Vehicle service demand processing method, device, equipment, medium and program product

Abstract

The present disclosure relates to a vehicle business demand processing method, device, equipment, medium and program product, and relates to the technical field of vehicles. The method comprises: in response to a functional demand corresponding to a vehicle business, determining a vehicle system demand corresponding to the functional demand according to a tracing relationship, wherein the tracing relationship is determined according to a data correlation degree of adjacent demand levels. Determining an electronic control unit demand according to the vehicle system demand and the tracing relationship, and sending the vehicle system demand and the electronic control unit demand to a corresponding target object. In this way, the tracing relationship between the demand data of the upstream and downstream demand levels of the vehicle is automatically established based on the data correlation degree, ensuring that the functional demand corresponding to the vehicle business can be quickly and accurately associated to the vehicle system demand and the electronic control unit demand through the tracing relationship, and the allocation efficiency and accuracy of the vehicle business demand at each demand level are improved.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle technology, and in particular to a method, apparatus, equipment, medium, and program product for processing vehicle business requirements. Background Technology

[0002] With the rapid development of automotive intelligence technology and the continuous expansion of vehicle application scenarios, the various business needs of vehicles are becoming increasingly complex and diverse across multiple scenarios. Currently, vehicle functions are realized through the collaborative efforts of multiple business domains, including intelligent driving, cockpit, body, and powertrain. The implementation of a single vehicle function often requires cross-domain collaboration between these business domains. In this context, how to handle vehicle business needs more rationally and efficiently is one of the key research and development priorities. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a method, apparatus, equipment, medium, and program product for processing vehicle business requirements.

[0004] According to a first aspect of the present disclosure, a method for processing vehicle business demands is provided, comprising: In response to the functional requirements corresponding to vehicle services, the vehicle system requirements corresponding to the functional requirements are determined based on the traceability relationship; wherein, the traceability relationship is determined based on the data correlation between adjacent requirement levels; The electronic control unit requirements are determined based on the vehicle system requirements and the traceability relationship. The vehicle system requirements and the electronic control unit requirements are sent to the corresponding target objects.

[0005] By adopting the above implementation method, the traceability relationship between the demand data of the upstream and downstream demand levels of the vehicle is automatically established based on the data correlation degree. This ensures that the functional requirements corresponding to the vehicle business can be quickly and accurately linked to the vehicle system requirements and electronic control unit requirements through the traceability relationship, thereby improving the efficiency and accuracy of the allocation of vehicle business requirements at each demand level.

[0006] In some possible implementations, the traceability relationship is determined in the following ways: Obtain the first requirement data corresponding to multiple requirement levels. Calculate the data correlation degree between the first demand data at adjacent demand levels, whereby the data correlation degree is used to characterize the degree of matching between the first demand data. The traceability relationship between the first demand data in adjacent demand levels is determined based on the data correlation.

[0007] By adopting the above implementation method, the traceability relationship between the demand data of upstream and downstream demand levels is automatically established through data correlation, which reduces the workload of manually maintaining the traceability relationship and improves the accuracy of the traceability relationship.

[0008] In some possible implementations, the data correlation degree includes at least one of semantic correlation degree, structural correlation degree, and type correlation degree; The semantic correlation degree is used to characterize the semantic similarity between the first requirement data, the structural correlation degree is used to characterize the proximity of the first requirement data in the topological position in the database, and the type correlation degree is used to characterize the matching degree between the data categories of the first requirement data.

[0009] By adopting the above implementation method, based on semantic correlation, structural correlation and type correlation, the inherent correlation of demand data can be quantified from multiple dimensions, which improves the comprehensiveness and accuracy of correlation identification.

[0010] In some possible implementations, the data correlation degree includes semantic correlation degree; calculating the data correlation degree between the first demand data at adjacent demand levels includes: Obtain the demand vector corresponding to the first demand data; Based on the demand vector, the semantic correlation between the first demand data of adjacent demand levels is calculated.

[0011] By adopting the above implementation method, since the demand vector represents the deep semantic features of the demand text, the semantic correlation degree can be determined based on the demand vector, which can accurately identify the inherent semantic correlation between demand data and improve the accuracy of semantic correlation degree.

[0012] In some possible implementations, calculating the semantic correlation between the first demand data at adjacent demand levels based on the demand vector includes: For two first-demand data at adjacent demand levels, calculate the cosine similarity between the demand vectors; The cosine similarity is used as the semantic relevance.

[0013] By employing the above implementation method, the cosine similarity between demand data is calculated as the semantic correlation, which can accurately quantify the similarity of demand vectors in the vector space, thereby effectively representing the semantic correlation between different demand data.

[0014] In some possible implementations, the data correlation degree includes structural correlation degree; calculating the data correlation degree between the first demand data at adjacent demand levels includes: Obtain the topological location information of the first requirement data in the database; The structural correlation degree is calculated based on the topological location information.

[0015] By adopting the above implementation method, the structural correlation degree is determined based on the topological location information, which can accurately quantify the structural correlation degree between cross-level demand data according to the hierarchical path and branch relationship of demand data in the database.

[0016] In some possible implementations, the data correlation degree includes type correlation degree; calculating the data correlation degree between the first demand data at adjacent demand levels includes: Obtain the type information of the first required data; The type association degree is calculated based on the type information.

[0017] By adopting the above implementation method, the type matching relationship between different requirements can be determined based on the category attributes of the requirement data, and the degree of correlation in the type dimension can be accurately quantified.

