A method, apparatus and terminal device for determining a vehicle diagnostic path

Abstract

Embodiments of the present application relate to the field of vehicle diagnosis, and provide a vehicle diagnosis path determination method, device and terminal equipment. The vehicle diagnosis path determination method obtains target data of a vehicle battery pack and a condition selection command input by a user; filters data in a first database according to the condition selection command and the target data, obtains at least one piece of sub-information associated with the condition selection command and the target data, and sorts the sub-information to generate a test menu and display the test menu; when the test menu includes multiple pieces of sub-information, analyzes and compares the target data with each piece of sub-information respectively to obtain a corresponding similarity, determines a candidate diagnosis path from the test menu according to multiple similarities, and takes the candidate diagnosis path as an optimal diagnosis path of the vehicle battery pack. The present application can quickly recommend a diagnosis path to a user, thereby improving the diagnosis speed of the battery pack, finding out a fault problem in time, and avoiding personnel injury and property loss.

Description

Technical Field

[0001] This invention relates to the field of vehicle diagnostics, and more specifically, to a method, apparatus, and terminal device for determining vehicle diagnostic paths. Background Technology

[0002] With the emergence of new energy sources and strong policy support, virtually all modes of transportation now rely on new energy sources for power, with electric vehicles using battery packs as their energy carrier. As market demands for longer driving ranges for electric vehicles continue to increase, battery packs are becoming larger and storing larger amounts of energy, resulting in high voltage and high current characteristics. However, while battery packs fulfill many functions, they also tend to generate numerous problems.

[0003] For example, regarding battery pack range, it's necessary to control the total voltage, total current, individual cell voltage, and individual cell current during charging and discharging. It's also crucial to control the internal temperature of the battery pack and the duration of its operating time. If a fault occurs within the battery pack and is not detected promptly, significant safety hazards can arise. Especially during charging and discharging, failure to identify and resolve the fault can easily lead to an explosion, resulting in property damage or personal injury. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, and terminal device for determining vehicle diagnostic paths.

[0005] In a first aspect, the present invention provides a method for determining a vehicle diagnostic path, the method comprising:

[0006] Obtain target data for the vehicle battery pack and user-input conditional selection commands;

[0007] The data in the first database is filtered according to the conditional selection command and the target data to obtain at least one piece of sub-information associated with the conditional selection command and the target data, and the sub-information is sorted to generate a test menu and display it;

[0008] When the test menu includes multiple pieces of information, the target data is analyzed and compared with each piece of information to obtain the corresponding similarity. Based on the multiple similarities, a candidate diagnostic path is determined from the test menu, and the candidate diagnostic path is used as the optimal diagnostic path for the vehicle battery pack.

[0009] In an optional implementation, the method further includes:

[0010] If the test menu contains only one of the sub-information items, then the candidate diagnostic path corresponding to the unique sub-information item will be taken as the optimal diagnostic path.

[0011] In an optional implementation, the step of filtering data in the first database according to the conditional selection command and the target data to obtain at least one piece of sub-information associated with the conditional selection command and the target data includes:

[0012] The data in the first database is filtered according to the conditional selection command to obtain the target record, and the target record is stored in the second database. The first database includes information on various vehicle battery packs.

[0013] Based on the target data, at least one piece of sub-information associated with the target data is retrieved from the second database storing the target record.

[0014] In an optional implementation, the conditional selection command includes a first selection command and a second selection command, and the step of filtering the data in the first database according to the conditional selection command to obtain the target record includes:

[0015] According to the first selection command, records including the target vehicle model data are selected from the first database as the filtering results;

[0016] According to the second selection command, select the record containing the target year data from the filtering results as the target record.

[0017] In an optional implementation, the method further includes:

[0018] When no sub-information associated with the target data exists in the second database, the target data is stored in the first database and uploaded and fed back in the form of logs.

[0019] In an optional implementation, determining candidate diagnostic paths from the test menu based on a plurality of similarities includes:

[0020] The multiple similarities are arranged in descending order of magnitude to obtain a similarity sequence;

[0021] The candidate diagnostic path corresponding to the first similarity in the similarity sequence is determined from the test menu.

[0022] In an optional implementation, determining the candidate diagnostic path corresponding to the first similarity in the similarity sequence from the test menu includes:

[0023] Based on the similarity ranking first in the similarity sequence, the corresponding sub-information is determined from the test menu as the target sub-information, wherein each sub-information is pre-associated with a candidate diagnostic path;

[0024] Based on the target sub-information, the corresponding candidate diagnostic path is determined.

