Vehicle capability parameter grouping method, device, electronic device and storage medium

Abstract

The present application provides a method, device, electronic device and storage medium for grouping vehicle capability parameters. The method includes: obtaining the capability parameters corresponding to the vehicle model code, dividing the capability parameters into action data and condition data; creating a condition classification container array, an action classification container array and an unordered set; when traversing the condition data or action data, calling the filtering method of the unordered set to determine whether the category corresponding to the condition data or action data is a new major category; when it belongs to a new major category, creating a new major category data model, and when it does not belong to a new major category, searching for the corresponding major category data model; adding the condition data or action data of the category parameters of the traversed major category array to the major category array, and adding the updated major category data model to the condition classification container array or the action classification container array. The present application improves the integrity and comprehensiveness of capability parameter grouping and realizes accurate grouping of vehicle model capability parameters.

Description

Technical Field

[0001] The present application relates to the field of new energy vehicle technology, and in particular to a method, device, electronic device, and storage medium for grouping vehicle capability parameters. Background Art

[0002] With the development of vehicle technology, various performance and functional parameters of vehicles are becoming more and more abundant. In order to facilitate manufacturers, maintenance personnel and users to understand and use these parameters, they usually need to be effectively classified and organized. However, there are some obvious problems and shortcomings in traditional technologies.

[0003] First, the capability parameters in existing technologies are not comprehensive. This means that for certain vehicles or certain specific scenarios, it may not be possible to obtain a complete description of the vehicle's capabilities, which will result in manufacturers, maintenance personnel or users being unable to accurately understand the full range of functions and performance of the vehicle. Secondly, although some technologies attempt to group these capability parameters, the grouping is often incomplete. Even if grouping is performed, it is often not done in the order of the source data, resulting in a chaotic order after grouping, which makes it more difficult to find specific parameters. Furthermore, many systems do not load the corresponding capability parameters based on the specific model. This will result in insufficient or redundant capability parameter information for some models, making it difficult for manufacturers, maintenance personnel and users to find capability parameters that fully match a specific model.

[0004] Therefore, how to accurately group scattered and disordered capability data into corresponding categories so that the capability parameters of different models can be displayed comprehensively and orderly has become a challenge facing current technology. Summary of the Invention

[0005] In view of this, the embodiments of the present application provide a method, device, electronic device and storage medium for grouping vehicle capability parameters to solve the problems in the prior art of incomplete grouping of capability parameters, chaotic order after grouping, and inability to achieve accurate grouping.

[0006] In a first aspect of an embodiment of the present application, a method for grouping vehicle capability parameters is provided, comprising: assigning a vehicle model code to a preset capability parameter interface, so that the capability parameter interface obtains the capability parameters corresponding to the model code from the server; traversing the capability parameters, and dividing the capability parameters into action data and condition data according to the parameter identifier corresponding to each capability parameter; adding the condition data to a condition temporary container array, adding the action data to an action temporary container array, and creating a condition classification container array, an action classification container array, and an unordered set for loading large classification names; traversing the condition temporary container array and the action temporary container array, and calling the unordered set process when traversing each condition data or action data. A filtering method is used to determine whether the category corresponding to the condition data or action data is a new major category based on the category parameters corresponding to the condition data or action data; when the category belongs to a new major category, a new major category data model is created, and the major category data model has a major category array for storing all condition data or action data under the major category. When the category does not belong to the new major category, the major category data model corresponding to the category is searched in the condition category container array or the action category container array; the traversed condition data or action data belonging to the category parameters corresponding to the major category array are added to the major category array under the major category data model, and the updated major category data model is added to the condition category container array or the action category container array.

[0007] According to a second aspect of an embodiment of the present application, a device for grouping vehicle capability parameters is provided, including: an acquisition module configured to assign a vehicle model code to a preset capability parameter interface, so that the capability parameter interface obtains the capability parameters corresponding to the model code from the server; a division module configured to traverse the capability parameters, and divide the capability parameters into action data and condition data according to the parameter identifier corresponding to each capability parameter; an addition module configured to add the condition data to the condition temporary container array, add the action data to the action temporary container array, and create a condition classification container array, an action classification container array and an unordered set for loading large classification names; a traversal module configured to traverse the condition temporary container array and the action temporary container array, and when traversing each condition data or action data , calling the filtering method of the unordered set, judging whether the category corresponding to the condition data or action data is a new major category according to the category parameters corresponding to the condition data or action data; the creation module is configured to create a new major category data model when the category belongs to a new major category, and the major category data model has a major category array for storing all condition data or action data under the major category. When the category does not belong to the new major category, the major category data model corresponding to the category is searched in the condition category container array or the action category container array; the grouping module is configured to add the traversed condition data or action data belonging to the category parameters corresponding to the major category array to the major category array under the major category data model, and add the updated major category data model to the condition category container array or the action category container array.