[0018] In some possible implementations, the data correlation degree includes semantic correlation degree, structural correlation degree, and type correlation degree; calculating the data correlation degree between the first requirement data at different requirement levels includes: The data correlation degree is obtained by weighting and summing the semantic correlation degree, structural correlation degree and type correlation degree according to the preset weight coefficients.

[0019] By adopting the above implementation method, combining semantic correlation, structural correlation and type correlation, the inherent correlation of data with different needs can be quantified from multiple dimensions, improving the comprehensiveness and accuracy of correlation identification.

[0020] In some possible implementations, determining the traceability relationship between the first demand data at adjacent demand levels based on the data correlation degree includes: For the first demand data of each demand level, the first demand data corresponding to the demand data in the upstream demand level is determined according to the data correlation degree; The traceability relationship is determined based on the first demand data of the demand level and the first demand data of the upstream demand level.

[0021] By adopting the above implementation method, the corresponding demand data of each demand data at the upstream demand level is determined, and the traceability relationship is determined based on the demand data. This enables the complete construction of upstream and downstream cross-level demand links, automatic construction of demand association relationships, reduction of omissions in manual operation, and improvement of the efficiency and accuracy of establishing traceability relationships.

[0022] In some possible implementations, determining the traceability relationship based on the first demand data of the demand level and the first demand data of the upstream demand level includes: For each of the first demand data, in response to the confirmation operation of the second demand data in the first demand data of the upstream demand level, a traceability relationship is established between the second demand data and the first demand data of the demand level.

[0023] By adopting the above implementation method, the correlation between upstream and downstream demand data can be confirmed manually, which can improve the efficiency of correlation while ensuring the accuracy and effectiveness of the traceability relationship.

[0024] In some possible implementations, the method further includes: The demand coverage rate for each demand level is determined based on the traceability relationship. The demand coverage rate is used to characterize the proportion of demand allocated to downstream demand levels for each demand level.

[0025] By adopting the above implementation method, the degree of implementation of each level of requirements down to the downstream sub-level can be intuitively quantified, problems of missing requirements in the breakdown of requirements can be discovered in a timely manner, and unfinished requirements and related nodes can be accurately located.

[0026] In some possible implementations, the method further includes: The requirement completion rate corresponding to the highest requirement level is determined based on the traceability relationship. The requirement completion rate is used to characterize the proportion of requirements completed in the requirement level.

[0027] By adopting the above implementation method, the completion progress of requirements can be quantified layer by layer, so that users can know the specific progress status of the system in a timely manner.

[0028] According to a second aspect of the present disclosure, a vehicle service demand processing apparatus is provided, comprising: The first determining module is configured to, in response to the functional requirements corresponding to vehicle services, determine the vehicle system requirements corresponding to the functional requirements based on a traceability relationship; the traceability relationship is determined based on the data correlation between adjacent requirement levels. The second determining module is configured to determine the electronic control unit requirements based on the vehicle system requirements and the traceability relationship; The sending module is configured to send the vehicle system requirements and the electronic control unit requirements to the corresponding target objects.

[0029] In some possible implementations, the tracing relationship is determined by a relationship determination device, the relationship determination device comprising: The acquisition module is configured to acquire the first requirement data corresponding to multiple requirement levels; The third determining module is configured to determine the data correlation degree between the first demand data at adjacent demand levels, wherein the data correlation degree is used to characterize the degree of matching between the demand data; The fourth determining module is configured to determine the traceability relationship between the first required data based on the data correlation degree.

[0030] In some possible implementations, the data correlation degree includes at least one of semantic correlation degree, structural correlation degree, and type correlation degree; The semantic correlation degree is used to characterize the semantic similarity between the first requirement data, the structural correlation degree is used to characterize the proximity of the first requirement data in the topological position in the database, and the type correlation degree is used to characterize the matching degree between the data categories of the first requirement data.

[0031] In some possible implementations, the data correlation degree includes semantic correlation degree, structural correlation degree, and type correlation degree; the third determining module is configured as follows: The data correlation degree is obtained by weighting and summing the semantic correlation degree, structural correlation degree and type correlation degree according to the preset weight coefficients.

[0032] According to a third aspect of the present disclosure, an electronic device is provided, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to execute processor-executable instructions in the memory to implement the steps of the method described in the first aspect of the present disclosure.

[0033] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in the first aspect of the present disclosure.

[0034] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in the first aspect of the present disclosure.

[0035] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: This disclosure first addresses the functional requirements corresponding to vehicle services by determining the corresponding vehicle system requirements based on traceability relationships. These traceability relationships are determined by the data correlation between adjacent requirement levels. Then, based on the vehicle system requirements and the traceability relationships, the electronic control unit (ECU) requirements are determined and sent to the corresponding target objects. In this way, traceability relationships between requirement data at the upstream and downstream requirement levels of the vehicle business are automatically established based on data correlation, ensuring that the functional requirements corresponding to vehicle services can be quickly and accurately linked to vehicle system requirements and ECU requirements through these traceability relationships. This improves the efficiency and accuracy of allocating vehicle service requirements at each requirement level.

[0036] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0038] Figure 1 This is a flowchart illustrating a vehicle business demand processing method according to an exemplary embodiment.

[0039] Figure 2 This is a flowchart illustrating a method for determining a traceability relationship according to an exemplary embodiment.

[0040] Figure 3 This is a flowchart illustrating another method for determining a traceability relationship according to an exemplary embodiment.

