Method, system, device and medium for automatic generation and optimization of routing of a communication matrix

Abstract

The application provides a communication matrix route automatic generation and optimization method, system, device and medium, and belongs to the technical field of vehicles, and the method comprises the following steps: acquiring an initial whole vehicle communication matrix, a network topology model of the whole vehicle, and a routing strategy and constraint library; constructing a global multi-objective optimization problem of signal path selection based on the network topology model and the initial whole vehicle communication matrix; solving the global multi-objective optimization problem based on the routing strategy and constraint library to obtain a solution result; backfilling the solution result to the initial whole vehicle communication matrix, generating an enhanced whole vehicle communication matrix, and generating a configuration file according to the enhanced whole vehicle communication matrix. The application automatically enumerates all feasible paths based on the network topology model of the whole vehicle, replaces manual analysis of a topology graph, and realizes automatic generation of routing optimization. The global multi-objective optimization model and a solving algorithm are introduced, all signal path selections are considered in the same decision framework, and the overall efficiency, real-time performance and robustness of the whole vehicle network are improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a method, system, device, and medium for automatic generation and optimization of communication matrix routing. Background Technology

[0002] As automotive electronic and electrical architectures evolve towards domain-centralized and centrally computed models, vehicle network topologies are becoming increasingly complex, typically including multiple CAN / CAN FD network segments and multiple hierarchical intelligent gateways. When designing communication matrices based on functional requirements, having solved the automated generation and verification of logical communication relationships (i.e., "who sends the signal and to whom") and clarified the logical transmission and reception relationships of signals, it is still necessary to further address the "how to arrive" problem within the complex physical topology. Without clear physical routing planning, the communication matrix cannot be directly used to guide the integration of network signal matrices and gateway configuration, causing a break in the forward design link at the "physical implementation" stage.

[0003] This method typically relies on manually analyzing the network topology to plan routing paths for each cross-segment signal. However, it suffers from drawbacks such as inefficiency, error-proneness, lack of global optimization, and slow response to changes. Summary of the Invention

[0004] This invention aims to solve at least one of the technical problems existing in the prior art, and proposes a method, system, device and medium for automatic generation and optimization of communication matrix routing.

[0005] In a first aspect, embodiments of the present invention provide a method for automatic generation and optimization of routing in a communication matrix, comprising:

[0006] Obtain the initial vehicle communication matrix, the vehicle network topology model, and the routing strategy and constraint library;

[0007] Based on the network topology model and the initial vehicle communication matrix, a global multi-objective optimization problem for signal path selection is constructed.

[0008] The global multi-objective optimization problem is solved based on the routing strategy and constraint library, and the solution results are obtained.

[0009] The solution results are backfilled into the initial vehicle communication matrix to generate an enhanced vehicle communication matrix, and a configuration file is generated based on the enhanced vehicle communication matrix.

[0010] In some embodiments, the acquisition of the initial vehicle communication matrix, the vehicle network topology model, and the routing strategy and constraint library...

[0011] Obtain the candidate scheme set of incremental matrix based on atomic operation analysis;

[0012] The initial vehicle communication matrix is ​​generated by merging the incremental matrix candidate scheme set and the original communication matrix.

[0013] Obtain the network topology model of the entire vehicle, as well as the routing strategy and constraint library.

[0014] In some embodiments, the step of generating an initial vehicle communication matrix by merging the incremental matrix candidate scheme set and the original communication matrix includes:

[0015] Determine the operation type identifier of the incremental matrix candidate scheme set, and dynamically load the predefined rule chain according to the operation type identifier;

[0016] Based on the rule chain, the candidate scheme set of the incremental matrix is ​​verified and conflict arbitration is performed in real time to obtain the target incremental matrix;

[0017] The target incremental matrix is ​​integrated with the original communication matrix to generate the initial vehicle communication matrix.

[0018] In some embodiments, obtaining the candidate scheme set of incremental matrix based on atomic operation analysis includes:

[0019] Obtain change request information and abstract the change request information into atomic operation instructions; wherein, the atomic operation instructions include add signal instructions, modify signal attribute instructions, and delete signal instructions;

[0020] Based on the platform signal library, platform context-aware differential processing is performed on each atomic operation instruction to generate candidate implementation results of the atomic operation;

[0021] An incremental matrix candidate scheme set is generated based on the candidate implementation results.

[0022] In some embodiments, the global multi-objective optimization problem for signal path selection based on the network topology model and the initial vehicle communication matrix includes:

[0023] Based on the network topology model and the initial vehicle communication matrix, feasible path enumeration and constraint filtering are performed to obtain feasible paths for the signal.

[0024] A global multi-objective optimization problem is constructed based on the feasible paths of the signal.

[0025] In some embodiments, the step of performing feasible path enumeration and constraint filtering based on the network topology model and the initial vehicle communication matrix to obtain feasible signal paths includes:

[0026] Determine each receiver of each signal that needs to be transmitted across network segments in the initial vehicle communication matrix;

[0027] Based on the network topology model, an initial path is searched for each receiver from the sender.

[0028] The initial path is filtered based on the maximum allowable delay constraint of the signal and the estimated delay of the path to obtain a feasible path for the signal.

[0029] In some embodiments, solving the global multi-objective optimization problem based on the routing policy and constraint library to obtain the solution result includes:

[0030] The global multi-objective optimization problem is solved based on the routing policy and constraint library to determine the selected physical routing path for each signal-receiver pair; wherein, the decision variables of the global multi-objective optimization problem are the specific path selected for each signal-receiver pair, and the optimization objectives include minimizing the maximum gateway load, minimizing the worst-case end-to-end delay, and minimizing the total number of routing hops;

[0031] The physical routing path is used as the solution result.

[0032] In some embodiments, the step of backfilling the solution result into the initial vehicle communication matrix, generating an enhanced vehicle communication matrix, and generating a configuration file based on the enhanced vehicle communication matrix includes:

[0033] The physical routing paths obtained from the solution are backfilled into the initial vehicle communication matrix in the form of structured fields to generate an enhanced vehicle communication matrix.

[0034] Based on the enhanced vehicle communication matrix, a configuration file is automatically generated for each gateway in the network; wherein the configuration file includes a routing table.

[0035] In some embodiments, the method further includes:

[0036] After generating the enhanced vehicle communication matrix, the system automatically detects whether there are any violations of hard constraints in the solution results;

[0037] If there are violations of mandatory constraints, provide suggested solutions;

[0038] A network performance analysis report is automatically generated based on the enhanced vehicle communication matrix.

[0039] The generated enhanced vehicle communication matrix, configuration file, and network performance analysis report are associated and stored with the initial vehicle communication matrix and network topology model;

[0040] When the initial vehicle communication matrix or network topology model changes, incremental route replanning is initiated.

[0041] Secondly, embodiments of the present invention provide an automatic routing generation and optimization system for a communication matrix, comprising:

[0042] The multi-source input module is used to obtain the initial vehicle communication matrix, the vehicle network topology model, and the routing strategy and constraint library;

[0043] The path search and optimization module is used to construct a global multi-objective optimization problem for signal path selection based on the network topology model and the initial vehicle communication matrix.

