vehicles will experience a certain fault. The application then checks whether the user has selected the platform's pre-set safety testing algorithm. If the pre-set safety testing algorithm is selected, the user is redirected to the pre-set safety testing algorithm's secondary development page, where they can drag and drop testing components to add or remove them, completing the development of the pre-set safety testing algorithm. Alternatively, if the user does not select the pre-set safety testing algorithm, the application enters a blank secondary development page, where they can drag and drop testing components to complete algorithm development. After algorithm development is complete, the algorithm is deployed, debugged, verified, tested, and approved for publication to the pre-set safety testing algorithm using a dataset in the developer center. A detection sub-algorithm is selected to output the test results, and the user is asked to select a default visualization report. If the default visualization report is used, the algorithm execution report (the target visualization report) is directly run. If not, a custom visualization report is created on the low-code development platform, and the algorithm execution report is run. The report is saved locally, completing the battery safety testing.

[0235] To achieve the above objectives, the present invention further provides a battery safety detection device. Figure 10 The following is a structural diagram of a battery safety detection device according to an embodiment of the present application. The battery safety detection device includes:

[0236] The acquisition module 10 is configured to acquire battery data representing the operating condition of the battery in response to a triggered battery detection instruction.

[0237] The determination module 20 is configured to obtain the battery type.

[0238] The determination module 20 is further used to determine a target detection sub-algorithm in a preset safety detection algorithm based on the battery type and battery data, wherein the preset safety detection algorithm encapsulates multiple detection sub-algorithms, and the multiple detection sub-algorithms are used to perform safety detection on different types of batteries.

[0239] The detection module 30 is used to perform safety detection on battery data through a target detection sub-algorithm.

[0240] This embodiment is achieved by

[0241] In one embodiment, the determination module 20 is further used to obtain a type parameter based on the battery type; obtain a first detection rule for each detection sub-algorithm in a preset safety detection algorithm, the first detection rule characterizing the battery type that the detection sub-algorithm can detect; traverse the first detection rule through the type parameter to obtain a first detection rule that matches the type parameter; screen the initial detection sub-algorithm through the battery data to obtain a target detection sub-algorithm, the initial detection sub-algorithm being the detection sub-algorithm corresponding to the first detection rule that matches the type parameter.

[0242] In one embodiment, the determination module 20 is further used to extract key fields from the battery data to obtain battery key fields; obtain a second detection rule in the initial detection sub-algorithm, the second detection rule characterizing the battery parameters that can be detected by each detection sub-algorithm; traverse the second detection rule through the battery key field to obtain a second detection rule that matches the battery key field; obtain a target detection sub-algorithm based on the second detection rule that matches the battery key field, the target detection sub-algorithm being the initial detection sub-algorithm corresponding to the second detection rule that matches the battery key field.

[0243] In one embodiment, the determination module 20 is further used to obtain detection sub-algorithm sample data and battery sample data characterizing the battery operating conditions; analyze the detection sub-algorithm sample data based on the battery sample data to determine the battery type and battery parameters that can be detected by each detection sub-algorithm; establish a first detection rule based on the battery type that can be detected by each detection sub-algorithm, and establish a second detection rule based on the battery parameter that can be detected by each detection sub-algorithm; encapsulate the detection sub-algorithm sample data through the first detection rule and the second detection rule to obtain a preset safety detection algorithm.

[0244] In one embodiment, the target detection sub-algorithm includes multiple detection components, and the determination module 20 is also used to obtain the detection process of the target detection sub-algorithm; create corresponding detection components according to the detection process; establish a correspondence between the detection components; sort the detection components based on the correspondence to generate a target detection sub-algorithm.

[0245] In one embodiment, the determination module 20 is further configured to determine a detection order of the detection components based on the corresponding relationship; sort the detection components according to the detection order, and obtain a target detection sub-algorithm through the sorted detection components.

[0246] In one embodiment, the detection module 30 is further configured to call each detection component in the target detection sub-algorithm; and calculate the battery data through each detection component to obtain a detection result.

[0247] In one embodiment, the detection module 30 is further configured to generate a target visualization report based on the detection results; and display the target visualization report.

