Control optimization method and system based on comprehensive industrial energy monitoring system

Abstract

The invention provides a control optimization method and system based on a comprehensive industrial energy monitoring system. The method comprises the steps of obtaining historical data of a subsystem, training to obtain a corresponding energy consumption demand prediction model, obtaining real-time data of the subsystem, inputting the real-time data into the energy consumption demand prediction model for processing, obtaining energy consumption demand prediction data of the subsystem, processing the energy consumption demand prediction data and real-time energy supply data, and obtaining a supply and demand deviation rate. The energy supply and demand states of the subsystems are obtained through threshold comparison, the real-time energy consumption data and the energy price data are obtained and processed in combination with preset expected energy cost data, the energy cost deviation rate of the subsystems is obtained, processing is carried out in combination with the supply and demand deviation rate, and the sub-operation evaluation indexes of the subsystems are obtained. And further processing to obtain a system operation evaluation index, finally determining the operation state of the comprehensive industrial energy monitoring system through threshold comparison, and optimizing energy supply among subsystems according to the operation state. Therefore, energy control optimization in the subsystems and among the subsystems is realized.

Description

Technical Field

[0001] The present application relates to the field of industrial energy management technology, and more specifically, to a control optimization method and system based on a comprehensive industrial energy monitoring system. Background Art

[0002] In the modern industrial production process, energy consumption is huge and involves multiple energy forms, such as electricity, coal, natural gas, steam, etc. Traditional industrial energy management is often extensive, lacking accurate monitoring and effective control of energy consumption, resulting in low energy utilization, increased production costs and greater pressure on the environment. Traditional industrial energy monitoring systems often rely on centralized data processing and control, and have defects such as data transmission delays, high cloud computing pressure and high requirements for network stability. It is difficult to meet the real-time and autonomy requirements of industrial sites. The existing system still has many shortcomings in control optimization, making it difficult to achieve rapid data processing and local decision-making, and the efficiency and reliability of industrial energy monitoring and control are insufficient.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of the present application is to provide a control optimization method and system based on a comprehensive industrial energy monitoring system, which can obtain the historical weather data and historical production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset time period, process them, obtain the historical energy impact coefficient, combine the historical operating parameter data and the corresponding historical energy data for training, obtain the energy demand forecasting model corresponding to the subsystem, obtain the set operating parameter data, predicted weather data and planned production capacity data of the subsystem for a preset time period, process them in combination with the energy demand forecasting model, obtain the energy demand forecasting data corresponding to the subsystem, obtain the real-time energy supply data of the subsystem, and process them in combination with the energy demand forecasting data to obtain the subsystem. The supply and demand deviation rate of the system is compared with the threshold value to obtain the energy supply and demand status of the subsystem, and the operating parameter data is adjusted according to the energy supply status. The real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the subsystem are obtained and processed in combination with the preset expected energy cost data to obtain the energy cost deviation rate of the subsystem. Combined with the supply and demand deviation rate, the subsystem operation evaluation index is obtained. Further processing is performed to obtain the system operation evaluation index of the comprehensive industrial energy monitoring system. Finally, the operating status of the comprehensive industrial energy monitoring system is determined through the threshold comparison results, and the energy supply between subsystems is optimized according to the operating status, thereby realizing the control optimization of energy within and between subsystems.

[0005] The present application also provides a control optimization method based on an integrated industrial energy monitoring system, comprising the following steps: Obtain historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset historical period of time, and process the historical weather data and historical production capacity data to obtain the historical energy consumption impact coefficient; Training is performed based on the historical operating parameter data, the historical energy consumption impact coefficient, and the corresponding historical energy consumption data to obtain an energy consumption demand prediction model corresponding to the subsystem; Obtaining the set operating parameter data, forecast weather data and planned production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset time period, and processing them in combination with the energy demand forecasting model to obtain the energy demand forecasting data corresponding to the subsystem; Acquire real-time energy supply data of the subsystem of the comprehensive industrial energy monitoring system, process the real-time energy supply data and the energy demand forecast data to obtain the supply and demand deviation rate of the subsystem, compare the supply and demand deviation rate with a preset supply and demand deviation rate threshold, obtain the energy supply and demand status of the subsystem, and adjust the set operating parameter data according to the energy supply status; Obtain the real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the comprehensive industrial energy monitoring system subsystem, and process the real-time energy consumption data and energy price data in combination with the preset expected energy cost data to obtain the energy cost deviation rate of the subsystem; Processing is performed according to the supply-demand deviation rate and the energy cost deviation rate to obtain a sub-operation evaluation index corresponding to the subsystem, and processing is performed according to the sub-operation evaluation index to obtain a system operation evaluation index of the comprehensive industrial energy monitoring system; The system operation evaluation index is compared with a preset system operation evaluation threshold, the operation status of the comprehensive industrial energy monitoring system is determined according to the threshold comparison result, and the energy supply between subsystems is optimized according to the operation status.