[0041] Figure 4 This is a flowchart illustrating another method for determining a traceability relationship according to an exemplary embodiment.

[0042] Figure 5 This is a block diagram illustrating a vehicle business demand processing apparatus according to an exemplary embodiment.

[0043] Figure 6 This is a block diagram illustrating a relationship determination apparatus according to an exemplary embodiment.

[0044] Figure 7 This is a block diagram illustrating another relationship determining apparatus according to an exemplary embodiment.

[0045] Figure 8 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0046] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0047] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0048] For the development of complex systems, the waterfall development approach is commonly used. It views software development as a sequential, phased, and document-driven process. Under this paradigm, requirements development is elevated to an unprecedented level of importance. The typical characteristics of the waterfall development approach are as follows.

[0049] 1. Strict phase division: Requirements development is an independent and must-have phase. Its output (requirements specification) is an unshakable input and contract for subsequent design, coding, testing and other phases. Errors or omissions in the requirements phase will result in exponentially increasing costs of fixing them later.

[0050] 2. Detailed documentation: The goal of the waterfall development approach is to produce a perfect, complete, and unambiguous Software Requirements Specification (SRS) that uses natural language and diagrams (such as data flow diagrams and state transition diagrams) to describe all functional and non-functional requirements.

[0051] 3. Approval as a marker: The end of the requirements phase is marked by the formal review and approval of the SRS by stakeholders. After this, the requirements are frozen in principle, and any changes must go through a strict change control process.

[0052] As system complexity increases, waterfall development has gradually incorporated the MBSE (Model-Based Systems Engineering) model to address shortcomings in traditional development processes. The MBSE model offers the following significant advantages: 1. Improved quality and consistency: Early detection of requirement errors through simulation eliminates defects in their nascent stage. 2. Significantly reduced change risks and costs: Automated impact analysis makes change decisions data-driven, avoiding catastrophic omissions. 3. Accelerated development process: Although initial modeling investment increases, overall project cycles are shortened by reducing later rework and refactoring. 4. Enhanced team collaboration and communication: A unified model provides all interdisciplinary teams with an unambiguous single source of fact.

[0053] Taking the vehicle field as an example, in the process of automobile development, vehicle functions are realized collaboratively by multiple business domains (such as intelligent driving domain, cockpit domain, body domain, powertrain domain, etc.). The implementation of a single vehicle function often requires cross-domain collaboration. For example, implementing a camping mode requires the cockpit domain to control the screen, the body domain to control the window lights, and the powertrain domain to maintain the power supply. In this process, it is necessary to ensure that all requirements have downstream requirements to support them. Simultaneously, changes in upstream requirements also require understanding which downstream requirements are affected, conducting impact analysis, and establishing traceability relationships between requirements. This is a crucial component of requirements management. Against this backdrop, requirements management faces significant challenges: 1. The demand chain is long and complex: A top-level product demand needs to be decomposed and allocated to multiple business domains, and finally transformed into executable software requirements for multiple ECUs (Electronic Control Units). The demand decomposition process is extremely complex.

[0054] 2. Difficulty in cross-departmental collaboration: Requests from upstream domains need to be confirmed, negotiated, and integrated by downstream domains. This process traditionally relies on meetings and documents, which is inefficient and makes it difficult to trace agreements.

[0055] 3. Difficulty in maintaining traceability: Since demand is not one-to-one, for example, one upstream demand may generate multiple downstream demands, and vice versa, multiple upstream demands may be integrated into one downstream demand. It is difficult to establish and maintain this many-to-many traceability relationship manually, leading to loss of control over demand management.

[0056] In view of this, embodiments of this disclosure propose a method, apparatus, device, medium, and program product for processing vehicle business requirements. The method first responds to the functional requirements corresponding to vehicle business requirements, determining the corresponding vehicle system requirements based on traceability relationships. These traceability relationships are determined based on the data correlation between adjacent requirement levels. Then, based on the vehicle system requirements and the traceability relationships, the electronic control unit (ECU) requirements are determined, and these requirements are sent to the corresponding target objects. In this way, traceability relationships between requirement data at upstream and downstream requirement levels of the vehicle are automatically established based on data correlation, ensuring that functional requirements corresponding to vehicle business requirements can be quickly and accurately associated with vehicle system requirements and ECU requirements through traceability relationships, thereby improving the efficiency and accuracy of allocating vehicle business requirements at various requirement levels.

[0057] Figure 1 This is a flowchart illustrating a vehicle business demand processing method according to an exemplary embodiment, such as... Figure 1 As shown, the method may include the following steps.

[0058] In step S101, in response to the functional requirements corresponding to the vehicle service, the vehicle system requirements corresponding to the functional requirements are determined according to the tracing relationship.

[0059] In step S102, the requirements for electronic control units are determined based on vehicle system requirements and traceability relationships.

[0060] For example, multiple requirement levels can be pre-built in the MBSE database. These levels, from top to bottom, could include: functional requirements, vehicle system requirements, and electronic control unit (ECU) requirements. The vehicle system requirement level can include vehicle-level system requirements and subsystem requirements. Functional requirements are the top-level, user- and market-oriented product descriptions. Vehicle-level system requirements are the first engineering translation of the function, defining the behavior, performance, and constraints that the entire vehicle system as a whole must meet to achieve a specific function. Subsystem requirements are the technical decomposition and allocation of system requirements, assigning system-level responsibilities to one or more subsystems and defining the interfaces and collaboration relationships between them. ECU requirements are the final executable, programmable instructions, representing the final allocation and refinement of subsystem requirements on a specific ECU. ECU requirements can include software instructions and hardware designs used to implement functional requirements. By establishing a multi-level requirement structure, user-perceptible and usable functions are progressively broken down into software-programmable requirements.