[0025] Secondly, the present invention provides a vehicle diagnostic path determination device, the device comprising:

[0026] The acquisition module is used to acquire target data of the vehicle battery pack and user-input conditional selection commands;

[0027] The display module is used to filter data in the first database according to the conditional selection command and the target data, obtain at least one piece of sub-information associated with the conditional selection command and the target data, sort the sub-information to generate a test menu and display it;

[0028] The determination module is used to analyze and compare the target data with each of the sub-information items when the test menu includes multiple sub-information items, obtain the corresponding similarity, and determine the candidate diagnostic path from the test menu based on the multiple similarity items. The candidate diagnostic path is used as the optimal diagnostic path for the vehicle battery pack.

[0029] Thirdly, the present invention provides a terminal device, including a memory and a processor, wherein the memory stores a computer program, and the computer program executes the vehicle diagnostic path determination method when it is run on the processor.

[0030] Fourthly, the present invention provides a readable storage medium storing a computer program that, when run on a processor, executes the method for determining the vehicle diagnostic path.

[0031] The beneficial effects of the embodiments of the present invention are:

[0032] This application provides a method for determining a vehicle diagnostic path. The method acquires target data of a vehicle battery pack and a conditional selection command input by the user. Based on the conditional selection command and the target data, data in a first database is filtered to obtain at least one piece of sub-information associated with the conditional selection command and the target data. This sub-information is then sorted to generate and display a test menu. When the test menu includes multiple pieces of sub-information, the target data is analyzed and compared with each piece of sub-information to obtain the corresponding similarity. Based on multiple similarities, candidate diagnostic paths are determined from the test menu, and these candidate diagnostic paths serve as the optimal diagnostic path for the vehicle battery pack. This application can quickly recommend diagnostic paths to the user, thereby improving the diagnostic speed of the battery pack, promptly identifying faults, and avoiding personal injury and property damage.

[0033] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0034] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope of protection of the present invention. In the various drawings, similar components are numbered similarly.

[0035] Figure 1 This illustration shows a first flowchart of a method for determining a vehicle diagnostic path according to an embodiment of this application;

[0036] Figure 2 This illustration shows a flowchart of the process for determining sub-information in a method for determining a vehicle diagnostic path according to an embodiment of this application.

[0037] Figure 3 This illustration shows a flowchart of the process for determining candidate diagnostic paths in a vehicle diagnostic path determination method according to an embodiment of this application.

[0038] Figure 4 This illustration shows a second flowchart of a method for determining a vehicle diagnostic path according to an embodiment of this application;

[0039] Figure 5 A schematic diagram of a vehicle diagnostic path determination device provided in an embodiment of this application is shown.

[0040] Explanation of key component symbols:

[0041] 10-Vehicle diagnostic path determination device; 11-Acquisition module; 12-Display module; 13-Determination module. Detailed Implementation

[0042] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0043] The components of the embodiments of the invention described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0044] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of the invention, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0045] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0046] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of the invention pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of the invention.

[0047] Example 1

[0048] Please refer to Figure 1 This application provides a method for determining a vehicle diagnostic path. In fact, the method for determining a vehicle diagnostic path includes steps S100 to S300.

[0049] Step S100: Obtain the target data of the vehicle battery pack and the conditional selection command input by the user.

[0050] In this application, an OBD (On-Board Diagnostics) connector will be used to connect to the vehicle's battery pack, thereby obtaining all data from the vehicle's battery pack. This data includes, but is not limited to, the type of the vehicle's battery pack, individual cell voltage, individual cell current, total battery voltage, total battery current, charging temperature, discharging temperature, and CAN bus ID. The battery pack type can be lithium iron phosphate, lithium hydrogen hydride, ternary lithium, etc. The CAN bus ID is a pre-set indicator, which is the address or name of a CAN node.

[0051] Understandably, each CAN bus ID has a corresponding baud rate. When acquiring all data from the battery pack, at least one CAN bus ID and its corresponding baud rate will be obtained. Any CAN bus ID from the at least one CAN bus ID will be selected as the target CAN bus ID, and the target baud rate corresponding to that target CAN bus ID will be determined. In other words, all data from the vehicle battery pack will be collected, and a target CAN bus ID and target baud rate will be selected from this data as the target data. After acquiring the target data and selecting the target CAN bus ID, the user-input conditional selection command will be received.

[0052] Step S200: Filter the data in the first database according to the conditional selection command and target data to obtain at least one piece of sub-information associated with the conditional selection command and target data, and sort the sub-information to generate a test menu and display it.