[0044] The optimization problem-solving module is used to solve the global multi-objective optimization problem based on the routing strategy and constraint library, and obtain the solution result;

[0045] The matrix generation and configuration module is used to backfill the solution results into the initial vehicle communication matrix, generate an enhanced vehicle communication matrix, and generate a configuration file based on the enhanced vehicle communication matrix.

[0046] Thirdly, embodiments of the present invention provide an electronic device, including:

[0047] One or more processors;

[0048] Memory, used to store one or more programs;

[0049] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the methods described above.

[0050] Fourthly, embodiments of the present invention provide a computer-readable medium on which a computer program is stored, the computer program being executed by a processor to implement the steps of any of the methods described above.

[0051] The present invention provides an automatic routing generation and optimization method for communication matrices, comprising: acquiring an initial vehicle communication matrix, a vehicle network topology model, and a routing strategy and constraint library; constructing a global multi-objective optimization problem for signal path selection based on the network topology model and the initial vehicle communication matrix; solving the global multi-objective optimization problem based on the routing strategy and constraint library to obtain the solution result; backfilling the solution result into the initial vehicle communication matrix to generate an enhanced vehicle communication matrix and generating a configuration file based on the enhanced vehicle communication matrix. The present invention automatically enumerates all feasible paths based on the vehicle's network topology model, replacing manual analysis of the topology map and achieving automated generation of optimal routes. By introducing a global multi-objective optimization model and solution algorithm, it can comprehensively consider the path selection of all signals within the same decision framework, achieving global load balancing of gateways and buses, avoiding local overload and critical path delay exceeding limits, and improving the overall efficiency, real-time performance, and robustness of the vehicle network. The solution results of the gateway routing are directly marked in the vehicle communication matrix, and a deployable configuration file is automatically generated, thereby completing the entire link closed loop of forward design from functional requirements to logical communication relationships and physical routing implementation. Attached Figure Description

[0052] Figure 1 A flowchart illustrating an automatic routing generation and optimization method for a communication matrix provided in an embodiment of the present invention;

[0053] Figure 2 This is a flowchart illustrating an optional specific implementation method involved in an embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of the topology graph structure involved in the embodiments of the present invention;

[0055] Figure 4 This invention provides a structural block diagram of an automatic routing generation and optimization system for a communication matrix.

[0056] Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0057] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0058] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0059] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0060] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0061] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0062] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0063] In related technologies, a vehicle speed signal (ESC transmission) needs to be simultaneously delivered to the Electric Power Steering (EPS) system on the same network segment, the instrument panel (ICD) on the audio / video network segment, and the DCU (door controller) located under the secondary gateway. This involves direct connection, single gateway forwarding, and multiple gateway forwarding, respectively. The lack of clear physical routing planning means the communication matrix cannot be directly used to guide network integration and gateway configuration, causing a break in the forward design link at the "physical implementation" stage.

[0064] Related technologies rely on manually analyzing the topology map to plan routing paths for each cross-network segment signal. This method has significant drawbacks: when dealing with hundreds or thousands of signals, manual planning is extremely labor-intensive, inefficient, and prone to errors, making it difficult to ensure consistency and easily leading to omissions or misconfigurations; manual routing cannot comprehensively consider all signal flows, cannot evaluate and optimize overall network performance in real time, lacks global optimization, and is prone to causing excessive load on local gateways or excessive latency on critical paths, affecting network robustness; when network topology or communication requirements change, routing paths and gateway configurations need to be manually re-evaluated and updated, resulting in slow change response, cumbersome processes, and a tendency to generate iteration errors.

[0065] Therefore, there is an urgent need for an automated and intelligent solution that can automatically complete the planning, optimization, and configuration generation of physical routes after the communication matrix logic design is completed, and realize a full-link digital closed loop of forward design from "function" to "logic" to "physical".

[0066] To address at least one of the technical problems existing in the aforementioned related technologies, the present invention provides a method for automatic generation and optimization of communication matrix routing. Figure 1 This is a flowchart illustrating an automatic routing generation and optimization method for a communication matrix provided in an embodiment of the present invention.

[0067] As one embodiment of the present invention, such as Figure 1 As shown, the automatic route generation and optimization method for the communication matrix includes:

[0068] Step S1: Obtain the initial vehicle communication matrix, the vehicle network topology model, and the routing strategy and constraint library;

[0069] Step S2: Construct a global multi-objective optimization problem for signal path selection based on the network topology model and the initial vehicle communication matrix;

[0070] Step S3: Solve the global multi-objective optimization problem based on the routing strategy and constraint library to obtain the solution result;

[0071] Step S4: Fill the solution results back into the initial vehicle communication matrix, generate an enhanced vehicle communication matrix, and generate a configuration file based on the enhanced vehicle communication matrix.

[0072] It should be noted that the execution subject in this embodiment can be an electronic device, which can be a computer device with data processing function, or other devices that can achieve the same or similar functions. This embodiment does not limit this. In this embodiment, the execution subject is a computer device as an example for explanation.

[0073] Specifically, the automatic generation and optimization method for communication matrix routing in this embodiment can be used in the fields of automotive electronic and electrical architecture design and vehicle network communication. Specifically, it involves a method for automatically planning and optimizing physical routing paths for cross-network segment signals and generating gateway configurations based on the vehicle network topology during the forward generation process of a demand-driven in-vehicle (Controller Area Network, CAN / CAN FD) communication matrix. The aim is to achieve a fully automated closed loop from logical communication relationships to physical routing implementation.

[0074] For example, this embodiment of the method is applicable to the automatic routing generation of in-vehicle CAN signals based on topology awareness and multi-objective optimization. It aims to fill gaps and deficiencies in related technologies, providing an automated and intelligent solution to achieve optimal automated route generation. For signals transmitted across network segments, there are multiple transmission routes in the vehicle network topology, requiring a decision on the transmission route based on a routing rule base. The route decision, while meeting signal performance constraints, achieves global load balancing between the gateway and the bus, improving overall network efficiency and robustness. This embodiment directly marks the gateway routing results in the vehicle communication matrix and automatically generates a deployable gateway routing table configuration file, thus completing a closed-loop forward design from "functional requirements" to "logical communication relationships" and then to "physical routing implementation." The following describes the specific steps.

[0075] In some embodiments, an initial vehicle communication matrix, a vehicle network topology model, and a routing strategy and constraint library are obtained; an incremental matrix candidate scheme set based on atomic operation parsing is obtained; the initial vehicle communication matrix is ​​generated by merging the incremental matrix candidate scheme set and the original communication matrix; and the vehicle network topology model and routing strategy and constraint library are obtained.

[0076] Specifically, such as Figure 2 As shown, multi-source input modeling and data preparation are performed. Three types of input data are received and integrated: input A (initial vehicle communication matrix), input B (vehicle network topology model), and input C (routing strategy and constraint library).