[0061] For example, the traceability relationship is determined based on the data correlation between the first requirement data of adjacent requirement levels. The data correlation degree is used to characterize the degree of matching between the first requirement data. The data correlation degree can be a single-dimensional correlation degree or a combination of correlation degrees of multiple dimensions. The traceability relationship can be used to characterize the upstream and downstream relationships between different requirement levels, and can reflect the complete link from the top-level definition to the bottom-level implementation of requirements. Based on the traceability relationship, functional requirements can be traced from top to bottom according to the requirement level, down to vehicle-level system requirements, subsystem requirements, and electronic control unit requirements.

[0062] In step S103, the vehicle system requirements and electronic control unit requirements are sent to the corresponding target objects.

[0063] In this step, different requirements can correspond to different target objects. For example, the target objects corresponding to each vehicle-level system requirement, each subsystem requirement, and each electronic control unit requirement can be different. At least one object's identification information can be pre-associated with each first requirement data. After determining the vehicle system requirements and electronic control unit requirements, the requirement can be sent to the corresponding target object based on the identification information associated with each requirement.

[0064] For example, the target object could be a target R&D unit, a target user, etc. After receiving the requirement data, the target object can design a development solution based on the requirement data. For example, after receiving the vehicle system requirements, the system R&D unit can design the overall system solution, including: dividing the internal subsystem interfaces, the timing of vehicle signal interaction, etc.; after receiving the electronic control unit requirements, the R&D engineer can write code and design hardware circuits according to the electronic control unit requirements.

[0065] Figure 2 This is a flowchart illustrating a method for determining a traceability relationship according to an exemplary embodiment, such as... Figure 2 As shown, the method may include the following steps.

[0066] In step S201, the first demand data corresponding to multiple demand levels is obtained.

[0067] The scope of business coverage varies depending on the level of requirement. The first requirement data is used to characterize the functional requirements of the business. The first requirement data may include: a unique requirement identifier, the requirement text, the requirement level, the business domain to which it belongs, and other potentially useful attribute data (such as creation time, author, etc.).

[0068] In step S202, the data correlation between the first demand data of adjacent demand levels is determined.

[0069] In other embodiments, after obtaining the first requirement data, data preprocessing can be performed, and then the data relevance can be calculated based on the processed first requirement data. Data preprocessing may include: removing irrelevant characters such as newline characters and special markers, standardizing and unifying synonyms through technical terminology, and extracting nouns that represent the core entity keywords of the requirement, etc.

[0070] In other embodiments, for each first demand data at each demand level, the data correlation degree between the first demand data and each first demand data at the upstream demand level can be calculated.

[0071] For example, data correlation can include at least one of semantic correlation, structural correlation, and type correlation. Data correlation can also be determined based on at least one of these three factors. The data correlation between different adjacent demand levels can be the same or different. Semantic correlation can characterize the semantic similarity between first-demand data, structural correlation can characterize the proximity of first-demand data in the database's topological location, and type correlation can characterize the degree of matching between data categories of first-demand data. Thus, based on semantic correlation, structural correlation, and type correlation, the inherent correlation of demand data can be quantified from multiple dimensions, improving the comprehensiveness and accuracy of correlation identification.

[0072] In some embodiments, where data relevance includes semantic relevance, a demand vector corresponding to the first demand data can be obtained, and the semantic relevance between first demand data at different demand levels can be determined based on the demand vector. Thus, since the demand vector represents the deep semantic features of the demand text, determining the semantic relevance based on the demand vector can accurately identify the inherent semantic relationships between demand data, improving the accuracy of semantic relevance.

[0073] For example, a first model can be pre-trained, which may be a large language embedding model, to convert demand data into demand vectors. After obtaining the first demand data, it can be input into the first model to obtain the demand vector output by the model. The demand vector can be a high-dimensional floating-point vector, such as a 1536-dimensional vector, which is the numerical representation of the demand data. In this way, the pre-trained model can accurately identify the deep features implicit in the demand data, thereby transforming the demand data into a standardized low-dimensional feature vector, providing reliable feature data for calculating semantic relevance.

[0074] For example, for two first-level demand data at adjacent demand levels, the cosine similarity between the demand vectors is determined; this cosine similarity is used as the semantic association degree. The cosine similarity can be obtained by calculating the ratio of the inner product of the two demand vectors to the product of their respective vector magnitudes. Cosine similarity can be used to characterize the directional similarity of demand vectors in a high-dimensional feature space. The closer the cosine similarity value is to 1, the higher the semantic association degree between the two demand data. In this way, the similarity of demand vectors can be accurately quantified in the vector space, thus effectively representing the semantic association relationship between different demand data.

[0075] In other embodiments, after obtaining the traceability relationship between the first demand data at the upstream and downstream demand levels, the model parameters of the first model can be updated according to the traceability relationship.

[0076] For example, traceable data can be used as labeled data to fine-tune the first model, thereby iteratively optimizing the model parameters of the first model to make it more closely match actual needs.

[0077] In other embodiments, where data correlation includes structural correlation, the topological location information of the first required data in the database can be obtained, and the structural correlation can be determined based on the topological location information.

