Asphalt mixing plant monitoring system and method

By introducing the DBSCAN clustering algorithm and hierarchical early warning mechanism into asphalt mixing plants, a set of qualified production modes is constructed, which solves the problem that existing technologies cannot identify complex abnormal working conditions. This enables refined and forward-looking monitoring of the asphalt mixing process, improving the accuracy and efficiency of quality control.

CN121211005APending Publication Date: 2025-12-26HEBEI FLAME MECHANICAL ENG CO LTD
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Patent Information

Application Number
CN202511347146.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-19
Publication Date
2025-12-26

AI Technical Summary

Technical Problem

Existing quality control systems for asphalt mixing plants rely on formula settings and static threshold alarms based on programmable logic controllers. They cannot identify complex abnormal conditions caused by minor fluctuations in multiple parameters within permissible ranges in advance, and lack the ability to recognize and evaluate the overall state of the production process, resulting in economic losses and resource waste.

Method used

The DBSCAN clustering algorithm is used to construct a set of qualified production patterns. By comparing real-time production data with historical superior batch data, the deviation of the patterns and the comprehensive risk level are calculated. Combined with a graded early warning mechanism, the asphalt mixing process can be monitored in a forward-looking manner.

Benefits of technology

It improves the monitoring accuracy and reliability of the asphalt mixing process, enabling early identification of potential production risks, avoiding the generation of substandard mixtures, and reducing economic losses and resource waste.

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Abstract

The invention relates to the technical field of asphalt concrete production, in particular to an asphalt mixing plant monitoring system and method. The method comprises the following steps: acquiring real-time production operation data of the asphalt mixing plant, and constructing a real-time production state vector; acquiring production process data of all superior product batches in history as a training data set; performing clustering processing on the training data set to obtain a qualified mode set; when the real-time production state vector does not belong to any qualified production mode cluster, calculating a mode deviation degree, and further calculating a comprehensive risk level; performing grading early warning based on a size relationship between the comprehensive risk grade and a preset risk threshold value, and monitoring the asphalt mixing plant; and the monitoring accuracy of the asphalt stirring process is improved.
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