An energy-saving optimization method and system based on inter-factory energy consumption analysis

Abstract

The application discloses an energy-saving optimization method and system based on workshop energy consumption analysis, relates to the technical field of data processing, and comprises the following steps: collecting production plan parameters, production energy consumption parameters and air conditioner energy consumption parameters in a target workshop, predicting a production plan completion rate, obtaining the plan completion rate, calculating a plan deviation rate, configuring a production optimization step to adjust the production energy consumption parameters, obtaining first production energy consumption parameters, predicting a first plan completion rate, adjusting the air conditioner energy consumption parameters, obtaining first air conditioner energy consumption parameters, predicting a second plan completion rate, obtaining second production energy consumption parameters and second air conditioner energy consumption parameters, calculating an energy-saving fitness, completing a first round of optimization, and continuously performing multiple rounds of optimization to obtain optimal parameters. The application solves the technical problem that the energy consumption optimization method in the prior art lacks collaborative optimization analysis of the energy consumption of production equipment and air conditioner systems, and the reliability and accuracy of the final optimization scheme are difficult to improve.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to an energy-saving optimization method and system based on workshop energy consumption analysis. Background Technology

[0002] In manufacturing processes, workshop energy consumption primarily includes production energy consumption from the operation of production equipment and air conditioning energy consumption for maintaining the workshop environment. Production energy consumption typically originates from the electricity or gas consumption of various machine tools, stamping equipment, assembly lines, etc.; air conditioning energy consumption includes the energy consumption of equipment such as refrigeration units and fans in regulating the workshop's temperature and humidity. In actual production, production energy consumption and air conditioning energy consumption are mutually influential. For example, changes in air conditioning operating parameters may affect the operating temperature of equipment, thereby indirectly altering the equipment's energy consumption level.

[0003] Most existing workshop energy management methods only optimize a single aspect, such as independently adjusting the operation strategy of production equipment or optimizing the operation mode of the air conditioning system. They lack comprehensive analysis and collaborative optimization of the coupling relationship between the two, making it difficult to improve the reliability and accuracy of the final optimization solution.

[0004] Therefore, existing workshop energy consumption optimization methods lack collaborative optimization analysis of energy consumption of production equipment and air conditioning systems, resulting in technical problems that make it difficult to improve the reliability and accuracy of the final optimization scheme. Summary of the Invention

[0005] This application provides an energy-saving optimization method and system based on workshop energy consumption analysis, solving the technical problem that existing workshop energy consumption optimization methods lack coordinated optimization analysis of energy consumption of production equipment and air conditioning systems, leading to difficulties in improving the reliability and accuracy of the final optimization scheme. By real-time collection and analysis of workshop production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters, and using predictive and classification models to calculate and configure production optimization step sizes and air conditioning optimization step sizes, through multiple rounds of energy-saving adaptive iterative optimization, coordinated adaptive adjustment of production energy consumption and air conditioning energy consumption is achieved, significantly reducing the total energy consumption of the workshop while ensuring the completion of production tasks.

[0006] This application provides an energy-saving optimization method based on workshop energy consumption analysis. The method includes: collecting production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters in a target workshop; predicting the production plan completion rate to obtain the plan completion rate; calculating the plan deviation rate based on the plan completion rate; adjusting the production energy consumption parameters using the plan deviation rate to configure a production optimization step size to obtain a first production energy consumption parameter; predicting the first plan completion rate by combining the air conditioning energy consumption parameter and the production plan parameters; analyzing the production energy consumption offset rate based on the first production energy consumption parameter and the air conditioning energy consumption parameter; calculating and configuring the air conditioning optimization step size based on the first plan completion rate; adjusting the air conditioning energy consumption parameter to obtain a first air conditioning energy consumption parameter; and predicting a second plan completion rate; fusing the production energy consumption parameter, the first production energy consumption parameter, the air conditioning energy consumption parameter, and the second air conditioning energy consumption parameter based on 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 conditioning energy consumption parameter; calculating the energy-saving adaptability to complete the first round of optimization; and continuing to perform multiple rounds of optimization to obtain the optimal production energy consumption parameter and the optimal air conditioning energy consumption parameter.

[0007] In the implementation method, production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters in the target workshop are collected to predict the production plan completion rate and obtain the plan completion rate. This includes: collecting the current production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters in the target workshop; inputting the production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters into the production forecaster and outputting the plan completion rate.

[0008] In the implementation, the training steps of the production predictor include: collecting a sample production plan parameter set, a sample production energy consumption parameter set, and a sample air conditioning energy consumption parameter set based on historical production data from the workshop, and collecting a sample plan completion rate set; constructing a production predictor based on machine learning; supervising the training of the production predictor using the sample production plan parameter set, sample production energy consumption parameter set, sample air conditioning energy consumption parameter set, and sample plan completion rate set; testing the accuracy of the production predictor, and completing the training when it is greater than or equal to a preset accuracy threshold.

[0009] In the implementation method, the plan deviation rate is calculated based on the plan completion rate. The production energy consumption parameters are adjusted using the plan deviation rate to configure the production optimization step size, thereby obtaining the first production energy consumption parameter. Combined with the air conditioning energy consumption parameter and the production plan parameter, the first plan completion rate is predicted. This includes: calculating the plan deviation rate based on the plan completion rate; configuring the plan deviation rate as the production optimization step size; adjusting the production energy consumption parameter using the production optimization step size to obtain the first production energy consumption parameter; and predicting the production plan completion rate based on the first production energy consumption parameter, the air conditioning energy consumption parameter, and the production plan parameter to obtain the first plan completion rate.

