An artificial intelligence-based garbage sorting method

By fusing heterogeneous data and using a three-dimensional spatial grid system, a topology map of waste migration trajectory is constructed, which solves the problems of difficulty in fusing multi-source data and insufficient description of waste migration paths, and realizes efficient and accurate analysis of waste sorting.

CN120644394BActive Publication Date: 2025-12-05GUANGZHOU KUAIYIDA CLEANING SERVICE CO LTD
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Patent Information

Application Number
CN202511089531.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-08-05
Publication Date
2025-12-05
Estimated Expiration
2045-08-05

AI Technical Summary

Technical Problem

Existing waste sorting technologies rely on manual and mechanical sorting, which are difficult to adapt to large-scale, multi-category waste processing. Furthermore, multi-source data is difficult to integrate and analyze, and there is a lack of dynamic description of waste migration paths, resulting in insufficient sorting efficiency and accuracy.

Method used

By integrating heterogeneous data, semantic alignment, transfer learning models, and a three-dimensional spatial grid system, a topological map of waste migration trajectories is constructed to quantify material flow intensity, generate a dynamic index matrix, and achieve multi-dimensional clustering analysis.

Benefits of technology

It enables unified analysis of multi-source waste characteristic data, accurately describes waste migration paths, improves the efficiency and accuracy of waste sorting, and provides dynamic sorting behavior references.

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Abstract

The application relates to the technical field of intelligent garbage sorting, and discloses a garbage sorting method based on artificial intelligence. The method performs heterogeneous data fusion on a multi-source garbage feature data set through a semantic alignment module, unifies feature dimensions and eliminates redundant items, and outputs a standardized feature set. Spatial position identifiers and physical state identifiers of garbage entities are extracted, a migration learning model is used to dynamically divide time slices to generate a periodic garbage flow data set. The starting point and ending point coordinates of sorting are converted to a three-dimensional space grid to construct a migration trajectory topology graph, the flow data and the physical state identifiers are combined, the sorting path is analyzed according to material categories, the material flow intensity in a specific period in a target area is quantified, and a dynamic index matrix is generated. The method realizes spatiotemporal correlation analysis of multi-modal garbage data, and improves the adaptability of sorting path planning in a complex environment.
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