CN-121998357-A - Multi-energy digital twin collaborative scheduling method, device, equipment and medium

CN121998357ACN 121998357 ACN121998357 ACN 121998357ACN-121998357-A

Abstract

The application discloses a multi-energy digital twin collaborative scheduling method, a device, equipment and a medium, which relate to the technical field of computers and are applied to an industrial energy management system, and comprise the steps of preprocessing data based on operation data of industrial equipment and edge computing nodes; the method comprises the steps of determining a calibrated twin model based on a current digital twin model and a preprocessing result in a digital twin layer in a system, determining a global energy scheduling strategy by carrying out multi-equipment and multi-energy coupling collaborative energy scheduling analysis through cloud collaborative layers in the system and related data of the calibrated twin model, verifying the global energy scheduling strategy based on a blockchain verification layer and a cross-chain verification protocol in the system, and issuing the global energy scheduling strategy to an edge computing node for execution when verification passes, so that collaborative scheduling is completed. The application effectively realizes the collaborative optimization of multiple devices and multiple energy sources, and improves the coping capability of complex working condition scenes.

Inventors

YANG JIAAO
LI JIA
QI GUANGPENG
SHANG GUANGYONG
LUO TAO

Assignees

浪潮云洲工业互联网有限公司

Dates

Publication Date: 20260508
Application Date: 20260128

Claims (10)