[0010] In the implementation method, based on the first production energy consumption parameter and the air conditioning energy consumption parameter, the production energy consumption deviation rate is analyzed and obtained. Combined with the first plan completion rate, the air conditioning optimization step size is calculated, the air conditioning energy consumption parameter is adjusted to obtain the first air conditioning energy consumption parameter, and the second plan completion rate is predicted. This includes: inputting the first production energy consumption parameter and the air conditioning energy consumption parameter into a production energy consumption deviation classifier to classify and obtain the production energy consumption deviation rate, wherein the production energy consumption deviation classifier is constructed based on a sample production energy consumption parameter set, a sample air conditioning energy consumption parameter set, and a sample production energy consumption deviation rate set, and the production energy consumption deviation rate includes the proportion of change of the production energy consumption parameter under the influence of the air conditioning energy consumption parameter; calculating the first plan deviation rate based on the first plan completion rate; calculating the air conditioning optimization step size based on the production energy consumption deviation rate and the first plan deviation rate; adjusting the air conditioning energy consumption parameter using the air conditioning optimization step size to obtain the first air conditioning energy consumption parameter; and predicting the production plan completion rate based on the first air conditioning energy consumption parameter, the first production energy consumption parameter, and the production plan parameter to obtain the second plan completion rate.

[0011] In the implementation method, the production energy consumption parameter, the first production energy consumption parameter, the air conditioning energy consumption parameter, and the first air conditioning energy consumption parameter are fused according to the plan completion rate, the first plan completion rate, and the second plan completion rate to obtain the second production energy consumption parameter and the second air conditioning energy consumption parameter. This includes: performing a weighted calculation on the production energy consumption parameter and the first production energy consumption parameter according to the plan completion rate and the first plan completion rate to obtain the second production energy consumption parameter; and performing a weighted calculation on the air conditioning energy consumption parameter and the first air conditioning energy consumption parameter according to the plan completion rate and the second plan completion rate to obtain the second air conditioning energy consumption parameter.

[0012] In the implementation method, the energy-saving adaptability is calculated to complete the first round of optimization, and multiple rounds of optimization are continued to obtain the optimal production plan parameters. This includes: calculating the ratio of the preset energy consumption parameter to the sum of the first production energy consumption parameter and the first air conditioning energy consumption parameter to obtain the first energy-saving coefficient; calculating the first energy-saving adaptability based on the first energy-saving 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 conditioning energy consumption parameter to obtain the second energy-saving coefficient; calculating the second energy-saving adaptability based on the second energy-saving coefficient and the second plan completion rate; calculating and obtaining the third energy-saving coefficient of the second production energy consumption parameter and the second air conditioning energy consumption parameter, and processing to obtain the third energy-saving adaptability; continuing multiple rounds of optimization until the optimization rounds converge to obtain the optimal production energy consumption parameter and the optimal air conditioning energy consumption parameter with the maximum energy-saving adaptability.

[0013] This application also provides an energy-saving optimization system based on workshop energy consumption analysis, comprising: a plan completion prediction module, used to collect production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters in the target workshop, predict the production plan completion rate, and obtain the plan completion rate; a production energy consumption adjustment module, used to calculate the plan deviation rate based on the plan completion rate, adjust the production energy consumption parameters by configuring the production optimization step size using the plan deviation rate, obtain a first production energy consumption parameter, and predict the first plan completion rate by combining the air conditioning energy consumption parameter and the production plan parameters; and an air conditioning energy consumption adjustment module, used to analyze the first production energy consumption parameter and the air conditioning energy consumption parameter. The system obtains the production energy consumption offset rate, combines it with the first plan completion rate, calculates the configuration air conditioning optimization step size, adjusts the air conditioning energy consumption parameters, obtains the first air conditioning energy consumption parameter, and predicts the second plan completion rate. An iterative optimization module is used to fuse the production energy consumption parameter, the first production energy consumption parameter, the air conditioning energy consumption parameter, and the second air conditioning energy consumption parameter based on the plan completion rate, the first plan completion rate, and the second plan completion rate, to obtain the second production energy consumption parameter and the second air conditioning energy consumption parameter. The module then calculates the energy-saving adaptability, completes the first round of optimization, and continues with multiple rounds of optimization to obtain the optimal production energy consumption parameter and the optimal air conditioning energy consumption parameter.