[0006] Optionally, in the control optimization method based on the comprehensive industrial energy monitoring system described in the present application, the acquisition of historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a historical preset time period, and processing according to the historical weather data and the historical production capacity data to obtain the historical energy consumption impact coefficient include: The historical operation parameter data includes equipment historical operation parameter data and energy historical operation parameter data; The historical weather data includes historical temperature difference mean data and historical sunshine duration data; The historical production capacity data includes historical production data and historical single-unit energy consumption data; The historical temperature difference mean data and the historical sunshine duration data as well as the historical output data and the historical single-unit energy consumption data are input into a preset energy consumption impact coefficient evaluation model for processing to obtain a historical energy consumption impact coefficient.

[0007] Optionally, in the control optimization method based on the comprehensive industrial energy monitoring system described in the present application, the acquisition of set operating parameter data, forecast weather data and planned production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset time period, combined with the energy demand forecasting model for processing, to obtain the energy demand forecasting data corresponding to the subsystem, includes: Obtain the set operating parameter data, forecast weather data and planned production capacity data for the pre-set time period of the integrated industrial energy monitoring system subsystem; The set operation parameter data includes equipment set operation parameter data and energy set operation parameter data; The predicted weather data includes predicted temperature difference mean data and predicted sunshine duration data; The planned production capacity data includes planned production data and calibrated single-unit energy consumption data; Input the predicted temperature difference mean data and the predicted sunshine duration data as well as the planned output data and the calibrated single-unit energy consumption data into a preset energy consumption impact coefficient evaluation model for processing to obtain a predicted energy consumption impact coefficient; The equipment setting operation parameter data, energy setting operation parameter data and predicted energy consumption impact coefficient are input into the energy consumption demand prediction model for processing to obtain the energy consumption demand prediction data corresponding to the subsystem.

[0008] Optionally, in the control optimization method based on the comprehensive industrial energy monitoring system described in the present application, the real-time energy supply data of the subsystem of the comprehensive industrial energy monitoring system is obtained, and the real-time energy supply data and the energy demand forecast data are processed to obtain the supply and demand deviation rate of the subsystem, and the supply and demand deviation rate is compared with a preset supply and demand deviation rate threshold to obtain the energy supply and demand state of the subsystem, and the set operating parameter data is adjusted according to the energy supply state, including: Obtain real-time energy supply data of subsystems of the comprehensive industrial energy monitoring system; Processing the real-time energy supply data and the energy demand forecast data to obtain a supply-demand deviation rate of the subsystem; Comparing the supply-demand deviation rate with a preset supply-demand deviation rate threshold, obtaining the energy supply-demand state of the subsystem, including a supply-demand balance state or a supply-demand imbalance state; If the supply-demand deviation rate is less than or equal to a preset supply-demand deviation rate threshold, it is determined that the energy supply-demand state of the subsystem is a supply-demand balance state; If the supply-demand deviation rate is greater than a preset supply-demand deviation rate threshold, the energy supply-demand state of the subsystem is determined to be a supply-demand imbalance state, and the set operating parameter data is adjusted.

[0009] Optionally, in the control optimization method based on the comprehensive industrial energy monitoring system described in the present application, the real-time energy consumption data and energy price data of multiple energy sources in a preset time period of the subsystem of the comprehensive industrial energy monitoring system are obtained, and the energy cost deviation rate of the subsystem is obtained by processing the real-time energy consumption data and energy price data in combination with preset expected energy cost data, including: Obtain real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the comprehensive industrial energy monitoring system subsystem; The real-time energy consumption data is multiplied by the energy price data and the sum is processed to obtain the energy consumption cost data of the subsystem; The energy cost deviation rate of the subsystem is obtained by processing the energy cost data and the preset expected energy cost data.

[0010] Optionally, in the control optimization method based on the comprehensive industrial energy monitoring system described in the present application, the processing is performed according to the supply-demand deviation rate and the energy cost deviation rate to obtain a sub-operation evaluation index corresponding to the subsystem, and the processing is performed according to the sub-operation evaluation index to obtain a system operation evaluation index of the comprehensive industrial energy monitoring system, including: Processing is performed according to the supply-demand deviation rate and the energy cost deviation rate to obtain a sub-operation evaluation index corresponding to the subsystem; Obtain type characteristic data of the subsystem, query a preset weight coefficient list according to the type characteristic data, and obtain the weight coefficient corresponding to the subsystem; The system operation evaluation index of the comprehensive industrial energy monitoring system is obtained by processing the plurality of sub-operation evaluation indexes and the corresponding weight coefficients.

[0011] Optionally, in the control optimization method based on the comprehensive industrial energy monitoring system described in the present application, the system operation evaluation index is compared with a preset system operation evaluation threshold, the operation state of the comprehensive industrial energy monitoring system is determined according to the threshold comparison result, and the energy supply between subsystems is optimized according to the operation state, including: Processing the system operation evaluation index with a preset system operation evaluation benchmark index to obtain an operation evaluation relative value; Comparing the operation evaluation relative value with a preset system operation evaluation threshold value to obtain the operation status of the comprehensive industrial energy monitoring system, including a normal operation status or an abnormal operation status; If the operation evaluation relative value is less than or equal to the preset system operation evaluation threshold, the operation state of the comprehensive industrial energy monitoring system is determined to be an abnormal operation state, and the energy supply between subsystems is optimized according to the operation state; If the operation evaluation relative value is greater than a preset system operation evaluation threshold, the operation state of the comprehensive industrial energy monitoring system is determined to be a normal operation state.

