Distributed industrial energy operation optimization platform for automatically constructing intelligent model and algorithm

Abstract

The invention discloses a distributed industrial energy operation optimization platform capable of automatically constructing an intelligent model and an algorithm. The platform is divided into a modeling end, a server end and an operation end. The modeling end encapsulates algorithm models such as data preprocessing, energy production-elimination-storage trend prediction and optimization scheduling decision making into visual modules oriented to different production scenes, and convenient construction of complex function models and algorithms is achieved in a module dragging mode. And the server automatically constructs sequences of training samples, production plans / manufacturing signals and the like corresponding to equipment model requirements of each edge side, interacts with the trained intelligent model through a corresponding interface, outputs a calculation result and stores the calculation result in a specified relational database. And the operation end displays the calculation result through a friendly user interface, adjusts the real-time state of the working condition through man-machine interaction and feeds back the real-time state to the cloud server, and the cloud server rapidly triggers parameter updating of each edge side model to enable the edge side model to rapidly adapt to working condition change, so that edge-cloud cooperation of the platform is realized.

Description

technical field [0001] The invention belongs to the field of information technology, relates to technologies such as data-driven modeling, multi-objective optimization, and edge-cloud collaboration, and is a distributed industrial energy operation optimization platform that automatically builds intelligent models and algorithms. The platform is divided into three parts: modeling end, server end and operation end. On the modeling side, an automated intelligent modeling framework is proposed, which encapsulates algorithm models such as data preprocessing, energy production-consumption-storage trend prediction, and optimal scheduling decisions into visualized modules for different production scenarios. This method realizes the convenient construction of complex function models and algorithms. At the same time, each module is embedded with an adaptive algorithm to dynamically optimize parameters, and can be saved as an intelligent model file compatible with multiple programming l...

AI Technical Summary

Problems solved by technology

However, the above-mentioned platforms only provide real-time simulation functions of the system, and cannot judge the future operation trend of the energy system and give reasonable optimization scheduling suggestions

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