[0014] This application proposes an energy-saving optimization method and system based on workshop energy consumption analysis. The method involves collecting production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters from a target workshop to predict the production plan completion rate and obtain the plan completion rate. Based on the plan completion rate, a plan deviation rate is calculated. The production energy consumption parameters are then adjusted using the plan deviation rate to configure the production optimization step size, resulting in a first production energy consumption parameter. This first plan completion rate is then predicted by combining the air conditioning energy consumption parameter and the production plan parameters. Finally, based on the first production energy consumption parameter and the air conditioning energy consumption parameter, a production energy consumption offset rate is analyzed and obtained. Combining the first plan completion rate, the air conditioning optimization step size is calculated and configured, and the air conditioning energy consumption parameters are adjusted to obtain the first air conditioning energy consumption parameter. A second plan completion rate is then predicted. Based on the plan completion rate, the first plan completion rate, and the second plan completion rate, the production energy consumption parameter, the first production energy consumption parameter, the air conditioning energy consumption parameter, and the second air conditioning energy consumption parameter are fused to obtain the second production energy consumption parameter and the second air conditioning energy consumption parameter. The energy-saving adaptability is calculated, completing the first round of optimization. Multiple rounds of optimization are then performed to obtain the optimal production energy consumption parameter and the optimal air conditioning energy consumption parameter. This solves the technical problem in existing workshop energy consumption optimization methods that lack collaborative optimization analysis of production equipment and air conditioning system energy consumption, leading to difficulties in improving the reliability and accuracy of the final optimization scheme. By real-time collection and analysis of workshop production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters, and using prediction and classification models to calculate and configure the production optimization step size and the air conditioning optimization step size, through multiple rounds of energy-saving adaptability iterative optimization, collaborative adaptive adjustment of production energy consumption and air conditioning energy consumption is achieved, significantly reducing the total energy consumption of the workshop while ensuring the completion of production tasks. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0016] Figure 1 A schematic diagram of an energy-saving optimization method based on workshop energy consumption analysis provided for an embodiment of this application;

[0017] Figure 2 This is a schematic diagram of an energy-saving optimization system based on workshop energy consumption analysis, provided as an embodiment of this application.

[0018] Figure labeling: Module 11 for plan completion prediction, Module 12 for production energy consumption adjustment, Module 13 for air conditioning energy consumption adjustment, and Module 14 for iterative optimization. Detailed Implementation

[0019] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0022] This application provides an energy-saving optimization method and system based on workshop energy consumption analysis, such as... Figure 1 As shown, the method includes:

[0023] The production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters in the target workshop are collected to predict the production plan completion rate and obtain the plan completion rate. Based on the plan completion rate, the plan deviation rate is calculated. The production energy consumption parameters are adjusted by configuring the production optimization step size using the plan deviation rate to obtain the first production energy consumption parameter. Combined with the air conditioning energy consumption parameter and the production plan parameters, the first plan completion rate is predicted.

[0024] By collecting production planning parameters, production energy consumption parameters, and air conditioning energy consumption parameters within the target workshop, the production planning parameters are specific production task information, such as the types and quantities of products planned for production. The production energy consumption parameters are energy consumption data of production equipment during operation, such as electricity consumption data or other energy consumption data generated by machine operation. The air conditioning energy consumption parameters are energy consumption data of the air conditioning system used to maintain the workshop environment (temperature, humidity, etc.), such as cooling power consumption and air supply power consumption. Based on the production planning parameters, production energy consumption parameters, and air conditioning energy consumption parameters, the production plan completion rate is predicted to obtain the plan completion rate. Subsequently, based on the plan completion rate, the plan deviation rate is calculated, which is 1 minus the plan completion rate. The production energy consumption parameters are adjusted using the plan deviation rate to configure the production optimization step size, obtaining the first production energy consumption parameter. Combining the air conditioning energy consumption parameter and the production planning parameters, the first plan completion rate is predicted.

[0025] The method provided in this application embodiment further includes: collecting current production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters in the target workshop; inputting the production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters into a production forecaster, and outputting the plan completion rate.

[0026] The process involves collecting production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters within the target workshop, predicting the production plan completion rate, and obtaining the plan completion rate. This includes: acquiring the production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters of the target workshop at the current time node with a preset start time, using IoT devices and workshop production big data. The preset start time is the start time of the current production task. Subsequently, the production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters are input into a production forecaster. Based on the production forecaster's prediction, the production plan completion rate is obtained, and the plan completion rate output by the production forecaster is obtained.

[0027] The method provided in this application embodiment further includes: collecting a sample production plan parameter set, a sample production energy consumption parameter set, and a sample air conditioning energy consumption parameter set based on historical production data from the workshop, and collecting a sample plan completion rate set; constructing a production predictor based on machine learning; supervising the training of the production predictor using the sample production plan parameter set, sample production energy consumption parameter set, sample air conditioning energy consumption parameter set, and sample plan completion rate set; testing the accuracy of the production predictor, and completing the training when it is greater than or equal to a preset accuracy threshold.

[0028] The training steps of the production predictor include: acquiring historical production data from the workshop; collecting multiple sample production plan parameter sets, sample production energy consumption parameter sets, and sample air conditioning energy consumption parameter sets from the historical production data; and collecting sample plan completion rate sets corresponding to the sample parameters. The sample plan completion rate set is the ratio of the actual product quantity completed to the sample production plan parameters under the conditions of the sample production energy consumption parameters and sample air conditioning energy consumption parameters. Further, a production predictor is constructed based on machine learning methods, such as random forests and long short-term memory networks. The production predictor predicts the workshop's plan completion rate under current conditions by analyzing the correlation between historical data and current data. The sample production plan parameter sets, sample production energy consumption parameter sets, and sample air conditioning energy consumption parameter sets are used as actual inputs, and the sample plan completion rate sets are used as supervised data for supervised learning of the model. By continuously adjusting the model parameters, the accuracy of the output prediction result is made to meet a preset requirement, i.e., the accuracy of the production predictor is greater than or equal to a preset accuracy threshold. If it is less than the preset accuracy threshold, supervised training of the model continues based on the training data. The preset accuracy threshold is the accuracy threshold of the model output result. When it is greater than the threshold, the accuracy of the corresponding model output result is relatively high, and vice versa.

