Target embedded code generation method and device, electronic equipment and storage medium

By extracting hardware entities and their interaction relationships from the embedded knowledge graph to generate embedded code, the problem of low accuracy of embedded code in the existing technology is solved, and more accurate code generation is achieved.

CN120704694AActive Publication Date: 2025-09-26LANGCHAO ELECTRONIC INFORMATION IND CO LTD
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Patent Information

Application Number
CN202511199556.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-26
Publication Date
2025-09-26
Estimated Expiration
2045-08-26

AI Technical Summary

Technical Problem

The existing embedded code generation technology lacks hardware interaction when generating embedded code, resulting in low accuracy of the generated code.

Method used

By receiving the target code generation request, parsing the request description text and extracting the entity objects and their interaction relationships associated with the target hardware from the embedded knowledge graph, generating target prompt words, and inputting them into the code generation model to generate accurate embedded code.

Benefits of technology

It improves the accuracy of embedded code and can handle complex hardware-related details such as communication protocols, interface types, and signal parameters, ensuring that the generated code meets hardware characteristics and interaction requirements.

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Abstract

The invention discloses a target embedded code generation method and device, electronic equipment and a storage medium, and relates to the technical field of code generation. The target embedded code generation method comprises the steps that a target code generation request is received, and a request description text in the target code generation request is analyzed; the entity objects closely related to the target hardware and the interaction relation of the entity objects can be extracted from the embedded knowledge graph. According to the process, natural language description is effectively converted into understanding of hardware characteristics, and therefore prompt words capable of accurately reflecting the hardware characteristics and requirements are generated. Then the generated target cue word is input into a code generation model, an embedded code matched with the target code generation request can be obtained, complex details related to hardware can be processed, the complex details comprise the interaction relation between entities and specific hardware attributes such as a communication protocol, an interface type and signal parameters, and the embedded code can be obtained. Therefore, the accuracy of the generated embedded code can be improved, and the technical problem that the accuracy of the generated embedded code is low is solved.
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