[0012] In a second aspect, the present application provides a control optimization system based on an integrated industrial energy monitoring system, the system comprising: a memory and a processor, the memory comprising a program of a control optimization method based on an integrated industrial energy monitoring system, the program of the control optimization method based on an integrated industrial energy monitoring system being executed by the processor to implement the following steps: Obtain historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset historical period of time, and process the historical weather data and historical production capacity data to obtain the historical energy consumption impact coefficient; Training is performed based on the historical operating parameter data, the historical energy consumption impact coefficient, and the corresponding historical energy consumption data to obtain an energy consumption demand prediction model corresponding to the subsystem; Obtaining the set operating parameter data, forecast weather data and planned production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset time period, and processing them in combination with the energy demand forecasting model to obtain the energy demand forecasting data corresponding to the subsystem; Acquire real-time energy supply data of the subsystem of the comprehensive industrial energy monitoring system, process the real-time energy supply data and the energy demand forecast data to obtain the supply and demand deviation rate of the subsystem, compare the supply and demand deviation rate with a preset supply and demand deviation rate threshold, obtain the energy supply and demand status of the subsystem, and adjust the set operating parameter data according to the energy supply status; Obtain the real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the comprehensive industrial energy monitoring system subsystem, and process the real-time energy consumption data and energy price data in combination with the preset expected energy cost data to obtain the energy cost deviation rate of the subsystem; Processing is performed according to the supply-demand deviation rate and the energy cost deviation rate to obtain a sub-operation evaluation index corresponding to the subsystem, and processing is performed according to the sub-operation evaluation index to obtain a system operation evaluation index of the comprehensive industrial energy monitoring system; The system operation evaluation index is compared with a preset system operation evaluation threshold, the operation status of the comprehensive industrial energy monitoring system is determined according to the threshold comparison result, and the energy supply between subsystems is optimized according to the operation status.

[0013] Optionally, in the control optimization system based on the comprehensive industrial energy monitoring system described in the present application, the acquisition of historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of the comprehensive industrial energy monitoring system subsystem in a historical preset time period, and processing according to the historical weather data and the historical production capacity data to obtain the historical energy consumption impact coefficient include: The historical operation parameter data includes equipment historical operation parameter data and energy historical operation parameter data; The historical weather data includes historical temperature difference mean data and historical sunshine duration data; The historical production capacity data includes historical production data and historical single-unit energy consumption data; The historical temperature difference mean data and the historical sunshine duration data as well as the historical output data and the historical single-unit energy consumption data are input into a preset energy consumption impact coefficient evaluation model for processing to obtain a historical energy consumption impact coefficient.

[0014] Optionally, in the control optimization system based on the comprehensive industrial energy monitoring system described in the present application, the acquisition of set operating parameter data, forecast weather data and planned production capacity data of a subsystem of the comprehensive industrial energy monitoring system for a preset time period, combined with the energy demand forecasting model for processing, to obtain energy demand forecasting data corresponding to the subsystem, includes: Obtain the set operating parameter data, forecast weather data and planned production capacity data for the pre-set time period of the integrated industrial energy monitoring system subsystem; The set operation parameter data includes equipment set operation parameter data and energy set operation parameter data; The predicted weather data includes predicted temperature difference mean data and predicted sunshine duration data; The planned production capacity data includes planned production data and calibrated single-unit energy consumption data; Input the predicted temperature difference mean data and the predicted sunshine duration data as well as the planned output data and the calibrated single-unit energy consumption data into a preset energy consumption impact coefficient evaluation model for processing to obtain a predicted energy consumption impact coefficient; The equipment setting operation parameter data, energy setting operation parameter data and predicted energy consumption impact coefficient are input into the energy consumption demand prediction model for processing to obtain the energy consumption demand prediction data corresponding to the subsystem.

[0015] From the above, it can be seen that the control optimization method and system based on the comprehensive industrial energy monitoring system provided by the present application obtains the historical weather data and historical production capacity data of the historical preset time period of the subsystem of the comprehensive industrial energy monitoring system for processing, obtains the historical energy consumption impact coefficient, combines the historical operating parameter data and the corresponding historical energy consumption data for training, obtains the energy demand prediction model corresponding to the subsystem, obtains the set operating parameter data, predicted weather data and planned production capacity data of the subsystem for a preset time period, combines the energy demand prediction model for processing, obtains the energy demand prediction data corresponding to the subsystem, obtains the real-time energy supply data of the subsystem, combines the energy demand prediction data for processing, and obtains the subsystem. The supply and demand deviation rate of the system is compared with the threshold value to obtain the energy supply and demand status of the subsystem, and the operating parameter data is adjusted according to the energy supply status. The real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the subsystem are obtained and processed in combination with the preset expected energy cost data to obtain the energy cost deviation rate of the subsystem. Combined with the supply and demand deviation rate, the subsystem operation evaluation index is obtained. Further processing is performed to obtain the system operation evaluation index of the comprehensive industrial energy monitoring system. Finally, the operating status of the comprehensive industrial energy monitoring system is determined through the threshold comparison results, and the energy supply between subsystems is optimized according to the operating status, thereby realizing the control optimization of energy within and between subsystems.