[0008] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0009] By assigning the vehicle model code to the preset capability parameter interface, the capability parameter interface can obtain the capability parameters corresponding to the vehicle model code from the server; traverse the capability parameters, and divide the capability parameters into action data and condition data according to the parameter identifier corresponding to each capability parameter; add the condition data to the condition temporary container array, add the action data to the action temporary container array, and create a condition classification container array, an action classification container array and an unordered collection for loading large classification names; traverse the condition temporary container array and the action temporary container array, and when traversing each condition data or action data, call the filtering method of the unordered collection, according to the condition data or action data According to the corresponding category parameters, determine whether the category corresponding to the conditional data or action data is a new major category; when the category belongs to a new major category, create a new major category data model, and the major category data model has a major category array for storing all conditional data or action data under the major category. When the category does not belong to a new major category, search for the major category data model corresponding to the category in the conditional category container array or the action category container array; add the traversed conditional data or action data belonging to the category parameters corresponding to the major category array to the major category array under the major category data model, and add the updated major category data model to the conditional category container array or the action category container array. The present application can accurately group scattered and disordered capability parameters into corresponding major categories. The grouping results are complete and comprehensive, and the order after grouping is not chaotic. It can achieve accurate grouping of capability parameters according to vehicle models. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 1 is a flow chart of a method for grouping vehicle capability parameters provided in an embodiment of the present application;

[0012] Figure 2 Schematic diagram of the structure of the vehicle capability parameter grouping device provided in an embodiment of the present application;

[0013] Figure 3 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0014] In the following description, specific details such as specific system structures and technologies are provided for illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present application with unnecessary details.

[0015] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0016] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0017] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0018] A method, device, electronic device, and storage medium for grouping vehicle capability parameters according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0019] Figure 1 It is a flowchart of the method for grouping vehicle capability parameters provided in an embodiment of the present application. Figure 1 The grouping method of vehicle capability parameters can be executed by the background server. Figure 1 As shown, the grouping method of the vehicle capability parameters may specifically include:

[0020] S101, assigning the vehicle model code to a preset capability parameter interface, so that the capability parameter interface obtains capability parameters corresponding to the vehicle model code from the server;

[0021] S102, traversing the capability parameters and dividing the capability parameters into action data and condition data according to the parameter identifier corresponding to each capability parameter;

[0022] S103, adding the condition data to the condition temporary container array, adding the action data to the action temporary container array, and creating a condition category container array, an action category container array, and an unordered set for storing large category names;

[0023] S104, traversing the condition temporary container array and the action temporary container array, and when traversing each condition data or action data, calling the unordered set filtering method, and judging whether the category corresponding to the condition data or action data is a new major category based on the category parameter corresponding to the condition data or action data;

[0024] S105: When the category belongs to a new major category, a new major category data model is created, wherein the major category data model has a major category array for storing all condition data or action data under the major category. When the category does not belong to the new major category, the major category data model corresponding to the category is searched in the condition category container array or the action category container array.

[0025] S106, adding the traversed conditional data or action data belonging to the category parameters corresponding to the large classification array to the large classification array under the large classification data model, and adding the updated large classification data model to the conditional classification container array or the action classification container array.

[0026] First, some technical terms involved in the embodiments of this application are explained, which may include the following:

[0027] modeCode: The parameter representing the vehicle model in the interface, that is, the vehicle model code.

[0028] type: Parameter representing the capability type.

[0029] insert: A method for inserting elements into an unordered collection in iOS. This method has a return value of type BOOl. A true value indicates that the element to be inserted does not exist in the collection, indicating a successful insertion; a false value indicates that the element to be inserted already exists in the collection, indicating a failed insertion.

[0030] Ability Data: The functions or conditions that a car can achieve.

[0031] Condition Data: Data that causes the car to respond in a certain way when certain conditions are met.

[0032] Action Data: The specific actions that the car can perform.

