CN117312312A - Grouping method and device for vehicle capability parameters, electronic equipment and storage medium - Google Patents

Abstract

The application provides a grouping method and device of vehicle capability parameters, electronic equipment and a storage medium. The method comprises the following steps: acquiring capability parameters corresponding to the vehicle type codes, and 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 the action data, invoking a filtering method of the unordered set, and judging whether the category corresponding to the condition data or the action data is a new large category or not; when the new large classification is included, a new large classification data model is created, and when the new large classification is not included, a corresponding large classification data model is searched; and adding the condition data or the action data of the class parameters of the traversed large classification array into the large classification array, and adding the updated large classification data model into the condition classification container array or the action classification container array. The method and the device improve the integrity and the comprehensiveness of the capacity parameter grouping, and realize the accurate grouping of the vehicle type capacity parameters.

Description

Grouping method and device for vehicle capability parameters, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of new energy automobiles, and in particular, to a method and apparatus for grouping vehicle capability parameters, an electronic device, and a storage medium.

Background

As vehicle technology evolves, various performance and functional parameters of vehicles become increasingly rich, which typically require efficient classification and organization for ease of understanding and use by manufacturers, service personnel and users. However, there are some significant problems and disadvantages in the conventional art.

First, the capability parameters of the prior art are not comprehensive. This means that for some vehicles or for some specific situations, a complete vehicle capability description may not be available, which may result in a manufacturer, serviceman or user not being able to accurately understand the full functionality and performance of the vehicle. Second, while some techniques attempt to group these capability parameters, the grouping tends to be incomplete. Even if the grouping is performed, it is often not performed in the order of the source data, resulting in a disorder of the order after the grouping, which makes it more difficult to find a specific parameter. Furthermore, many systems do not load corresponding capability parameters according to a particular vehicle model. This can result in insufficient or redundant capability parameter information for a portion of the vehicle model, making it difficult for manufacturers, maintenance personnel, and users to find capability parameters that exactly match a particular vehicle model.

Therefore, how to accurately group scattered and unordered capability data into corresponding large categories, so that capability parameters of different vehicle types are comprehensively and orderly displayed, and the method becomes a challenge facing the current technology.

Disclosure of Invention

In view of this, the embodiments of the present application provide a method, an apparatus, an electronic device, and a storage medium for grouping capability parameters of a vehicle, so as to solve the problem that in the prior art, the grouping of capability parameters is incomplete, and the sequence after grouping is disordered, so that accurate grouping cannot be achieved.

In a first aspect of an embodiment of the present application, a method for grouping vehicle capability parameters is provided, including: assigning the vehicle type code of the vehicle to a preset capacity parameter interface so that the capacity parameter interface obtains capacity parameters corresponding to the vehicle type code from a server; traversing the capacity parameters, and dividing the capacity parameters into action data and condition data according to the parameter identification corresponding to each capacity parameter; adding condition data into a condition temporary container array, adding action data into 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 a filtering method of the unordered set when traversing each condition data or action data, and judging whether the category corresponding to the condition data or the action data is a new large category according to the category parameter corresponding to the condition data or the action data; when the category belongs to a new large category, a new large category data model is created, a large category array used for storing all condition data or action data under the large category is arranged in the large category data model, and when the category does not belong to the new large category, a large category data model corresponding to the category is searched in a condition category container array or an action category container array; and adding the traversed condition data or action data belonging to the class parameters corresponding to the large classification data array into the large classification data array under the large classification data model, and adding the updated large classification data model into the condition classification container array or the action classification container array.

In a second aspect of the embodiments of the present application, there is provided a grouping apparatus for vehicle capability parameters, including: the acquisition module is configured to assign the vehicle type code of the vehicle to a preset capacity parameter interface so that the capacity parameter interface acquires capacity parameters corresponding to the vehicle type code from the server; the dividing module is configured to traverse the capability parameters and divide the capability parameters into action data and condition data according to the parameter identification corresponding to each capability parameter; an adding module configured to add condition data to the condition temporary container array, to add action data to the action temporary container array, and to create a condition classification container array, an action classification container array, and an unordered set for loading large classification names; the traversing module is configured to traverse the condition temporary container array and the action temporary container array, and when each condition data or action data is traversed, the filtering method of the unordered set is called, and whether the category corresponding to the condition data or the action data is a new large category is judged according to the category parameter corresponding to the condition data or the action data; the creating module is configured to create a new large-classification data model when the category belongs to a new large classification, wherein the large-classification data model is provided with a large-classification array for storing all condition data or action data under the large classification, and when the category does not belong to the new large classification, the large-classification data model corresponding to the category is searched in a condition classification container array or an action classification container array; the grouping module is configured to add the traversed condition data or action data belonging to the class parameters corresponding to the large classification data array into the large classification data array under the large classification data model, and add the updated large classification data model into the condition classification container array or the action classification container array.