1. The multi-energy digital twin collaborative scheduling method is characterized by being applied to an industrial energy management system and comprising the following steps: Performing data preprocessing based on equipment operation data of industrial equipment and edge computing nodes local to the equipment to determine preprocessing results, wherein the edge computing nodes comprise an edge intelligent computing model; Performing model calibration based on a current digital twin model in a digital twin layer in the industrial energy management system and the preprocessing result to determine a calibrated twin model; Performing multi-equipment and multi-energy coupling collaborative energy scheduling analysis through a cloud collaborative layer in the industrial energy management system and related data of the calibrated twin model to determine a global energy scheduling strategy; Verifying the global energy scheduling strategy based on a blockchain verification layer and a cross-chain verification protocol in the industrial energy management system to determine a strategy verification result, wherein the blockchain verification layer adopts a double-chain architecture with separated private chains and alliance chains; And when the strategy verification result shows that verification is passed, the global energy scheduling strategy is issued to the corresponding edge computing node, and strategy execution operation is triggered to complete cooperative scheduling operation, and a cooperative scheduling result is obtained.
2. The multi-energy digital twin collaborative scheduling method according to claim 1, wherein the industrial equipment-based equipment operation data and an edge computing node local to the equipment perform data preprocessing to determine a preprocessing result, wherein the edge computing node comprises an edge intelligent computing model comprising: data acquisition is performed based on heterogeneous sensors deployed on industrial equipment to determine equipment operation data; based on the local edge computing node of the industrial equipment, carrying out Kalman filtering on the equipment operation data to finish data denoising operation and obtain denoised data; based on an equipment-level energy consumption prediction model in the edge intelligent calculation model, and combining the noise-reduced data, carrying out local energy consumption light-weight prediction to determine a local energy scheduling strategy corresponding to the industrial equipment; And determining a preprocessing result based on the noise-reduced data and the local energy scheduling strategy.
3. The multi-energy digital twin collaborative scheduling method according to claim 2, wherein the performing model calibration based on a current digital twin model in a digital twin layer in the industrial energy management system and the preprocessing results to determine a calibrated twin model comprises: receiving the pretreatment result uploaded according to a preset communication protocol through a digital twin modeling layer in the industrial energy management system; The energy consumption prediction method comprises the steps of carrying out energy consumption prediction based on a current digital twin model in a digital twin modeling layer and the noise-reduced data to determine an energy consumption prediction result, wherein the current digital twin model comprises a device-level digital twin model and a system-level digital twin model; judging whether the difference between the energy consumption prediction result and the energy consumption prediction value in the local energy scheduling strategy is larger than a preset dynamic threshold value or not so as to determine a difference judgment result; If the difference value judging result is yes, performing Bayesian updating on the current digital twin model based on the noise-reduced data so as to determine a calibrated twin model; and sending the model parameters of the calibrated twin model to the corresponding edge computing nodes, and triggering model parameter coverage operation.
4. The multi-energy digital twin collaborative scheduling method according to claim 1, wherein the performing collaborative energy scheduling analysis of multi-device, multi-energy coupling to determine a global energy scheduling policy through a cloud collaborative layer in the industrial energy management system and related data of the calibrated twin model comprises: Receiving the equipment health index generated by the digital twin layer through a cloud cooperative layer in the industrial energy management system, and giving the equipment health index to determine the weight value of each industrial equipment; And performing multi-equipment and multi-energy coupling collaborative energy scheduling analysis through the cloud collaborative layer, the calibrated twin model, the weight value, the distributed optimization algorithm and the deep learning algorithm to determine a global energy scheduling strategy.
5. The multi-energy digital twin co-scheduling method according to claim 2 or 3, wherein after the data acquisition based on heterogeneous sensors deployed on the industrial equipment to determine the equipment operation data, further comprising: Determining a hash value based on a preset hash algorithm and the equipment operation data; Uploading the hash value and the equipment signature information corresponding to the industrial equipment to the private chain; And verifying the received hash value and the device signature information based on the private chain and a preset improved PBFT consensus mechanism, and writing the hash value and the device signature information into a corresponding block after verification is passed.
6. The multi-energy digital twin collaborative scheduling method according to claim 5, wherein validating the global energy scheduling policy based on a blockchain validation layer and a cross-chain validation protocol in the industrial energy management system comprises: Based on the private chain and a preset improved PBFT consensus mechanism, verifying the temperature constraint and the power grid capacity constraint of the scheduling instruction in the global energy scheduling strategy by combining an intelligent contract to determine a strategy verification result.
7. The multi-energy digital twin co-scheduling method of claim 6, further comprising, after determining the policy validation result: If the strategy verification result shows that verification is passed, in the process that the edge computing node executes the global energy scheduling strategy, verifying and privately storing the acquired equipment operation data and the executed scheduling instruction based on the blockchain verification layer and the double-storing strategy so as to determine the storing information; Based on the alliance chain, invoking a precompiled contract to verify the certificate storage information, and triggering alliance chain certificate storage operation when verification is passed; After the alliance chain certificate storing operation corresponding to a plurality of blocks in the alliance chain is completed, the final certificate storing operation is completed by calling the intelligent contract corresponding to the main chain in the block chain verification layer.
8. The utility model provides a many energy digital twin cooperation dispatch device which characterized in that is applied to industrial energy management system, includes: the data preprocessing module is used for preprocessing data based on equipment operation data of the industrial equipment and edge computing nodes local to the equipment so as to determine preprocessing results, wherein the edge computing nodes comprise an edge intelligent computing model; The model calibration module is used for carrying out model calibration based on a current digital twin model in a digital twin layer in the industrial energy management system and the preprocessing result so as to determine a calibrated twin model; The scheduling analysis module is used for carrying out multi-equipment and multi-energy coupling collaborative energy scheduling analysis through a cloud collaborative layer in the industrial energy management system and the related data of the calibrated twin model so as to determine a global energy scheduling strategy; The system comprises a strategy verification module, a global energy scheduling strategy, a strategy verification module and a control module, wherein the strategy verification module is used for verifying the global energy scheduling strategy based on a blockchain verification layer and a cross-chain verification protocol in the industrial energy management system so as to determine a strategy verification result; And the strategy execution module is used for issuing the global energy scheduling strategy to the corresponding edge computing node when the strategy verification result shows that the verification is passed, triggering the strategy execution operation to complete the cooperative scheduling operation and obtaining the cooperative scheduling result.
9. An electronic device, comprising: A memory for storing a computer program; a processor for executing the computer program to implement the multi-energy digital twin co-scheduling method of any one of claims 1 to 7.
10. A computer readable storage medium for storing a computer program which when executed by a processor implements the multi-energy digital twin co-scheduling method of any one of claims 1 to 7.