[0029] The method provided in this application embodiment further includes: calculating the plan deviation rate based on the plan completion rate; configuring the plan deviation rate as the production optimization step size; adjusting the production energy consumption parameters using the production optimization step size to obtain a first production energy consumption parameter; and predicting the production plan completion rate based on the first production energy consumption parameter, the air conditioning energy consumption parameter, and the production plan parameter to obtain a first plan completion rate.

[0030] Based on the planned completion rate, a planned deviation rate is calculated. The production energy consumption parameters are adjusted using the planned deviation rate to configure a production optimization step size, resulting in a first production energy consumption parameter. Combining the air conditioning energy consumption parameter and the production plan parameter, a first planned completion rate is predicted, including: calculating the planned deviation rate based on the predicted planned completion rate, where the planned deviation rate is 1 minus the planned completion rate. Further, a production optimization step size is configured based on the planned deviation rate. A larger planned deviation rate corresponds to a lower production progress. Configuring the production optimization step size based on the planned deviation rate allows for adaptive adjustment of the energy consumption optimization step size according to the predicted production progress. Subsequently, the production energy consumption parameters are adjusted using the production optimization step size to obtain the first production energy consumption parameter, i.e., adding the energy consumption parameter corresponding to the production optimization step size to the current energy consumption. Based on the first production energy consumption parameter, the air conditioning energy consumption parameter, and the production plan parameter, the above data is input into the production predictor to perform production plan completion rate prediction, obtaining the first planned completion rate. At this point, the first planned completion rate is the planned completion rate obtained after adjusting the production energy consumption.

[0031] Based on the first production energy consumption parameter and the air conditioning energy consumption parameter, the production energy consumption deviation rate is analyzed and obtained. Combined with the first plan completion rate, the air conditioning optimization step size is calculated, and the air conditioning energy consumption parameter is adjusted to obtain the first air conditioning energy consumption parameter. The second plan completion rate is predicted and obtained. Based on the plan completion rate, the first plan completion rate, and the second plan completion rate, the production energy consumption parameter, the first production energy consumption parameter, the air conditioning energy consumption parameter, and the first air conditioning energy consumption parameter are fused to obtain the second production energy consumption parameter and the second air conditioning energy consumption parameter. The energy-saving adaptability is calculated and the first round of optimization is completed. Multiple rounds of optimization are then performed to obtain the optimal production energy consumption parameter and the optimal air conditioning energy consumption parameter.

[0032] When production energy consumption parameters are adjusted, air conditioning energy consumption parameters may become mismatched. Incompatibility can lead to changes in production energy consumption, such as increased equipment temperature leading to higher energy consumption. Based on the first production energy consumption parameter and the air conditioning energy consumption parameter, a production energy consumption deviation rate is obtained. Combined with the first plan completion rate, the air conditioning optimization step size is calculated. The production energy consumption deviation rate includes the proportion of change in production energy consumption parameters under the influence of air conditioning energy consumption parameters. The air conditioning optimization step size is the adjustment range of air conditioning energy consumption determined based on the production energy consumption deviation rate and the plan deviation rate. It is used to adjust the air conditioning energy consumption parameters and obtain the first air conditioning energy consumption parameter. Based on the first air conditioning energy consumption parameter, the first production energy consumption parameter, and the production plan parameters, a production forecaster is used to predict the production plan completion rate, obtaining the second plan completion rate. Finally, based on the planned completion rate, the first planned completion rate, and the second planned completion rate, the production energy consumption parameters, the first production energy consumption parameters, the air conditioning energy consumption parameters, and the first air conditioning energy consumption parameters are fused to obtain the second production energy consumption parameters and the second air conditioning energy consumption parameters. Energy-saving adaptability is calculated to complete the first round of optimization, and multiple rounds of optimization are performed to obtain the optimal production energy consumption parameters and the optimal air conditioning energy consumption parameters. This solves the technical problem in existing workshop energy consumption optimization methods that lack collaborative optimization analysis of the energy consumption of production equipment and air conditioning systems, leading to difficulties in improving the reliability and accuracy of the final optimization scheme. By real-time collection and analysis of workshop production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters, and by using predictive and classification models to calculate and configure the production optimization step size and the air conditioning optimization step size, through multiple rounds of iterative optimization based on energy-saving adaptability, collaborative adaptive adjustment of production energy consumption and air conditioning energy consumption is achieved, significantly reducing the total energy consumption of the workshop while ensuring the completion of production tasks.

[0033] The method provided in this application embodiment further includes: inputting a first production energy consumption parameter and an air conditioning energy consumption parameter into a production energy consumption offset classifier, classifying and obtaining 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 conditioning energy consumption parameter set, and a sample production energy consumption offset rate set, and the production energy consumption offset rate includes the proportion of change of the production energy consumption parameter under the influence of the air conditioning energy consumption parameter; calculating a first plan deviation rate based on the first plan completion rate; calculating an air conditioning optimization step size based on the production energy consumption offset rate and the first plan deviation rate; adjusting the air conditioning energy consumption parameter using the air conditioning optimization step size to obtain a first air conditioning energy consumption parameter; and predicting the production plan completion rate based on the first air conditioning energy consumption parameter, the first production energy consumption parameter, and the production plan parameter to obtain a second plan completion rate.

