Method, system, device and vehicle for generating vehicle component architecture

Abstract

The present invention discloses a method, system, device, and vehicle for generating a vehicle component architecture. The method comprises: obtaining requirement data for an object, wherein the requirement data represents the object's requirements for interactions between multiple components to be created in the vehicle, wherein the components include vehicle parts; determining attribute characteristics of the multiple components based on the requirement data; determining target creation data for the multiple components based on the attribute characteristics; and creating a component architecture based on the target creation data. The present invention addresses the technical problem of low efficiency in constructing a vehicle component architecture.

Description

Technical Field

[0001] The present invention relates to the field of vehicles, and in particular to a method, system, device and vehicle for generating a vehicle component architecture. Background Art

[0002] At present, the design process of vehicle component architecture in related technologies is very cumbersome, requiring manual design of the detailed design content of each module of the entire vehicle based on vehicle components, such as the abstract functions of parts and equipment. Since manual design will lead to problems such as long generation cycle and high error rate of vehicle component architecture, there is still a technical problem of low construction efficiency of vehicle component architecture.

[0003] Currently, no effective solution has been proposed to address the technical problem of low efficiency in constructing vehicle component architecture in the above-mentioned related technologies. Summary of the Invention

[0004] Embodiments of the present invention provide a method, system, device, and vehicle for generating a vehicle component architecture, so as to at least solve the technical problem of low efficiency in constructing a vehicle component architecture.

[0005] According to one aspect of an embodiment of the present invention, a method for generating a vehicle component architecture is provided. The method may include: obtaining requirement data of an object, wherein the requirement data is used to represent the object's requirements for interactions between multiple components to be created in a vehicle, wherein the components include vehicle parts; determining attribute characteristics of the multiple components based on the requirement data; determining target creation data for the multiple components based on the attribute characteristics of the multiple components; and creating a component architecture based on the target creation data.

[0006] Optionally, determining the attribute characteristics of the multiple components based on the demand data includes: converting the demand data into stream data; and extracting the stream data to obtain the attribute characteristics of the multiple components.

[0007] Optionally, determining target creation data for the multiple components based on the attribute characteristics of the multiple components includes: determining similarity between the attribute characteristics and at least one data in the data set; and determining the target creation data from the data set based on the similarity.

[0008] Optionally, based on similarity, target creation data is determined from the data set, including: sorting at least one data based on similarity, removing data with low similarity in at least one data, and obtaining a sorting result; based on the matching result, generating at least one initial creation data from the sorting result; and determining the target creation data from the at least one initial data.

[0009] Optionally, determining the target creation data from at least one initial creation data includes: determining the target creation data from the at least one initial creation data in response to a selection operation of the object.

[0010] Optionally, a scoring result of the object on the target creation data is obtained; and the matching module is updated based on the scoring result.

[0011] According to another aspect of an embodiment of the present invention, a system for generating a vehicle component architecture is provided. The system may include: an operation unit for acquiring operation data of an object, wherein the operation data includes selection data and input data; a data processing unit for processing the operation data to generate target creation data; and a data access unit for storing data in the generation system.

[0012] Optionally, the data processing unit further includes: an artificial intelligence matching module for generating target creation data; an image drawing module for drawing the target creation data; and an output module for outputting the target creation data to the operating unit and displaying it.

[0013] According to another aspect of an embodiment of the present invention, a device for generating a vehicle component architecture is provided. The device may include: an acquisition unit for acquiring requirement data of an object, wherein the requirement data is used to represent the object's requirements for interactions between multiple components to be created in a vehicle, wherein the components include vehicle parts; a first determination unit for determining attribute characteristics of the multiple components based on the requirement data; a second determination unit for determining target creation data for the multiple components based on the attribute characteristics of the multiple components; and a creation unit for creating a component architecture based on the target creation data.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device containing the computer-readable storage medium is controlled to execute the method for generating a vehicle component architecture according to an embodiment of the present invention.

[0015] According to another aspect of an embodiment of the present invention, a processor is provided, which is configured to run a program, wherein the program, when running, executes the method for generating a vehicle component architecture according to an embodiment of the present invention.

[0016] According to another aspect of an embodiment of the present invention, a vehicle is provided, which is used to execute the method for generating a vehicle component architecture according to an embodiment of the present invention.

[0017] In an embodiment of the present invention, demand data of an object is obtained, wherein the demand data is used to characterize the object's demand for interactions between multiple components to be created in a vehicle, wherein the components include parts in the vehicle; attribute characteristics of the multiple components are determined based on the demand data; target creation data of the multiple components are determined based on the attribute characteristics of the multiple components; and a component architecture is created based on the target creation data. In other words, the embodiment of the present invention obtains the object's demand data for components to be created in a vehicle and interactions between the components, processes the demand data, obtains the attribute characteristics of the multiple components, determines the target creation data based on the attribute characteristics, constructs a component architecture based on the target creation data, and displays the created component architecture on an interactive interface, so that the created component architecture can be visualized, thereby solving the technical problem of low efficiency in constructing the vehicle component architecture and achieving the technical effect of improving the efficiency in constructing the vehicle component architecture. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 is a flow chart of a method for generating a vehicle component architecture according to an embodiment of the present invention;