The above-mentioned at least one technical scheme that this application embodiment adopted can reach following beneficial effect:

assigning the vehicle type code of the vehicle to a preset capacity parameter interface so that the capacity parameter interface obtains capacity parameters corresponding to the vehicle type code from a server; traversing the capacity parameters, and dividing the capacity parameters into action data and condition data according to the parameter identification corresponding to each capacity parameter; adding condition data into a condition temporary container array, adding action data into 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 a filtering method of the unordered set when traversing each condition data or action data, and judging whether the category corresponding to the condition data or the action data is a new large category according to the category parameter corresponding to the condition data or the action data; when the category belongs to a new large category, a new large category data model is created, a large category array used for storing all condition data or action data under the large category is arranged in the large category data model, and when the category does not belong to the new large category, a large category data model corresponding to the category is searched in a condition category container array or an action category container array; and adding the traversed condition data or action data belonging to the class parameters corresponding to the large classification data array into the large classification data array under the large classification data model, and adding the updated large classification data model into the condition classification container array or the action classification container array. The method and the device can accurately group scattered and unordered capacity parameters into corresponding large categories, the grouping result is complete and comprehensive, the grouping sequence is not disordered, and the capacity parameters can be accurately grouped according to the vehicle type.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for grouping vehicle capability parameters provided by an embodiment of the present application;

FIG. 2 is a schematic structural diagram of a grouping device for vehicle capability parameters according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below. It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

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

Fig. 1 is a flow chart of a method for grouping vehicle capability parameters according to an embodiment of the present application. The grouping method of the vehicle capability parameters of fig. 1 may be performed by a background server. As shown in fig. 1, the grouping method of the vehicle capability parameters may specifically include:

s101, assigning a vehicle model code of a vehicle to a preset capacity parameter interface so that the capacity parameter interface obtains capacity parameters corresponding to the vehicle model code from a server;

s102, traversing the capability parameters, and dividing the capability parameters into action data and condition data according to the parameter identification corresponding to each capability parameter;

s103, adding the condition data into a condition temporary container array, adding the action data into 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;

s104, traversing the condition temporary container array and the action temporary container array, and calling a filtering method of the unordered set when traversing each condition data or action data, and judging whether the category corresponding to the condition data or the action data is a new large category according to the category parameter corresponding to the condition data or the action data;

S105, when the category belongs to a new large category, creating a new large category data model, wherein the large category data model is provided with a large category data array for storing all condition data or action data under the large category, and when the category does not belong to the new large category, searching the large category data model corresponding to the category in a condition category container array or an action category container array;

s106, adding the traversed condition data or action data belonging to the class parameters corresponding to the large classification data array into the large classification data array under the large classification data model, and adding the updated large classification data model into the condition classification container array or the action classification container array.

First, some technical terms involved in the embodiments of the present application are explained, and specifically may include the following:

modeCode: parameters representing the vehicle type, namely the vehicle type code, are displayed in the interface.

type: parameters representing the type of capability.

insert: the method for inserting elements into unordered set in iOS has a BOOl type return value, and represents that no element to be inserted exists in the set for true, and represents that the insertion is successful; an element to be inserted already exists in the false representation set, and represents an insertion failure.

Capability parameter (capability Data): functions or conditions that can be fulfilled by an automobile.

Condition Data (Condition Data): when certain conditions are met, the car will respond with data of a certain kind.

Action Data (Action Data): specific operations that may be performed by the automobile.