[0034] Based on the first production energy consumption parameter and the air conditioning energy consumption parameter, the production energy consumption deviation rate is analyzed and obtained. Combined with the first plan completion rate, the air conditioning optimization step size is calculated, the air conditioning energy consumption parameter is adjusted, the first air conditioning energy consumption parameter is obtained, and the second plan completion rate is predicted. This includes: inputting the first production energy consumption parameter and the air conditioning energy consumption parameter into a production energy consumption deviation classifier. The production energy consumption deviation classifier is used to analyze the production energy consumption deviation rate based on the production energy consumption parameter and the air conditioning energy consumption parameter, and obtain the deviation ratio of the production energy consumption parameter and the production energy consumption parameter under normal conditions caused by the mismatch of air conditioning energy consumption. The neural network model is trained under supervision based on the sample production energy consumption parameter set, the sample air conditioning energy consumption parameter set, and the sample production energy consumption deviation rate set. Training is completed when the accuracy of the production energy consumption deviation rate output by the model meets the requirements, and the production energy consumption deviation classifier is obtained. The production energy consumption deviation rate includes the change ratio of the production energy consumption parameter under the influence of the air conditioning energy consumption parameter. Subsequently, based on the first plan completion rate, the first plan deviation rate is calculated, which is 1 minus the first plan completion rate. Based on the production energy consumption offset rate and the first plan deviation rate, the air conditioning optimization step size is calculated. The air conditioning optimization step size is 1 plus the product of the calculated absolute value of the production energy consumption offset rate and the first plan deviation rate. For example, if the offset rate is 5% and the deviation rate is 2%, then the air conditioning optimization step size is 2% * (1 + 5%) = 2.1%. Finally, using the air conditioning optimization step size, the air conditioning energy consumption parameters are adjusted to obtain the first air conditioning energy consumption parameter. Taking an air conditioning optimization step size of 2.1% as an example, the air conditioning energy consumption parameter is adjusted by increasing the energy consumption by 2.1% based on the current air conditioning energy consumption, and the corresponding first air conditioning energy consumption parameter, i.e., the adjusted energy consumption data, is obtained. Further, based on the first air conditioning energy consumption parameter, the first production energy consumption parameter, and the production plan parameter, a production predictor is used to predict the production plan completion rate to obtain the second plan completion rate.

[0035] The method provided in this application embodiment further includes: performing a weighted calculation on the production energy consumption parameter and the first production energy consumption parameter based on the plan completion rate and the first plan completion rate to obtain a second production energy consumption parameter; and performing a weighted calculation on the air conditioning energy consumption parameter and the first air conditioning energy consumption parameter based on the plan completion rate and the second plan completion rate to obtain a second air conditioning energy consumption parameter.

[0036] Based on the planned completion rate, the first planned completion rate, and the second planned completion rate, the production energy consumption parameter, the first production energy consumption parameter, the air conditioning energy consumption parameter, and the first air conditioning energy consumption parameter are fused to obtain the second production energy consumption parameter and the second air conditioning energy consumption parameter. This includes: weighting the production energy consumption parameter and the first production energy consumption parameter based on the obtained planned completion rate and the first planned completion rate, wherein the weight of the production energy consumption parameter is the ratio of the planned completion rate to the sum of the planned completion rate and the first planned completion rate, and the weight of the first production energy consumption parameter is the ratio of the first planned completion rate to the sum of the planned completion rate and the first planned completion rate. The second production energy consumption parameter is obtained based on the weighted calculation result. Based on the planned completion rate and the second planned completion rate, the air conditioning energy consumption parameter and the first air conditioning energy consumption parameter are weighted to obtain the second air conditioning energy consumption parameter. The weight of the air conditioning energy consumption parameter is the ratio of the planned completion rate to the sum of the planned completion rate and the second planned completion rate, and the weight of the first air conditioning energy consumption parameter is the ratio of the second planned completion rate to the sum of the planned completion rate and the second planned completion rate.

[0037] The method provided in this application embodiment further includes: calculating the ratio of the preset energy consumption parameter to the sum of the first production energy consumption parameter and the air conditioning energy consumption parameter to obtain a first energy-saving coefficient; calculating a first energy-saving adaptability based on the first energy-saving coefficient and the first plan completion rate; calculating a second energy-saving coefficient based on the ratio of the preset energy consumption parameter to the sum of the first production energy consumption parameter and the first air conditioning energy consumption parameter; calculating a second energy-saving adaptability based on the second energy-saving coefficient and the second plan completion rate; calculating and obtaining a third energy-saving coefficient of the second production energy consumption parameter and the second air conditioning energy consumption parameter, and processing to obtain the third energy-saving adaptability; continuing to perform multiple rounds of optimization until the optimization rounds converge to obtain the optimal production energy consumption parameter and the optimal air conditioning energy consumption parameter with the highest energy-saving adaptability.