[0033] In some embodiments, the capability parameters are traversed and divided into action data and conditional data according to the parameter identifier corresponding to each capability parameter, including: traversing the capability parameters, checking the parameter identifier of each capability parameter during the traversal process, judging whether the capability parameter belongs to action data or conditional data based on the parameter identifier, when the parameter identifier is 0, classifying the capability parameter as action data, and when the parameter identifier is not 0, classifying the capability parameter as conditional data, wherein the conditional data and the action data have a unified data structure.

[0034] Specifically, the capability parameters of the corresponding vehicle model are obtained from the server according to the vehicle model code (modeCode). The capability parameters are specifically divided into two categories: conditional data and action data. Since the software and hardware configurations of different vehicle models are not exactly the same, not all vehicle models will have the same capability parameters. In order to obtain the capability parameters of a specific vehicle model, the background of the embodiment of the present application provides a capability parameter interface, which includes a vehicle model code modeCode. When obtaining the capability data of a specific vehicle model, the vehicle model of the vehicle model is assigned to the modelCode parameter and sent to the server. The server will then return all relevant capability parameters of the vehicle model.

[0035] Furthermore, after obtaining the capability parameters, since all conditional data and action data are mixed together, they need to be divided into two categories: conditional data and action data. The classification principle is: since both conditional data and action data have the same data structure and have a parameter type (parameter identifier), the parameter "type" can be used to distinguish conditional data from action data. For example, when the parameter identifier is 0, it means that the capability parameter is action data; when the parameter identifier is not 0, it means that the capability parameter is conditional data.

[0036] In actual applications, before starting the classification operation, you need to create two temporary container arrays, named temCDataSource (condition temporary container array) and temADataSource (action temporary container array), which are used to temporarily load the condition data and action data generated by the intermediate steps respectively.

[0037] Traverse all capability parameters returned in the above embodiment and perform the following judgment: if the type of the capability parameter is 0, it means that the capability parameter belongs to action data, and then add the capability parameter to te mADataSource; if the type of the capability parameter is not 0, it means that the capability parameter is conditional data, and then add the capability parameter to temCDataSource.

[0038] At this point, all conditional data and action data have been added to their respective containers. At this point, they are simply divided into conditional data and action data, but the data is still not clearly categorized. For example, within the "time" category, sunrise and sunset both fall under the "time" category, but at this point, they still belong to the "conditional data" category. Therefore, further subdivision is required.

[0039] In some embodiments, the condition temporary container array is traversed, and when traversing each condition data, the filtering method of the unordered set is called, and according to the category parameters corresponding to the condition data, it is judged whether the category corresponding to the condition data is a new major category, including: traversing each condition data in the condition temporary container array one by one, calling the filtering method of the unordered set, and inputting the category parameters of the currently traversed condition data into the filtering method; judging whether the category of the current condition data is classified as a known major category based on the return value of the filtering method, and when the return value is true, judging that the category of the current condition data is a new major category that has not been identified.

[0040] Specifically, before performing subclassification, you need to create two container arrays, named cDataS ource (conditional classification container array) and aDataSource (action classification container array), which are used to load the final subclassification results; then create an unordered set categorieSets, which is used to load the names of the major categories in the subclassification process.

[0041] The principle of subdividing the data in the temporary container array in the embodiment of the present application is: each capability parameter has a category (category parameter), and the category parameter indicates the category name of a certain large category to which the capability parameter belongs. Therefore, when multiple capability parameters have the same category, the capability parameters with the same category can be classified under the same large category.

[0042] Furthermore, taking the classification of conditional data as an example, we first traverse the conditional temporary container array temCDataS ource. In each traversal, we call the insert method (filtering method) of the categorieSets collection provided by the system, and pass the category value of each conditional capability (that is, the value of the category parameter) to the insert method. The return value of the function is judged. If the return value is true, it means that the current conditional capability belongs to a new major category. At this time, we need to create a new major category data model to store the data of the current conditional capability.

[0043] In some embodiments, the action temporary container array is traversed, and when traversing each action data, the filtering method of the unordered set is called, and according to the category parameters corresponding to the action data, it is judged whether the category corresponding to the action data is a new major category, including: traversing each action data in the action temporary container array one by one, calling the filtering method of the unordered set, and inputting the category parameters of the currently traversed action data into the filtering method; judging whether the category of the current action data is classified into a known major category based on the return value of the filtering method, and when the return value is true, judging that the category of the current action data is a new major category that has not been identified.