In some embodiments, traversing the capability parameters, dividing the capability parameters into action data and condition data according to parameter identifiers corresponding to each capability parameter, including: and traversing the capacity parameters, checking the parameter identification of each capacity parameter in the traversing process, judging that the capacity parameter belongs to the action data or the condition data according to the parameter identification, classifying the capacity parameter into the action data when the parameter identification is 0, and classifying the capacity parameter into the condition data when the parameter identification is not 0, wherein the condition data and the action data have a uniform data structure.

Specifically, capability parameters of corresponding vehicle types are obtained from a server according to a vehicle type code (modeCode). The capability parameters are specifically divided into two types, namely condition data and action data, and because the software and hardware configurations of different vehicle types are not completely the same, not all vehicle types can possess the same capability parameters, in order to obtain the capability parameters of a specific vehicle type, the background of the embodiment of the application provides a capability parameter interface, the interface comprises a vehicle type code modeCode, when the capability data of a specific vehicle type is obtained, the vehicle type of the vehicle type is assigned to a model code parameter and sent to a server, and the server returns all relevant capability parameters of the vehicle type.

Further, after the capability parameters are acquired, since all the condition data and the action data are mixed together, they need to be divided into two major categories, namely, condition data and action data, and the classification principle is as follows: since the data structures of the condition data and the action data are identical and all possess one parameter type (parameter identification), the condition data and the action data can be distinguished by the parameter "type". For example, when the parameter identification is 0, representing the capability parameter as action data; when the parameter identification is not 0, the capability parameter is represented as conditional data.

In practical applications, two temporary container arrays, named temCDataSource (conditional temporary container array) and temADataSource (action temporary container array), respectively, are created before the classification operation is started, and are used for temporarily loading the condition data and the action data generated in the intermediate step.

Traversing all the capability parameters returned in the above embodiment, and executing the following judgment: if the type of the capability parameter is 0, indicating that the capability parameter belongs to the action data, adding the capability parameter into the te mADataSource; if the type of the capability parameter is not 0, indicating that the capability parameter is conditional data, the capability parameter is added to temCDataSource.

So far, all condition data and action data are added to the respective containers, respectively, at which time they are simply divided into condition data and action data by a generalization, but these data are still not clearly classified. Such as: for the large category "time", sunrise and sunset both belong to the category "time", i.e. they both fall under the large category "time", but at this time they still fall under the large category "condition data", and therefore further sub-division of the category is required.

In some embodiments, traversing the condition temporary container array, and when traversing each condition data, invoking a filtering method of the unordered set, and judging whether the category corresponding to the condition data is a new large category according to the category parameter corresponding to the condition data, including: traversing each condition data in the condition temporary container array one by one, calling a filtering method of the unordered set, and inputting category parameters of the condition data traversed currently into the filtering method; judging whether the category of the current condition data is classified into a known large category according to the return value of the filtering method, and judging that the category of the current condition data is a new large category which is not recognized when the return value is true.

Specifically, before classifying, two container arrays named cDataS source (conditional classification container array) and aadatasource (action classification container array), respectively, are created for loading the final classification result; an unordered set of categories is created again, which is used to load the names of the large categories in the fine-categorization process.

The principle of classifying data in the temporary container array according to the embodiment of the application is as follows: each capability parameter has a category parameter, which indicates that the capability parameter belongs to a category name of a large category, so that when a plurality of capability parameters have the same category, the capability parameters with the same category can be classified under the same large category.

Further, taking classification of condition data as an example, firstly traversing a condition temporary container array temCDataS source, in each traversal, calling an insert method (filtering method) of a category set provided by a system, transmitting a category value (i.e. a value of a category parameter) of each condition capability to the insert method, judging a return value of the function, and if the return value is true, indicating that the current condition capability belongs to a new large classification, wherein a new large classification data model is required to be created to store the data of the current condition capability.

In some embodiments, traversing the action temporary container array, and when traversing each action data, invoking a filtering method of the unordered set, and judging whether the category corresponding to the action data is a new large category according to the category parameter corresponding to the action data, including: traversing each action data in the action temporary container array one by one, calling a filtering method of the unordered set, and inputting category parameters of the action data traversed currently into the filtering method; judging whether the category of the current action data is classified into a known large category according to the return value of the filtering method, and judging that the category of the current action data is a new large category which is not recognized when the return value is true.