[0038] The process involves calculating and obtaining the energy-saving adaptability to complete the first round of optimization. Multiple rounds of optimization are then performed to obtain the optimal production plan parameters. This includes: calculating the ratio of the preset energy consumption parameter to the sum of the first production energy consumption parameter and the air conditioning energy consumption parameter to obtain the first energy-saving coefficient. The preset energy consumption parameter is the target energy consumption value set by the system during the design phase, serving as the benchmark for energy-saving evaluation. The first energy-saving coefficient is multiplied by the first plan completion rate to calculate the first energy-saving adaptability, which reflects the energy-saving adaptability under the operating environment of the first production energy consumption parameter and the air conditioning energy consumption parameter. The ratio of the preset energy consumption parameter to the sum of the first production energy consumption parameter and the first air conditioning energy consumption parameter is calculated to obtain the second energy-saving coefficient. The second energy-saving coefficient is multiplied by the second plan completion rate to calculate the second energy-saving adaptability. Using the same calculation method as the energy-saving coefficient, a third energy-saving coefficient is calculated for the second production energy consumption parameter and the second air conditioning energy consumption parameter, and then processed to obtain the third energy-saving adaptability. Thus, repeat the above steps to continue multiple rounds of optimization until the optimization rounds converge, for example, reaching 100 rounds of optimization, to obtain the optimal production energy consumption parameters and the optimal air conditioning energy consumption parameters with the greatest energy-saving adaptability.

[0039] In the above text, refer to Figure 1 A method for energy-saving optimization based on workshop energy consumption analysis according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 This invention describes an energy-saving optimization system based on workshop energy consumption analysis according to an embodiment of the present invention.

[0040] An energy-saving optimization system based on workshop energy consumption analysis, according to an embodiment of the present invention, solves the technical problem that existing workshop energy consumption optimization methods lack coordinated optimization analysis of the energy consumption of production equipment and air conditioning systems, leading to difficulties in improving the reliability and accuracy of the final optimization scheme. By real-time collection and analysis of workshop production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters, and by using prediction and classification models to calculate and configure the production optimization step size and air conditioning optimization step size, through multiple rounds of energy-saving adaptive iterative optimization, coordinated adaptive adjustment of production energy consumption and air conditioning energy consumption is achieved, significantly reducing the total energy consumption of the workshop while ensuring the completion of production tasks. An energy-saving optimization system based on workshop energy consumption analysis includes: a plan completion prediction module 11, a production energy consumption adjustment module 12, an air conditioning energy consumption adjustment module 13, and an iterative optimization module 14.

[0041] The production plan completion prediction module 11 is used to collect production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters in the target workshop, predict the production plan completion rate, and obtain the plan completion rate. The production energy consumption adjustment module 12 is used to calculate the plan deviation rate based on the plan completion rate, adjust the production energy consumption parameters by configuring the production optimization step size according to the plan deviation rate, obtain the first production energy consumption parameter, and predict the first plan completion rate by combining the air conditioning energy consumption parameter and the production plan parameter. The air conditioning energy consumption adjustment module 13 is used to analyze and obtain the production energy consumption deviation rate based on the first production energy consumption parameter and the air conditioning energy consumption parameter, and combine it with... The first plan completion rate is used to calculate the air conditioning optimization step size, adjust the air conditioning energy consumption parameters to obtain the first air conditioning energy consumption parameters, and predict the second plan completion rate. The iterative optimization module 14 is used to perform fusion processing on the production energy consumption parameters, the first production energy consumption parameters, the air conditioning energy consumption parameters, and the first air conditioning energy consumption parameters based on the plan completion rate, the first plan completion rate, and the second plan completion rate to obtain the second production energy consumption parameters and the second air conditioning energy consumption parameters. The energy-saving adaptability is calculated, the first round of optimization is completed, and multiple rounds of optimization are continued to obtain the optimal production energy consumption parameters and the optimal air conditioning energy consumption parameters.

[0042] The specific configuration of the plan completion prediction module 11 will be described in detail below. The plan completion prediction module 11 further includes: collecting production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters within the target workshop; predicting the production plan completion rate; and obtaining the plan completion rate. This includes: collecting the current production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters within the target workshop; inputting the production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters into the production predictor; and outputting the plan completion rate.

[0043] The specific configuration of the production prediction module 11 will be described in detail below. The training steps of the production predictor include: collecting sample production plan parameter sets, sample production energy consumption parameter sets, and sample air conditioning energy consumption parameter sets based on historical production data from the workshop, and collecting sample plan completion rate sets; constructing a production predictor based on machine learning; supervising the training of the production predictor using the sample production plan parameter sets, sample production energy consumption parameter sets, sample air conditioning energy consumption parameter sets, and sample plan completion rate sets; testing the accuracy of the production predictor, and completing the training when it is greater than or equal to a preset accuracy threshold.

[0044] The specific configuration of the production energy consumption adjustment module 12 will be described in detail below. The production energy consumption adjustment module 12 further includes: calculating the plan deviation rate based on the plan completion rate; adjusting the production energy consumption parameters using the plan deviation rate to configure a production optimization step size to obtain a first production energy consumption parameter; and predicting the first plan completion rate by combining the air conditioning energy consumption parameter and the production plan parameter. This includes: calculating the plan deviation rate based on the plan completion rate; configuring the plan deviation rate as a production optimization step size; adjusting the production energy consumption parameter using the production optimization step size to obtain the first production energy consumption parameter; and predicting the production plan completion rate based on the first production energy consumption parameter, the air conditioning energy consumption parameter, and the production plan parameter to obtain the first plan completion rate.