[0044] Similarly, taking the classification of action data as an example, first traverse the action temporary container array temADataSource. In each traversal, call the insert method (filter method) of the categorieSets collection provided by the system, pass the category value of each action capability (that is, the value of the category parameter) to the insert method, and judge the return value of the function. If the return value is true, it means that the current action capability belongs to a new major category. At this time, you need to create a new major category data model to store the data of the current action capability.

[0045] In some embodiments, when a category belongs to a new major category, a new major category data model is created, and the major category data model has a major category array for storing all conditional data or action data under the major category, including: creating a new major category data model, the major category data model contains a major category array and category parameters corresponding to the major category array, wherein the major category array is used to store all conditional data or action data belonging to the major category, and the category parameters of the major category array include the major category name corresponding to the major category array.

[0046] Specifically, when it is necessary to create a new large category data model, the embodiment of the present application designs a data model structure called "caListModel". This caListModel data model is designed to have two main attributes, namely atomModels and category. Among them, the atomModels attribute represents the large category array, which is an array structure that can be dynamically expanded. Its main task is to load all conditional data or action data belonging to the large category. These data exist in the form of capability parameters, which can be performance parameters, function descriptions, etc. of different vehicle models. The category attribute is used to identify the name of the current large category array. For example, when the parameters related to the vehicle's power system are taken as a large category, this category may be "power system".

[0047] Furthermore, when the system traverses the temporary container array and identifies some capability parameters that need to be classified into a new category, it first creates a caListModel instance and then adds these capability parameters to the atomModels property of the newly created caListModel instance.

[0048] After completing this step, the system needs to decide which container array to add the newly created caListModel instance to. Specifically, if the current data being processed is conditional, the caListModel instance is added to the condition classification container array cDataSource; if the data being processed is action, the caListModel instance is added to the action classification container array aDataSource.

[0049] In addition, it is important to note that whenever new capability parameters are added to the atomModels attribute of caListModel, the system will check and ensure that these parameters are consistent with the category attribute of caListModel to ensure data integrity and accuracy.

[0050] Through the above embodiments, the present application provides a clear and structured way to create, manage and organize large classification data models, thereby achieving accurate and orderly classification and display of vehicle capability parameters.

[0051] In some embodiments, when the category does not belong to a new major category, the major category data model corresponding to the category is searched in the conditional category container array or the action category container array, including: when the return value is false, it is determined that the conditional category container array or the action category container array contains the major category corresponding to the conditional data or action data, and according to the category parameters corresponding to the conditional data or action data, the major category data model corresponding to the category parameters is searched in the conditional category container array or the action category container array.

[0052] Specifically, if the return value is false, it means that the major category already exists, indicating that the currently traversed capability data does not belong to a new major category. Then, it is necessary to search for a major category data model with the same category as the current capability parameter in the existing conditional category container array cDataSource or the category container array aDataSource. After finding it, add the current capability data to the major category array atomModels under the major category data model just found.

[0053] Specifically, in order to ensure that the capability parameters can be correctly classified and organized, when processing the capability parameters, a certain judgment mechanism (such as the above-mentioned filtering method) will first be used to determine whether the parameter belongs to a new major category. If the return value is false, it is judged that the current capability parameters do not belong to the new major category, that is, a major category data model has been created for the major category, and the current capability parameters are stored in the major category array under the existing major category data model.

[0054] In one example, when traversing the temporary container array and obtaining a certain capability parameter, a filtering method or a similar mechanism is first used to classify it. If the judgment result is false, this means that the category to which the current capability parameter belongs already exists in the conditional classification container array cDataSource or the action classification container array aDataSource. Therefore, it is necessary to further search these two container arrays, with the goal of finding a large classification data model with the same category attribute value as the current capability parameter. In order to improve efficiency, this application can use hash tables or other efficient data structures and algorithms for fast searches. Once a matching large classification data model is found, the current capability parameter can be added to the atomModels attribute of this data model. In this way, it is ensured that the capability parameters are accurately classified and stored in the correct location.

[0055] Through the above embodiments, the present application can ensure that all capability parameters are accurately and orderly classified and stored, regardless of whether they belong to new or existing categories. This not only improves the efficiency of data organization, but also ensures the integrity and accuracy of the data.

