An energy intelligent scheduling method and system for food production

CN122047950BActive Publication Date: 2026-06-19TIANJIN THERMAL POWER DESIGNING INST +1

Patent Information

Authority / Receiving Office: CN · China
Patent Type: Patents(China)
Current Assignee / Owner: TIANJIN THERMAL POWER DESIGNING INST
Filing Date: 2026-04-16
Publication Date: 2026-06-19

AI Technical Summary

Technical Problem

The current food production process has a crude configuration of heat sources and a low degree of cold-heat coupling, making it difficult to achieve the cascade utilization of waste heat of different grades and the dynamic matching between heat pumps, boilers and heat storage units.

Method used

By collecting and preprocessing multi-source datasets, a standardized thermal dataset is generated, heat supply and demand objects are identified, load curves and waste heat output are predicted, a virtual energy network is constructed, an optimized scheduling model is established, pre-peak heat storage scheduling is carried out, the optimal heat scheduling strategy is generated, and the model is corrected through operational feedback.

Benefits of technology

It improves the reliability and orderliness of heat scheduling, reduces the possibility of scheduling inaccuracies during long-term operation, and realizes the refinement of heat source configuration and the optimization of cold-heat coupling.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention discloses an intelligent energy scheduling method and system for food production, relating to the field of energy scheduling technology. The method includes: collecting raw multi-source datasets and preprocessing them to generate standardized thermal datasets; identifying process hot water demand, secondary sterilization hot water demand, cooling load demand, and waste heat resources from the standardized thermal datasets to form a heat supply and demand object set; constructing a virtual energy network based on predicted supply and demand sequences; mapping and constraining the virtual energy network to establish an optimized scheduling model; controlling the operation of heat pumps, circulating pumps, valves, and boilers according to the optimal heat scheduling strategy; collecting operational feedback data and process compliance results; and continuously revising the optimized scheduling model based on the operational feedback data and process compliance results, outputting revised scheduling instructions. This invention reduces the possibility of inaccurate scheduling results during long-term operation by performing collaborative optimization again and outputting revised scheduling instructions.

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