[0078] For example, a requirement identifier for the first requirement data can be obtained. The requirement identifier can be used to uniquely represent the first requirement data, and the requirement identifier can include the topological location information of the first requirement data in the requirement hierarchy architecture. For example, the requirement identifier can carry the hierarchy number, node number, and superior / subordinate codes. Based on the requirement identifier, the topological location of the first requirement data in the database hierarchy structure can be directly located without the need to store additional coordinate information.

[0079] For another example, the topological distance between the first set of demand data can be calculated based on the topological location information, and then normalized to obtain the structural similarity. The topological distance can be understood as the number of edges on the shortest path between the nodes containing the two sets of demand data; the smaller the topological distance, the higher the structural similarity.

[0080] For example, the demand identifiers for demand data in the vehicle's braking domain include SYS-BRAKE-001 and SS-BRAKE-010, and the demand identifiers for demand data in the vehicle's battery domain include SS-POWER-005. The structural similarity between SYS-BRAKE-001 and SS-BRAKE-010 is higher than that between SYS-BRAKE-001 and SS-POWER-005.

[0081] As another example, topological location information can be input into a pre-trained second model to obtain the structural similarity output by the second model.

[0082] In this way, by determining the structural correlation based on topological location information, the structural correlation between cross-level demand data can be accurately quantified according to the hierarchical path and branch relationship of demand data in the database.

[0083] In other embodiments, where data correlation includes type correlation, type information of the first required data can be obtained, and type correlation can be determined based on the type information.

[0084] For example, each requirement data can be pre-labeled with a type tag as type information. These type tags can be customized according to actual business needs, such as performance requirements, interface requirements, and security requirements. Furthermore, the association level between multiple type tags can be defined. A higher association level indicates a higher degree of type association; for example, the association between downstream interface requirement data and upstream interface requirement data is higher than the association with upstream functional requirement data.

[0085] For example, after obtaining the type information of the requirement data, the association level can be used as the type association degree, and the association level between type information can be normalized to obtain the type association degree.

[0086] As another example, type information can be input into a pre-trained third model to obtain the type similarity output by the third model.

[0087] In this way, the type matching relationship between different requirements can be determined based on the category attributes of the requirement data, and the degree of correlation in the type dimension can be accurately quantified.

[0088] In other embodiments, when the data relevance includes semantic relevance, structural relevance, and type relevance, the data relevance can be obtained by weighting and summing the semantic relevance, structural relevance, and type relevance according to preset weight coefficients. The preset weight coefficients can be specifically set according to actual business needs, and the weight coefficient for semantic relevance can be greater than the weight coefficients for structural relevance and type relevance.

[0089] For example, the degree of data correlation can be calculated using the following formula: S_final = α * S_semantic + β * S_structural + γ * S_typological (Formula 1) Where S_final represents the data relevance, S_semantic represents the semantic relevance, S_structural represents the structural relevance, and S_typological represents the type relevance. α, β, and γ are the weighting coefficients for the semantic, structural, and type relevance, respectively. α + β + γ = 1, and α > β, γ.

[0090] In this way, by combining semantic relevance, structural relevance, and type relevance, the inherent relevance of data with different needs can be quantified from multiple dimensions, improving the comprehensiveness and accuracy of relevance identification.

[0091] In step S203, the traceability relationship between the first required data is determined based on the data correlation degree.

[0092] In some embodiments, for each demand level of first demand data, the second demand data corresponding to the first demand data in the upstream demand level is determined based on the data correlation; and the traceability relationship is determined based on the first demand data and the second demand data.

[0093] For example, for each first demand data point, a correlation threshold can be determined firstly based on the data correlation between the first demand data point and all first demand data points in the upstream demand level. For instance, the data correlation can be sorted in ascending order, the difference between adjacent data points after sorting can be calculated, and the data gap with the largest difference can be located. The median value of adjacent data points before and after the gap can be used as the correlation threshold, thus achieving dynamic determination of the correlation threshold. After determining the correlation threshold, for each first demand data point in the downstream demand level, the data correlation between that first demand data point and each first demand data point in the upstream demand level can be calculated. Then, the first demand data points with a correlation greater than the correlation threshold can be used as the corresponding first demand data points in the upstream demand level.

[0094] For example, the first demand data at the current demand level can be displayed on the screen along with the corresponding first demand data at the upstream demand level. Manual verification of this upstream demand data is then performed. Manual verification can be performed on each upstream demand level's first demand data, along with deletion. Verification establishes the traceability relationship between the current and upstream demand levels' first demand data, while deletion removes this relationship. Manual intervention can also add relationships between the first demand data and other demand data in the upstream demand level. This manual verification of the relationships between upstream and downstream demand data improves efficiency while ensuring the accuracy and effectiveness of the traceability relationships.

[0095] As another example, upon receiving confirmation of the second demand data from the first demand data at the upstream demand level, a traceability relationship can be established between the second demand data and the first demand data.

[0096] In this way, by identifying the corresponding demand data at the upstream demand level for each demand data, and determining the traceability relationship based on this demand data, a complete upstream and downstream cross-level demand chain can be built, demand association relationships can be automatically constructed, and the efficiency and accuracy of establishing traceability relationships can be improved.

[0097] Among them, the data correlation between the first demand data of adjacent demand levels can be the same or different.

