CN-121072882-B - Energy-saving optimization method and system based on workshop energy consumption analysis

CN121072882BCN 121072882 BCN121072882 BCN 121072882BCN-121072882-B

Abstract

The invention discloses an energy-saving optimization method and system based on workshop energy consumption analysis, and relates to the technical field of data processing, wherein the method comprises the following steps: and the production plan completion rate is predicted by collecting production plan parameters, production energy consumption parameters and air conditioner energy consumption parameters in the target workshop, so as to obtain the plan completion rate. Calculating to obtain a plan deviation rate, configuring a production optimization step length to adjust production energy consumption parameters, obtaining a first production energy consumption parameter, and predicting to obtain a first plan completion rate. And adjusting the energy consumption parameters of the air conditioner to obtain the energy consumption parameters of the first air conditioner, and predicting to obtain the completion rate of the second plan. And obtaining a second production energy consumption parameter and a second air conditioner energy consumption parameter, calculating to obtain energy-saving fitness, completing the first round of optimization, and continuing to perform multiple rounds of optimization to obtain optimal parameters. The technical problem that the reliability and accuracy of a final optimization scheme are difficult to improve due to the fact that the workshop energy consumption optimization method in the prior art lacks collaborative optimization analysis on energy consumption of production equipment and an air conditioning system is solved.

Inventors

FU ZHIBO
TAN YINXIANG
DENG ZHENHAO
LI ZHIJIE

Assignees

广东携成智能装备有限公司

Dates

Publication Date: 20260512
Application Date: 20250903

Claims (6)