Similarly, taking classification of action data as an example, firstly traversing an action temporary container array temadata source, calling an insert method (filtering method) of a category set provided by a system in each traversal, transmitting a category value (namely a value of a category parameter) of each action capability to the insert method, judging a return value of the function, and if the return value is true, indicating that the current action capability belongs to a new large classification, and creating a new large classification data model to store the data of the current action capability.

In some embodiments, when a category belongs to a new large category, a new large category data model is created having therein a large category array for storing all condition data or action data under the large category, comprising: creating a new large-classification data model, wherein the large-classification data model comprises a large-classification array and class parameters corresponding to the large-classification array, the large-classification array is used for storing all condition data or action data belonging to large classification, and the class parameters of the large-classification array comprise large-classification names corresponding to the large-classification array.

Specifically, when a new large classification data model needs to be created, embodiments of the present application design a data model structure called "calist model". This calist model data model is designed to possess two main attributes, namely atoms and category. The attimodes attribute represents a large-class array, and is an array structure capable of being dynamically expanded. Its main task is to load all condition data or action data belonging to the large class. These data are all in the form of capability parameters, which may be performance parameters, function descriptions, etc. of different vehicle models. The category attribute is then used to identify the name of the current large category array. For example, when the parameters related to the powertrain of the vehicle are classified as a large category, the category may be "powertrain".

Further, when the system traverses the temporary container array and recognizes that certain capability parameters need to fall into a new large class, it will first create a calist model instance. The system will then add these capability parameters to the atom models properties of the just created calist model instance.

After this step is completed, the system needs to decide which container array to add this newly created calist model instance to. Specifically, if condition data is currently being processed, then the caListMo del instance is added to the condition classification container array cDataSource; if action data is processed, it is added to the action classification container array aDataSource.

Further, it should be noted that whenever new capability parameters are added to the atom Mo dels attribute of the caListModel, the system will check and ensure that these parameters are consistent with the category attribute of the caListModel, ensuring the integrity and accuracy of the data.

By the embodiment, a clear and structured manner is provided for creating, managing and organizing large classified data models, so that accurate and orderly classification and display of capability parameters of vehicles are realized.

In some embodiments, when the category does not belong to a new large category, searching the large category data model corresponding to the category in the condition classification container array or the action classification container array includes: when the return value is false, judging that the condition classification container array or the action classification container array contains large classification corresponding to the condition data or the action data, and searching a large classification data model corresponding to the classification parameter in the condition classification container array or the action classification container array according to the classification parameter corresponding to the condition data or the action data.

Specifically, if the return value is false, which means that the large class already exists, and indicates that the capability data currently traversed does not belong to a new large class, then the large class data model identical to the category of the current capability parameter needs to be searched in the existing conditional classification container array cDataSource or the classification container array aadatasource, and after the large class data model is found, the current capability data is added to the large class array atomModels under the large class data model just found.

Specifically, to ensure that the capability parameter can be correctly classified and sorted, when the capability parameter is processed, it is first determined by a certain determination mechanism (such as the filtering method described above) whether the parameter belongs to a new large class, if the return value is false, it is determined that the current capability parameter does not belong to the new large class, i.e. a large class data model has been created for the large class, and the current capability parameter is stored in a large class array under the existing large class data model.

In one example, when a temporary container array is traversed and certain capability parameters are obtained, it is first classified using a filtering method or similar mechanism. If the judgment result is false, the current capability parameter belongs to the category which is already existed in the condition classification container array cDataSource or the action classification container array aDataSource. Thus, it is necessary to further search the two container arrays, with the goal of finding a large classified data model that is the same as the category attribute value of the current capability parameter. To increase efficiency, the present application may use hash tables or other efficient data structures and algorithms for fast lookup. Once a matching large classification data model is found, the current capability parameters can be added to the atomModels properties of this data model. In this way it is ensured that the capability parameters are accurately categorized and stored in the correct locations.

By means of the above embodiments, the present application is able to ensure that all capability parameters are accurately and orderly categorized and stored, whether they belong to a new large category or an existing large category. This not only improves the data arrangement efficiency, but also ensures the integrity and accuracy of the data.

In some embodiments, the method further comprises: responding to a vehicle model code input or selected by a user through a user interface, acquiring corresponding capability parameters from a server through a preset capability parameter interface, and carrying out grouping processing on the capability parameters; and displaying the grouped capability parameters on a user interface according to categories, wherein each large category comprises condition data or action data belonging to the large category.