[0016] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a control optimization method based on a comprehensive industrial energy monitoring system provided in an embodiment of the present application; Figure 2 A flow chart of obtaining energy demand forecast data corresponding to a subsystem of a control optimization method based on a comprehensive industrial energy monitoring system provided in an embodiment of the present application; Figure 3 A flow chart of obtaining the energy cost deviation rate of a subsystem in a control optimization method based on a comprehensive industrial energy monitoring system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 1 is a flow chart of a control optimization method based on an integrated industrial energy monitoring system in some embodiments of the present application. The control optimization method based on an integrated industrial energy monitoring system is used in a terminal device, such as a computer, a mobile phone terminal, etc. The control optimization method based on an integrated industrial energy monitoring system includes the following steps: S11, obtaining historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of the comprehensive industrial energy monitoring system subsystem for a historical preset time period, and processing the historical weather data and historical production capacity data to obtain a historical energy consumption impact coefficient; S12, training according to the historical operating parameter data, the historical energy consumption impact coefficient and the corresponding historical energy consumption data to obtain an energy consumption demand prediction model corresponding to the subsystem; S13, obtaining the set operating parameter data, forecast weather data and planned production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset time period, and processing them in combination with the energy demand forecasting model to obtain the energy demand forecasting data corresponding to the subsystem; S14, obtaining real-time energy supply data of the subsystem of the comprehensive industrial energy monitoring system, processing the real-time energy supply data and the energy demand forecast data to obtain the supply and demand deviation rate of the subsystem, comparing the supply and demand deviation rate with a preset supply and demand deviation rate threshold, obtaining the energy supply and demand state of the subsystem, and adjusting the set operating parameter data according to the energy supply state; S15, obtaining real-time energy consumption data and energy price data of multiple energy sources in a preset time period of the comprehensive industrial energy monitoring system subsystem, and processing the real-time energy consumption data and energy price data in combination with preset expected energy cost data to obtain the energy cost deviation rate of the subsystem; S16, performing processing according to the supply-demand deviation rate and the energy cost deviation rate to obtain a sub-operation evaluation index corresponding to the subsystem, and performing processing according to the sub-operation evaluation index to obtain a system operation evaluation index of the comprehensive industrial energy monitoring system; S17. Perform a threshold comparison between the system operation evaluation index and a preset system operation evaluation threshold, determine the operation status of the comprehensive industrial energy monitoring system according to the threshold comparison result, and optimize the energy supply between subsystems according to the operation status.

[0022] It should be noted that, in order to achieve the control optimization of energy within and between subsystems in the comprehensive industrial energy monitoring system, each subsystem in the comprehensive industrial energy monitoring system can be preset and determined by technical personnel in this field according to specific circumstances, such as large motors, heating furnaces, production line distribution boxes, etc. Comprehensive industrial energy involves multiple energy forms, such as electricity, coal, natural gas, steam, etc. First, obtain historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of multiple historical preset time periods of the subsystems of the comprehensive industrial energy monitoring system, including historical operating parameter data of equipment and historical operating parameter data of energy, and process them according to the historical weather data and historical production capacity data to obtain the historical energy consumption impact coefficient. According to the historical operating parameter data, the historical energy consumption impact coefficient and the corresponding historical energy consumption data, the initial energy demand prediction model is trained to obtain the energy demand prediction model corresponding to the trained subsystem, and then the set operating parameter data, predicted weather data and planned production capacity data of the subsystems of the comprehensive industrial energy monitoring system in the preset time period are obtained, and the processed data are input into the energy demand prediction model to obtain the energy demand prediction data corresponding to the subsystem. At the same time, the real-time energy supply data of the subsystem is obtained, and the energy demand prediction data is combined with the energy demand prediction data for processing to obtain The supply and demand deviation rate of the subsystem is compared with the preset supply and demand deviation rate threshold to obtain the energy supply and demand status of the subsystem, including the supply and demand balance status or the supply and demand imbalance status. If it is the supply and demand imbalance status, adjust the set operating parameter data to optimize the energy supply and demand of the subsystem; then obtain the real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the subsystem, and process them in combination with the preset expected energy cost data to obtain the energy cost deviation rate of the subsystem, evaluate the energy cost of the subsystem, and process them in combination with the supply and demand deviation rate of the subsystem to obtain the subsystem operation evaluation index corresponding to the subsystem. The index is processed to obtain the system operation evaluation index of the comprehensive industrial energy monitoring system, which is used to evaluate the operation of the entire comprehensive industrial energy system. Finally, the system operation evaluation index is compared with the preset system operation evaluation threshold. The operation status of the comprehensive industrial energy monitoring system is determined according to the threshold comparison result, which is the normal operation status or the abnormal operation status. If it is the abnormal operation status, the energy supply between subsystems is optimized, thereby realizing the optimization of energy monitoring and control on the subsystem side and between subsystems, improving the autonomy and independence within the subsystem, and fully coordinating regional resources, realizing full resource utilization and reducing energy waste.