[0020] Figure 2 is a schematic diagram of an electronic and electrical software architecture automation platform according to an embodiment of the present invention;

[0021] Figure 3 is a flowchart of the operation of an artificial intelligence algorithm matching module according to an embodiment of the present invention;

[0022] Figure 4 is a schematic diagram of a system for generating a vehicle component architecture according to an embodiment of the present invention;

[0023] Figure 5 2 is a schematic diagram of a device for generating a vehicle component architecture according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Example 1

[0027] According to an embodiment of the present invention, an embodiment of a vehicle data processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] Figure 1 is a flow chart of a method for generating a vehicle component according to an embodiment of the present invention, such as Figure 1 As shown, the method may include the following steps:

[0029] Step S102: Obtain the object's demand data.

[0030] In the technical solution provided in step S102 of the present invention, demand data of an object can be obtained, where the object can be, for example, a user using the vehicle or an engineer designing the vehicle. The demand data can include multiple components to be created in the vehicle and the interaction requirements between these components, or it can be specific business data. Components can include vehicle components, such as the vehicle air conditioner, vehicle audio system, camera, driving recorder, low beam headlights, high beam headlights, and parking sensors. This is for illustrative purposes only and is not intended to be limiting.

[0031] Optionally, the object operates the controls on the interactive interface and can obtain the object's demand data for components in the vehicle by acquiring the input information collected by the interactive interface. In order to facilitate reference and aggregation, the demand data can be stored in the form of a standardized demand table. It should be noted that there are no specific restrictions on the location of deploying the interactive interface and the method of obtaining the demand data.

[0032] For example, the high beam in the body domain can interact with the braking system (Idea BeautifyChina, abbreviated as IBC) in the chassis domain, the intelligent high and low beam control system (Intelligence High Control, abbreviated as IHC) in the autonomous driving domain, and the low beam in the body domain, and requires information on the low beam such as the left front low beam status, the right front low beam status, and the low beam status, the vehicle speed information of IBC, and the high beam control information of IHC as demand data. The interactive interface can obtain the above-mentioned demand data of the object. The types of demand data here are only for illustration and are not specifically limited.

[0033] Step S104: determining attribute characteristics of multiple components based on the demand data.

[0034] In the technical solution provided in the above step S104 of the present invention, the demand data can be processed to obtain a standardized demand form, and the standardized demand form can be parsed and feature extracted to obtain attribute characteristics of multiple components, wherein the attribute characteristics can be relevant information of the component, and may include information such as control, feedback and status of the component.

[0035] Optionally, the relevant demand data of the above-mentioned high beam can be drawn into a standardized demand table, the header of which can include the domain to which the component belongs, the component name, the domain to which the interactive component belongs, the interactive component name and the required signal, so as to achieve the purpose of classifying and storing the demand data for easy aggregation and reference.

[0036] It should be noted that there are no specific restrictions on the drawing method and header content of the standardized requirement table here, it is only for illustration.

[0037] For example, the demand data submitted by the object is processed to obtain a standardized demand form, which can then be parsed and features extracted through artificial intelligence algorithms. For example, the demand data corresponding to the high beam only needs to extract the status information in the form.

[0038] Step S106: determining target creation data of the multiple components based on the attribute characteristics of the multiple components.

[0039] In the technical solution provided in the above step S106 of the present invention, the generated attribute features can be compared with the relevant data for similarity and sorted from high to low similarity to obtain target creation data, wherein the target creation data may include the interaction relationship between multiple components, and can be visualization data of the architecture obtained by constructing multiple components, for example, it can be an interaction model or interaction diagram of the component architecture. This is only for illustration and does not impose specific restrictions on the form of the target creation data.

[0040] For example, similarity sorting can be performed through an artificial intelligence algorithm to obtain a sorting result, and redundant data irrelevant to the required data, that is, data with low similarity, can be filtered out. For data with high similarity, the visual data of the data can be transmitted to the interactive interface and provided to the object for selection. The object can make a selection by clicking the relevant controls to determine the target creation data, thereby improving the technical effect of the construction efficiency of the vehicle component architecture. It should be noted that the determination of the target creation data based on the artificial intelligence algorithm here is only an example, and does not specifically limit the method of similarity sorting and the method and process of determining the target creation data.

[0041] Step S108: creating a component architecture based on the target creation data.

[0042] In the technical solution provided in the above step S108 of the present invention, data can be created based on the target, and multiple components can be connected to achieve interaction, thereby creating a component architecture, wherein the component architecture corresponds one-to-one to the target creation data.

[0043] Optionally, after creating the component architecture, the target creation data can be exported and stored in formats such as spreadsheets (Excel), portable document formats (PDF), or images, to facilitate the object to export and download the target creation data. It should be noted that this is only an example and does not impose specific restrictions on the storage method of the target creation data.