Description

Multi-energy digital twin collaborative scheduling method, device, equipment and medium Technical Field The invention relates to the technical field of computers, in particular to a multi-energy digital twin collaborative scheduling method, a device, equipment and a medium. Background Along with the rapid development of economy and society, the current industrial energy management system faces the problems of data island, insufficient energy and production cooperation, poor renewable energy volatility, poor energy storage economy, poor system stability and the like. However, in the existing related energy scheduling solutions, multiple focusing is a single technology (for example, only using blockchain and mainly used for data sharing and transaction recording), which results in that in practical application, the lack of accurate equipment running state is taken as a basis, resource waste or unbalance of supply and demand is easily caused, the overall energy efficiency management effect is affected, complex coupling relation between multiple equipment and multiple energy sources is difficult to process, and coping capability of complex working condition scenes is lacking. Therefore, how to adjust the energy distribution strategy according to the real-time working condition of the industrial equipment under the complex working condition scene is a problem to be solved in the current field. Disclosure of Invention In view of the above, the invention aims to provide a multi-energy digital twin collaborative scheduling method, a device, equipment and a medium, which can effectively realize collaborative optimization of multiple equipment and multiple energy sources, improve the coping capability of complex working condition scenes, solve the problems of data island, insufficient collaboration of energy sources and production, poor renewable energy source volatility, poor energy storage economy and poor system stability in the existing scheme, and improve the energy utilization efficiency. The specific scheme is as follows: in a first aspect, the present application provides a multi-energy digital twin collaborative scheduling method, applied to an industrial energy management system, comprising: Performing data preprocessing based on equipment operation data of industrial equipment and edge computing nodes local to the equipment to determine preprocessing results, wherein the edge computing nodes comprise an edge intelligent computing model; Performing model calibration based on a current digital twin model in a digital twin layer in the industrial energy management system and the preprocessing result to determine a calibrated twin model; Performing multi-equipment and multi-energy coupling collaborative energy scheduling analysis through a cloud collaborative layer in the industrial energy management system and related data of the calibrated twin model to determine a global energy scheduling strategy; Verifying the global energy scheduling strategy based on a blockchain verification layer and a cross-chain verification protocol in the industrial energy management system to determine a strategy verification result, wherein the blockchain verification layer adopts a double-chain architecture with separated private chains and alliance chains; And when the strategy verification result shows that verification is passed, the global energy scheduling strategy is issued to the corresponding edge computing node, and strategy execution operation is triggered to complete cooperative scheduling operation, and a cooperative scheduling result is obtained. Optionally, the device operation data based on the industrial device and the edge computing node local to the device perform data preprocessing to determine a preprocessing result, where the edge computing node includes an edge intelligent computing model, and includes: data acquisition is performed based on heterogeneous sensors deployed on industrial equipment to determine equipment operation data; based on the local edge computing node of the industrial equipment, carrying out Kalman filtering on the equipment operation data to finish data denoising operation and obtain denoised data; based on an equipment-level energy consumption prediction model in the edge intelligent calculation model, and combining the noise-reduced data, carrying out local energy consumption light-weight prediction to determine a local energy scheduling strategy corresponding to the industrial equipment; And determining a preprocessing result based on the noise-reduced data and the local energy scheduling strategy. Optionally, the performing model calibration based on the current digital twin model in the digital twin layer in the industrial energy management system and the preprocessing result to determine a calibrated twin model includes: receiving the pretreatment result uploaded according to a preset communication protocol through a digital twin modeling layer in the industrial energy manageme