AI-based MCAL automatic configuration method, electronic equipment and medium

Abstract

The invention discloses an AI-based MCAL automatic configuration method, electronic equipment and a medium. The method can comprise the steps of obtaining generated MCAL code data; taking the generated MCAL code data as training data, and training an AI model; and inputting technical requirements of a new project into the trained AI model to obtain an automatically configured MCAL code. According to the method, the MCAL configuration requirements can be quickly and accurately analyzed and identified, and the MCAL configuration scheme adaptive to a specific application scene and a hardware environment is intelligently generated.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and more particularly, to an AI-based MCAL automatic configuration method, an electronic device, and a medium. Background Art

[0002] After generating the software architecture of the controller during the project development process, AUTOSAR engineers need to configure and generate the controller software code for the software architecture. Among them, the software code generation of the MCAL layer, which is most closely combined with the controller hardware bottom layer, is generated by an EB tool.

[0003] In actual projects, AUTOSAR engineers need to have rich experience and technical capabilities at the MCAL level because this part has relatively high requirements for them.

[0004] Therefore, it is necessary to develop an AI-based MCAL automatic configuration method, an electronic device, and a medium to relieve the burden on AUTOSAR engineers.

[0005] The information disclosed in the background art part of the present invention is only intended to deepen the understanding of the general background art of the present invention, and should not be regarded as an admission or any form of implication that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0006] The present invention proposes an AI-based MCAL automatic configuration method, an electronic device, and a medium, which can quickly and accurately analyze and identify MCAL configuration requirements, and intelligently generate an MCAL configuration scheme adapted to a specific application scenario and hardware environment.

[0007] In a first aspect, an embodiment of the present disclosure provides an AI-based MCAL automatic configuration method, including:

[0008] Obtain the generated MCAL code data;

[0009] Use the generated MCAL code data as training data to train an AI model;

[0010] Input the technical requirements of a new project into the trained AI model to obtain automatically configured MCAL code.

[0011] Preferably, the generated MCAL code data includes the generated MCAL code and the corresponding technical requirements.

[0012] Preferably, obtaining the generated MCAL code and the corresponding technical requirements includes:

[0013] Collect the documents generated by the MCAL layer of controller products in the field of intelligent driving and the list of technical requirement parameters matched thereto.

[0014] Preferably, the document includes format files in the forms of *.c, *.h, *.arxml, and *.dbc.

[0015] Preferably, the AI model is trained through machine learning or deep learning.

[0016] Preferably, the technical requirements include the number of configured CAN buses, the type of CAN buses, the number of in-vehicle ethers, the type of in-vehicle ethers, the required clock of the MCU, timers, and the number of cores.

[0017] Preferably, it further includes:

[0018] Verify the automatically configured MCAL code through the DaVinci configuration tool.

[0019] In a second aspect, an embodiment of the present disclosure further provides an electronic device, which includes:

[0020] A memory storing executable instructions;

[0021] A processor that runs the executable instructions in the memory to implement the above-mentioned AI-based MCAL automatic configuration method.

[0022] In a third aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores a computer program that implements the above-mentioned AI-based MCAL automatic configuration method when executed by a processor.

[0023] The beneficial effects are as follows:

[0024] 1. Improve configuration efficiency: The AI-based MCAL automatic configuration method can automatically analyze and identify MCAL configuration requirements, reducing the time for engineers to manually adapt, debug, and develop, and greatly improving the configuration efficiency;

[0025] 2. Improve accuracy and quality: The AI-based configuration method can accurately generate MCAL configuration solutions adapted to specific scenarios and hardware environments, avoiding human configuration errors and application incompatibility problems, and improving the accuracy and quality of the configuration;

[0026] 3. Reduce costs: The automated and intelligent MCAL configuration method reduces the investment in human resources and saves related costs.

[0027] In summary, the present invention has fast, accurate, and intelligent configuration capabilities, can greatly improve the work efficiency of AUTOSAR engineers, and reduce the configuration costs.

[0028] The method and apparatus of the present invention have other characteristics and advantages, which will be apparent from the accompanying drawings incorporated herein and the subsequent detailed description, or will be described in detail in the accompanying drawings incorporated herein and the subsequent detailed description, and these accompanying drawings and detailed description are used together to explain the specific principles of the present invention. Description of the Drawings

[0029] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more apparent, wherein, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0030] Figure 1 A flowchart showing the steps of an AI-based MCAL automatic configuration method according to an embodiment of the present invention is shown. Detailed Description of the Embodiments

[0031] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0032] To facilitate understanding of the solutions and effects of the embodiments of the present invention, four specific application examples are given below. Those skilled in the art should understand that this example is only for facilitating the understanding of the present invention, and any specific details are not intended to limit the present invention in any way.

[0033] Example 1

[0034] Figure 1 A flowchart showing the steps of an AI-based MCAL automatic configuration method according to an embodiment of the present invention is shown.

[0035] As Figure 1 shown, the AI-based MCAL automatic configuration method includes:

[0036] Step 101, obtaining the generated MCAL code data;

[0037] Step 102, using the generated MCAL code data as training data to train an AI model;

[0038] Step 103, inputting the technical requirements of a new project into the trained AI model to obtain the automatically configured MCAL code.

[0039] In one example, the generated MCAL code data includes the generated MCAL code and the corresponding technical requirements.