[0023] According to an embodiment of the present invention, the acquisition of historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of a subsystem of the comprehensive industrial energy monitoring system for a preset historical time period, and processing the historical weather data and the historical production capacity data to obtain the historical energy consumption impact coefficient include: The historical operation parameter data includes equipment historical operation parameter data and energy historical operation parameter data; The historical weather data includes historical temperature difference mean data and historical sunshine duration data; The historical production capacity data includes historical production data and historical single-unit energy consumption data; The historical temperature difference mean data and the historical sunshine duration data as well as the historical output data and the historical single-unit energy consumption data are input into a preset energy consumption impact coefficient evaluation model for processing to obtain a historical energy consumption impact coefficient.

[0024] It should be noted that the historical operating parameter data includes the historical operating parameter data of the equipment and the historical operating parameter data of the energy, wherein the historical operating parameter data of the equipment and the historical operating parameter data of the energy are specifically set by technical personnel in this field according to the subsystem, such as the real-time speed, rated voltage, rated current, efficiency and power factor of the subsystem motor, the real-time current, real-time voltage, frequency fluctuation value and harmonic content of the electric energy; the historical weather data includes the historical temperature difference mean data and the historical sunshine duration data, and the historical production capacity data includes the historical output data and the historical single-unit energy consumption data, wherein the historical temperature difference mean data refers to the average value of the difference between multiple historical temperatures and the preset standard temperature within a preset time period; the historical temperature difference mean data and the historical sunshine duration data as well as the historical output data and the historical single-unit energy consumption data are input into the preset energy consumption impact coefficient evaluation model for processing to obtain the historical energy consumption impact coefficient; The calculation formula of the historical energy consumption impact coefficient in the energy consumption impact coefficient evaluation model is: ; in, Historical energy consumption impact coefficient, , , , They are historical temperature difference average data, historical sunshine duration data, historical production data and historical single-unit energy consumption data. , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset comprehensive industrial energy monitoring platform).

[0025] Please refer to Figure 2 , Figure 2 It is a flowchart of obtaining energy demand forecast data corresponding to a subsystem of a control optimization method based on an integrated industrial energy monitoring system in some embodiments of the present application. According to an embodiment of the present invention, the set operating parameter data, forecast weather data and planned production capacity data of a subsystem of the integrated industrial energy monitoring system for a preset time period are obtained, and the energy demand forecast data corresponding to the subsystem is obtained by processing in combination with the energy demand forecast model, including: S21, obtaining set operating parameter data, forecast weather data and planned production capacity data for a preset time period of the subsystem of the comprehensive industrial energy monitoring system; S22, the set operating parameter data includes equipment set operating parameter data and energy set operating parameter data; S23, the predicted weather data includes predicted temperature difference mean data and predicted sunshine duration data; S24, the planned production capacity data includes planned production data and calibrated single-unit energy consumption data; S25, inputting the predicted temperature difference mean data and the predicted sunshine duration data as well as the planned output data and the calibrated single-unit energy consumption data into a preset energy consumption impact coefficient evaluation model for processing, to obtain a predicted energy consumption impact coefficient; S26, inputting the equipment setting operation parameter data, energy setting operation parameter data and predicted energy consumption impact coefficient into the energy consumption demand prediction model for processing, and obtaining the energy consumption demand prediction data corresponding to the subsystem.

[0026] It should be noted that in order to predict the energy demand of the subsystem, firstly, the set operating parameter data of the subsystem in the preset time period, including the set operating parameter data of the equipment and the set operating parameter data of the energy, including the predicted weather data of the predicted temperature difference mean data and the predicted light duration data, wherein the predicted temperature difference mean data refers to the average value of the difference between multiple predicted temperatures and the preset standard temperature in the preset time period, including the planned production data and the planned production capacity data of the calibrated single energy consumption data, the predicted temperature difference mean data and the predicted light duration data as well as the planned production data and the calibrated single energy consumption data are input into the preset energy consumption impact coefficient evaluation model for processing, and the predicted energy consumption impact coefficient is obtained; The calculation formula for predicting the energy impact coefficient in the energy impact coefficient evaluation model is: ; in, Predict the energy consumption impact coefficient, , , , They are the predicted temperature difference mean data, predicted sunshine duration data, planned output data and calibrated single unit energy consumption data. , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset comprehensive industrial energy monitoring platform); The equipment setting operating parameter data, energy setting operating parameter data and predicted energy consumption impact coefficient are input into the trained energy demand prediction model for processing to obtain the energy demand prediction data corresponding to the subsystem.