1. An energy-saving optimization method based on workshop energy consumption analysis, which is characterized by comprising the following steps: Acquiring production plan parameters, production energy consumption parameters and air conditioner energy consumption parameters in a target workshop, and predicting the production plan completion rate to obtain the plan completion rate; calculating a plan deviation rate according to the plan completion rate, configuring a production optimization step length by adopting the plan deviation rate to adjust production energy consumption parameters to obtain first production energy consumption parameters, and predicting and obtaining the first plan completion rate by combining the air conditioner energy consumption parameters and the production plan parameters; According to the first production energy consumption parameter and the air conditioner energy consumption parameter, analyzing to obtain a production energy consumption offset rate, calculating and configuring an air conditioner optimization step length by combining the first plan completion rate, adjusting the air conditioner energy consumption parameter to obtain a first air conditioner energy consumption parameter, and predicting to obtain a second plan completion rate; according to the plan completion rate, the first plan completion rate and the second plan completion rate, carrying out fusion processing on the production energy consumption parameter, the first production energy consumption parameter, the air conditioner energy consumption parameter and the first air conditioner energy consumption parameter to obtain a second production energy consumption parameter and a second air conditioner energy consumption parameter, carrying out processing calculation to obtain energy-saving fitness, completing first round optimization, and continuing to carry out multi-round optimization to obtain an optimal production energy consumption parameter and an optimal air conditioner energy consumption parameter; according to the plan completion rate, calculating to obtain a plan deviation rate, adopting the plan deviation rate to configure a production optimization step length to adjust production energy consumption parameters to obtain first production energy consumption parameters, and predicting to obtain the first plan completion rate by combining the air conditioner energy consumption parameters and the production plan parameters, wherein the method comprises the following steps: Calculating to obtain a plan deviation rate according to the plan completion rate; configuring the planned deviation rate as a production optimization step; Adopting the production optimization step length to adjust the production energy consumption parameters to obtain first production energy consumption parameters; According to the first production energy consumption parameter, the air conditioner energy consumption parameter and the production plan parameter, predicting the production plan completion rate to obtain a first plan completion rate; according to the first production energy consumption parameter and the air conditioner energy consumption parameter, analyzing to obtain a production energy consumption offset rate, calculating and configuring an air conditioner optimizing step length by combining the first plan completion rate, adjusting the air conditioner energy consumption parameter to obtain a first air conditioner energy consumption parameter, and predicting to obtain a second plan completion rate, wherein the method comprises the following steps: Inputting the first production energy consumption parameter and the air conditioner energy consumption parameter into a production energy consumption offset classifier, and classifying to obtain a production energy consumption offset rate, wherein the production energy consumption offset classifier is constructed based on a sample production energy consumption parameter set, a sample air conditioner energy consumption parameter set and a sample production energy consumption offset rate set, and the production energy consumption offset rate comprises the change proportion of the production energy consumption parameter under the influence of the air conditioner energy consumption parameter; calculating to obtain a first plan deviation rate according to the first plan completion rate; calculating and configuring an air conditioner optimization step length according to the production energy consumption offset rate and the first planning offset rate; adjusting the air conditioner energy consumption parameters by adopting the air conditioner optimization step length to obtain first air conditioner energy consumption parameters; and predicting the production plan completion rate according to the first air conditioner energy consumption parameter, the first production energy consumption parameter and the production plan parameter to obtain a second plan completion rate.
2. The energy saving optimization method based on workshop energy consumption analysis according to claim 1, wherein the steps of collecting production plan parameters, production energy consumption parameters and air conditioner energy consumption parameters in a target workshop, predicting a production plan completion rate, and obtaining a plan completion rate include: collecting current production plan parameters, production energy consumption parameters and air conditioner energy consumption parameters in a target workshop; And inputting the production plan parameters, the production energy consumption parameters and the air conditioner energy consumption parameters into a production predictor, and outputting to obtain the plan completion rate.
3. The energy saving optimization method based on the plant energy consumption analysis according to claim 2, wherein the training step of the production predictor comprises: according to historical production data of workshops, collecting a sample production plan parameter set, a sample production energy consumption parameter set and a sample air conditioner energy consumption parameter set, and collecting a sample plan completion rate set; Constructing a production predictor based on machine learning; Using the sample production plan parameter set, the sample production energy consumption parameter set, the sample air conditioner energy consumption parameter set and the sample plan completion rate set to supervise and train the production predictor; and testing the accuracy of the production predictor, and completing training when the accuracy is greater than or equal to a preset accuracy threshold.
4. The energy saving optimization method based on workshop energy consumption analysis according to claim 1, wherein the fusion processing is performed on the production energy consumption parameter, the first production energy consumption parameter, the air conditioner energy consumption parameter and the first air conditioner energy consumption parameter according to the planned completion rate, the first planned completion rate and the second planned completion rate, so as to obtain a second production energy consumption parameter and a second air conditioner energy consumption parameter, and the method comprises the following steps: According to the plan completion rate and the first plan completion rate, carrying out weighted calculation on the production energy consumption parameter and the first production energy consumption parameter to obtain a second production energy consumption parameter; And according to the planned completion rate and the second planned completion rate, carrying out weighted calculation on the air-conditioning energy consumption parameter and the first air-conditioning energy consumption parameter to obtain a second air-conditioning energy consumption parameter.
5. The energy-saving optimization method based on workshop energy consumption analysis according to claim 1, wherein the processing calculation to obtain the energy-saving fitness, completing the first round of optimization, and continuing the multiple rounds of optimization to obtain the optimal production plan parameters comprises: Calculating the ratio of the preset energy consumption parameter to the sum of the first production energy consumption parameter and the air conditioner energy consumption parameter to obtain a first energy saving coefficient; calculating to obtain a first energy conservation fitness according to the first energy conservation coefficient and the first plan completion rate; calculating the ratio of the preset energy consumption parameter to the sum of the first production energy consumption parameter and the first air conditioner energy consumption parameter to obtain a second energy saving coefficient; calculating to obtain a second energy-saving fitness according to the second energy-saving coefficient and the second plan completion rate; Calculating and acquiring a third energy-saving coefficient of the second production energy consumption parameter and the second air conditioner energy consumption parameter, and processing and acquiring a third energy-saving fitness; And continuing to perform multi-round optimization until the optimization rounds are converged, and obtaining the optimal production energy consumption parameter and the optimal air conditioner energy consumption parameter with the maximum energy-saving fitness.
6. An energy saving optimization system based on plant energy consumption analysis, characterized in that the system is adapted to perform the method of any of claims 1-5, the system comprising: the plan completion prediction module is used for acquiring production plan parameters, production energy consumption parameters and air conditioner energy consumption parameters in the target workshop, predicting the production plan completion rate and obtaining the plan completion rate; the production energy consumption adjustment module is used for calculating and obtaining a plan deviation rate according to the plan completion rate, adjusting production energy consumption parameters by adopting the plan deviation rate to configure production optimization step length, obtaining first production energy consumption parameters, and predicting and obtaining the first plan completion rate by combining the air conditioner energy consumption parameters and the production plan parameters; the air conditioner energy consumption adjustment module is used for analyzing and obtaining a production energy consumption offset rate according to the first production energy consumption parameter and the air conditioner energy consumption parameter, calculating and configuring an air conditioner optimization step length by combining the first plan completion rate, adjusting the air conditioner energy consumption parameter to obtain a first air conditioner energy consumption parameter, and predicting and obtaining a second plan completion rate; and the iterative optimization module is used for carrying out fusion processing on the production energy consumption parameter, the first production energy consumption parameter, the air conditioner energy consumption parameter and the first air conditioner energy consumption parameter according to the plan completion rate, the first plan completion rate and the second plan completion rate to obtain a second production energy consumption parameter and a second air conditioner energy consumption parameter, carrying out processing calculation to obtain energy-saving fitness, completing first round optimization, and continuing to carry out multi-round optimization to obtain an optimal production energy consumption parameter and an optimal air conditioner energy consumption parameter.