[0098] In other embodiments, the demand coverage rate for each demand tier can be determined based on the traceability relationship. The demand coverage rate can be used to characterize the proportion of demand allocated to downstream demand tiers for each demand tier. For each demand tier, the demand coverage rate can be the ratio between the quantity of demand allocated to downstream demand tiers and the total quantity of demand in this demand tier.

[0099] In this way, based on the calculation of demand coverage, the degree of implementation of each level of demand down to the downstream sub-level can be intuitively quantified, problems of demand breakdown can be discovered in a timely manner, and unfinished demands and related nodes can be accurately located.

[0100] In other embodiments, the requirement completion rate corresponding to the highest requirement level can be determined based on the traceability relationship. The requirement completion rate characterizes the proportion of requirements completed within a requirement level, and can be the ratio between the number of lower-level requirements that are fully completed and the total number of requirements in the current requirement level.

[0101] For example, taking a top-down hierarchy of requirements, including functional requirements, system requirements, subsystem requirements, and electronic control unit requirements, the requirement completion rate corresponding to the highest requirement level can be used to characterize the proportion of functional requirements completed within the functional requirements level. The requirement completion rate for each requirement level can be determined first based on the traceability relationship. Furthermore, the requirement completion rate corresponding to the highest requirement level can be the ratio between the total number of completed functional requirements and the total number of functional requirements.

[0102] In this way, the completion rate of requirements can be used to quantify the progress of different levels of requirements, so that users can know the specific progress status of the system in a timely manner.

[0103] Figure 3 This is a flowchart illustrating another method for determining a traceability relationship according to an exemplary embodiment, such as... Figure 3 As shown, the steps are as follows: Step 1: Obtain upstream and downstream demand data; Step 2: Calculate the demand vector corresponding to the demand data; Step 3: Calculate the multi-dimensional data correlation degree; Step 4: Generate a list of demand data that meets the conditions; Step 5: Manually confirm the traceability relationship. The traceability relationship obtained in Step 5 can be fed back to the embedding model in Step 2 for iterative updates.

[0104] Figure 4 This is a flowchart illustrating another method for determining a traceability relationship according to an exemplary embodiment, such as... Figure 4 As shown, the demand allocation relationship is first established using the MBSE (Maintenance, Execution, and Sequence) approach. This involves creating upstream demand levels and allocating them to downstream demand levels. If an upstream demand is a single demand, a corresponding downstream demand can be created, and a traceability relationship between it and the upstream demand is automatically established. If there are multiple upstream demands, after creating the corresponding downstream demands, the data correlation between the downstream demand data and the upstream demand data can be calculated to identify upstream demand data with traceability relationships. These traceability relationships are then manually verified. This significantly reduces the complexity, time, and manpower required for demand traceability.

[0105] In summary, this disclosure first responds to the functional requirements corresponding to vehicle services, and determines the corresponding vehicle system requirements based on traceability relationships, whereby the traceability relationships are determined based on the data correlation between adjacent requirement levels. Then, based on the vehicle system requirements and traceability relationships, the electronic control unit (ECU) requirements are determined, and these requirements are sent to the corresponding target objects. In this way, traceability relationships between requirement data at the upstream and downstream requirement levels of the vehicle are automatically established based on data correlation, ensuring that the functional requirements corresponding to vehicle services can be quickly and accurately linked to vehicle system requirements and ECU requirements through traceability relationships, thereby improving the efficiency and accuracy of allocating vehicle service requirements at various requirement levels.

[0106] Figure 5 This is a block diagram illustrating a vehicle service demand processing apparatus according to an exemplary embodiment, such as... Figure 5 As shown, the device 300 may include the following modules.

[0107] The first determining module 301 is configured to, in response to the functional requirements corresponding to the vehicle business, determine the vehicle system requirements corresponding to the functional requirements based on the traceability relationship; the vehicle system requirements include: vehicle-level system requirements and subsystem requirements; the traceability relationship is determined based on the data correlation between the first requirement data of adjacent requirement levels.

[0108] The second determining module 302 is configured to determine the electronic control unit requirements based on the vehicle system requirements and the traceability relationship.

[0109] The sending module 303 is configured to send the vehicle system requirements and the electronic control unit requirements to the corresponding target objects.

[0110] Figure 6 This is a block diagram illustrating a relationship determination apparatus according to an exemplary embodiment, the apparatus being used to determine traceability relationships, such as... Figure 6 As shown, the device 400 may include the following modules.

[0111] The acquisition module 401 is configured to acquire first requirement data corresponding to multiple requirement levels. The business coverage of different requirement levels is different. The first requirement data is used to characterize the functional requirements of the business. The third determining module 401 is configured to determine the data correlation degree between the first demand data of adjacent demand levels, wherein the data correlation degree is used to characterize the degree of matching between the demand data; The fourth determining module 403 is configured to determine the traceability relationship between the first required data based on the data correlation degree.

[0112] In some embodiments, the data correlation degree includes at least one of semantic correlation degree, structural correlation degree, and type correlation degree; the semantic correlation degree is used to characterize the semantic similarity between the first requirement data, the structural correlation degree is used to characterize the proximity of the first requirement data in the topological position in the database, and the type correlation degree is used to characterize the matching degree between the data categories of the first requirement data.

[0113] In other embodiments, the data correlation degree includes semantic correlation degree; the third determining module 402 is configured to: obtain the demand vector corresponding to the first demand data; and determine the semantic correlation degree between the first demand data at adjacent demand levels based on the demand vector.