[0044] For example, the demand data submitted by the object regarding high beams can be obtained, such as the demand data for the interaction between high beams and IBC systems, IHC systems, and low beams. The demand data can be processed to obtain a standardized demand form, which can be parsed and feature extracted through an artificial intelligence algorithm to obtain attribute characteristics of components such as high beams and low beams. The attribute characteristics can be compared with related data for similarity, and multiple component architectures and component architecture interaction diagrams can be determined. One of the component architecture interaction diagrams among the interaction diagrams of all the above component architectures can be used as target creation data. Assuming that the obtained component interaction diagram is a component interaction model for the interaction between high beams and low beams, IBC systems, and IHC systems, the high beams can be connected to the low beams, IBC systems, and IHC systems based on the above target creation data to obtain the component architecture of the high beams and realize the interaction between components. The target creation data can then be stored in PDF format, and the object can export the target creation data by entering keywords for field matching on the interactive interface, and can download the target creation data by clicking on relevant controls.

[0045] In the above steps S102 to S108 of the present application, the object's demand data is obtained, wherein the demand data is used to characterize the object's demand for interactions between multiple components to be created in the vehicle, wherein the components include parts in the vehicle; based on the demand data, the attribute characteristics of the multiple components are determined; based on the attribute characteristics of the multiple components, the target creation data of the multiple components are determined; and based on the target creation data, multiple components are created. In other words, the embodiment of the present invention obtains the object's demand data for components to be created in the vehicle and interactions between the components, and obtains the attribute characteristics of the multiple components by processing the demand data. Based on the attribute characteristics, the target creation data can be determined, and the component architecture can be constructed based on the target creation data. The created component architecture can be displayed on the interactive interface, so that the created component architecture can be visualized, thereby solving the technical problem of low efficiency in constructing the vehicle component architecture and achieving the technical effect of improving the efficiency of constructing the vehicle component architecture.

[0046] The above method of this embodiment is further introduced below.

[0047] As an optional embodiment, step S104, determining the attribute characteristics of the multiple components based on the demand data, includes: converting the demand data into stream data; and extracting the stream data to obtain the attribute characteristics of the multiple components.

[0048] In this embodiment, the demand data can be converted into stream data, and features can be extracted from the stream data. The extracted features can be abstracted into attribute features of multiple components, where the stream data can be a dynamic data set that grows infinitely over time, such as field stream data. This is only for illustration and no specific restrictions are placed on the stream data.

[0049] For example, the demand data can be drawn as a normalized demand table, and the table can be parsed. The text in the demand table can be converted into field stream data through an algorithm or program. For example, the text in the table can be converted into a digital signal composed of 0 and 1 through a computer programming language (Java or C language). The decoder can extract features from the digital signal based on key blocking, thereby realizing feature extraction of the field stream data. The extracted features can be abstracted as attribute features of the component. It should be noted that there is no specific restriction on the parsing of the normalized demand table and the feature extraction method of the field stream data. As long as the normalized demand table is parsed and converted into stream data, and the method and process for feature extraction are performed, they are within the protection scope of the embodiments of the present invention.

[0050] As an optional embodiment, step S106, determining target creation data of multiple components based on attribute characteristics of multiple components, includes: determining the similarity between the attribute characteristics and at least one data in the data set; and determining the target creation data from the data set based on the similarity.

[0051] In this embodiment, the similarity between the attribute characteristics of the component and at least one data stored in the data set can be determined, and the similarities can be sorted from large to small. If the similarity is small, it can be indicated that the attribute characteristics of the component stored in the data set are irrelevant to the required data, and the data with small similarity is filtered; if the similarity is large, one or more data in the data set corresponding to the similarity can be extracted, so that the target creation data can be determined, wherein the data set can include a preset software framework library and / or an electronic and electrical component model library.

[0052] Optionally, the attribute characteristics of the component can be compared for similarity with the data in the preset software framework in the data set, or with the data in the electronic and electrical component model library in the data set, or with the data in both, so that the target creation data can be determined from the data in the data set based on the similarity.

[0053] For example, an artificial intelligence algorithm or a similarity sorting algorithm can be used to determine the similarity between attribute features and data in a data set, and to sort the similarity from large to small to obtain a similarity sorting result. Based on the similarity sorting result, the target creation data can be determined. In the embodiment of the present invention, an artificial intelligence algorithm can be used for sorting, thereby avoiding the technical problem of low efficiency in constructing the vehicle component architecture caused by human factors. It should be noted that the above-mentioned method of determining similarity and performing similarity sorting is only for illustration, and no specific limitation is made here. As long as the method and process of comparing attribute features with data in a data set to obtain similarity are performed, they are within the protection scope of the embodiment of the present invention.

[0054] As an optional implementation method, step S106, based on similarity, determines the target creation data from the data set, including: sorting at least one data based on similarity, removing data with low similarity in at least one data, and obtaining a sorting result; based on a matching module, generating at least one initial creation data from the sorting result; and determining the target creation data from the at least one initial creation data.