Specifically, in an actual application scenario, a user may need to acquire and view corresponding capability parameters according to a specific model code. Such a need represents a convenience and individualization of information retrieval. The application also provides the following methods to meet the personalized display requirements of users, which specifically can comprise the following contents:

setting a user interaction interface: first, an interactive friendly interface is provided for the user. On this interface, the user can easily input or select the model code of interest to them from the drop down list.

Capability parameter interface call: once the user determines the selection and submission, the system communicates with the server through a preset capability parameter interface. This interface is designed to receive a model code as an input parameter and return a capability parameter associated with this model code.

Packet processing of capability parameters: after the capability parameters are obtained, the application uses the grouping mechanism described before for processing. I.e., based on the nature and class of the capability parameters, the present application will group them into different large categories including, but not limited to, condition data and action data.

And (5) displaying the treatment results: after the processing is finished, the grouped capability parameters are displayed on a user interface. To ensure that the user can easily understand and find the desired information, the present application uses explicit labels or icons on the interface to represent each large category. Under each large category, the user can see all of the condition data or action data belonging to that category.

User interaction functionality enhancement: to enhance the user experience, the present application may provide further interactive functionality for each large category and data items therein, such as: expanding/collapsing large category content, highlighting keywords for user searches, or providing ranking functionality, etc.

Data updating and synchronization: considering that capability parameters of a vehicle model can be updated with time, a timing task or a trigger is set in the background, so that data acquired from a server side are always up to date. When the data of the server side changes, the content displayed on the user interface is updated accordingly.

According to the technical scheme provided by the embodiment of the application, the scattered unordered capability data are processed, and the data are effectively and accurately grouped into corresponding large categories. This not only makes the data organization more ordered, but also provides convenience for subsequent data manipulation. The solution ensures the integrity and comprehensiveness of the capability parameters, all of which are considered and placed in the appropriate categories according to their characteristics during the grouping process. The scheme particularly pays attention to the original sequence of the source data, and the sequence inside the source data is reserved even though the data is reorganized, so that the continuity and the integrity of the data are ensured, and possible data interpretation errors caused by disturbing the original sequence are avoided. According to the scheme, corresponding capability parameters are acquired and loaded from the server according to the specific vehicle model code input by the user, so that the loaded parameters are ensured to be completely matched with the given vehicle model, and the system is neither missing nor redundant. Such personalized loading provides a highly customized data viewing experience for the user. After the data traversal is completed, all action data is accurately grouped in its original order. This ensures that the logic flow and original intent of the action data is maintained, providing strong support for subsequent action data analysis and applications.

The following are device embodiments of the present application, which may be used to perform 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.

Fig. 2 is a schematic structural diagram of a grouping device for vehicle capability parameters according to an embodiment of the present application. As shown in fig. 2, the grouping device of vehicle capability parameters includes:

the acquiring module 201 is configured to assign a vehicle type code of the vehicle to a preset capability parameter interface, so that the capability parameter interface acquires capability parameters corresponding to the vehicle type code from the server;

the dividing 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;

an adding module 203 configured to add condition data to the condition temporary container array, to add action data to the action temporary container array, and to create a condition classification container array, an action classification container array, and an unordered set for loading large classification names;

the traversing 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, invoke a filtering method of the unordered set, and judge whether the category corresponding to the condition data or the action data is a new large category according to the category parameter corresponding to the condition data or the action data;

A creating module 205 configured to create a new large classification data model when the category belongs to a new large classification, the large classification data model having therein a large classification array for storing all condition data or action data under the large classification, and to find a large classification data model corresponding to the category in a condition classification container array or an action classification container array when the category does not belong to the new large classification;

the grouping module 206 is configured to add the traversed condition data or action data belonging to the class parameters corresponding to the large classification array under the large classification data model, and add the updated large classification data model to the condition classification container array or the action classification container array.

In some embodiments, the partitioning module 202 of fig. 2 traverses the capability parameters, checks the parameter identifier of each capability parameter during the traversal, determines that the capability parameter belongs to the action data or the condition data according to the parameter identifier, classifies the capability parameter as the action data when the parameter identifier is 0, and classifies the capability parameter as the condition data when the parameter identifier is not 0, wherein the condition data and the action data have a uniform data structure.