[0040] In one example, obtaining the generated MCAL code and the corresponding technical requirements includes:

[0041] Collect the documents generated by the MCAL layer of the controller products in the field of intelligent driving and the list of matching technical requirement parameters.

[0042] In one example, the documents include format files such as *.c, *.h, *.arxml, and *.dbc.

[0043] In one example, train an AI model through machine learning or deep learning.

[0044] In one example, the technical requirements include the number of configured CAN buses, CAN bus types, the number of in-vehicle ethers, in-vehicle ether types, the required clock of the MCU, timers, and the number of cores.

[0045] In one example, it also includes:

[0046] Verify the automatically configured MCAL code through the DaVinci configuration tool.

[0047] Specifically, the present invention is an AI-based automatic MCAL configuration method, aiming to simplify the AUTOSAR configuration process and improve the configuration efficiency and accuracy.

[0048] Data collection: Collect the documents generated by the MCAL layer of the controller products in the field of intelligent driving and the corresponding list of technical requirements. These documents include format files such as.c,.h,.arxml, and.dbc.

[0049] AI model training: Use the collected documents and requirement lists as training data, and utilize AI technologies such as machine learning or deep learning to train the AI model. This model needs to be based on the rule-based MCAL requirement list and be trained according to AI technologies. This rule-based AI model can use a sequence-to-sequence model or other models with similar functions.

[0050] Automatic MCAL code configuration process:

[0051] Input technical requirements: In a new project, engineers input the corresponding list of technical requirement parameters to the AI model. These requirement lists contain configured parameters and limitations, etc. For example, the middleware needs to configure parameters such as the number of CAN buses, CAN bus types, the number of in-vehicle ethers, types of each in-vehicle ether, the required clock of the MCU, timers, and the number of cores.

[0052] Automatically configure MCAL: Based on the learned knowledge, the AI model generates corresponding MCAL configurations according to the input list of technical requirement parameters. These configurations include, but are not limited to, the settings and adjustments of communication, timing, memory, and processing resources.

[0053] Verification and debugging: The generated configured MCAL code is verified through configuration tools such as DaVinci. Incompatible and non-compliant configurations can be promptly detected and debugged.

[0054] Through the above technical solution, by leveraging the learning and generation capabilities of the AI model, the process of automatic MCAL configuration is achieved. Engineers only need to input the list of technical requirement parameters to the AI model to quickly obtain the MCAL configuration for a specific scenario and hardware environment. This greatly simplifies the complexity of the configuration, reduces the configuration workload and time of engineers. Finally, through verification and debugging, it is ensured that the generated configuration meets the AUTOSAR specifications and system requirements.

[0055] In summary, the AI-based MCAL automatic configuration tool of the present invention realizes the automation and intelligence of AUTOSAR configuration for controllers in the field of intelligent driving through data collection and AI model training. It has the advantages of improving configuration efficiency and accuracy, and provides a new solution for the development of intelligent driving systems.

[0056] Example 2

[0057] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; a processor that runs the executable instructions in the memory to implement the above-mentioned AI-based MCAL automatic configuration method.

[0058] The electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0059] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0060] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0061] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain good user experience effects, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included in the protection scope of this disclosure.

[0062] For the detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0063] Example 3

[0064] The embodiments of the present disclosure provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for automatically configuring MCAL based on AI as described above is implemented.

[0065] According to the computer-readable storage medium of the embodiments of the present disclosure, non-temporary computer-readable instructions are stored thereon. When the non-temporary computer-readable instructions are run by a processor, all or part of the steps of the methods of the foregoing embodiments of the present disclosure are executed.

[0066] The above computer-readable storage medium includes but is not limited to: optical storage media (such as CD-ROM and DVD), magneto-optical storage media (such as MO), magnetic storage media (such as magnetic tapes or external hard drives), media with built-in rewritable non-volatile memories (such as memory cards), and media with built-in ROMs (such as ROM cartridges).

[0067] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any example given.

[0068] The above has described the embodiments of the present invention. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. An AI-based MCAL automatic configuration method, characterized in that: include: Get the generated MCAL code data; Using the generated MCAL code data as training data to train the AI ​​model; Input the technical requirements of the new project into the trained AI model to obtain the automatically configured MCAL code.

2. The AI-based MCAL automatic configuration method according to claim 1, wherein: The generated MCAL code data includes the generated MCAL code and corresponding technical requirements.

3. The AI-based MCAL automatic configuration method according to claim 2, wherein: Obtaining the generated MCAL code and the corresponding technical requirements include: Collect the documents generated by the MCAL layer of controller products in the field of intelligent driving and the matching technical requirement parameter list.

4. The AI-based MCAL automatic configuration method according to claim 3, wherein: The documents include files in the formats of *.c, *.h, *.arxml and *.dbc.

5. The AI-based MCAL automatic configuration method according to claim 1, wherein: The AI ​​model is trained through machine learning or deep learning.

6. The AI-based MCAL automatic configuration method according to claim 1, wherein: The technical requirements include the number of configured CAN buses, the type of CAN buses, the number of on-board Ethernet networks, the type of on-board Ethernet networks, and the MCU required clocks, timers, and number of cores.

7. The AI-based MCAL automatic configuration method according to claim 1, wherein: Also includes: The MCAL code for automatic configuration is verified using the DaVinci Configuration Tool.

8. An electronic device, characterized in that: The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the AI-based MCAL automatic configuration method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the AI-based MCAL automatic configuration method described in any one of claims 1 to 7 is implemented.

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