[0077] For example, input A (initial vehicle communication matrix): receives the initial version of the vehicle communication matrix (initial vehicle communication matrix) which is a combination of the first matrix and the second matrix (an incremental matrix solidified version based on the platform signal library). The initial vehicle communication matrix includes the logical transmit and receive attributes (logical sender ECU and logical receiver ECU) of each signal; message attributes (message period, message length) and signal delay attributes (maximum delay time).

[0078] For example, input B (network topology model): obtain a structured definition of the vehicle network topology. The network topology model abstracts the vehicle network as a graph consisting of nodes and edges. Nodes include ECUs, gateways and their ports; edges represent physical links (such as CAN or CANFD buses) and have bandwidth and network segment attributes.

[0079] For example, input C (routing policy and constraint library): predefined policies and hard constraints required for routing decisions, including the maximum allowable load rate threshold for each network segment, the routing and protocol conversion capabilities of each type of gateway, the link transmission delay model, and the priority of optimization objectives (e.g., load balancing priority, or worst-case minimum delay priority).

[0080] In some embodiments, generating an initial vehicle communication matrix by merging the incremental matrix candidate scheme set and the original communication matrix includes: determining the operation type identifier of the incremental matrix candidate scheme set; dynamically loading a predefined rule chain based on the operation type identifier; performing real-time verification and conflict arbitration on the incremental matrix candidate scheme set based on the rule chain to obtain a target incremental matrix; and integrating the target incremental matrix with the original communication matrix to generate the initial vehicle communication matrix.

[0081] In some embodiments, obtaining a candidate scheme set for an incremental matrix based on atomic operation parsing includes: obtaining change requirement information and abstracting the change requirement information into atomic operation instructions; wherein, the atomic operation instructions include adding signal instructions, modifying signal attribute instructions, and deleting signal instructions; performing platform context-aware differential processing on each atomic operation instruction based on the platform signal library to generate candidate implementation results of the atomic operation; and generating a candidate scheme set for an incremental matrix based on the candidate implementation results.

[0082] Specifically, the initial version of the vehicle communication matrix (initial vehicle communication matrix) is formed by merging the first matrix and the second matrix (the fixed version of the incremental matrix based on the platform signal library). The first matrix is ​​the original communication matrix, and the second matrix is ​​the target incremental matrix.

[0083] For example, a candidate scheme set is received from the atomic operation parsing engine. Each candidate scheme includes: operation type identifier, naming candidate, message matching candidate, layout candidate, and other context data. The operation type identifier includes, for example, ADD_SIGNAL (add signal), MODIFY_SIGNAL_ATTRIBUTE (modify signal attributes), or DELETE_SIGNAL (delete signal); the naming candidate includes, for example, for the ADD operation, the candidate name after renaming; the message matching candidate includes, for example, for the ADD operation, a list of candidate messages (each message has attributes such as ID, period, load rate, and remaining space) and a matching score; the layout candidate includes, for example, for the ADD and MODIFY operations, the layout scheme (start bit, length, message load rate, etc.); and other context data includes, for example, signal definition attributes and logical transmit / receive attributes.

[0084] For example, signal change sets from multiple Electronic Control Units (ECUs) are received. These sets are submitted based on structured templates, and each change request includes at least change type attributes, signal definition attributes, signal semantic attributes, logical transmit / receive attributes, and signal delay attributes. Each change request in the signal change set is parsed and converted into a corresponding atomic operation instruction (ADD, MODIFY, DELETE) based on the change type attribute. Each instruction is associated with a globally unique identifier for the target signal and a complete set of change attributes. Based on the platform's signal library, each atomic operation is processed differently directly within the platform library. During processing, each key decision point (e.g., candidate message selection, layout scheme selection, attribute change feasibility, etc.) is submitted to an external rule engine for verification. The verification result determines whether to continue the current path or whether adjustments are needed.

[0085] Specifically, for the ADD_SIGNAL operation, the processing flow of the atomic operation instruction (ADD_SIGNAL) includes: performing a platform library existence check, and searching for a matching existing signal in the platform signal library based on the signal semantics, globally unique identifier, or attribute combination provided by the ECU. Each signal in the platform signal library has a globally unique name and coded identifier to ensure accurate matching.

[0086] For example, for the ADD_SIGNAL operation, in case one, the signal already exists in the platform signal library. If a matching signal exists, it is directly confirmed that the signal is defined in the platform library, and no creation operation is required. The complete standard attributes of the signal (including message definition attributes, signal definition attributes, signal location attributes, logical transmit / receive attributes, and signal delay attributes) exist in the platform library and can be directly referenced by all vehicle model projects.

[0087] For example, for the ADD_SIGNAL operation, case two: the signal does not exist in the platform signal library. If no matching signal exists, it is treated as a completely new signal, requiring a creation operation in the platform signal library: a) signal naming correction; b) intelligent matching within the platform library; c) automatic spatial optimization layout; d) supplementing logical transmit / receive attributes and signal delay attributes. Through the above creation operation, the new signal is completely created in the platform signal library, and all its attributes are defined at the platform level, allowing direct reference by all current and future vehicle model projects.

[0088] For example, signal naming correction: The signal naming standardization correction module is called to verify and correct the original signal name according to the platform's predefined naming rules (e.g., length ≤ 32, only numbers / letters / underscores, first character must be a letter, English abbreviation standard, etc.), generate a standardized signal name that conforms to the standard, and write the name into the library as the globally unique identifier of the signal in the platform library.

[0089] For example, the platform library performs intelligent matching by invoking the signal-message intelligent matching subsystem to calculate the matching degree of candidate messages based on the following multi-dimensional features: signal semantic similarity: the degree of semantic matching between the signal semantic attributes provided by the ECU and the message functional classification; period matching degree: the degree of matching between the expected transmission period of the signal and the message period; data length compatibility: whether the remaining space of the message meets the signal length requirements; historical correlation: the historical layout preference of signals from the same ECU or the same functional domain. Messages that may carry the signal (including existing messages and new message options) are selected from the platform signal library, and a matching degree score is calculated, outputting the N candidate schemes with the highest scores. For new message options, a formal CAN ID is assigned according to the period-ID mapping rule: the ID range to which the signal belongs is determined based on the signal's transmission period (e.g., a 10ms message corresponds to an ID range of 0x10~0xFF, a 20ms message corresponds to 0x100~0x1FF, etc.), and an ID is selected from the currently available ID pool within that range as the formal ID. A dynamically updated ID usage status table is maintained. Each time an ID is assigned to a new message, it is marked as occupied in the ID pool for the corresponding period range, ensuring that subsequent new messages do not select the same ID repeatedly. The initial state of the ID pool is generated based on existing message IDs in the platform's signal library. The complete definition of a new message (including message ID, period, length, etc.) is also written into the platform's signal library. Candidate schemes are submitted to an external rule engine for verification, and the final selected message is determined based on the verification results.

[0090] For example, automatic spatial optimization layout: For the finally selected message (whether an existing message or a newly created message), based on the current bit field occupancy map of the message (which records the start bit, length, and reserved area of ​​the allocated signals), an idle bit field query is performed. From the continuous idle bit field intervals that can accommodate the signal length, the start bit is selected according to a preset optimization strategy (e.g., best fit, first fit, load balancing priority), and a layout scheme is generated. This layout scheme is submitted to an external rule engine for verification. After the verification is passed, the signal position attributes (start byte, arrangement format) are written into the platform signal library.