[0114] In other embodiments, the third determining module 402 is configured to: input the first demand data into a pre-trained first model to obtain the demand vector output by the first model.

[0115] Figure 7 This is a block diagram illustrating another relationship determining apparatus according to an exemplary embodiment, such as... Figure 7 As shown, the device 400 may also include the following modules.

[0116] The update module 404 is configured to update the model parameters of the first model according to the traceability relationship.

[0117] In other embodiments, the third determining module 402 is configured to: determine the cosine similarity between the demand vectors for two first demand data at adjacent demand levels; and use the cosine similarity as the semantic relevance.

[0118] In other embodiments, the data correlation degree includes structural correlation degree; the third determining module 402 is configured to: obtain the topological location information of the first demand data in the database; and determine the structural correlation degree based on the topological location information.

[0119] In other embodiments, the data correlation degree includes type correlation degree; the third determining module 402 is configured to: obtain type information of the first required data; and determine the type correlation degree based on the type information.

[0120] In other embodiments, the data correlation degree includes semantic correlation degree, structural correlation degree and type correlation degree; the third determining module 402 is configured to: perform a weighted summation of the semantic correlation degree, structural correlation degree and type correlation degree according to a preset weight coefficient to obtain the data correlation degree.

[0121] In other embodiments, the fourth determining module 403 is configured to: for each first demand data at each demand level, determine the first demand data corresponding to the first demand data at the upstream demand level based on the data correlation; and determine the traceability relationship based on the first demand data at the demand level and the first demand data at the upstream demand level.

[0122] In other embodiments, the fourth determining module 403 is configured to: for each first demand data, in response to the confirmation operation of the second demand data in the first demand data of the upstream demand level, establish a traceability relationship between the second demand data and the first demand data of the demand level.

[0123] In other embodiments, the fourth determining module 403 is further configured to: determine the demand coverage rate of each demand level according to the traceability relationship, the demand coverage rate being used to characterize the proportion of demand allocated to downstream demand levels for each demand level.

[0124] In other embodiments, the fourth determining module 403 is further configured to: determine the demand completion rate corresponding to the highest demand level based on the traceability relationship, wherein the demand completion rate is used to characterize the proportion of demand completion in the demand level.

[0125] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0126] In summary, this disclosure first responds to the functional requirements corresponding to vehicle services, and determines the corresponding vehicle system requirements based on traceability relationships, whereby the traceability relationships are determined based on the data correlation between adjacent requirement levels. Then, based on the vehicle system requirements and traceability relationships, the electronic control unit (ECU) requirements are determined, and these requirements are sent to the corresponding target objects. In this way, traceability relationships between requirement data at the upstream and downstream requirement levels of the vehicle are automatically established based on data correlation, ensuring that the functional requirements corresponding to vehicle services can be quickly and accurately linked to vehicle system requirements and ECU requirements through traceability relationships, thereby improving the efficiency and accuracy of allocating vehicle service requirements at various requirement levels.

[0127] This disclosure also provides a non-transitory computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the steps of the vehicle business requirement processing method provided in this disclosure.

[0128] Figure 8This is a block diagram illustrating an electronic device according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0129] Reference Figure 8 The electronic device 800 may include one or more of the following components: processing component 802, memory 804, power supply component 806, multimedia component 808, audio component 810, input / output interface 812, sensor component 814, and communication component 816.

[0130] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the vehicle business requirement processing method described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0131] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of such data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0132] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0133] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). In some embodiments, the multimedia component 808 includes a front-facing camera and / or a rear-facing camera.

[0134] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0135] Input / output interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0136] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0137] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 further includes a near-field communication (NFC) module to facilitate short-range communication.

[0138] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described vehicle business requirement processing method.

[0139] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of an electronic device 800 to complete the aforementioned vehicle business requirement processing method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0140] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described vehicle business demand processing method when executed by the programmable device.

[0141] Those skilled in the art will also understand that the various illustrative logical blocks and steps listed in the embodiments of this application can be implemented by electronic hardware, computer software, or a combination of both. Whether such functionality is implemented through hardware or software depends on the specific application and the overall system design requirements. Those skilled in the art can implement the described functionality using various methods for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of this application.

[0142] Furthermore, the term “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous compared to other aspects or designs. Rather, the use of the term “exemplary” is intended to present the concept in a concrete manner. As used herein, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from the context, “X applies A or B” is intended to mean any of the natural inclusive arrangements. That is, “X applies A or B” satisfies any of the foregoing instances if X applies A; X applies B; or both X applies A and B. Additionally, unless otherwise specified or clear from the context to refer to the singular form, the articles “a” and “an” as used in this application and the appended claims are generally understood to mean “one or more.”

[0143] Similarly, although this disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art upon reading and understanding this specification and the accompanying drawings. This disclosure includes all such modifications and variations and is limited only by the scope of the claims. In particular, with respect to the various functions performed by the components described above (e.g., elements, resources, etc.), unless otherwise indicated, the terminology used to describe such components is intended to correspond to any component (functionally equivalent) that performs the specific function of the described component, even if structurally not equivalent to the disclosed structure. Furthermore, although specific features of this disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations, as may be desired and advantageous to any given or particular application. Moreover, with regard to the terms “comprising,” “owning,” “having,” “having,” or variations thereof as used in the detailed description or claims, such terms are intended to be inclusive in a manner similar to the term “including.”