[0027] According to an embodiment of the present invention, the real-time energy supply data of the subsystem of the comprehensive industrial energy monitoring system is obtained, the real-time energy supply data and the energy demand forecast data are processed to obtain the supply and demand deviation rate of the subsystem, the supply and demand deviation rate is compared with a preset supply and demand deviation rate threshold, the energy supply and demand state of the subsystem is obtained, and the set operating parameter data is adjusted according to the energy supply state, including: Obtain real-time energy supply data of subsystems of the comprehensive industrial energy monitoring system; Processing the real-time energy supply data and the energy demand forecast data to obtain a supply-demand deviation rate of the subsystem; Comparing the supply-demand deviation rate with a preset supply-demand deviation rate threshold, obtaining the energy supply-demand state of the subsystem, including a supply-demand balance state or a supply-demand imbalance state; If the supply-demand deviation rate is less than or equal to a preset supply-demand deviation rate threshold, it is determined that the energy supply-demand state of the subsystem is a supply-demand balance state; If the supply-demand deviation rate is greater than a preset supply-demand deviation rate threshold, the energy supply-demand state of the subsystem is determined to be a supply-demand imbalance state, and the set operating parameter data is adjusted.

[0028] It should be noted that in order to evaluate the energy supply and demand of the subsystem, the real-time energy supply data of the subsystem is first obtained, and then processed with the obtained energy demand forecast data to obtain the supply and demand deviation rate of the subsystem. The supply and demand deviation rate refers to the ratio of the absolute value of the difference between the real-time energy supply data and the energy demand forecast data to the real-time energy supply data. For example, the real-time energy supply data is 10 and the energy demand forecast data is 8. The obtained supply and demand deviation rate is compared with the preset supply and demand deviation rate threshold to obtain the energy supply and demand state of the subsystem. In this embodiment, the supply and demand deviation rate threshold is set to (0, 0.25], (0.25, 1], corresponding to the supply and demand balance state and the supply and demand imbalance state, respectively. For example, if the obtained supply and demand deviation rate is 0.2, which is less than the preset supply and demand deviation rate threshold, the energy supply and demand state of the subsystem is determined to be the supply and demand balance state. If the obtained supply and demand deviation rate is 0.3, which is greater than the preset supply and demand deviation rate threshold, the energy supply and demand state of the subsystem is determined to be the supply and demand imbalance state, and the operation parameter data is adjusted and set according to the supply and demand deviation rate to balance the energy supply and demand of the subsystem.

[0029] Please refer to Figure 3 , Figure 3It is a flow chart of obtaining the energy cost deviation rate of a subsystem based on the control optimization method of the comprehensive industrial energy monitoring system in some embodiments of the present application. According to an embodiment of the present invention, the real-time energy consumption data and energy price data of multiple energy sources in a preset time period of the subsystem of the comprehensive industrial energy monitoring system are obtained, and the energy cost deviation rate of the subsystem is obtained by processing the real-time energy consumption data and energy price data in combination with the preset expected energy cost data, including: S31, obtaining real-time energy consumption data and energy price data of multiple energy sources in a preset time period of the comprehensive industrial energy monitoring system subsystem; S32, multiplying the real-time energy consumption data by the energy price data and summing the results to obtain energy consumption cost data of the subsystem; S33: Process the energy cost data and the preset expected energy cost data to obtain the energy cost deviation rate of the subsystem.

[0030] It should be noted that in order to understand the energy usage cost, first obtain the real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the subsystem, multiply the real-time energy consumption data by the energy price data and sum them up to obtain the energy cost data of the subsystem; The energy cost data calculation formula is: ; in, For energy cost data, , are the real-time energy consumption data and energy price data of the i-th energy source; The energy cost deviation rate of the subsystem is obtained by processing the obtained energy cost data and the preset expected energy cost data; The energy cost deviation rate calculation formula is: .

[0031] in, is the energy cost deviation rate, , They are energy cost data and preset expected energy cost data respectively.

[0032] According to an embodiment of the present invention, the processing according to the supply-demand deviation rate and the energy cost deviation rate to obtain the sub-operation evaluation index corresponding to the subsystem, and the processing according to the sub-operation evaluation index to obtain the system operation evaluation index of the comprehensive industrial energy monitoring system include: Processing is performed according to the supply-demand deviation rate and the energy cost deviation rate to obtain a sub-operation evaluation index corresponding to the subsystem; Obtain type characteristic data of the subsystem, query a preset weight coefficient list according to the type characteristic data, and obtain the weight coefficient corresponding to the subsystem; The system operation evaluation index of the comprehensive industrial energy monitoring system is obtained by processing the plurality of sub-operation evaluation indexes and the corresponding weight coefficients.

[0033] It should be noted that the operation of the subsystem is comprehensively evaluated based on the energy supply and demand and cost usage of the subsystem. First, the obtained supply and demand deviation rate and energy cost deviation rate are processed to obtain the sub-operation evaluation index corresponding to the subsystem; The calculation formula of the sub-operation evaluation index is: ; in, is the sub-run evaluation index, , are the supply-demand deviation rate and the energy cost deviation rate, respectively. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset comprehensive industrial energy monitoring platform); Further obtain the type characteristic data of the subsystem, query the preset weight coefficient list according to the type characteristic data, and obtain the weight coefficient corresponding to the subsystem, wherein the preset weight coefficient list is obtained by querying the preset comprehensive industrial energy monitoring platform, and the system operation evaluation index of the comprehensive industrial energy monitoring system is obtained by processing according to the multiple sub-operation evaluation indexes and the corresponding weight coefficients; The calculation formula of the system operation evaluation index is: ; in, It is the system operation evaluation index. , are the sub-operation evaluation index and weight coefficient of the j-th subsystem, It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset comprehensive industrial energy monitoring platform).