[0091] For example, supplement the logical transmit / receive attributes and signal delay attributes: extract the logical transmit / receive attributes (logical sender ECU and logical receiver ECU) and signal delay attributes (maximum delay time) from the ECU change set, and write these attributes as the default attributes of the signal into the platform signal library.

[0092] Specifically, for the MODIFY_SIGNAL_ATTRIBUTE operation: the atomic operation instruction is the signal attribute modification instruction (MODIFY_SIGNAL_ATTRIBUTE). The processing flow includes: change impact analysis; layout recalculation; and attribute update. Change impact analysis: Locate the target signal in the platform's signal library and analyze whether attribute changes (e.g., signal length, precision, offset, range, etc.) affect the current physical layout. For example, special attention needs to be paid to changes in signal length, as length changes may directly lead to insufficient bit fields in the original layout. Layout recalculation: If the change affects the layout (e.g., increased length leading to insufficient space in the original position), the layout recalculation algorithm is invoked. While maintaining the original message ID, free bit fields are queried based on the current message's bit field occupancy map. Attempts are made to rearrange the signal bit fields, prioritizing finding new free areas within the current message to accommodate the signal, while minimizing the movement of other signals within the same message. If the signal cannot be accommodated, multiple layout adjustment schemes are generated, including but not limited to: moving within the current message, extending the message length, and migrating to other messages. Multiple candidate schemes are submitted to an external rule engine for verification, and the final scheme is selected based on the verification results. Attribute Update: Based on the rule engine's validation results, the signal position attributes (starting byte) and signal definition attributes (such as precision and range) are synchronously updated in the platform signal library. The modified signal will be used as the new version of the platform library for subsequent vehicle model projects.

[0093] Specifically, for the DELETE_SIGNAL operation, the atomic operation instruction for deleting a signal (DELETE_SIGNAL) includes the following processing steps: deactivation marking or removal. Based on the platform management policy, one of the following operations is performed: If the signal may continue to be used by some vehicle models, the signal is marked as "deactivated" in the platform signal library for the current vehicle model context, releasing the message bit field resources it occupies in the current vehicle model, but retaining the signal definition for reuse by other vehicle models; if the signal is no longer used in all vehicle models, the signal and its related attributes are completely removed from the platform signal library, and the ID and bit field resources are reclaimed. The feasibility of the deletion operation (e.g., whether there are dependent references) can be verified through an external rule engine.

[0094] In this embodiment, the change requirements are abstracted into atomic operations and incrementally differentiated based on the platform signal library. This avoids the loss of original design data caused by the overall coverage method and preserves the layout results already completed in the first communication matrix. At the same time, by combining atomic operations with incremental matrix merging, the structured and automated integration of multi-source requirements is realized, which significantly improves the generation efficiency of the communication matrix and reduces the risk of data inconsistency caused by human error.

[0095] Specifically, the rule chain is dynamically loaded and scheduled: the rule chain scheduler dynamically loads predefined rule chains based on the operation type identifier and executes them sequentially. The predefined rule chains include: the add signal operation rule chain (ADD_SIGNAL operation rule chain), the modify signal attribute operation rule chain (MODIFY_SIGNAL_ATTRIBUTE operation rule chain), and the delete signal operation rule chain (DELETE_SIGNAL operation rule chain).

[0096] For example, the ADD_SIGNAL operation rule chain includes: signal naming compliance rules → signal-message matching decision rules → physical layout conflict detection rules; the MODIFY_SIGNAL_ATTRIBUTE operation rule chain includes: impact analysis rules (determining whether the layout has changed) → physical layout conflict detection rules; and the DELETE_SIGNAL operation rule chain includes: deletion feasibility rules.

[0097] Specifically, rule execution and conflict detection. The following explains the specific implementation logic of each rule. For the newly added signal ADD_SIGNAL, the new signal operation rule chain is executed: signal naming compliance rule → signal-message matching decision rule → physical layout conflict detection rule.

[0098] For example, signal naming compliance rules include: verifying whether candidate names conform to platform specifications: length ≤ 32, containing only numbers / letters / underscores, starting with a letter, and conforming to English abbreviation standards, etc. The system checks the uniqueness of names in the platform's signal library and whether they are duplicates of other newly added signal names. If the name format is non-compliant, a status code NEED_HUMAN is returned with details of the format error; if there is a uniqueness conflict, a status code CONFLICT is returned with conflict details (such as suggestions for existing similar names); otherwise, a status code SUCCESS is returned, and the name is selected.

[0099] For example, the signal-message matching decision rules include: resource conflict rules, ID-period logic matching rules, and matching degree threshold rules. Specifically, the resource conflict rule checks whether the pre-assigned temporary ID is unique in the platform library and in this new addition if the candidate solution involves creating a new message. Since the rule engine has already selected from the available ID pool, it is usually unique, but it needs to be confirmed whether other parallel additions in concurrent scenarios occupy the same ID. If there is a conflict, the status code CONFLICT is returned and a backtracking is triggered, requiring the rule engine to reselect the ID; if the ID is unique, the process continues. The ID-period logic matching rule verifies whether the message ID conforms to the period-ID mapping relationship defined by the platform (e.g., a 10ms message ID must be in the range of 0x10~0xFF). If it does not conform, the status code CONFLICT is returned and the solution is eliminated. The matching degree threshold rule checks whether the candidate solution score is higher than a preset threshold (e.g., 80%). If the highest-scoring solution meets the criteria, the message is automatically selected, and the status code SUCCESS is returned. If multiple high-scoring solutions exist (all meet the criteria) or all solutions fail to meet the criteria, the meta-arbitrator is triggered, and the rule returns the status code SUCCESS with a multi-solution flag. It should be noted that if all candidate solutions are eliminated due to severe conflicts, the status code CONFLICT is returned.

[0100] For example, physical layout conflict detection rules include: bit field overlap rules, reserved bit / extended bit constraint rules, and load rate warning rules. The bit field overlap rule checks whether the layout scheme overlaps with the bit fields of other concurrently added signals (from the same batch, temporary states not yet merged into the platform library) within the same message. Since the rule engine generates layouts sequentially, multiple new signals may be assigned to the same bit field interval in the same message; this rule is used to capture such concurrent conflicts. If overlap is found, the status code CONFLICT is returned. The reserved bit / extended bit constraint rule confirms that the layout does not encroach on reserved areas or extended bits defined by the platform (such as diagnostic reserved bits, security extended bits). If encroachment is found, the status code CONFLICT is returned. The load rate warning rule triggers a warning if the message load rate exceeds a threshold (e.g., 85%) after layout, but can be allowed to continue according to project policy (returning the status code SUCCESS with an additional warning). If the layout scheme is marked as "layout unsolvable" (the rule engine cannot find a free bit field), the status code CONFLICT is returned directly.

[0101] Specifically, for modifying the signal attribute MODIFY_SIGNAL_ATTRIBUTE, the signal attribute operation rule chain is modified, including: impact analysis rule (determining whether the layout has changed) → physical layout conflict detection rule.