In some embodiments, the traversal module 204 of fig. 2 traverses each condition data in the condition temporary container array one by one, invokes the filtering method of the unordered set, and inputs the category parameters of the condition data currently traversed into the filtering method; judging whether the category of the current condition data is classified into a known large category according to the return value of the filtering method, and judging that the category of the current condition data is a new large category which is not recognized when the return value is true.

In some embodiments, the traversal module 204 of fig. 2 traverses each action data in the action temporary container array one by one, invokes the filtering method of the unordered set, and inputs 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 large category according to the return value of the filtering method, and judging that the category of the current action data is a new large category which is not recognized when the return value is true.

In some embodiments, the creation module 205 of fig. 2 creates a new large classification data model, where the large classification data model includes a large classification array and class parameters corresponding to the large classification array, where the large classification array is used to store all condition data or action data belonging to the large classification, and the class parameters of the large classification array include a large classification name corresponding to the large classification array.

In some embodiments, when the return value is false, the creation module 205 of fig. 2 determines that the condition classification container array or the action classification container array includes a large classification corresponding to the condition data or the action data, and searches the condition classification container array or the action classification container array for a large classification data model corresponding to the classification parameter according to the classification parameter corresponding to the condition data or the action data.

In some embodiments, the display module 207 of fig. 2 is configured to obtain, through a preset capability parameter interface, corresponding capability parameters from the server in response to a vehicle model code input or selected by a user through a user interface, and perform grouping processing on the capability parameters; and displaying the grouped capability parameters on a user interface according to categories, wherein each large category comprises condition data or action data belonging to the large category.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.

Fig. 3 is a schematic structural diagram of the electronic device 3 provided in the embodiment of the present application. As shown in fig. 3, the electronic apparatus 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. The steps of the various method embodiments described above are implemented when the processor 301 executes the computer program 303. Alternatively, the processor 301, when executing the computer program 303, performs the functions of the modules/units in the above-described apparatus embodiments.

Illustratively, the computer program 303 may be partitioned into one or more modules/units, which are stored in the memory 302 and executed by the processor 301 to complete the present application. One or more of the modules/units may be a series of computer program instruction segments capable of performing a specific function for describing the execution of the computer program 303 in the electronic device 3.

The electronic device 3 may be an electronic device such as a desktop computer, a notebook computer, a palm computer, or a cloud server. The electronic device 3 may include, but is not limited to, a processor 301 and a memory 302. It will be appreciated by those skilled in the art that fig. 3 is merely an example of the electronic device 3 and does not constitute a limitation of the electronic device 3, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may also include an input-output device, a network access device, a bus, etc.

The processor 301 may be a central processing unit (Central Processing Unit, CPU) or other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 302 may be an internal storage unit of the electronic device 3, for example, a hard disk or a memory of the electronic device 3. The memory 302 may also be an external storage device of the electronic device 3, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 3. Further, the memory 302 may also include both an internal storage unit and an external storage device of the electronic device 3. The memory 302 is used to store computer programs and other programs and data required by the electronic device. The memory 302 may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in this application, it should be understood that the disclosed apparatus/computer device and method may be implemented in other ways. For example, the apparatus/computer device embodiments described above are merely illustrative, e.g., the division of modules or elements is merely a logical functional division, and there may be additional divisions of actual implementations, multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow in the methods of the above embodiments, or may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program may implement the steps of the respective method embodiments described above when executed by a processor. The computer program may comprise computer program code, which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.

The above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims (10)

1. A method of grouping vehicle capability parameters, comprising:

assigning a vehicle model code of a vehicle to a preset capacity parameter interface so that the capacity parameter interface obtains capacity parameters corresponding to the vehicle model code from a server;

traversing the capability parameters, and dividing the capability parameters into action data and condition data according to the parameter identification 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 a filtering method of the unordered set when traversing each condition data or action data, and judging whether the category corresponding to the condition data or the action data is a new large category according to the category parameters corresponding to the condition data or the action data;

when the category belongs to a new large category, a new large category data model is created, the large category data model is provided with a large category array for storing all condition data or action data under the large category, and when the category does not belong to the new large category, the large category data model corresponding to the category is searched in the condition category container array or the action category container array;

and adding the traversed condition data or action data belonging to the class parameters corresponding to the large classification array into the large classification array under the large classification data model, and adding the updated large classification data model into the condition classification container array or the action classification container array.

