Multi-parameter collaborative intelligent dosing system and device fusing BIM and Internet of Things

By using neural network algorithms and fuzzy PID control, combined with BIM models, the dosage of chemicals is automatically adjusted, solving the problem of chemical dosage relying on manual experience in sewage treatment. This achieves precise control and water quality assessment, thereby improving sewage treatment efficiency.

CN121158864APending Publication Date: 2025-12-19山东宁川新材料科技有限公司
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Patent Information

Application Number
CN202511368011.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-24
Publication Date
2025-12-19

AI Technical Summary

Technical Problem

In current wastewater treatment, the dosage of chemicals relies on manual experience, resulting in inaccurate turbidity control, a large workload, and delays, making it difficult to achieve precise control.

Method used

A water quality prediction model is constructed based on a neural network algorithm. Combining the pipeline layout and equipment location information of the BIM model, a fuzzy PID control strategy is adopted. The dosing pump is driven by a PLC controller to automatically adjust the dosing amount and generate a water quality assessment report.

Benefits of technology

It enables precise control of chemical dosage, improves water treatment efficiency, and can assess the degree of water pollution based on the condition of components, reducing human intervention.

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Abstract

The invention discloses a BIM (Building Information Modeling) and Internet of Things integrated multi-parameter collaborative intelligent dosing system and device, and relates to the technical field of sewage treatment intellectualization, and the system comprises a BIM modeling unit, a data acquisition unit, a multi-parameter collaborative analysis unit, a dosing execution unit and a water quality analysis unit; according to the method, a 3D building information model is exported through a Revit interface to obtain a water treatment BIM model, a unique identifier is allocated to each component in the water treatment BIM model, a water quality prediction model is constructed based on a neural network algorithm, and the water quality prediction model is constructed by combining pipeline layout and equipment position information in the water treatment BIM model, dynamically calculating the dosage demand and adopting a fuzzy PID control strategy. The frequency of a dosing pump is automatically adjusted according to the deviation between real-time water quality parameters and target values, the dosing pump, a disinfectant preparation device and a proportioning system are driven through a PLC, a dosing adjustment instruction is executed, component state labels are updated, historical component state labels are obtained, water quality state differences are evaluated according to component state difference values, and a water quality evaluation report is generated.
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Citation Information

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