[0102] For example, the impact analysis rule receives the impact analysis results provided by the rule engine and determines whether the attribute change involves a layout change. If not, it directly returns SUCCESS; if so, it continues with subsequent layout conflict detection rules. The physical layout conflict detection rules are described in the above embodiment.

[0103] Specifically, for the DELETE_SIGNAL signal, the signal deletion operation rule chain includes: deletion feasibility rules. Deletion feasibility rules: verify whether the target signal exists in the current vehicle model project; check whether the target signal is referenced by other dependencies in the current vehicle model, including but not limited to cross-signal calculation functions, diagnostic events, network management related configurations, etc.; if dependencies exist, return the status code CONFLICT with dependency details (e.g., dependency type, location); if no dependencies exist, return the status code SUCCESS, allowing the signal to be marked as "disabled" in the application context of the current vehicle model.

[0104] In this embodiment, rule chain scheduling is introduced. Based on the received atomic operation type identifier (e.g., ADD_SIGNAL, MODIFY_SIGNAL_ATTRIBUTE, DELETE_SIGNAL), a rule chain uniquely corresponding to that operation type is dynamically loaded from a predefined rule base and executed sequentially. Each rule chain consists of multiple independent rules (e.g., naming compliance rules, message matching decision rules, physical layout conflict detection rules, etc.). The technique of dynamically loading rule chains using rule chain scheduling decouples the rule logic from the execution scheduling logic. When a new operation type needs to be added or the verification logic of a certain type of operation needs to be adjusted, only the rule chain definition or the rule itself in the rule base needs to be modified, without changing the core scheduling code, significantly improving flexibility and scalability when dealing with different operation types.

[0105] In some embodiments, constructing a global multi-objective optimization problem for signal path selection based on the network topology model and the initial vehicle communication matrix includes: performing feasible path enumeration and constraint filtering based on the network topology model and the initial vehicle communication matrix to obtain feasible paths for the signal; and constructing a global multi-objective optimization problem based on the feasible paths for the signal.

[0106] In some embodiments, feasible path enumeration and constraint filtering are performed based on the network topology model and the initial vehicle communication matrix to obtain feasible paths for signals, including: determining each receiver of each signal that needs to be transmitted across network segments in the initial vehicle communication matrix; searching for initial paths from the sender to each receiver based on the network topology model; and filtering the initial paths based on the maximum allowable delay constraint of the signal and the estimated delay of the path to obtain feasible paths for the signal.

[0107] In some embodiments, solving the global multi-objective optimization problem based on the routing policy and constraint library to obtain the solution result includes: solving the global multi-objective optimization problem based on the routing policy and constraint library to determine the selected physical routing path for each signal-receiver pair; wherein, the decision variables of the global multi-objective optimization problem are the specific paths selected for each signal-receiver pair, and the optimization objectives include minimizing the maximum gateway load, minimizing the worst-case end-to-end delay, and minimizing the total number of routing hops; and using the physical routing path as the solution result.

[0108] Specifically, such as Figure 2 As shown, topology-aware global path search and multi-objective optimization are performed. Based on the network topology model, the following operations are performed for each signal in the communication matrix that needs to be transmitted across network segments: feasible path enumeration, constraint filtering, and global optimization decision.

[0109] For example, feasible path enumeration: In the topology graph, all possible paths from the sender to each receiver are searched. Each path includes the sequence of gateways traversed and the ingress and egress ports, the total number of hops, and the estimated transmission delay.

[0110] For example, constraint filtering: Paths that do not meet the timeliness requirements are filtered out based on the maximum allowable delay constraint of the signal and the estimated delay of the path.

[0111] For example, global optimization decision-making: The path selection problem for all signals is constructed as a global multi-objective optimization problem. The decision variable is the specific path selected for each signal-receiver pair. The optimization objectives include at least: a) minimizing the maximum gateway load: avoiding a single gateway becoming a performance bottleneck; b) minimizing the worst-case end-to-end latency: ensuring the real-time performance of critical signals; c) minimizing the total number of routing hops: reducing overall network complexity and cumulative latency. An algorithm is used to solve this optimization problem, assigning appropriate physical routing paths to all signals.

[0112] The goal is to select a specific path for each signal-receiver pair from the set of feasible paths for all signals, making a set of global optimization objectives optimal while satisfying all hard constraints. An example solution is provided using a Pareto-optimal non-dominated sorting genetic algorithm (e.g., NSGA-II). The decision variable is the specific path selected for each signal-receiver pair; only one path can be selected for the same signal-receiver pair. Hard constraints include: network segment load rate constraint (for each network segment, its load rate ≤ maximum allowable load rate); gateway capacity constraint (for each gateway, the total number of packets it needs to route ≤ the gateway's maximum number of routed packets); and signal delay constraint (for each signal-receiver pair, the end-to-end delay of its selected path ≤ the maximum allowable end-to-end delay of that signal). Optimization objectives include: minimizing the maximum gateway load (i.e., minimizing the highest load rate among all gateways), minimizing the worst-case end-to-end delay (i.e., minimizing the largest end-to-end delay among all signal-receiver pairs), and minimizing the total number of route hops (i.e., minimizing the sum of the number of hops for all signal route paths).

[0113] For example, the solution algorithm can employ a multi-objective optimization solver based on a genetic algorithm. In this embodiment, a Pareto-optimal non-dominated sorting genetic algorithm (e.g., NSGA-II) is used for the solution. The input includes a list of feasible paths for all signal-receiver pairs, parameters of the hard constraints, and the priority of the optimization objectives (e.g., load balancing priority, while also considering latency).

[0114] In one example, the population is initialized as follows: An initial population of N individuals is randomly generated. Each individual represents a global routing decision scheme, i.e., a set of solutions containing all signal-receiver pair assignments. When generating each individual, it must be ensured that each signal-receiver pair randomly selects a feasible path. Fitness evaluation: For each individual in the current population, the global path selection scheme is decoded. The satisfaction of all hard constraints (penalty function) under this scheme is calculated. The values ​​of all optimization objective functions under this scheme are calculated. Based on the objective function values ​​and the penalty function, the fitness of the individual (rank and crowding distance) is calculated. Genetic operations: Selection: Based on fitness, a tournament selection method is used to select parent individuals for reproducing the next generation; Crossover: Crossover is performed between two parent individuals with a certain probability, for example, selecting one or more subnets and exchanging the signal routing path selections of the two parents within that subnet; Mutation: With a certain probability, the path selection of some signal-receiver pairs in the offspring individuals is randomly changed (randomly selecting another path from their list of feasible paths). Generate a new population: The next generation of population is generated through selection, crossover, and mutation operations. Termination condition determination: The fitness evaluation step is repeated until the new population generation step is reached, until the preset maximum number of iterations is reached, or the change in the optimization objective value across multiple generations is less than a preset threshold. Solution output: After iteration, the optimal Pareto optimal solution or a set of optimal solutions is selected from the final population according to a preset optimization priority (e.g., load balancing priority). The global routing path scheme corresponding to this solution is output.