[0055] In this embodiment, the data of the components in the data set can be sorted in descending order of similarity based on multiple similarities. If the similarity is small, the data can be removed and the data with higher similarity can be retained to obtain a sorting result. The sorting result can be transmitted to a matching module, and initial creation data corresponding to the sorting result can be generated, and then the target creation data can be determined from the initial creation data, wherein the sorting result can include data with high similarity and the corresponding similarity size. The matching module can be used to generate initial creation data corresponding to the data set. The initial creation data can include the interaction relationship between multiple components, and can be visualization data of the architecture constructed for multiple components corresponding to data with high similarity, for example, it can be an interaction model or interaction diagram of the component architecture, and the target creation data can be selected from the initial creation data.

[0056] For example, a threshold value for the number of initial creation data can be set, and the threshold number of initial creation data can be extracted from the ranking results from the front to the back. For example, the threshold value for the number of initial creation data can be set to three, and the first three can be extracted from the ranking results as the initial creation data. This is only an example and no specific limitation is imposed.

[0057] Optionally, in the matching module, the similarity can be determined by determining the Euclidean distance between the attribute features and the data in the data set through an artificial intelligence algorithm. The smaller the Euclidean distance, the greater the similarity between the two. Conversely, the smaller the similarity between the two. The similarities are sorted from large to small, and the data with smaller similarity are removed to obtain the sorting result. Based on the sorting result, the corresponding initial creation data can be generated, and the target creation data can be further determined from the initial creation data. It should be noted that the above method and process for determining similarity based on Euclidean distance are only for example and are not specifically limited here. The similarity can also be calculated by other methods such as cosine distance. As long as the method and process for determining the similarity between the two are within the protection scope of the embodiments of the present invention,

[0058] In an embodiment of the present invention, the similarity between attribute features and data in a data set is automatically determined based on an artificial intelligence algorithm and sorted, data with low similarity is filtered, and initial creation data is automatically generated for data with high similarity, thereby avoiding the technical problem of low construction efficiency of the vehicle component architecture caused by human factors and achieving the technical effect of improving the construction efficiency of the vehicle component architecture.

[0059] As an optional embodiment, step S106, determining target creation data from at least one initial creation data, includes: determining target creation data from at least one initial creation data in response to a selection operation of the object.

[0060] In this embodiment, when the object performs a selection operation on at least one initial creation data in the interactive interface, target creation data can be determined from the at least one initial creation data, thereby creating a component architecture based on the target creation data.

[0061] Optionally, the initial creation data may be transmitted to an interactive interface, and the object may operate controls on the interactive interface to select target creation data from multiple initial creation data. The interactive interface may obtain the target creation data selected by the object.

[0062] Optionally, if there is no need to select the target creation data, the initial creation data corresponding to the first data in the sorting result can be directly determined as the target creation data. Based on the target creation data, the corresponding component architecture can be generated. It should be noted that this is only an example and does not impose specific restrictions on the selection method and process of the target creation data.

[0063] As an optional embodiment, step S106 is to obtain the scoring result of the object on the target creation data; and update the matching module based on the scoring result.

[0064] In this embodiment, the object operates the controls on the interactive interface to obtain a scoring result of the object's satisfaction with the target creation data. If the scoring result is too low, it means that the initial creation data and the target creation data do not meet the object's needs. The matching module can be updated based on the scoring result to achieve the technical effect of optimizing the matching process and accuracy.

[0065] Optionally, the interactive interface obtains the object's scoring results for all initial creation data displayed on the interactive interface. If all the scoring results are too low, it means that the initial creation data cannot meet the object's requirements. It can further be explained that the similarity between the initial creation data and the attribute characteristics is inaccurate. The matching module can be optimized and updated to ensure the accuracy of the matching.

[0066] For example, a scoring threshold can be set. If the scoring result is greater than or equal to the scoring threshold, it can be said that the initial creation data and the target creation data meet the requirements of the object. If the scoring result is less than the scoring threshold, it can be said that the requirements of the object are not met this time. The matching model can be optimized and updated to determine the accuracy of the match.

[0067] In an embodiment of the present invention, demand data of an object is obtained, wherein the demand data is used to characterize the object's demand for interactions between multiple components to be created in a vehicle, wherein the components include parts in the vehicle; attribute characteristics of the multiple components are determined based on the demand data; target creation data of the multiple components are determined based on the attribute characteristics of the multiple components; and multiple components are created based on the target creation data. In other words, the embodiment of the present invention obtains the object's demand data for components to be created in a vehicle and interactions between components, processes the demand data, obtains the attribute characteristics of the multiple components, determines the target creation data based on the attribute characteristics, constructs a component architecture based on the target creation data, and displays the created component architecture on an interactive interface, so that the created component architecture can be visualized, thereby solving the technical problem of low efficiency in constructing the vehicle component architecture and achieving the technical effect of improving the efficiency in constructing the vehicle component architecture.

[0068] Example 2

[0069] The technical solutions of the embodiments of the present invention are described below with reference to preferred implementation methods.

[0070] Currently, the design process for traditional automotive electrical and electronic architecture is extremely complex, requiring engineers to conduct detailed design of each vehicle module based on the abstract functions of automotive components and equipment. Relying solely on manual design can lead to significant design flaws, resulting in long modeling and design iteration cycles, high error rates, and even rework. Furthermore, traditional automotive electrical and electronic architecture design cannot directly represent business logic structures, resulting in low reusability of design models and a lack of standardization of design structures.

