Automatic batching and stirring control system based on industrial computer

By integrating multi-parameter monitoring and intelligent control modules, combining fuzzy PID and machine learning, the new wall material production control system is optimized, and the problem of insufficient real-time and automation is solved, and the system stability and resource utilization are improved.

CN120447331APending Publication Date: 2025-08-08青岛绿帆盛达新型墙体材料科技有限公司
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Patent Information

Application Number
CN202510345854.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-24
Publication Date
2025-08-08

AI Technical Summary

Technical Problem

The existing new wall material production control system has insufficient real-time, multi-parameter collaborative optimization, environmental adaptability and automation, resulting in low resource utilization, poor system stability and waste of resources.

Method used

Integrate multi-parameter dynamic monitoring module, intelligent prediction and correction module, adaptive valve system, self-cleaning and waste recycling module, formula adaptive management module, environmental compensation unit, stirring control module and feeding control module, combined with fuzzy PID control algorithm and machine learning model to achieve real-time data feedback and dynamic parameter optimization.

Benefits of technology

It improves the real-time and reliability of data acquisition, reduces the first error of the new formula, enhances the stability and automation of the system, improves the recycling rate of waste, and reduces manual intervention and resource waste.

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Abstract

The invention discloses an automatic batching and stirring control system based on an industrial computer, belongs to the field of new wall material production control systems, and aims to solve the problems that the real-time performance is poor, and the influence of raw material characteristic changes on a fall value cannot be pre-judged. Weighing, humidity, infrared flow velocity, temperature, pressure, torque and airflow sensors are integrated, multi-dimensional parameters such as material weight, flow velocity, humidity and air pressure in a bin are monitored in real time, the influence of environmental interference on the sensors is dynamically compensated through multi-parameter collaborative feedback, the real-time performance and reliability of data acquisition are improved, a fuzzy PID control algorithm and a machine learning model are fused, and the real-time performance of the system is improved. A nonlinear mapping relation is established through a training data set, the initial valve opening degree and the fall value are predicted, errors are corrected in real time, dependence on historical data is reduced, the first-time use error of a new formula is reduced, the response time for coping with sudden environment changes is shortened, a single adjustable-caliber electromagnetic valve is adopted, the inner wall is coated with a nano anti-sticking coating, and the service life of the valve is prolonged. And the opening degree is dynamically adjusted in combination with a PID algorithm.
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