[0115] In some embodiments, the solution results are backfilled into the initial vehicle communication matrix to generate an enhanced vehicle communication matrix and a configuration file is generated based on the enhanced vehicle communication matrix. This includes: backfilling the physical routing paths, which are the solution results, into the initial vehicle communication matrix in the form of structured fields to generate an enhanced vehicle communication matrix; and automatically generating a configuration file for each gateway in the network based on the enhanced vehicle communication matrix; wherein the configuration file includes a routing table.

[0116] Specifically, such as Figure 2 As shown, routing information labeling, conflict resolution, and matrix enhancement are performed. Result labeling: The final routing path result obtained from the optimization solution (solution result) is automatically backfilled into the original communication matrix in the form of structured fields to generate an "enhanced vehicle communication matrix". For each transmitter and receiver of each signal, its routing path sequence, gateways passed through, routing type (direct connection / gateway forwarding), and calculated expected delay are independently labeled.

[0117] Specifically, such as Figure 2As shown, configuration and intelligent report generation are executable. Gateway configuration is automatically generated: based on the enhanced vehicle communication matrix, a dedicated, directly deployable configuration file (e.g., ARXML, DBC extension file) is automatically generated for each gateway in the network. This configuration file explicitly defines: a routing table, specifying the input port, output port, and forwarding conditions for signals that need to be forwarded.

[0118] For example, a multi-dimensional network performance analysis report is automatically generated, which includes: load rate statistics and trend charts for each CAN / CAN FD network segment; CPU and memory load estimates for each gateway; end-to-end latency analysis of key signal flows to identify latency bottlenecks; and analysis of the impact of routing changes to provide quantitative basis for architecture optimization.

[0119] In some embodiments, the method further includes: after generating the enhanced vehicle communication matrix, automatically detecting whether there is a violation of hard constraints in the solution results; if there is a violation of hard constraints, providing a solution suggestion.

[0120] Specifically, conflict detection and resolution automatically detects whether the optimization results violate hard constraints (e.g., a gateway routing table entry exceeds hardware capacity, a link bandwidth exceeds limit). If a conflict is found, it provides resolution suggestions, such as: suggesting adjusting the signal cycle, suggesting adding a gateway or upgrading the link in the topology, or returning to the topology-aware global path search and multi-objective optimization steps to re-optimize with stricter constraints.

[0121] In some embodiments, the method further includes: automatically generating a network performance analysis report based on the enhanced vehicle communication matrix; associating and storing the generated enhanced vehicle communication matrix, configuration file, and network performance analysis report with the initial vehicle communication matrix and network topology model; and initiating incremental route replanning when the initial vehicle communication matrix or network topology model changes.

[0122] Specifically, such as Figure 2 As shown, change synchronization and end-to-end traceability are implemented. All outputs generated from this routing plan (enhanced vehicle communication matrix, gateway configuration files, network performance analysis reports) are associated and stored with the input communication matrix baseline and topology model version. When the inputs (requirements, matrix, topology) change, incremental route replanning can be quickly initiated, clearly identifying the affected signals, gateway configurations, and network performance metrics changes, achieving end-to-end traceability of the impact of changes from physical routes back to logical requirements.

[0123] It should be noted that the technical solution of this embodiment's method will be described in detail below with reference to specific embodiments. In vehicle model project A, a draft of the vehicle communication matrix (initial vehicle communication matrix) after merging the first and second matrices has been generated. Now, physical routing planning needs to be completed for this initial vehicle communication matrix. The vehicle network topology is as follows: Figure 3 As shown, the network includes multiple segments: BCANFD (Body Domain), ICANFD (Intelligent Cockpit Domain), ECANFD (New Energy Domain), MCAN (Motor Subnet), CCANFD (Chassis Domain), and CAN1 (ZCU Subnet). These segments are interconnected via the central gateway OIB and the regional gateway ZCU. The signal "ESC_VehSpd" is sent by the ESC (Electronic Stability Controller) on the CCANFD chassis domain and needs to be received by the EPS (Electric Power Steering) within the same segment, the ICD (Instrument Display) across segments, the DCU (Door Controller) on the CAN1 ZCU subnet, and the MCU1 (Motor Controller 1) on the MCAN motor subnet. The DCU requires forwarding through two gateways (OIB and ZCU), while the MCU1 has multiple selectable paths.

[0124] Specifically, Step 1 involves multi-source input modeling. Input A: Import the initial draft of the vehicle communication matrix (initial vehicle communication matrix) from the intelligent matching and layout execution engine module for Project A, and obtain the complete attributes of the "ESC_VehSpd" signal: Sender ECU = "ESC", Logical Receiver ECU List = ["EPS", "ICD", "DCU", "MCU1"], Signal Period = 20ms, Message Length = 8 bytes, Maximum Allowable End-to-End Delay = 20ms. Input B: Load the vehicle network topology model file (e.g., XML format). Parse the topology diagram into structured data, with nodes including: OIB, ZCU, VIU, EPS, ESC, BMS, OBC, IVI, ICD, HUD, MCU1, MCU2, DCU, SAC. The OIB has multiple ports: OIB@BCANFD, OIB@ICANFD, OIB@ECANFD, OIB@MCAN, and OIB@CCANFD; the ZCU has two ports: ZCU@BCANFD and ZCU@CAN1; and the VCU has two ports: VIU@CCANFD and VIU@MCAN. Edges represent physical links, and each network segment has bandwidth attributes and maximum load rate constraints. Input C: Load predefined constraints from the routing policy and constraint library. These predefined constraints include: a) The maximum allowable load rate for each CAN / CAN FD network segment is 55%; b) Gateway routing capability: Each gateway supports a maximum of 50 routing packets, and the internal routing processing time is 2ms; c) Optimization objective priority: Prioritize load balancing while minimizing end-to-end latency.

[0125] For example, step two involves global path search and optimization. The routing optimization engine, based on the topology model, searches for feasible paths for each receiver of the "ESC_VehSpd" signal: a) To EPS: Only one path: ESC (transmit) → EPS (receive), a direct connection within the same network segment, requiring no gateway forwarding, estimated latency 0ms. b) To ICD: Only one path: ESC (transmit) → OIB@CCANFD (receive) → OIB@ICANFD (transmit) → ICD (receive), involving one gateway forwarding, estimated latency 2ms. c) To DCU: Only one path: ESC (transmit) → OIB@CCANFD (receive) → OIB@BCANFD (transmit) → ZCU@BCANFD (receive) → ZCU@CAN1 (transmit) → DCU (receive), involving two levels of gateway forwarding, estimated latency 4ms. d) To MCU1: There are two feasible paths: Path1: ESC (transmit) → OIB@CCANFD (receive) → OIB@MCAN (transmit) → MCU1 (receive); Path2: ESC (transmit) → VCU@CCANFD (receive) → VCU@MCAN (transmit) → MCU1 (receive).