Description

Energy-saving optimization method and system based on workshop energy consumption analysis Technical Field The application relates to the technical field of data processing, in particular to an energy-saving optimization method and system based on workshop energy consumption analysis. Background In the manufacturing process, the energy consumption of the workshop mainly comprises the production energy consumption generated by the operation of production equipment and the air conditioner energy consumption for maintaining the workshop environment. The production energy consumption is usually derived from electricity consumption or gas consumption of various machine tools, stamping equipment, assembly lines and the like, and the air conditioning energy consumption comprises energy consumption of equipment such as a refrigerating unit, a blower and the like in the process of adjusting the temperature and the humidity of a workshop. In actual production, there is a relationship between production energy consumption and air conditioner energy consumption, for example, a change of an air conditioner operation parameter may affect an operating temperature of equipment, so as to indirectly change an energy consumption level of the equipment. Most of the existing workshop energy consumption management methods are optimized only for a single link, such as independently adjusting the operation strategy of production equipment or independently optimizing the operation mode of an air conditioning system, and the comprehensive analysis and collaborative optimization of the coupling relation between the two are lacking, so that the reliability and accuracy of the final optimization scheme are difficult to improve. Therefore, in the prior art, the workshop energy consumption optimization method lacks collaborative optimization analysis on energy consumption of production equipment and an air conditioning system, so that the reliability and accuracy of a final optimization scheme are difficult to improve. Disclosure of Invention The application provides an energy-saving optimization method and system based on workshop energy consumption analysis, which solve the technical problem that the reliability and accuracy of a final optimization scheme are difficult to improve due to the fact that the workshop energy consumption optimization method lacks collaborative optimization analysis on energy consumption of production equipment and an air conditioning system in the prior art. The production optimization step length and the air conditioner optimization step length are calculated and configured by utilizing the prediction model and the classification model through real-time collection and analysis of workshop production planning parameters, production energy consumption parameters and air conditioner energy consumption parameters, and the cooperative self-adaptive adjustment of production energy consumption and air conditioner energy consumption is realized through multi-round energy-saving fitness iterative optimization, so that the total energy consumption of the workshop is obviously reduced while the completion of production tasks is ensured. The application provides an energy-saving optimization method based on workshop energy consumption analysis, which comprises the steps of collecting production plan parameters, production energy consumption parameters and air conditioner energy consumption parameters in a target workshop, predicting production plan completion rates to obtain plan completion rates, calculating to obtain plan deviation rates, adjusting the production energy consumption parameters by adopting plan deviation rates to allocate production optimization steps to obtain first production energy consumption parameters, predicting to obtain first plan completion rates by combining the air conditioner energy consumption parameters and the production plan parameters, analyzing to obtain production energy consumption offset rates by combining the first production energy consumption parameters and the air conditioner energy consumption parameters, calculating to allocate air conditioner optimization steps by combining the first plan completion rates, adjusting the air conditioner energy consumption parameters to obtain first air conditioner energy consumption parameters, predicting to obtain second plan completion rates, carrying out fusion processing on the production energy consumption parameters, the first production energy consumption parameters, the air conditioner energy consumption parameters and the first air conditioner energy consumption parameters to obtain second production energy consumption parameters and second energy consumption parameters, carrying out processing and calculation to obtain energy-saving fitness, calculating to complete first optimization, optimizing to obtain optimal production energy consumption parameters and optimal air conditioner energy consumption parameters, and continuing optim