[0248] In one embodiment, the detection module 30 is also used to determine a target report component in a preset statistical report set in response to a triggered report selection instruction; generate a target report through the target report component; load the detection results into the target report, and generate a target visualization report.

[0249] In one embodiment, the detection module 30 is further configured to obtain a custom report component in response to a triggered report creation instruction; create a target report using the custom report component; and load the result into the target report to generate a target visual report.

[0250] In one embodiment, the detection module 30 is also used to collect detection result samples of the battery under different operating conditions based on a preset safety detection algorithm; collect corresponding statistical indicator samples based on the detection result samples; create corresponding report component data based on the statistical indicator samples; and generate a preset statistical report set based on the report component data.

[0251] In one embodiment, the acquisition module 10 is also used to obtain battery sample data of different batteries in different states; perform keyword extraction on the battery sample data to obtain battery keyword samples that characterize the battery operating conditions; and establish a physical model based on the battery keyword samples, wherein the physical model is used to identify corresponding battery keywords.

[0252] In one embodiment, the acquisition module 10 is further used to obtain initial battery data of the battery; the initial battery data is input into the physical model to obtain battery data representing the battery operating conditions, and the physical model is used to identify battery keywords in the initial battery data.

[0253] To achieve the above objectives, an embodiment of the present invention further provides a battery safety testing platform, on which the above-mentioned battery safety testing method is executed.

[0254] To achieve the above objectives, an embodiment of the present invention further proposes a battery safety detection device, which includes: a memory, a processor, and a battery safety detection program stored in the memory and executable on the processor, wherein the battery safety detection program is configured to implement the battery safety detection method as described above.

[0255] Since the battery safety detection device adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described one by one here.

[0256] To achieve the above objectives, an embodiment of the present invention further provides a storage medium, on which a battery safety detection program is stored. When the battery safety detection program is executed by a processor, the battery safety detection method as described above is implemented.

[0257] Since the storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described one by one here.

[0258] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A battery safety detection method, characterized in that: The battery safety detection method comprises: In response to the triggered battery detection instruction, obtaining battery data representing the operating condition of the battery; Get the battery type; Determining a target detection sub-algorithm in a preset safety detection algorithm based on the battery type and the battery data, wherein the preset safety detection algorithm encapsulates a plurality of detection sub-algorithms, and the plurality of detection sub-algorithms are used to perform safety detection on different types of batteries; The battery data is safety-checked by the target detection sub-algorithm.

2. The battery safety detection method according to claim 1, characterized in that: The determining of a target detection sub-algorithm in a preset safety detection algorithm based on the battery type and the battery data includes: obtain a type parameter based on the battery type; Obtaining a first detection rule for each detection sub-algorithm in a preset safety detection algorithm, wherein the first detection rule represents a battery type that can be detected by the detection sub-algorithm; Traversing the first detection rule through the type parameter to obtain a first detection rule matching the type parameter; The initial detection sub-algorithm is screened through the battery data to obtain a target detection sub-algorithm, wherein the initial detection sub-algorithm is a detection sub-algorithm corresponding to a first detection rule matching the type parameter.

3. The battery safety detection method according to claim 2, characterized in that: The initial detection sub-algorithm is screened by the battery data to obtain a target detection sub-algorithm, including: Extracting key fields from the battery data to obtain key fields of the battery; Acquire a second detection rule in the initial detection sub-algorithm, where the second detection rule represents a battery parameter detectable by each of the detection sub-algorithms; Traversing the second detection rule through the battery key field to obtain a second detection rule matching the battery key field; A target detection sub-algorithm is obtained based on a second detection rule that matches the battery key field, and the target detection sub-algorithm is an initial detection sub-algorithm corresponding to the second detection rule that matches the battery key field.

