一种基于田面平整度与浆层特征的整地质量提升方法
By analyzing the cross-layer spatial morphological variation ratio and plow bottom topological redundancy of the spatial multidimensional elevation data on the paddy field slurry leveling operation trajectory, the problem of insufficient identification of hidden defects in the bottom layer under the cover of mud self-leveling was solved, and more accurate evaluation of land preparation quality and operation status was achieved, thus improving the safety and efficiency of agricultural machinery operations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JILIN ACAD OF AGRI SCI
- Filing Date
- 2026-05-19
- Publication Date
- 2026-07-17
AI Technical Summary
Existing land preparation quality evaluation algorithms cannot identify hidden defects in the underlying layer covered by mud self-leveling, resulting in distorted evaluation results that cannot accurately reflect the actual working condition after paddy field preparation, and are prone to misjudgment and overcorrection.
By collecting spatial multidimensional elevation data on the paddy field slurry preparation operation trajectory, cross-layer spatial morphological variation ratio analysis and plow bottom spatial topological redundancy analysis are performed to obtain spatial cross-layer difference penalty factor and terrain smoothing compensation factor. The land preparation quality score is then fused and reconstructed to identify hidden defects in the bottom layer and distinguish between safe gentle slope terrain and dangerous abrupt pitfalls.
It improves the ability to identify hidden defects such as deep mud pits and localized deep ruts, reduces the risk of seedling drift, machine getting stuck and operational instability during subsequent rice transplanting operations, enhances the pertinence and credibility of evaluation results, and optimizes the efficiency of agricultural machinery operations.
Smart Images
Figure CN122221112B_ABST