[0053] In this application, data that meets the conditions is first filtered from a pre-set first database based on the obtained conditional selection command, and the filtered data is stored in a pre-set second database. At least one piece of sub-information associated with the conditional selection command and the target data is retrieved from the second database based on the target data, and the sub-information is sorted in the order in which it is obtained to obtain a test menu, which is then displayed on a terminal display device.

[0054] In one implementation, such as Figure 2 As shown, step S200 includes sub-steps S210 to S220.

[0055] Sub-step S210: Based on the conditional selection command, filter the data in the first database to obtain the target record, and store the target record in the second database. The first database includes information on various vehicle battery packs.

[0056] In this application, the first database includes pre-set information on various vehicle battery packs, such as any one or more combinations of pre-set vehicle model, vehicle year, battery pack type, CAN bus ID, baud rate, and candidate diagnostic paths. Before searching based on target data, i.e., the target CAN bus ID and the target baud rate corresponding to the target CAN bus ID, a conditional screening will be performed. This screening includes, but is not limited to, screening for vehicle model, year, and battery pack type. Vehicle models include Audi, BMW, and Volkswagen, and vehicle years include 2019, 2018, and 2020. The CAN bus ID is associated with the baud rate; that is, the first database includes at least one record containing the CAN bus ID and the baud rate corresponding to that CAN bus ID.

[0057] The conditional selection commands include a first selection command and a second selection command. The first selection command selects records containing target vehicle model data, and the second selection command selects records containing target year data. Based on the user-inputted first selection command, records with the target vehicle model data are selected from the first database as the filter results. Based on the user-inputted second selection command, records with the target year data are selected from the determined filter results as the target records. The selected target records are stored in a pre-built second database; in other words, the second database includes all records associated with the target vehicle model data and the target year data.

[0058] In one implementation, the condition selection command further includes a third selection command, which selects records including target type data. After selecting and determining the target record, the user can select records from the target records whose battery pack type is the target type data according to the third selection command input by the user, obtain type records, and store the selected type records in a pre-set second database. In other words, the records in the second database are all records associated with target vehicle model data, target year data, and target type data.

[0059] Understandably, the conditional selection command can be set according to the user's actual needs, including but not limited to screening data in a pre-set first database based on information corresponding to the vehicle or battery pack.

[0060] As an example, when the user selects the target vehicle model data based on the first selection command entered by the user, a vehicle model submenu will be displayed on the display interface. The vehicle model submenu includes data such as Audi, BMW and Volkswagen. When the user selects the target year data, a year submenu will also be displayed. The year submenu includes 2019, 2018, 2020, etc. The user will select the data in the vehicle model submenu and year submenu according to the actual situation, that is, enter the corresponding conditional selection command.

[0061] Sub-step S220: Based on the target data, retrieve at least one piece of sub-information that is associated with the target data from the second database that stores the target records.

[0062] After storing the target records obtained from the conditional screening into the second database, a search will be performed in the second database based on the target data, specifically the target CAN bus ID and the target baud rate corresponding to that CAN bus ID, to determine all records associated with that target CAN bus ID and target baud rate. Each record includes the corresponding CAN bus ID and baud rate; therefore, the CAN bus ID and baud rate in each record will be treated as sub-information. In other words, it will be determined whether at least one piece of sub-information related to the target data exists in the second database. When searching the second database containing the stored target records based on the target baud rate and target CAN bus ID, if a record associated with the target data exists in the second database, four search results (A1-A4) may be obtained, representing four possible scenarios involving sub-information.

[0063] A1. Obtain records with accurate baud rates but inaccurate CAN bus IDs, i.e., obtain records related to the target baud rate and non-target CAN bus IDs; A2. Obtain records with accurate IDs but inaccurate baud rates, i.e., obtain records related to the target CAN bus ID and non-target baud rates; A3. Obtain records with both accurate baud rates and accurate CAN bus IDs, i.e., obtain records corresponding to the target baud rate and target CAN bus ID; A4. Obtain records with inaccurate baud rates and CAN bus IDs, i.e., obtain records corresponding to non-target CAN bus IDs and non-target baud rates.

[0064] When searching the second database containing the stored target records based on the target CAN bus ID and the target baud rate corresponding to the target CAN bus ID to obtain search results (i.e., at least one sub-information among the four search results A1-A4), the obtained sub-information is sorted according to its order to generate a corresponding test menu, which is then displayed on the user terminal. This test menu includes at least one sub-information related to the target data, and each sub-information, i.e., each record, has an associated candidate diagnostic path.

[0065] In one implementation, step S200 further includes sub-step S230.