[0126] Specifically, the optimization engine constructs a global multi-objective optimization model using "ESC_VehSpd" along with all other signals in the matrix. Assuming multiple signals simultaneously travel from the chassis domain to the cockpit and motor domains, the engine solves the optimization model, making decisions with the objectives of minimizing the maximum gateway load and minimizing the total end-to-end delay, while satisfying constraints such as load rate of each network segment ≤ 55%, number of gateway routes ≤ 50, and delay of all signals ≤ 20ms. For the two paths to MCU1, the engine calculates that Path1 would increase the load on the OIB@ECANFD port, which already has many signals; Path2 utilizes the VCU for forwarding, which can distribute the load, and the VCU currently has a lower load. Under the premise of satisfying the delay constraint (both paths have a delay of 2ms), the engine decides to select Path2 based on the load balancing priority principle.

[0127] For example, step three involves route information labeling and matrix enhancement. The route results obtained from the optimization solution are backfilled into the original communication matrix as structured fields to generate an enhanced vehicle communication matrix. Each receiver of the "ESC_VehSpd" signal is independently labeled as shown in Table 1:

[0128] Table 1

[0129]

[0130] Specifically, step four involves configuration and report generation. Gateway configuration generation: Based on the enhanced vehicle communication matrix, routing table configuration files (e.g., ARXML format) are generated for the gateway nodes OIB, ZCU, and VCU respectively. The configuration content of each gateway is described in a structured manner below.

[0131] For example, OIB configuration: Routing rules for the signal "ESC_VehSpd" (signal identifier 0x1A0): Input port: OIB@CCANFD; Output port list: OIB@ICANFD: used for forwarding to ICD, OIB@BCANFD: used for forwarding to ZCU@BCANFD, ultimately reaching DCU; Action type: Copy forwarding, that is, after receiving the signal from the input port, copy it twice and forward it from the two output ports respectively; Protocol conversion: No conversion required, maintain CANFD format.

[0132] For example, the area gateway ZCU is configured with the following routing rules for the signal "ESC_VehSpd": Input port: ZCU@BCANFD (forwarded from OIB); Output port: ZCU@CAN1; Action type: direct forwarding; Protocol conversion: CANFD to CAN.

[0133] For example, the VCU gateway configuration includes the following routing rules for the signal "ESC_VehSpd": Input port: VCU@CCANFD; Output port: VCU@MCAN; Action type: Direct forwarding; Protocol conversion: CANFD to CAN.

[0134] Specifically, the network performance analysis report shows that: the chassis domain CCANFD load rate is 42%, the cockpit domain ICANFD load rate is 38%, and the new energy domain ECANFD load rate is 45%, all below the 55% threshold; the number of OIB routing messages is 38, the number of ZCU routing messages is 22, and the number of VCU routing messages is 15, all below the 50-message limit; the end-to-end delay of the "ESC_VehSpd" signal to the DCU is 4ms, meeting the 20ms constraint and having a 16ms margin; critical path delay analysis shows that the path forwarded by the VCU effectively balances the OIB load and avoids potential bottlenecks.

[0135] Specifically, step five involves change tracing. Assume a topology change occurs during subsequent development, such as adding a secondary gateway between OIB and ZCU. The new topology model is compared with the historical baseline to automatically identify affected signals. When a user queries the historical routing record of "ESC_VehSpd", the evolution of its routing path from "ESC→OIB→ZCU→DCU" to "ESC→OIB→New Gateway→ZCU→DCU" is displayed, along with a list of gateway configuration files and network performance metrics affected by this change (e.g., a 2ms increase in latency), achieving end-to-end change impact tracing. Meanwhile, the path to MCU1 remains unaffected, and its status is marked as "stable".

[0136] In this embodiment, a technique based on a topology model to automatically enumerate all feasible paths is adopted, replacing the step of manually analyzing the topology map. This eliminates route omissions and configuration errors caused by human factors, significantly improves the efficiency and accuracy of generating physical routes for cross-network segment signals, and realizes an automated closed loop from logical communication relationships to physical route planning.

[0137] In this embodiment, a global multi-objective optimization model and solution algorithm are introduced, which can take into account the path selection of all signals under the same decision framework, and achieve global balance of gateway and bus load under the premise of satisfying all signal performance constraints (such as delay), avoid local overload and critical path delay exceeding the standard, and improve the overall efficiency, real-time performance and robustness of the vehicle network.

[0138] In this embodiment, a version-related storage and incremental replanning mechanism for input and output data is established, which enables the automatic and rapid replanning of the affected parts when network topology or communication requirements change, and accurately traces the scope of the impact of the changes. This solves the problems of cumbersome process, long cycle and easy error in traditional manual change methods, and improves the response speed and iteration consistency of the forward design process to requirement changes.

[0139] The automatic routing generation and optimization method for communication matrices provided in this embodiment includes: acquiring an initial vehicle communication matrix, a vehicle network topology model, and a routing strategy and constraint library; constructing a global multi-objective optimization problem for signal path selection based on the network topology model and the initial vehicle communication matrix; solving the global multi-objective optimization problem based on the routing strategy and constraint library to obtain the solution result; backfilling the solution result into the initial vehicle communication matrix to generate an enhanced vehicle communication matrix and generating a configuration file based on the enhanced vehicle communication matrix. This embodiment automatically enumerates all feasible paths based on the vehicle's network topology model, replacing manual analysis of the topology map and achieving automated generation of optimal routes. The introduction of a global multi-objective optimization model and solution algorithm enables comprehensive consideration of all signal path selection within the same decision framework, achieving global load balancing between gateways and buses, avoiding local overload and critical path delay exceeding limits, and improving the overall efficiency, real-time performance, and robustness of the vehicle network. The solution results of the gateway routing are directly marked in the vehicle communication matrix, and a deployable configuration file is automatically generated, thereby completing the entire link closed loop of forward design from functional requirements to logical communication relationships and physical routing implementation.

[0140] Reference Figure 4 , Figure 4 This is a structural block diagram of an embodiment of the automatic routing generation and optimization system for the communication matrix of the present invention. Figure 4 As shown, the automatic route generation and optimization system for the communication matrix includes:

[0141] The multi-source input module 10 is used to acquire the initial vehicle communication matrix, the vehicle network topology model, and the routing strategy and constraint library;

[0142] The path search and optimization module 20 is used to construct a global multi-objective optimization problem for signal path selection based on the network topology model and the initial vehicle communication matrix.

[0143] The optimization problem-solving module 30 is used to solve the global multi-objective optimization problem based on the routing strategy and constraint library, and obtain the solution result;

[0144] The matrix generation and configuration module 40 is used to backfill the solution results into the initial vehicle communication matrix, generate an enhanced vehicle communication matrix, and generate a configuration file based on the enhanced vehicle communication matrix.

[0145] Specifically, the automatic route generation and optimization system for the communication matrix in this embodiment may include a topology modeling and data management module, a route optimization engine, a configuration and report generator, a conflict diagnosis and suggestion module, and a traceability and version management module.