[0144] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

[0145] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

[0146] It should be understood that, unless otherwise specifically indicated, features of various embodiments of this disclosure described herein can be combined with each other. As used herein, the term “and / or” includes any one of the relevant listed items and any combination of any two or more; similarly, “at least one of…” includes any one of the relevant listed items and any combination of any two or more.

[0147] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In this description, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for processing vehicle business requirements, characterized in that, include: In response to the functional requirements corresponding to vehicle services, the vehicle system requirements corresponding to the functional requirements are determined based on the traceability relationship; wherein, the traceability relationship is determined based on the data correlation between adjacent requirement levels; The electronic control unit requirements are determined based on the vehicle system requirements and the traceability relationship. The vehicle system requirements and the electronic control unit requirements are sent to the corresponding target objects.

2. The method according to claim 1, characterized in that, The traceability relationship is determined in the following way: Obtain the first requirement data corresponding to multiple requirement levels; Calculate the data correlation degree between the first demand data at adjacent demand levels, whereby the data correlation degree is used to characterize the degree of matching between the first demand data. The traceability relationship between the first demand data in adjacent demand levels is determined based on the data correlation.

3. The method according to claim 2, characterized in that, The data correlation degree includes at least one of semantic correlation degree, structural correlation degree, and type correlation degree; The semantic correlation degree is used to characterize the semantic similarity between the first requirement data, the structural correlation degree is used to characterize the proximity of the first requirement data in the topological position in the database, and the type correlation degree is used to characterize the matching degree between the data categories of the first requirement data.

4. The method according to claim 2, characterized in that, The data correlation degree includes semantic correlation degree; the calculation of the data correlation degree between the first demand data at adjacent demand levels includes: Obtain the demand vector corresponding to the first demand data; Based on the demand vector, the semantic correlation between the first demand data of adjacent demand levels is calculated.

5. The method according to claim 4, characterized in that, The step of calculating the semantic correlation between the first demand data at adjacent demand levels based on the demand vector includes: For two first-demand data at adjacent demand levels, calculate the cosine similarity between the demand vectors; The cosine similarity is used as the semantic relevance.

6. The method according to claim 2, characterized in that, The data correlation degree includes structural correlation degree; the calculation of the data correlation degree between the first demand data at adjacent demand levels includes: Obtain the topological location information of the first required data in the database; The structural correlation degree is calculated based on the topological location information.

7. The method according to claim 2, characterized in that, The data correlation degree includes type correlation degree; the calculation of the data correlation degree between the first demand data at adjacent demand levels includes: Obtain the type information of the first required data; The type association degree is calculated based on the type information.

8. The method according to claim 2, characterized in that, The data correlation degree includes semantic correlation degree, structural correlation degree, and type correlation degree; the calculation of the data correlation degree between the first requirement data at adjacent requirement levels includes: The data correlation degree is obtained by weighting and summing the semantic correlation degree, structural correlation degree and type correlation degree according to the preset weight coefficients.

9. The method according to claim 2, characterized in that, The step of determining the traceability relationship between the first demand data at adjacent demand levels based on the data correlation degree includes: For the first demand data at each demand level, the first demand data corresponding to the first demand data at the upstream demand level is determined based on the data correlation. The traceability relationship is determined based on the first demand data of the demand level and the first demand data of the upstream demand level.

10. The method according to claim 9, characterized in that, Determining the traceability relationship based on the first demand data at the demand level and the first demand data at the upstream demand level includes: For each of the first demand data, in response to the confirmation operation of the second demand data in the first demand data of the upstream demand level, a traceability relationship is established between the second demand data and the first demand data of the demand level.

11. The method according to any one of claims 1-10, characterized in that, The method further includes at least one of the following: The demand coverage rate for each demand level is determined based on the traceability relationship, and the demand coverage rate is used to characterize the proportion of demand that has been allocated to downstream demand levels for each demand level. The requirement completion rate corresponding to the highest requirement level is determined based on the traceability relationship. The requirement completion rate is used to characterize the proportion of requirements completed in the requirement level.

12. A vehicle business demand processing device, characterized in that, include: The first determining module is configured to, in response to the functional requirements corresponding to vehicle services, determine the vehicle system requirements corresponding to the functional requirements based on the tracing relationship; wherein, the tracing relationship is determined based on the data correlation between adjacent requirement levels. The second determining module is configured to determine the electronic control unit requirements based on the vehicle system requirements and the traceability relationship; The execution module is configured to send requests to the corresponding target object according to the requirements of the electronic control unit.

13. The apparatus according to claim 12, characterized in that, The tracing relationship is determined by a relationship determination device, which includes: The acquisition module is configured to acquire the first requirement data corresponding to multiple requirement levels; The third determining module is configured to calculate the data correlation degree between the first demand data at adjacent demand levels, wherein the data correlation degree is used to characterize the degree of matching between the first demand data. The fourth determining module is configured to determine the traceability relationship between the first demand data of adjacent demand levels based on the data correlation degree.

14. The apparatus according to claim 13, characterized in that, The data correlation degree includes at least one of semantic correlation degree, structural correlation degree, and type correlation degree; The semantic correlation degree is used to characterize the semantic similarity between the first requirement data, the structural correlation degree is used to characterize the proximity of the first requirement data in the topological position in the database, and the type correlation degree is used to characterize the matching degree between the data categories of the first requirement data.

15. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute processor-executable instructions in the memory to implement the steps of the method according to any one of claims 1-11.

16. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program performs the steps of the method described in any one of claims 1-11.

17. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-11.

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