[0071] The automotive electronic and electrical architecture in related technologies still requires human intervention in the detailed design of various modules of the entire vehicle, and does not consider automating the architecture. Therefore, there is still a technical problem of low efficiency in building the vehicle software component architecture.

[0072] In a related technology, a globally configurable data analysis software architecture design method for storage, computing and display is specified. This method divides the data analysis software architecture into an interface layer, an analysis layer, a data access layer and a plug-in layer, and the sum of the three layers, the data access layer, the analysis layer and the interface layer, is named the storage, computing and display layer. The interface layer provides a visual interface for interactive operations for software users; the analysis layer is responsible for executing algorithmic data analysis and processing the business logic of the software system; the data access layer obtains data from the data storage medium according to data analysis requirements and transmits it to the analysis layer; the plug-in layer provides software developers with a way to configure data and algorithms, parse the configuration of the above data and algorithms, and provide an interface for the parsing results. The data analysis software architecture design method provided in the related technology enables the designed software architecture to meet the original intention of software architecture design that is user-friendly, extensible, maintainable and highly available.

[0073] In another related technology, based on the current status of the electronic control units of the current automotive electronic and electrical architecture, a service-oriented architecture (SOA) service layered vehicle function implementation method and system is specified. The layered design concept is adopted to achieve flexible and diverse functions without changing the atomic services. Based on the current electronic and electrical architecture and the corresponding hardware resources, atomic services can be sorted out and defined. Then, based on the division of the electronic and electrical system of the whole vehicle, composite services can be defined, and the atomic services can be combined with each composite service. According to the user usage scenario, scenario services are defined, and the composite services are associated with each scenario service. In this way, a three-layer service architecture is constructed: atomic services, composite services, and scenario services. The three-layer services are deployed to different electronic controllers according to the electronic and electrical architecture and network topology.

[0074] However, none of the above methods considers automating the architecture, and therefore, there is still a technical problem of low efficiency in constructing the vehicle software component architecture.

[0075] However, this paper proposes an AI-based automated design method for automotive software architecture. This method proposes a universal design platform for customizable software components, encapsulating a pre-defined, scalable library of electronic and electrical component tools and providing unified interface call methods and standards. Users can submit standardized requirements forms to the platform, which then automatically matches the database with AI algorithms, recommending relevant electronic and electrical components and software frameworks that meet the requirements, and generating corresponding software component interaction design diagrams. The system platform architecture is divided into three layers: an interface layer, a business layer, and a data access layer. The business layer includes an AI algorithm matching module, a graphics rendering module, and a customized model output module, thereby addressing the technical issue of inefficient vehicle software component construction.

[0076] Next, the method for generating the vehicle component architecture according to the embodiment of the present invention will be further introduced.

[0077] Figure 2 Schematic diagram of an electronic and electrical software architecture automation platform according to an embodiment of the present invention, such as Figure 2 As shown, the electronic and electrical software architecture automation design platform 200 may include an interface layer 202, a business layer 204, a data access layer 206, a data repository 208, a preset software framework library 210 and an electronic and electrical component model library 212, wherein the business layer 204 may include an artificial intelligence algorithm matching module 2042, a graphics drawing module 2044 and a customized model output module 2046; the electronic and electrical component model library 212 may include custom components 2122 and basic components 2124.

[0078] Optionally, the interface layer 202 can be used to display specific business data to the user and collect information input by the user. The user can interact with the system server by clicking on the components on the interface layer, and transmit the collected standardized requirement form and the user's selection recommendation result information to the business layer 204, wherein the standardized requirement form can be used to record the user's needs, and may include standardized requirements such as management and interaction that the user requires the software to have. It should be noted that the above-mentioned interaction method between the interface layer and the system server is only an example, and can also be voice interaction or text interaction, etc., which is not specifically limited here. The above-mentioned standardized requirements are only an example, and different standardized requirement forms can be generated according to actual user needs. There are no specific restrictions.

[0079] Optionally, the business layer 204 can be used to process various business logics, integrating an artificial intelligence matching module, a graphics drawing module, and a customized model output module.

[0080] Optionally, the artificial intelligence algorithm matching module 2042 can be used to parse the standardized requirement form input by the user and extract attribute feature information. It can then perform similarity sorting based on the artificial intelligence algorithm feature information and related information in the preset software framework library 210 and the electronic and electrical component model library 212. It can then filter information and generate recommendation results. The recommendation result information can be stored in the data repository 208 by calling the data access layer 206. The user selects a recommendation result at the interface layer, and the business layer receives the selected recommendation result. The artificial intelligence algorithm can then be used to sort the similarities and score the user's final result, thereby optimizing the artificial intelligence algorithm model.

[0081] Optionally, after the user selects a recommendation result, the image drawing module 2044 may match the relevant electronic and electrical components with the software framework model according to the recommendation result, draw a corresponding electronic and electrical architecture interaction diagram, and store the drawing result in the data repository 208 .