[0146] For example, the topology modeling and data management module is used to import and manage the vehicle network topology model, communication matrix, and constraint policies. The routing optimization engine, as the core computing unit, integrates path search algorithms and multi-objective optimization solvers, performing global optimization decisions for topology-aware global path search and multi-objective optimization steps. The configuration and report generator performs executable configuration and intelligent report generation steps, transforming routing results into executable gateway configuration files and structured analysis reports. The conflict diagnosis and suggestion module performs conflict detection in routing information marking, conflict resolution, and matrix enhancement steps, and provides intelligent resolution suggestions. The traceability and version management module manages all data versions and relationships during the routing design process, supporting change impact analysis and end-to-end traceability.

[0147] The automatic routing generation and optimization system for communication matrices provided in this embodiment employs a technique based on a topology model to automatically enumerate all feasible paths, replacing the manual analysis of the topology map. This eliminates routing omissions and configuration errors caused by human factors, significantly improving the efficiency and accuracy of physical route generation for cross-network segment signals, and achieving an automated closed loop from logical communication relationships to physical route planning. The introduction of a global multi-objective optimization model and solution algorithm enables comprehensive consideration of all signal path selections within the same decision framework. This achieves global load balancing for gateways and buses while satisfying all signal performance constraints (e.g., latency), avoiding local overload and critical path latency exceeding limits, and improving the overall efficiency, real-time performance, and robustness of the vehicle network. The establishment of version-associative storage and incremental replanning mechanisms for input and output data allows for automatic and rapid replanning of affected parts when network topology or communication requirements change, accurately tracing the scope of the change's impact. This solves the problems of cumbersome processes, long cycles, and error-proneness in traditional manual change methods, improving the responsiveness and iterative consistency of the forward design process to requirement changes.

[0148] Furthermore, for any technical details not described in detail in the embodiments of the automatic route generation and optimization system for the communication matrix, please refer to the automatic route generation and optimization method for the communication matrix provided in any embodiment of the present invention, which will not be repeated here.

[0149] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 5As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the automatic route generation and optimization method for any of the communication matrices described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0150] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0151] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0152] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0153] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps in the automatic route generation and optimization method for any of the communication matrices described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0154] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-mentioned automatic routing generation and optimization method for the communication matrix.

[0155] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0156] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0157] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0158] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0159] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0160] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0161] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0162] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0163] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0164] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for automatic generation and optimization of routing in a communication matrix, characterized in that, include: Obtain the initial vehicle communication matrix, the vehicle network topology model, and the routing strategy and constraint library; Based on the network topology model and the initial vehicle communication matrix, a global multi-objective optimization problem for signal path selection is constructed. The global multi-objective optimization problem is solved based on the routing strategy and constraint library, and the solution results are obtained. The solution results are backfilled into the initial vehicle communication matrix to generate an enhanced vehicle communication matrix, and a configuration file is generated based on the enhanced vehicle communication matrix.

2. The method according to claim 1, characterized in that, The process involves obtaining the initial vehicle communication matrix, the vehicle network topology model, and the routing strategy and constraint library. Obtain the candidate scheme set of incremental matrix based on atomic operation analysis; The initial vehicle communication matrix is ​​generated by merging the incremental matrix candidate scheme set and the original communication matrix. Obtain the network topology model of the entire vehicle, as well as the routing strategy and constraint library.

3. The method according to claim 2, characterized in that, The step of generating an initial vehicle communication matrix by merging the incremental matrix candidate scheme set and the original communication matrix includes: Determine the operation type identifier of the incremental matrix candidate scheme set, and dynamically load the predefined rule chain according to the operation type identifier; Based on the rule chain, the candidate scheme set of the incremental matrix is ​​verified and conflict arbitration is performed in real time to obtain the target incremental matrix; The target incremental matrix is ​​integrated with the original communication matrix to generate the initial vehicle communication matrix.

4. The method according to claim 2, characterized in that, The process of obtaining the candidate scheme set of incremental matrix based on atomic operation analysis includes: Obtain change request information and abstract the change request information into atomic operation instructions; wherein, the atomic operation instructions include add signal instructions, modify signal attribute instructions, and delete signal instructions; Based on the platform signal library, platform context-aware differential processing is performed on each atomic operation instruction to generate candidate implementation results of the atomic operation; An incremental matrix candidate scheme set is generated based on the candidate implementation results.

5. The method according to claim 1, characterized in that, The global multi-objective optimization problem for signal path selection based on the network topology model and the initial vehicle communication matrix includes: Based on the network topology model and the initial vehicle communication matrix, feasible path enumeration and constraint filtering are performed to obtain feasible paths for the signal. A global multi-objective optimization problem is constructed based on the feasible paths of the signal.

6. The method according to claim 5, characterized in that, The process of performing feasible path enumeration and constraint filtering based on the network topology model and the initial vehicle communication matrix to obtain feasible signal paths includes: Determine each receiver of each signal that needs to be transmitted across network segments in the initial vehicle communication matrix; Based on the network topology model, an initial path is searched for each receiver from the sender. The initial path is filtered based on the maximum allowable delay constraint of the signal and the estimated delay of the path to obtain a feasible path for the signal.

7. The method according to claim 1, characterized in that, The solution to the global multi-objective optimization problem based on the routing strategy and constraint library yields the following results: The global multi-objective optimization problem is solved based on the routing policy and constraint library to determine the selected physical routing path for each signal-receiver pair; wherein, the decision variables of the global multi-objective optimization problem are the specific path selected for each signal-receiver pair, and the optimization objectives include minimizing the maximum gateway load, minimizing the worst-case end-to-end delay, and minimizing the total number of routing hops; The physical routing path is used as the solution result.

8. The method according to claim 1, characterized in that, The step of backfilling the solution result into the initial vehicle communication matrix, generating an enhanced vehicle communication matrix, and generating a configuration file based on the enhanced vehicle communication matrix includes: The physical routing paths obtained from the solution are backfilled into the initial vehicle communication matrix in the form of structured fields to generate an enhanced vehicle communication matrix. Based on the enhanced vehicle communication matrix, a configuration file is automatically generated for each gateway in the network; wherein the configuration file includes a routing table.

9. The method according to any one of claims 1 to 8, characterized in that, The method further includes: After generating the enhanced vehicle communication matrix, the system automatically detects whether there are any violations of hard constraints in the solution results; If there are violations of mandatory constraints, provide suggested solutions; A network performance analysis report is automatically generated based on the enhanced vehicle communication matrix. The generated enhanced vehicle communication matrix, configuration file, and network performance analysis report are associated and stored with the initial vehicle communication matrix and network topology model; When the initial vehicle communication matrix or network topology model changes, incremental route replanning is initiated.

10. A system for automatically generating and optimizing routes in a communication matrix, characterized in that, include: The multi-source input module is used to obtain the initial vehicle communication matrix, the vehicle network topology model, and the routing strategy and constraint library; The path search and optimization module is used to construct a global multi-objective optimization problem for signal path selection based on the network topology model and the initial vehicle communication matrix. The optimization problem-solving module is used to solve the global multi-objective optimization problem based on the routing strategy and constraint library, and obtain the solution result; The matrix generation and configuration module is used to backfill the solution results into the initial vehicle communication matrix, generate an enhanced vehicle communication matrix, and generate a configuration file based on the enhanced vehicle communication matrix.

11. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 9.

12. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 9.

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