[0066] Sub-step S230: When there is no sub-information associated with the target data in the second database, the target data is stored in the first database and uploaded and fed back in the form of logs.

[0067] If the first database does not contain a record corresponding to the target CAN bus ID and target baud rate, i.e., there is no sub-information associated with the target data, or if the obtained sub-information is any of the three cases A1, A2, or A4 mentioned above, it indicates that the first database does not contain the target CAN bus ID and target baud rate. In this case, the target CAN bus ID and target baud rate are undeveloped data. At this time, the target baud rate and target CAN bus ID will be stored in the first database as new data, i.e., a new record, and uploaded and fed back in the form of a log. This is used to redevelop the vehicle configuration corresponding to the target baud rate and target CAN bus ID, generate new records of candidate diagnostic paths associated with the target baud rate and target CAN bus ID, thereby further expanding the first database for subsequent comparison, and repeating the above determination steps.

[0068] Step S300: When the test menu includes multiple sub-information items, the target data is analyzed and compared with each sub-information item to obtain the corresponding similarity. Based on the multiple similarities, candidate diagnostic paths are determined from the test menu, and the candidate diagnostic paths are used as the optimal diagnostic paths for the vehicle battery pack.

[0069] Understandably, when the corresponding sub-information exists in the second database, the records obtained by searching using the target baud rate and target CAN bus ID are accurate. Therefore, the candidate diagnostic path information corresponding to the target sub-information that has the most overlap with multiple sub-information items in the test menu will be selected as the optimal diagnostic path. In other words, when searching based on the target baud rate and target CAN bus ID, the number of overlapping characters and the sorting position between the target data and the baud rate and CAN bus ID in the second database are relevant. The more overlapping characters the searched sub-information has with the target data, and the more records with the same sorting position (i.e., the highest similarity), the candidate diagnostic path corresponding to that record can be used as the optimal diagnostic path.

[0070] When the displayed test menu includes multiple sub-information items, the target data is compared with each of the sub-information items. Specifically, the target CAN bus ID and target baud rate corresponding to the target data are compared with the CAN bus ID and baud rate corresponding to each sub-information item to obtain the similarity score for each item. Based on the similarity score of each sub-information item, the target sub-information item is determined from the multiple sub-information items in the test menu. The candidate diagnostic path associated with the target sub-information item is then selected as the optimal diagnostic path for the vehicle battery pack, and normal intelligent diagnostics are performed according to the selected optimal diagnostic path.

[0071] In one implementation, such as Figure 3 As shown, step S300 includes sub-steps S310 to S320.

[0072] Sub-step S310: Sort the multiple similarities in descending order of size to obtain the similarity sequence.

[0073] It is understandable that after determining the similarity of multiple pieces of sub-information, the obtained similarities will be sorted in descending order to obtain a sorted similarity sequence. In other words, the multiple similarities in the similarity sequence are sorted in descending order.

[0074] Sub-step S320: Determine the candidate diagnostic path corresponding to the first similarity in the similarity sequence from the test menu.

[0075] Each piece of sub-information is pre-associated with a candidate diagnostic path. The first similarity in the similarity sequence is selected, that is, the largest similarity value among multiple similarities. Based on the first similarity, the sub-information corresponding to the first similarity is selected from the test menu as the target sub-information. Based on the target sub-information, the corresponding candidate diagnostic path is determined as the optimal diagnostic path, and normal diagnosis is performed according to the candidate diagnostic path.

[0076] In one implementation, such as Figure 4 As shown, the method for determining the vehicle diagnostic path also includes step S400.

[0077] Step S400: If the test menu contains only one sub-information, then the candidate diagnostic path corresponding to the unique sub-information is taken as the optimal diagnostic path.

[0078] In this application, when the test menu contains only one sub-information, the candidate diagnostic path corresponding to that sub-information will be selected as the optimal diagnostic path. For example, in case A3 above, when the search results only contain records corresponding to the target baud rate and the target CAN bus ID, i.e., when the test menu only contains target data, the candidate path corresponding to case A3 will be selected as the optimal diagnostic path for the vehicle battery pack.

[0079] In this application, multiple candidate diagnostic paths can be determined based on the collected battery pack data, and diagnostic paths can be quickly recommended to users, thereby improving the diagnostic speed of the battery pack, timely identifying faults, and avoiding personal injury and property damage.