[0082] Optionally, the user can export the generated electronic and electrical architecture image model into a customized document according to the required information through the customized model output module 2046. The customized model output module can call the interactive model information stored in the data repository through the data access layer, and can export the required customized document through keyword field matching. The user can download the customized document through the interface layer, where the customized document can be in the form of a spreadsheet, a portable document format, or a picture. It should be noted that the format of the customized document here is only for example and is not specifically limited.

[0083] Optionally, the data access layer 206 may provide basic data access functions without performing any processing on the business logic. Operations such as loading, writing, or deleting data may be performed through the data access layer.

[0084] Optionally, the electronic and electrical software architecture automation platform of an embodiment of the present invention adds three databases, namely, a data repository 208, a preset software framework library 210, and an electronic and electrical component model library 212. The data repository stores the recommendation results generated by the artificial intelligence algorithm module, the architecture interaction diagram drawn by the graphics drawing module, and the customized documents generated by the customized model output module; the preset software framework library stores preset electronic and electrical model software framework model information, which may include various software architecture design models such as Model View Controller (MVC), Automotive Open System Architecture Adaptive (AUTOSARAP), Automotive Open System Architecture Classic (AUTOSAR CP), and proxy mode, to provide relevant information for the graphics drawing module. It should be noted that the models involved in the above-mentioned software architecture are only for illustration and are not specifically limited here; the electronic and electrical component model library stores user-defined custom components and basic components to provide basic drawing primitive information for the graphics drawing module.

[0085] Optionally, Figure 3 FIG. 1 is a flowchart of an artificial intelligence algorithm matching module according to an embodiment of the present invention. Figure 3 As shown, the artificial intelligence algorithm matching module may include the following steps:

[0086] Step S302: Submit the standardized requirement form to the artificial intelligence algorithm matching module.

[0087] In the above step S302 of the present invention, based on the interaction between the interface layer and the system server, the user can input his or her own standardized requirements for the software into the interface layer. The interface layer collects the information input by the user, executes the creation of a standardized requirement form to record the user's standardized requirements, and can transmit it to the artificial intelligence algorithm matching module in the business layer, wherein the artificial intelligence algorithm matching module is used to execute the artificial intelligence algorithm model.

[0088] For example, when a user submits the demand data for high beams to the artificial intelligence algorithm matching module, for example, the demand data is for the high beam in the body domain to interact with the braking system IBC in the chassis domain, the intelligent high and low beam control system IHC in the autonomous driving domain, and the low beam in the body domain, and requires information on the low beam such as the left front low beam status, the right front low beam status, and the low beam status. The artificial intelligence algorithm matching module can draw the demand data into a standardized demand table. Table 1 is a standardized demand table for high beams. As shown in Table 1, by parsing the above high beam demand data, the demand data can be classified according to the table headers: component domain, component name, interactive component domain, interactive component name, and required signal, which is convenient for summary and reference.

[0089] Table 1: Standardized requirements for a high beam

[0090]

[0091] Step S304: Parse the normalized requirement table into field flow data and save it into the data repository.

[0092] In the above step S304 of the present invention, the artificial intelligence algorithm matching module can parse the normalized requirement form and save it to the data storage library. The normalized form can be parsed into field stream data. This is only an example and does not impose specific restrictions on the data format obtained by parsing the normalized requirement form.

[0093] Step S306: abstractly convert the field stream data into attribute features.

[0094] In the above step S306 of the present invention, the artificial intelligence algorithm matching module can extract and abstract the features of the field flow data obtained by parsing the standardized requirement table to obtain attribute features, thereby facilitating the next artificial intelligence algorithm matching step.

[0095] Step S308: Calculate similarity between the attribute feature data and the data set, and sort them.

[0096] In the above step S308 of the present invention, the similarity between the obtained attribute features and the data set can be calculated and sorted by an artificial intelligence algorithm to obtain a similarity result, wherein the data set can include a preset software framework library and an electronic and electrical component model library.

[0097] For example, the similarity between the attribute feature and the data set can be measured by determining the Euclidean distance between the two. If the Euclidean distance is smaller, it can be said that the similarity between the attribute feature and the data set is greater; if the Euclidean distance is larger, it can be said that the similarity between the attribute feature and the data set is smaller. It should be noted that the Euclidean distance between the attribute feature and the data set is used here to determine the similarity between the two for example only. Other calculation methods can also be used, such as cosine distance, etc. As long as the method and process of determining the similarity between the attribute feature and the data set is used, it is within the protection scope of the embodiments of the present invention.

[0098] Step S310 : generating initial creation data according to the similarity sorting result, and filtering out redundant data irrelevant to the normalization requirement.

[0099] In the above step S310 of the present invention, initial creation data can be generated based on the similarity results between the determined attribute feature data and the data in the data set through an artificial intelligence algorithm, and redundant data irrelevant to the normalization requirements can be filtered out, wherein the initial creation data can be used to characterize the component architecture that meets the user's normalization requirements, which can be one or more, and the redundant data can be used to characterize data with lower similarity.

[0100] Optionally, by calculating the similarity between the attribute feature and the data in the data set and arranging the sizes, the initial creation data can be generated based on the attribute feature with the greatest similarity. The attribute features other than the attribute feature with the greatest similarity are redundant data, which can be filtered and removed.

