Adaptive Fried Rice Sub-packaging with Dynamic Vision Recognition
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Solution Overview
Problem
The existing sub-packaging methods for fried rice in central kitchens, particularly the computer vision technology, struggle to adapt to changes in the scene, leading to fluctuations in product quality due to uneven distribution of specific side dishes, which affects the consistency and efficiency of the packaging process.
Innovation Solution
An adaptive quantitative sub-packaging method that involves self-construction and self-evaluation of a key side dish recognition model, using a central control module with image processing to optimize recognition rates and ensure consistent packaging of fried rice with multiple side dishes, incorporating a camera and light sources to adjust segmentation thresholds and bands for accurate recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional weight sub-packaging method is used, then total weight consistency is achieved, but side dish distribution uniformity deteriorates
Solution Approach 1:
The patent segments the sub-packaging process into two independent stages: first, visual identification and counting of specific side dishes using computer vision technology; second, weight-based packaging to achieve target weight. This segmentation allows simultaneous control of both side dish distribution uniformity and total weight consistency, resolving the contradiction between these two requirements.
2Productivity
If computer vision quantitative sub-packaging method is used, then recognition speed is improved, but adaptability to scene changes deteriorates
Solution Approach 1:
The patent implements dynamic adaptability by enabling the image processing program to be reconfigured when scene changes occur. The system can adjust recognition parameters and thresholds based on different fried rice varieties and side dish combinations, maintaining high recognition speed while adapting to various production scenarios without requiring complete system redesign.
Solution Approach 2:
The system changes recognition parameters such as color thresholds, shape criteria, and size ranges according to different fried rice types and side dishes. By adjusting these parameters dynamically, the computer vision system maintains high recognition accuracy and speed across different scene conditions, resolving the adaptability issue.
3Measurement precision
If image processing program is modified for scene changes, then recognition accuracy is improved, but production line downtime increases
Solution Approach 1:
The patent implements preliminary configuration of multiple image processing programs for different fried rice varieties and side dish combinations. When a scene change is detected, the system can switch to a pre-configured program rather than modifying the existing one, significantly reducing downtime while maintaining recognition accuracy.
Solution Approach 2:
The system incorporates automatic scene change detection and self-adjustment capabilities. When changes in fried rice composition or side dishes are detected, the system can automatically adjust recognition parameters or switch between pre-configured programs without requiring manual intervention, thereby maintaining high recognition accuracy while minimizing production line downtime.
Data Source
AI summary
The present invention belongs to the technical field of food processing, and in particular, relates to an adaptive quantitative sub-packaging method for fried rice with multiple side dishes in a central kitchen and an apparatus therefor. The present invention mainly includes self-construction of a key side dish recognition model, self-evaluation of the key side dish recognition model, adaptive quantification of batch fried rice, and the apparatus matched with the three-step operation.