Digital monitoring management method and system for solid waste treatment

Through real-time data acquisition and digital twin model training, combined with a multi-objective learning framework, real-time and optimization problems in solid waste treatment are solved, and more efficient and accurate processing process management is achieved.

CN119739985AActive Publication Date: 2025-04-01FEIBAO NANJING INTELLIGENT TECH CO LTD
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Patent Information

Application Number
CN202411810675.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-10
Publication Date
2025-04-01
Estimated Expiration
2044-12-10

AI Technical Summary

Technical Problem

The prior art has problems such as insufficient real-time performance, poor data integrity, insufficient digital twin model training in solid waste treatment, and the difficulty of traditional single-objective optimization methods to cope with multi-objective needs and dynamic adjustments.

Method used

The sensor collects data information from the solid waste treatment process in real time, uses edge computing and cloud platforms to form real-time archives, creates digital twin models for training, outputs efficiency prediction results, and continuously monitors and adjusts through a multi-objective learning framework.

Benefits of technology

Real-time monitoring and accurate prediction of solid waste treatment process are achieved, dynamic optimization capabilities of processing efficiency are improved, and the adaptability and robustness of the model in complex scenarios are enhanced.

✦ Generated by Eureka AI based on patent content.

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Patent Text Reader

Abstract

The invention discloses a digital monitoring management method and system for solid waste treatment, and the method comprises the steps: collecting the data information of each link of solid waste treatment in real time, carrying out the data analysis and efficiency prediction through a digital twin model, and finally optimizing the efficiency of each link in the solid waste treatment process, thereby improving the overall treatment effect; through the combination of the digital twin model and the multi-target learning framework, the efficiency of each link in the solid waste treatment process can be accurately predicted, high robustness can be kept under different interference conditions, the intelligent level and resource utilization efficiency of the solid waste treatment system are remarkably improved, and the method is suitable for popularization and application. The method has good economical efficiency, environmental adaptability and stability, and is suitable for solid waste treatment scenes of various scales.
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Citation Information

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Cited By

  • Solid waste storage and transportation system based on environmental control

    CN120450566A

  • A solid waste storage and transportation system based on environmental control

    CN120450566B