[0101] For example, the artificial intelligence algorithm can be used to determine the similarity between the component attribute characteristics of the demand data corresponding to the high beam and the data in the data set, and the data in the data set with low similarity can be filtered out. Further, the two data with higher similarity can be determined as the initially created data. For example, the first recommendation result can be "Low beam: left front low beam on / off status; right front low beam on / off status; total low beam on / off status", and the second recommendation result can be "Low beam: low beam fault status; IBC: vehicle speed status; IHC: high beam control".

[0102] Step S312: Score the recommendation results selected by the user and optimize the artificial intelligence algorithm model.

[0103] In the above step S312 of the present invention, when the user selects all the initial creation data generated by the artificial intelligence algorithm model, the operation information selected by the user can be collected through the interface layer and transmitted to the artificial intelligence algorithm matching module. The artificial intelligence algorithm matching model can score all the initial creation data generated by itself according to the user's selection, and optimize the artificial intelligence algorithm model according to the scoring results.

[0104] The embodiment of the present invention obtains the object's demand data on the components to be created in the vehicle and the interactions between the components, and obtains the attribute characteristics of multiple components by processing the demand data. Based on the attribute characteristics, the target creation data can be determined, and the component architecture can be constructed based on the target creation data. The created component architecture can be displayed on the interactive interface, so that the created component architecture can be visualized, thereby solving the technical problem of low construction efficiency of the vehicle component architecture and achieving the technical effect of improving the construction efficiency of the vehicle component architecture.

[0105] Example 3

[0106] According to an embodiment of the present invention, a system for generating a vehicle component architecture is also provided. It should be noted that the system for generating a vehicle component architecture can be used to execute the method for generating a vehicle component architecture in Example 1.

[0107] Figure 4 FIG. 1 is a schematic diagram of a system for generating a vehicle component architecture according to an embodiment of the present invention. Figure 4 As shown, the vehicle component architecture generation system 400 may include: an operation unit 402, a data processing unit 404 and a data access unit 406, wherein the data processing unit 404 also includes an artificial intelligence matching module 4042, an image drawing module 4044 and an output module 4046.

[0108] The operation unit 402 is used to obtain operation data of the object, where the operation data includes selection data and input data.

[0109] The data processing unit 404 is used to process the operation data and generate target creation data.

[0110] The data access unit 406 is used to store the data in the generation system.

[0111] Optionally, the data processing unit 404 may include: an artificial intelligence matching module 4042 for generating target creation data; a graphics drawing module 4044 for drawing the target creation data; and an output module 4046 for outputting the target creation data to the operation unit and displaying it.

[0112] In this embodiment, the operation data of the object is obtained through the operation unit, wherein the operation data includes selection data and input data; the operation data is processed by the data processing unit to generate target creation data; and the data in the generation system is stored by the data access unit, thereby solving the technical problem of low construction efficiency of the vehicle component architecture and achieving the technical effect of improving the construction efficiency of the vehicle component architecture.

[0113] Example 4

[0114] According to an embodiment of the present invention, a device for generating a vehicle component architecture is also provided. It should be noted that the device for generating a vehicle component architecture can be used to execute the method for generating a vehicle component architecture in Example 1.

[0115] Figure 5 is a schematic diagram of a device for generating a vehicle component architecture according to an embodiment of the present invention, such as Figure 5 As shown, the vehicle component architecture generation device 500 may include: an acquisition unit 502 , a first determination unit 504 , a second determination unit 506 and a creation unit 508 .

[0116] The acquisition unit 502 is configured to acquire demand data of the object, wherein the demand data is used to represent the object's demand for interaction between multiple components to be created in the vehicle, where the components include parts in the vehicle.

[0117] The first determining unit 504 is configured to determine attribute characteristics of multiple components based on the demand data.

[0118] The second determining unit 506 is configured to determine target creation data of the multiple components based on the attribute characteristics of the multiple components.

[0119] The creation unit 508 is configured to create a component architecture based on the target creation data.

[0120] Optionally, the first determining unit 504 may include: a conversion module, configured to convert the demand data into stream data; and an extraction module, configured to extract the stream data to obtain attribute features of multiple components.

[0121] Optionally, the second determining unit 506 may include: a first determining module for determining similarity between the attribute characteristic and at least one data in the data set; and a second determining module for determining target creation data from the data set based on the similarity.

[0122] Optionally, the second determination module may include: a removal submodule, used to sort at least one data based on similarity, remove data with low similarity in at least one data, and obtain a sorting result; a generation submodule, used to generate at least one initial creation data from the filming result based on the matching result; and a determination submodule, used to determine the target creation data from at least one initial data.

[0123] Optionally, the second determining unit 506 may further include: a third determining module, configured to determine target creation data from at least one type of initial creation data in response to an object selection operation.

[0124] Optionally, the second determining unit 506 may further include: an acquiring module configured to acquire a scoring result of the object on the target creation data; and an updating module configured to update the matching module based on the scoring result.