[0034] According to an embodiment of the present invention, the system operation evaluation index is compared with a preset system operation evaluation threshold, the operation state of the comprehensive industrial energy monitoring system is determined according to the threshold comparison result, and the energy supply between subsystems is optimized according to the operation state, including: Processing the system operation evaluation index with a preset system operation evaluation benchmark index to obtain an operation evaluation relative value; Comparing the operation evaluation relative value with a preset system operation evaluation threshold value to obtain the operation status of the comprehensive industrial energy monitoring system, including a normal operation status or an abnormal operation status; If the operation evaluation relative value is less than or equal to the preset system operation evaluation threshold, the operation state of the comprehensive industrial energy monitoring system is determined to be an abnormal operation state, and the energy supply between subsystems is optimized according to the operation state; If the operation evaluation relative value is greater than a preset system operation evaluation threshold, the operation state of the comprehensive industrial energy monitoring system is determined to be a normal operation state.

[0035] It should be noted that the obtained system operation evaluation index is processed with the preset system operation evaluation benchmark index to obtain an operation evaluation relative value. For example, if the system operation evaluation index is 7 and the preset system operation evaluation benchmark index is 10, then 7 / 10=0.7 is the operation evaluation relative value. Then, the operation evaluation relative value is compared with the preset system operation evaluation threshold to obtain the operation status of the comprehensive industrial energy monitoring system. In this embodiment, the preset system operation evaluation threshold is set to (0, 0.75], (0.75, 1], corresponding to the normal operation status and the abnormal operation status, respectively. For example, if the obtained operation evaluation relative value is 0.7, which is less than the preset system operation evaluation threshold, then the operation status of the comprehensive industrial energy monitoring system is determined to be an abnormal operation status, and the energy supply between subsystems is optimized according to the operation status; if the obtained operation evaluation relative value is 0.9, which is greater than the preset system operation evaluation threshold, then the operation status of the comprehensive industrial energy monitoring system is determined to be a normal operation status.

[0036] It is worth mentioning that according to an embodiment of the present invention, it also includes: Obtain energy consumption time data, total self-supplied energy data and total energy demand data from the comprehensive industrial energy monitoring system; Comparing the total self-supplied energy data with the total energy demand data; If the self-supplied energy total amount data is greater than the total energy demand data, the self-supplied energy total amount data and the total energy demand data are processed to obtain excess energy supply data; Query a preset energy price list according to the energy consumption time data to obtain real-time energy prices; Calculate the excess energy value data based on the excess energy supply data and the real-time energy price; Compare the excess energy value data with a preset energy value threshold, and determine an excess energy disposal plan based on the threshold comparison result; If it is greater than the preset energy value threshold, it will be sold on the grid; If it is less than or equal to the preset energy value threshold, the energy storage device energy storage response is initiated.

[0037] It should be noted that in order to reduce energy operating costs and maximize the advantages of the company's self-supply, the energy consumption time data, total self-supply data and total energy demand data of the comprehensive industrial energy monitoring system are obtained. First, the total self-supply data and the total energy demand data are compared. If the total self-supply data is greater than the total energy demand data, the difference between the total self-supply data and the total energy demand data is the excess energy supply data, indicating that the company's self-supply has a surplus and needs to be arranged reasonably. Further, the preset energy price list is queried according to the energy consumption time data to obtain the real-time energy price, and then the excess energy supply data and the real-time energy price are calculated. The excess energy value data is calculated based on the grid to obtain the excess energy value data. For example, the excess energy supply data is 100, and the real-time energy price is 0.55, then 100*0.55=55 is the excess energy value data; finally, the excess energy value data is compared with the preset energy value threshold, and the excess energy disposal plan is determined according to the threshold comparison result. In this embodiment, the energy value threshold is set to 100. For example, if the excess energy value data obtained is 55, which is less than the preset energy value threshold, the energy storage response of the energy storage device is started. If the excess energy value data obtained is 101, it is sold on the grid to improve profitability.

[0038] It is worth mentioning that according to an embodiment of the present invention, it also includes: If the self-supplied energy total amount data is less than the total energy demand data, the self-supplied energy total amount data and the total energy demand data are processed to obtain energy supply difference data; Performing a threshold comparison between the energy supply difference data and a preset difference threshold, and determining an energy supply adjustment plan according to the threshold comparison result; Extracting a first preset difference threshold and a second preset difference threshold according to the preset difference threshold, wherein the first preset difference threshold is smaller than the second preset difference threshold; If the energy supply difference data is less than or equal to a first preset difference threshold, adjusting the setting parameters of the self-powered device; If the energy supply difference data is greater than the first preset difference threshold and less than or equal to the second preset difference threshold, activating the external network energy supply response; If the energy supply difference data is greater than the second preset difference threshold, the setting parameters of the self-powered device are adjusted, and the external network energy supply response is activated.