[0080] The vehicle diagnostic path determination method based on the above embodiments, Figure 5 A schematic diagram of a vehicle diagnostic path determination device 10 according to an embodiment of this application is shown. The vehicle diagnostic path determination device 10 includes:

[0081] The acquisition module 11 is used to acquire the target data of the vehicle battery pack and the conditional selection command input by the user;

[0082] Display module 12 is used to filter data in the first database according to the conditional selection command and the target data, obtain at least one piece of sub-information associated with the conditional selection command and the target data, sort the sub-information to generate a test menu and display it;

[0083] The determination module 13 is used to analyze and compare the target data with each of the sub-information items when the test menu includes multiple sub-information items, obtain the corresponding similarity, and determine the candidate diagnostic path from the test menu based on the multiple similarity items. The candidate diagnostic path is used as the optimal diagnostic path for the vehicle battery pack.

[0084] The vehicle diagnostic path determination device 10 in this embodiment is used to execute the vehicle diagnostic path determination method of the above embodiment. The implementation schemes and beneficial effects involved in the above embodiments are also applicable in this embodiment, and will not be repeated here.

[0085] This application also provides a terminal device, including a memory and a processor. The memory stores a computer program, and the computer program executes the above-described method for determining vehicle diagnostic paths when it runs on the processor.

[0086] This application also provides a computer-readable storage medium storing a computer program that, when executed on a processor, implements the above-described method for determining vehicle diagnostic paths.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, as an alternative implementation, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0088] In addition, the functional modules or units in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0089] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0090] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining a vehicle diagnostic path, characterized in that, The method includes: Obtain target data for the vehicle battery pack and user-input conditional selection commands; The data in the first database is filtered according to the conditional selection command to obtain the target record, and the target record is stored in the second database. The first database includes information on various vehicle battery packs. Based on the target data, at least one piece of sub-information associated with the target data is retrieved from the second database storing the target record, and the sub-information is sorted to generate a test menu and display it; When the test menu includes multiple pieces of information, the target data is analyzed and compared with each piece of information to obtain the corresponding similarity. Based on the multiple similarities, a candidate diagnostic path is determined from the test menu, and the candidate diagnostic path is used as the optimal diagnostic path for the vehicle battery pack.

2. The method for determining the vehicle diagnostic path according to claim 1, characterized in that, The method further includes: If the test menu contains only one of the sub-information items, then the candidate diagnostic path corresponding to the unique sub-information item will be taken as the optimal diagnostic path.

3. The method for determining the vehicle diagnostic path according to claim 1, characterized in that, The conditional selection command includes a first selection command and a second selection command. The step of filtering data in the first database according to the conditional selection command to obtain the target record includes: According to the first selection command, records including the target vehicle model data are selected from the first database as the filtering results; According to the second selection command, select the record containing the target year data from the filtering results as the target record.

4. The method for determining the vehicle diagnostic path according to claim 1, characterized in that, The method further includes: When no sub-information associated with the target data exists in the second database, the target data is stored in the first database and uploaded and fed back in the form of logs.

5. The method for determining the vehicle diagnostic path according to claim 1, characterized in that, The step of determining candidate diagnostic paths from the test menu based on multiple similarities includes: The multiple similarities are arranged in descending order of magnitude to obtain a similarity sequence; The candidate diagnostic path corresponding to the first similarity in the similarity sequence is determined from the test menu.

6. The method for determining the vehicle diagnostic path according to claim 5, characterized in that, The step of determining the candidate diagnostic path corresponding to the first similarity in the similarity sequence from the test menu includes: Based on the similarity ranking first in the similarity sequence, the corresponding sub-information is determined from the test menu as the target sub-information, wherein each sub-information is pre-associated with a candidate diagnostic path; Based on the target sub-information, the corresponding candidate diagnostic path is determined.

7. A device for determining a vehicle diagnostic path, characterized in that, The device includes: The acquisition module is used to acquire target data of the vehicle battery pack and user-input conditional selection commands; The display module is used to filter data in the first database according to the conditional selection command to obtain the target record, and store the target record in the second database, wherein the first database includes information on various vehicle battery packs; and to retrieve at least one piece of sub-information associated with the target data from the second database where the target record is stored, and to sort the sub-information to generate a test menu and display it. The determination module is used to analyze and compare the target data with each of the sub-information items when the test menu includes multiple sub-information items, obtain the corresponding similarity, and determine the candidate diagnostic path from the test menu based on the multiple similarity items. The candidate diagnostic path is used as the optimal diagnostic path for the vehicle battery pack.

8. A terminal device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when run on the processor, executes the method for determining a vehicle diagnostic path as described in any one of claims 1 to 6.

9. A readable storage medium, characterized in that, It stores a computer program that, when run on a processor, executes the method for determining the vehicle diagnostic path as described in any one of claims 1 to 6.

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