[0125] According to an embodiment of the present invention, demand data of an object is acquired through an acquisition unit, wherein the demand data is used to characterize the object's demand for interaction between multiple components to be created in a vehicle, and the components include parts in the vehicle; attribute characteristics of the multiple components are determined based on the demand data through a first determination unit; target creation data of the multiple components are determined based on the attribute characteristics of the multiple components through a second determination unit; and multiple components are created based on the target creation data through a creation unit, thereby solving the technical problem of low construction efficiency of the vehicle component architecture and achieving the technical effect of improving the construction efficiency of the vehicle component architecture.

[0126] Example 5

[0127] According to an embodiment of the present invention, a computer-readable storage medium is further provided. The storage medium includes a stored program, wherein the program executes the method for generating a vehicle component architecture described in Example 1.

[0128] Example 6

[0129] According to an embodiment of the present invention, a processor is further provided. The processor is configured to run a program, wherein the program, when running, executes the method for generating a vehicle component architecture described in Example 1.

[0130] Example 7

[0131] According to an embodiment of the present invention, a vehicle is further provided. The vehicle is used to execute the method for generating a component architecture of a vehicle according to an embodiment of the present invention.

[0132] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0133] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0135] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0136] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, 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.

[0137] If the integrated 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 technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0138] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for generating a vehicle component architecture, characterized in that: The method comprises: Obtaining requirement data of an object, wherein the requirement data is used to represent the object's requirements for interactions between a plurality of components to be created in a vehicle, the components including parts in the vehicle; determining attribute characteristics of the plurality of components based on the demand data; Determining target creation data for the multiple components based on attribute characteristics of the multiple components, wherein the target creation data includes interaction relationships between the multiple components and visualization data of component architectures constructed for the multiple components; creating the component architecture based on the target creation data; Wherein, the target creation data of the multiple components is determined based on the attribute characteristics of the multiple components, including: sorting the at least one data based on the similarity between the attribute characteristics and at least one data in the data set, removing the data with low similarity in the at least one data, and obtaining a sorting result, wherein the data set includes a preset software framework library and / or an electronic and electrical component model library; based on the matching module, extracting a threshold number of the data from the sorting results in descending order of the similarity as the initial creation data; and determining the target creation data from at least one of the initial creation data.

2. The method according to claim 1, characterized in that Determining attribute characteristics of the plurality of components based on the demand data includes: Converting the demand data into stream data; The stream data is extracted to obtain attribute features of the multiple components.

3. The method according to claim 1, characterized in that Determining target creation data of the plurality of components based on attribute characteristics of the plurality of components includes: Determining the similarity between the attribute feature and at least one of the data in the data set; Based on the similarity, the target creation data is determined from the data set.

4. The method according to claim 1, wherein Determining the target creation data from at least one of the initial creation data comprises: In response to a selection operation of the object, the target creation data is determined from at least one of the initial creation data.

5. The method according to claim 4, characterized in that The method further comprises: Obtaining a scoring result of the object on the target creation data; The matching module is updated based on the scoring result.

6. A system for generating a vehicle component architecture, characterized in that: The system includes an operating unit, a data processing unit and a data access unit, wherein: The operation unit is used to obtain operation data of the object, wherein the operation data includes selection data and input data; The data processing unit is configured to process the operation data to generate target creation data, wherein the target creation data includes interaction relationships between multiple components and visualization data of a component architecture constructed by the multiple components; The data access unit is used to store data in the generation system; The data processing unit also includes: an artificial intelligence matching module, which is used to sort at least one of the data based on the similarity between the attribute characteristics of the multiple components and at least one of the data in the data set, and remove data with low similarity in at least one of the data to obtain a sorting result, wherein the data set includes a preset software framework library and / or an electronic and electrical component model library; based on the matching module, according to the similarity from large to small, extract a threshold number of the data from the sorting result as initial creation data; and determine the target creation data from at least one of the initial creation data.

7. The system according to claim 6, characterized in that The data processing unit further includes: An image drawing module, used for drawing the target creation data; The output module is used to output the target creation data to the operation unit and display it.

8. A device for generating a vehicle component architecture, characterized in that: The device comprises: an acquiring unit, configured to acquire demand data of an object, wherein the demand data is used to represent the interaction demand of the object on a plurality of components to be created in a vehicle, the components including parts in the vehicle; a first determining unit, configured to determine attribute characteristics of the plurality of components based on the demand data; A second determining unit is configured to determine target creation data of the plurality of components based on the attribute characteristics of the plurality of components, wherein the target creation data includes interaction relationships between the plurality of components and visualization data of component architectures constructed by the plurality of components; A creation unit, configured to create the component architecture based on the target creation data; Wherein, the second determination unit is used to determine the target creation data of the multiple components based on the attribute characteristics of the multiple components by performing the following steps: sorting the at least one data based on the similarity between the attribute characteristics and at least one data in the data set, removing the data with low similarity in the at least one data, and obtaining a sorting result, wherein the data set includes a preset software framework library and / or an electronic and electrical component model library; based on the matching module, extracting a threshold number of the data from the sorting results in descending order of similarity as initial creation data; and determining the target creation data from at least one of the initial creation data.

9. A vehicle, characterized in that: Used to perform the method according to any one of claims 1 to 5.

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