[0056] In some embodiments, the method further includes: in response to a vehicle model code input or selected by a user through a user interface, obtaining corresponding capability parameters from a server through a preset capability parameter interface, and grouping the capability parameters; displaying the grouped capability parameters by category on the user interface, wherein each major category contains conditional data or action data belonging to the major category.

[0057] Specifically, in actual application scenarios, users may need to obtain and view the corresponding capability parameters based on a specific vehicle model code. Such a requirement reflects the convenience and personalization of information retrieval. This application also provides the following methods to meet the user's personalized display needs, which may include the following:

[0058] User interface settings: First, provide users with an interactive and friendly interface where they can easily enter or select the model code they are interested in from a drop-down list.

[0059] Capability parameter interface call: Once the user confirms their selection and submits, the system communicates with the server through the preset capability parameter interface. This interface is designed to receive the vehicle model code as an input parameter and return the capability parameters associated with this vehicle model code.

[0060] Grouping of capability parameters: After acquiring the capability parameters, this application uses the grouping mechanism described above to process them. That is, based on the characteristics and categories of the capability parameters, this application groups them into different major categories, including but not limited to condition data and action data.

[0061] Displaying processing results: After processing is complete, this application will display the grouped capability parameters on the user interface. To ensure that users can easily understand and find the information they need, this application uses clear labels or icons to represent each major category on the interface. Under each major category, users can see all the condition data or action data belonging to that category.

[0062] Enhanced user interaction functions: In order to enhance the user experience, this application can provide further interactive functions for each major category and the data items therein, such as: expanding / collapse the major category content, highlighting the keywords searched by the user, or providing sorting functions, etc.

[0063] Data Update and Synchronization: Considering that vehicle capability parameters may change over time, this application also sets up scheduled tasks or triggers in the background to ensure that the data obtained from the server is always up to date. When the data on the server changes, the content displayed on the user interface will also be updated accordingly.

[0064] According to the technical solutions provided in the embodiments of this application, this application processes scattered and disordered capability data and effectively and accurately groups this data into corresponding major categories. This not only makes data organization more orderly but also facilitates subsequent data operations. This solution ensures the integrity and comprehensiveness of capability parameters. During the grouping process, all capability parameters are considered and placed into appropriate categories according to their characteristics. This solution places particular emphasis on the original order of the source data. Although the data is reorganized, its internal order is still preserved, ensuring data continuity and integrity and avoiding possible data interpretation errors caused by disrupting the original order. This solution obtains and loads the corresponding capability parameters from the server based on the specific vehicle model code entered by the user, ensuring that the loaded parameters fully match the given vehicle model, with neither missing nor redundant parameters. This personalized loading provides users with a highly customized data viewing experience. After completing the data traversal, all action data are accurately grouped according to their original order. This ensures that the logical flow and original intent of the action data are maintained, providing strong support for subsequent action data analysis and application.

[0065] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0066] Figure 2 Schematic diagram of the structure of the vehicle capability parameter grouping device provided in the embodiment of the present application. Figure 2 As shown, the grouping device of the vehicle capability parameters includes:

[0067] The acquisition module 201 is configured to assign the vehicle model code to a preset capability parameter interface, so that the capability parameter interface obtains the capability parameters corresponding to the vehicle model code from the server;

[0068] The division module 202 is configured to traverse the capability parameters and divide the capability parameters into action data and condition data according to the parameter identifier corresponding to each capability parameter;

[0069] An adding module 203 is configured to add condition data to a condition temporary container array, add action data to an action temporary container array, and create a condition category container array, an action category container array, and an unordered set for loading large category names;

[0070] The traversal module 204 is configured to traverse the condition temporary container array and the action temporary container array, and when traversing each condition data or action data, call the unordered set filtering method to determine whether the category corresponding to the condition data or action data is a new major category based on the category parameter corresponding to the condition data or action data;

[0071] The creation module 205 is configured to, when the category belongs to a new major category, create a new major category data model, wherein the major category data model has a major category array for storing all condition data or action data under the major category; and, when the category does not belong to the new major category, search the condition category container array or the action category container array for the major category data model corresponding to the category;

[0072] The grouping module 206 is configured to add the traversed conditional data or action data belonging to the category parameters corresponding to the large classification array to the large classification array under the large classification data model, and add the updated large classification data model to the conditional classification container array or the action classification container array.