2. The method of claim 1, wherein 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 comprises:

And traversing the capability parameters, checking the parameter identification of each capability parameter in the traversing process, judging that the capability parameter belongs to action data or condition data according to the parameter identification, classifying the capability parameter as the action data when the parameter identification is 0, and classifying the capability parameter as the condition data when the parameter identification is not 0, wherein the condition data and the action data have a uniform data structure.

3. The method of claim 1, wherein traversing the condition temporary container array, while traversing each of the condition data, invoking a filtering method of the unordered set, and determining, according to a category parameter corresponding to the condition data, whether a category corresponding to the condition data is a new large category, comprises:

traversing each condition data in the condition temporary container array one by one, calling a filtering method of the unordered set, and inputting category parameters of the condition data traversed currently into the filtering method; judging whether the category of the current condition data is classified into a known large category according to the return value of the filtering method, and judging that the category of the current condition data is a new large category which is not recognized when the return value is true.

4. The method of claim 1, wherein traversing the array of action temporary containers, while traversing each of the action data, invoking a filtering method of the unordered set, determining, according to a category parameter corresponding to the action data, whether a category corresponding to the action data is a new large category, comprises:

traversing each action data in the action temporary container array one by one, calling a filtering method of the unordered set, and inputting category parameters of the action data traversed currently into the filtering method; judging whether the category of the current action data is classified into a known large category according to the return value of the filtering method, and judging that the category of the current action data is a new large category which is not recognized when the return value is true.

5. The method of claim 1, wherein creating a new large classification data model when the category belongs to a new large classification, the large classification data model having therein a large classification array for storing all condition data or action data under the large classification, comprises:

creating a new large classification data model, wherein the large classification data model comprises a large classification array and class parameters corresponding to the large classification array, the large classification array is used for storing all condition data or action data belonging to the large classification, and the class parameters of the large classification array comprise large classification names corresponding to the large classification array.

6. The method of claim 4, wherein searching for a large classification data model corresponding to the category in the condition classification container array or the action classification container array when the category does not belong to a new large classification, comprises:

and when the return value is false, judging that the condition classification container array or the action classification container array contains the large classification corresponding to the condition data or the action data, and searching a large classification data model corresponding to the classification parameter in the condition classification container array or the action classification container array according to the classification parameter corresponding to the condition data or the action data.

7. The method according to claim 1, wherein the method further comprises:

responding to a vehicle model code input or selected by a user through a user interface, acquiring corresponding capability parameters from a server through a preset capability parameter interface, and carrying out grouping processing on the capability parameters; and displaying the grouped capability parameters on the user interface according to categories, wherein each large category comprises condition data or action data belonging to the large category.

8. A vehicle capability parameter grouping apparatus, comprising:

the acquisition module is configured to assign a vehicle model code of a vehicle to a preset capacity parameter interface so that the capacity parameter interface acquires capacity parameters corresponding to the vehicle model code from a server;

the dividing module is configured to traverse the capability parameters and divide the capability parameters into action data and condition data according to the parameter identification corresponding to each capability parameter;

an add module configured to add the condition data to a condition temporary container array, to add the action data to an action temporary container array, and to create a condition classification container array, an action classification container array, and an unordered set for loading large classification names;

the traversing module is configured to traverse the condition temporary container array and the action temporary container array, and when each condition data or action data is traversed, the filtering method of the unordered set is called, and whether the category corresponding to the condition data or the action data is a new large category is judged according to the category parameters corresponding to the condition data or the action data;

The creation module is configured to create a new large classification data model when the category belongs to a new large classification, wherein the large classification data model is provided with a large classification array used for storing all condition data or action data under the large classification, and when the category does not belong to the new large classification, the large classification data model corresponding to the category is searched in the condition classification container array or the action classification container array;

the grouping module is configured to add the traversed condition data or action data belonging to the class parameters corresponding to the large classification data array into the large classification data array under the large classification data model, and add the updated large classification data model into the condition classification container array or the action classification container array.

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 7 when the computer program is executed by the processor.

10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 7.