[0045] The specific configuration of the air conditioning energy consumption adjustment module 13 will be described in detail below. The air conditioning energy consumption adjustment module 13 further includes: analyzing and obtaining a production energy consumption deviation rate based on the first production energy consumption parameter and the air conditioning energy consumption parameter; calculating the configuration air conditioning optimization step size based on the first plan completion rate; adjusting the air conditioning energy consumption parameter to obtain a first air conditioning energy consumption parameter; and predicting and obtaining a second plan completion rate. This includes: inputting the first production energy consumption parameter and the air conditioning energy consumption parameter into a production energy consumption deviation classifier to classify and obtain a production energy consumption deviation rate, wherein the production energy consumption deviation classifier is constructed based on a sample production energy consumption parameter set, a sample air conditioning energy consumption parameter set, and a sample production energy consumption deviation rate set; the production energy consumption deviation rate includes the proportion of change of the production energy consumption parameter under the influence of the air conditioning energy consumption parameter; calculating a first plan deviation rate based on the first plan completion rate; calculating the configuration air conditioning optimization step size based on the production energy consumption deviation rate and the first plan deviation rate; adjusting the air conditioning energy consumption parameter using the air conditioning optimization step size to obtain a first air conditioning energy consumption parameter; and predicting the production plan completion rate based on the first air conditioning energy consumption parameter, the first production energy consumption parameter, and the production plan parameter to obtain a second plan completion rate.

[0046] The specific configuration of the iterative optimization module 14 will be described in detail below. The iterative optimization module 14 further includes: performing a fusion process on the production energy consumption parameter, the first production energy consumption parameter, the air conditioning energy consumption parameter, and the first air conditioning energy consumption parameter based on the plan completion rate, the first plan completion rate, and the second plan completion rate to obtain the second production energy consumption parameter and the second air conditioning energy consumption parameter. This includes: performing a weighted calculation on the production energy consumption parameter and the first production energy consumption parameter based on the plan completion rate and the first plan completion rate to obtain the second production energy consumption parameter; and performing a weighted calculation on the air conditioning energy consumption parameter and the first air conditioning energy consumption parameter based on the plan completion rate and the second plan completion rate to obtain the second air conditioning energy consumption parameter.

[0047] The specific configuration of the iterative optimization module 14 will be described in detail below. The iterative optimization module 14 further includes: processing and calculating the energy-saving adaptability, completing the first round of optimization, and continuing multiple rounds of optimization to obtain the optimal production plan parameters, including: calculating the ratio of the preset energy consumption parameter to the sum of the first production energy consumption parameter and the air conditioning energy consumption parameter to obtain a first energy-saving coefficient; calculating the first energy-saving adaptability based on the first energy-saving 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 conditioning energy consumption parameter to obtain a second energy-saving coefficient; calculating the second energy-saving adaptability based on the second energy-saving coefficient and the second plan completion rate; calculating and obtaining the third energy-saving coefficient of the second production energy consumption parameter and the second air conditioning energy consumption parameter, and processing to obtain the third energy-saving adaptability; continuing multiple rounds of optimization until the optimization rounds converge to obtain the optimal production energy consumption parameter and the optimal air conditioning energy consumption parameter with the highest energy-saving adaptability.

[0048] The energy-saving optimization system based on workshop energy consumption analysis provided in this embodiment of the invention can execute the energy-saving optimization method based on workshop energy consumption analysis provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0049] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0050] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An energy-saving optimization method based on workshop energy consumption analysis, characterized in that, The method includes: Collect production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters in the target workshop, predict the production plan completion rate, and obtain the plan completion rate. Based on the plan completion rate, the plan deviation rate is calculated. The production optimization step size is configured using the plan deviation rate to adjust the production energy consumption parameters and obtain the first production energy consumption parameter. Combined with the air conditioning energy consumption parameter and the production plan parameter, the first plan completion rate is predicted. Based on the first production energy consumption parameter and the air conditioning energy consumption parameter, the production energy consumption deviation rate is obtained by analysis. Combined with the first plan completion rate, the air conditioning optimization step size is calculated, the air conditioning energy consumption parameter is adjusted, the first air conditioning energy consumption parameter is obtained, and the second plan completion rate is predicted. Based on the planned completion rate, the first planned completion rate and the second planned completion rate, the production energy consumption parameter, the first production energy consumption parameter, the air conditioning energy consumption parameter and the first air conditioning energy consumption parameter are fused to obtain the second production energy consumption parameter and the second air conditioning energy consumption parameter. The energy-saving adaptability is calculated to complete the first round of optimization, and multiple rounds of optimization are continued to obtain the optimal production energy consumption parameter and the optimal air conditioning energy consumption parameter. Based on the planned completion rate, the planned deviation rate is calculated. The production optimization step size is then adjusted using this planned deviation rate to obtain the first production energy consumption parameter. Combining this parameter with the air conditioning energy consumption parameter and the production plan parameter, the first planned completion rate is predicted, including: The plan deviation rate is calculated based on the plan completion rate. Configure the planned deviation rate as the production optimization step size; Using the aforementioned production optimization step size, the production energy consumption parameters are adjusted to obtain the first production energy consumption parameter; Based on the first production energy consumption parameter, air conditioning energy consumption parameter, and production plan parameter, the production plan completion rate is predicted to obtain the first plan completion rate; Based on the first production energy consumption parameter and the air conditioning energy consumption parameter, the production energy consumption deviation rate is analyzed and obtained. Combined with the first plan completion rate, the air conditioning optimization step size is calculated, the air conditioning energy consumption parameter is adjusted, the first air conditioning energy consumption parameter is obtained, and the second plan completion rate is predicted, including: The first production energy consumption parameter and the air conditioning energy consumption parameter are input into the production energy consumption offset classifier, and the production energy consumption offset rate is obtained by classification. The production energy consumption offset classifier is constructed based on the sample production energy consumption parameter set, the sample air conditioning energy consumption parameter set, and the sample production energy consumption offset rate set. The production energy consumption offset rate includes the proportion of change of the production energy consumption parameter under the influence of the air conditioning energy consumption parameter. The first plan deviation rate is calculated based on the first plan completion rate; Calculate the air conditioning optimization step size based on the production energy consumption deviation rate and the first plan deviation rate; The air conditioning energy consumption parameters are adjusted using the air conditioning optimization step size to obtain the first air conditioning energy consumption parameter; Based on the first air conditioning energy consumption parameter, the first production energy consumption parameter, and the production plan parameter, the production plan completion rate is predicted to obtain the second plan completion rate.