[0073] In some embodiments, Figure 2The division module 202 traverses the capability parameters. During the traversal process, the parameter identifier of each capability parameter is checked, and the capability parameter is judged as action data or condition data based on the parameter identifier. When the parameter identifier is 0, the capability parameter is classified as action data. When the parameter identifier is not 0, the capability parameter is classified as condition data, wherein the condition data and the action data have a unified data structure.

[0074] In some embodiments, Figure 2 The traversal module 204 traverses each condition data in the condition temporary container array one by one, calls the filtering method of the unordered set, and inputs the category parameter of the currently traversed condition data into the filtering method; based on the return value of the filtering method, it is judged whether the category of the current condition data is classified into a known major category. When the return value is true, it is judged that the category of the current condition data is a new major category that has not been identified.

[0075] In some embodiments, Figure 2 The traversal module 204 traverses each action data in the action temporary container array one by one, calls the filtering method of the unordered set, and inputs the category parameter of the currently traversed action data into the filtering method; based on the return value of the filtering method, it is judged whether the category of the current action data is classified into a known major category. When the return value is true, it is judged that the category of the current action data is a new major category that has not been recognized.

[0076] In some embodiments, Figure 2 The creation module 205 creates a new large classification data model, which includes a large classification array and category parameters corresponding to the large classification array, wherein the large classification array is used to store all conditional data or action data belonging to the large classification, and the category parameters of the large classification array include the large classification name corresponding to the large classification array.

[0077] In some embodiments, Figure 2 When the return value of the creation module 205 is false, it is determined that the condition classification container array or the action classification container array contains the major classification corresponding to the condition data or the action data, and according to the category parameters corresponding to the condition data or the action data, the major classification data model corresponding to the category parameters is searched in the condition classification container array or the action classification container array.

[0078] In some embodiments, Figure 2 The display module 207 responds to the vehicle model code input or selected by the user through the user interface, obtains the corresponding capability parameters from the server through the preset capability parameter interface, and groups the capability parameters; the grouped capability parameters are displayed by category on the user interface, wherein each major category contains conditional data or action data belonging to the major category.

[0079] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0080] Figure 3 Schematic diagram of the structure of the electronic device 3 provided in the embodiment of the present application. Figure 3 As shown, the electronic device 3 of this embodiment includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program 303, the steps of the above-mentioned method embodiments are implemented. Alternatively, when the processor 301 executes the computer program 303, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0081] For example, computer program 303 may be divided into one or more modules / units, which are stored in memory 302 and executed by processor 301 to implement the present application. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of computer program 303 in electronic device 3.

[0082] The electronic device 3 may be a desktop computer, a notebook, a PDA, a cloud server or other electronic device. The electronic device 3 may include but is not limited to a processor 301 and a memory 302. Those skilled in the art will understand that Figure 3 It is only an example of electronic device 3 and does not constitute a limitation of electronic device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0083] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0084] The memory 302 can be an internal storage unit of the electronic device 3, such as a hard drive or memory of the electronic device 3. The memory 302 can also be an external storage device of the electronic device 3, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the electronic device 3. Furthermore, the memory 302 can include both an internal storage unit of the electronic device 3 and an external storage device. The memory 302 is used to store computer programs and other programs and data required by the electronic device. The memory 302 can also be used to temporarily store data that has been output or is about to be output.

[0085] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0086] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0087] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0088] In the embodiments provided in this application, it should be understood that the disclosed apparatus / computer equipment and methods can be implemented in other ways. For example, the apparatus / computer equipment embodiments described above are merely schematic. For example, the division of modules or units is merely a logical function division. In actual implementation, there may be other division methods. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of the apparatus or unit, which may be electrical, mechanical or other forms.

[0089] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0090] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0091] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0092] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for grouping vehicle capability parameters, characterized in that: include: Assigning the vehicle model code to a preset capability parameter interface so that the capability parameter interface obtains the capability parameter corresponding to the vehicle model code from the server; Traversing the capability parameters, and dividing the capability parameters into action data and condition data according to a parameter identifier corresponding to each capability parameter; Add the condition data to a condition temporary container array, add the action data to an action temporary container array, and create a condition category container array, an action category container array, and an unordered collection for loading large category names; Traversing the condition temporary container array and the action temporary container array, and when traversing each condition data or the action data, calling the filtering method of the unordered set, and judging whether the category corresponding to the condition data or the action data is a new major category according to the category parameter corresponding to the condition data or the action data; When the category belongs to a new major category, a new major category data model is created, wherein the major category data model has a major category array for storing all condition data or action data under the major category; when the category does not belong to the new major category, the major category data model corresponding to the category is searched in the condition category container array or the action category container array; The traversed conditional data or action data belonging to the category parameters corresponding to the large classification array are added to the large classification array under the large classification data model, and the updated large classification data model is added to the conditional classification container array or the action classification container array.

