An embedded engineering driver library automatic analysis and intelligent transplantation system

By using collaborative modules to generate engineering and driver semantic models, driver adaptation code is automatically generated, solving the problem of cross-platform porting of embedded system driver libraries and achieving efficient and low-cost automated porting of driver libraries.

CN122240168APending Publication Date: 2026-06-19QINGDAO PORTER RHYTHM TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
QINGDAO PORTER RHYTHM TECHNOLOGY CO LTD
Filing Date
2026-03-10
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing technologies cannot achieve automated cross-platform porting of embedded system driver libraries, resulting in high costs, low efficiency, low standardization, and a lack of in-depth understanding of engineering structures and hardware semantic modeling capabilities.

Method used

By employing the collaborative work of the engineering parsing module, engineering modeling module, driver semantic analysis module, driver modeling module, intelligent matching module, and driver adaptation generation module, driver adaptation code is automatically generated to form a driver adaptation layer by generating an engineering structure semantic model, a driver function semantic model, and an adaptation mapping relationship table.

Benefits of technology

It has achieved standardized and automated porting of embedded driver libraries, significantly reducing porting costs and error rates, improving cross-platform driver reuse efficiency, and realizing intelligent and systematic embedded driver porting.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention discloses an automatic analysis and intelligent porting system for embedded engineering driver libraries, relating to the field of embedded engineering. The system includes: an engineering parsing module for scanning embedded engineering directories, parsing target files, and generating an engineering structure semantic model. Compared with existing technologies, the advantages of this invention are: it automatically constructs an engineering semantic description structure and simultaneously generates a driver function semantic model, realizing a digital description of the engineering structure and driver capabilities; an intelligent matching module generates an adaptation mapping relationship based on feature alignment and similarity calculation, and a driver adaptation generation module automatically generates complete adaptation code accordingly, forming a driver adaptation layer. This system transforms the traditional driver porting process, which relies on manual experience, into a standardized and automated process, significantly reducing porting costs and error rates, improving cross-platform driver reuse efficiency, and realizing the intelligent and systematic nature of embedded driver porting.
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