2. The energy-saving optimization method based on workshop energy consumption analysis according to claim 1, characterized in that, Collect production planning parameters, production energy consumption parameters, and air conditioning energy consumption parameters within the target workshop to predict the production plan completion rate and obtain the plan completion rate, including: Collect current production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters within the target workshop; The production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters are input into the production forecaster, and the plan completion rate is output.

3. The energy-saving optimization method based on workshop energy consumption analysis according to claim 2, characterized in that, The training steps for the production forecaster include: Based on the workshop's historical production data, sample production plan parameter sets, sample production energy consumption parameter sets, and sample air conditioning energy consumption parameter sets were collected, along with a sample plan completion rate set. Building production predictors based on machine learning; The production predictor is trained under supervision using the sample production plan parameter set, sample production energy consumption parameter set, sample air conditioning energy consumption parameter set, and sample plan completion rate set. The accuracy of the production predictor is tested, and training is completed when it 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, characterized in that, Based on the planned completion rate, the first planned completion rate, and the second planned completion rate, the production energy consumption parameter, the first production energy consumption parameter, the air conditioning energy consumption parameter, and the first air conditioning energy consumption parameter are fused to obtain the second production energy consumption parameter and the second air conditioning energy consumption parameter, including: Based on the planned completion rate and the first planned completion rate, the production energy consumption parameter and the first production energy consumption parameter are weighted and calculated to obtain the second production energy consumption parameter. Based on the planned completion rate and the second planned completion rate, the air conditioning energy consumption parameter and the first air conditioning energy consumption parameter are weighted and calculated to obtain the second air conditioning energy consumption parameter.

5. The energy-saving optimization method based on workshop energy consumption analysis according to claim 1, characterized in that, The energy-saving adaptability is calculated to complete the first round of optimization, and multiple rounds of optimization are continued to obtain the optimal production plan parameters, including: Calculate the ratio of the preset energy consumption parameter to the sum of the first production energy consumption parameter and the air conditioning energy consumption parameter to obtain the first energy saving coefficient; The first energy-saving adaptability is calculated based on the first energy-saving coefficient and the first plan completion rate; Calculate the ratio of the preset energy consumption parameter to the sum of the first production energy consumption parameter and the first air conditioning energy consumption parameter to obtain the second energy saving coefficient; The second energy-saving adaptability is calculated based on the second energy-saving coefficient and the second plan completion rate. Calculate and obtain the third energy-saving coefficient of the second production energy consumption parameter and the second air conditioning energy consumption parameter, and process them to obtain the third energy-saving adaptability. Continue with multiple rounds of optimization until the optimization rounds converge, obtaining the optimal production energy consumption parameters and the optimal air conditioning energy consumption parameters with the greatest energy-saving adaptability.

6. An energy-saving optimization system based on workshop energy consumption analysis, characterized in that, The system is used to perform the method according to any one of claims 1-5, the system comprising: The plan completion prediction module is used to collect production plan parameters, production energy consumption parameters, and air conditioning energy consumption parameters in the target workshop, and to predict the production plan completion rate to obtain the plan completion rate. The production energy consumption adjustment module is used to calculate the plan deviation rate based on the plan completion rate, adjust the production energy consumption parameters by configuring the production optimization step size using the plan deviation rate, obtain the first production energy consumption parameter, and predict the first plan completion rate by combining the air conditioning energy consumption parameter and the production plan parameter. The air conditioning energy consumption adjustment module is used to analyze and obtain the production energy consumption deviation rate based on the first production energy consumption parameter and the air conditioning energy consumption parameter, calculate the configuration air conditioning optimization step size in combination with the first plan completion rate, adjust the air conditioning energy consumption parameter, obtain the first air conditioning energy consumption parameter, and predict and obtain the second plan completion rate. The iterative optimization module is used to fuse the production energy consumption parameters, the first production energy consumption parameters, the air conditioning energy consumption parameters, and the first air conditioning energy consumption parameters according to the plan completion rate, the first plan completion rate, and the second plan completion rate to obtain the second production energy consumption parameters and the second air conditioning energy consumption parameters. The module then calculates the energy-saving adaptability, completes the first round of optimization, and continues to perform multiple rounds of optimization to obtain the optimal production energy consumption parameters and the optimal air conditioning energy consumption parameters.

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