4. The battery safety detection method according to any one of claims 1 to 3, characterized in that: Before determining the target detection sub-algorithm in the preset safety detection algorithm based on the battery type and the battery data, the method further includes: Acquire detection sub-algorithm sample data and battery sample data representing battery operating conditions; Analyze the detection sub-algorithm sample data based on the battery sample data to determine the battery type and battery parameters that can be detected by each detection sub-algorithm; Establishing a first detection rule based on the battery type detectable by each detection sub-algorithm, and establishing a second detection rule based on the battery parameter detectable by each detection sub-algorithm; The detection sub-algorithm sample data is encapsulated by the first detection rule and the second detection rule to obtain a preset security detection algorithm.

5. The battery safety detection method according to any one of claims 1 to 4, characterized in that: The target detection sub-algorithm includes multiple detection components; Before determining the target detection sub-algorithm in the preset safety detection algorithm based on the battery type and the battery data, the method further includes: Obtaining the detection process of the target detection sub-algorithm; Creating a corresponding detection component according to the detection process; Establishing a corresponding relationship between the detection components based on the detection process; The detection components are sorted based on the corresponding relationship to generate a target detection sub-algorithm.

6. The battery safety detection method according to claim 5, characterized in that: The step of sorting the detection components based on the corresponding relationship to generate a target detection sub-algorithm includes: Determining a detection order of the detection components based on the corresponding relationship; The detection components are sorted according to the detection order, and a target detection sub-algorithm is obtained through the sorted detection components.

7. The battery safety detection method according to any one of claims 1 to 6, characterized in that: The performing safety detection on the battery data by using the target detection sub-algorithm includes: Calling each detection component in the target detection sub-algorithm; The battery data is calculated by each of the detection components to obtain a detection result.

8. The battery safety detection method according to claim 7, characterized in that: The battery safety detection method further includes: Generate target visualization reports based on the test results; The target visualization report is displayed.

9. The battery safety detection method according to claim 8, characterized in that: The generating of a target visualization report according to the detection results includes: In response to the triggered report selection instruction, determining a target report component in a preset statistical report set; Generate a target report through the target report component; The detection results are loaded into the target report to generate a target visualization report.

10. The battery safety detection method according to claim 8, characterized in that: The generating of a target visualization report according to the detection results includes: In response to the triggered report creation instruction, obtain a custom report component; Creating a target report using the custom report component; The detection results are loaded into the target report to generate a target visualization report.

11. The battery safety detection method according to claim 9, characterized in that: The method further includes, in response to the triggered report selection instruction, before determining the target report component in the preset statistical report set: Collect test result samples of the battery under different operating conditions based on the preset safety detection algorithm; Collect corresponding statistical indicator samples according to the test result samples; Creating corresponding report component data according to the statistical indicator sample; A preset statistical report set is generated according to the report component data.

12. The battery safety detection method according to any one of claims 1 to 11, characterized in that: Before obtaining the battery data representing the battery operating condition in response to the triggered battery detection instruction, the method further includes: Obtain battery sample data of different batteries in different states; Performing keyword extraction on the battery sample data to obtain a battery keyword sample representing a battery operating condition; A physical model is established according to the battery keyword sample, wherein the physical model is used to identify the corresponding battery keyword.

13. The battery safety detection method according to claim 12, characterized in that: The obtaining of battery data characterizing the battery operating condition includes: Get the initial battery data of the battery; The initial battery data is input into the physical model to obtain battery data characterizing the battery operating condition, and the physical model is used to identify battery keywords in the initial battery data.

14. A battery safety detection device, characterized in that: The battery safety detection device comprises: An acquisition module, configured to acquire battery data representing a battery operating condition in response to a triggered battery detection instruction; Determine the module for obtaining the battery type; The determination module is further used to determine a target detection sub-algorithm in a preset safety detection algorithm based on the battery type and the battery data, wherein the preset safety detection algorithm encapsulates a plurality of detection sub-algorithms, and the plurality of detection sub-algorithms are used to perform safety detection on different types of batteries; A detection module is used to perform safety detection on the battery data through the target detection sub-algorithm.

15. A battery safety testing platform, characterized in that: The battery safety detection platform performs the battery safety detection method as described in any one of claims 1 to 13.

16. A storage medium, characterized in that: The storage medium stores a battery safety detection program, and when the battery safety detection program is executed by the processor, the battery safety detection method according to any one of claims 1 to 13 is implemented.

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