2. The method according to claim 1, characterized in that The traversing of the capability parameters and dividing the capability parameters into action data and condition data according to the parameter identifier corresponding to each capability parameter includes: The capability parameters are traversed, and during the traversal process, the parameter identifier of each capability parameter is checked, and the capability parameter is judged as action data or conditional data based on the parameter identifier. When the parameter identifier is 0, the capability parameter is classified as action data, and when the parameter identifier is not 0, the capability parameter is classified as conditional data, wherein the conditional data and the action data have a unified data structure.

3. The method according to claim 1, characterized in that Traversing the condition temporary container array, when traversing each condition data, calling the filtering method of the unordered set, and judging whether the category corresponding to the condition data is a new major category according to the category parameter corresponding to the condition data, including: Each of the conditional data in the conditional temporary container array is traversed one by one, the filtering method of the unordered set is called, and the category parameter of the conditional data currently traversed is input into the filtering method; based on the return value of the filtering method, it is judged whether the category of the current conditional data is classified into a known major category. When the return value is true, it is judged that the category of the current conditional data is a new major category that has not been identified.

4. The method according to claim 1, wherein Traversing the action temporary container array, when traversing each action data, calling the filtering method of the unordered set, and judging whether the category corresponding to the action data is a new major category according to the category parameter corresponding to the action data, including: Each action data in the action temporary container array is traversed one by one, the filtering method of the unordered set is called, and the category parameter of the currently traversed action data is input into the filtering method; based on the return value of the filtering method, it is judged whether the category of the current action data is classified into a known major category. When the return value is true, it is judged that the category of the current action data is a new major category that has not been recognized.

5. The method according to claim 1, characterized in that When the category belongs to a new major category, a new major category data model is created, wherein the major category data model has a major category array for storing all condition data or action data under the major category, including: A new large classification data model is created, wherein the large classification data model includes the large classification array and the category parameters corresponding to the large classification array, wherein the large classification array is used to store all conditional data or action data belonging to the large classification, and the category parameters of the large classification array include the large classification name corresponding to the large classification array.

6. The method according to claim 4, characterized in that When the category does not belong to a new major category, searching for a major category data model corresponding to the category in the condition category container array or the action category container array includes: When the return value is false, it is determined that the condition classification container array or the action classification container array contains the major classification corresponding to the condition data or the action data, and according to the category parameters corresponding to the condition data or the action data, the major classification data model corresponding to the category parameters is searched in the condition classification container array or the action classification container array.

7. The method according to claim 1, characterized in that The method further comprises: In response to the vehicle model code input or selected by the user through the user interface, the corresponding capability parameters are obtained from the server through the preset capability parameter interface, and the capability parameters are grouped and processed; the grouped capability parameters are displayed by category on the user interface, wherein each of the major categories contains conditional data or action data belonging to the major category.

8. A vehicle capability parameter grouping device, characterized in that: include: An acquisition module is configured to assign the vehicle model code to a preset capability parameter interface, so that the capability parameter interface obtains the capability parameter corresponding to the vehicle model code from the server; a division module configured to traverse the capability parameters and divide the capability parameters into action data and condition data according to a parameter identifier corresponding to each capability parameter; An adding module is configured to add the condition data to a condition temporary container array, add the action data to an action temporary container array, and create a condition category container array, an action category container array, and an unordered collection for loading large category names; a traversal module configured to traverse the condition temporary container array and the action temporary container array, and when traversing each condition data or the action data, call the filtering method of the unordered set, and determine whether the category corresponding to the condition data or the action data is a new major category based on the category parameter corresponding to the condition data or the action data; a creation module configured to, when the category belongs to a new major category, create a new major category data model, the major category data model having a major category array for storing all condition data or action data under the major category, and, when the category does not belong to the new major category, search the condition category container array or the action category container array for the major category data model corresponding to the category; The grouping module is configured to add the traversed conditional data or action data belonging to the category parameters corresponding to the large classification array to the large classification array under the large classification data model, and add the updated large classification data model to the conditional classification container array or the action classification container array.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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