[0039] It should be noted that if the total self-supplied energy data is less than the total energy demand data, it means that the current self-supplied energy is insufficient and the energy supply capacity needs to be improved. In order to reduce the energy cost, the energy supply needs to be adjusted according to the specific situation. First, the total energy demand data is subtracted from the total self-supplied energy data to obtain the energy supply difference data. For example, if the total energy demand data is 100 and the total self-supplied energy data is 80, then 100-80=20 is the energy supply difference data. Then, the energy supply difference data is compared with the preset difference threshold. The energy supply adjustment plan is determined according to the threshold comparison result. The preset difference threshold is used to adjust the energy supply. The value includes a first preset difference threshold and a second preset difference threshold. In this embodiment, the first preset difference threshold is set to 25, and the second preset difference threshold is set to 30. For example, if the obtained energy supply difference data is 20, which is less than the first preset difference threshold, the setting parameters of the self-energy supply device are adjusted; if the obtained energy supply difference data is 28, which is greater than the first preset difference threshold and less than the second preset difference threshold, the external network energy supply response is activated; if the obtained energy supply difference data is 35, which is greater than the second preset difference threshold, the setting parameters of the self-energy supply device are adjusted, and the external network energy supply response is activated.

[0040] It is worth mentioning that according to an embodiment of the present invention, if the energy supply difference data is greater than the second preset difference threshold, the setting parameters of the self-powered device are adjusted, and the external network energy supply response is activated, which also includes: Obtain energy usage information of subsystems of the integrated industrial energy monitoring system; According to the energy consumption information, a preset energy consumption priority list is searched to obtain the energy consumption priority corresponding to the subsystem; Sort the energy usage priorities from high to low to obtain a subsystem energy usage sequence table; The energy allocation plan is adjusted according to the energy usage sequence table.

[0041] It should be noted that if self-supplied energy is insufficient, the energy supply of important equipment should be guaranteed first. First, the energy consumption information of the subsystem should be obtained, and the preset energy consumption priority list should be queried based on the energy consumption information to obtain the energy consumption priority corresponding to the subsystem. The preset energy consumption priority list is obtained by querying the preset comprehensive industrial energy monitoring platform, and then the energy consumption priorities are sorted from high to low to obtain the subsystem energy consumption sequence table. Finally, the energy allocation plan is adjusted according to the energy consumption sequence table to give priority to ensuring the energy consumption of subsystems with high energy consumption priorities.

[0042] The present invention also discloses a control optimization system based on an integrated industrial energy monitoring system, comprising a memory and a processor, wherein the memory comprises a control optimization method program based on an integrated industrial energy monitoring system, and when the control optimization method program based on an integrated industrial energy monitoring system is executed by the processor, the following steps are implemented: Obtain historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset historical period of time, and process the historical weather data and historical production capacity data to obtain the historical energy consumption impact coefficient; Training is performed based on the historical operating parameter data, the historical energy consumption impact coefficient, and the corresponding historical energy consumption data to obtain an energy consumption demand prediction model corresponding to the subsystem; Obtaining the set operating parameter data, forecast weather data and planned production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset time period, and processing them in combination with the energy demand forecasting model to obtain the energy demand forecasting data corresponding to the subsystem; Acquire real-time energy supply data of the subsystem of the comprehensive industrial energy monitoring system, process the real-time energy supply data and the energy demand forecast data to obtain the supply and demand deviation rate of the subsystem, compare the supply and demand deviation rate with a preset supply and demand deviation rate threshold, obtain the energy supply and demand status of the subsystem, and adjust the set operating parameter data according to the energy supply status; Obtain the real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the comprehensive industrial energy monitoring system subsystem, and process the real-time energy consumption data and energy price data in combination with the preset expected energy cost data to obtain the energy cost deviation rate of the subsystem; Processing is performed according to the supply-demand deviation rate and the energy cost deviation rate to obtain a sub-operation evaluation index corresponding to the subsystem, and processing is performed according to the sub-operation evaluation index to obtain a system operation evaluation index of the comprehensive industrial energy monitoring system; The system operation evaluation index is compared with a preset system operation evaluation threshold, the operation status of the comprehensive industrial energy monitoring system is determined according to the threshold comparison result, and the energy supply between subsystems is optimized according to the operation status.

[0043] It should be noted that, in order to achieve the control optimization of energy within and between subsystems in the comprehensive industrial energy monitoring system, each subsystem in the comprehensive industrial energy monitoring system can be preset and determined by technical personnel in this field according to specific circumstances, such as large motors, heating furnaces, production line distribution boxes, etc. Comprehensive industrial energy involves multiple energy forms, such as electricity, coal, natural gas, steam, etc. First, obtain historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of multiple historical preset time periods of the subsystems of the comprehensive industrial energy monitoring system, including historical operating parameter data of equipment and historical operating parameter data of energy, and process them according to the historical weather data and historical production capacity data to obtain the historical energy consumption impact coefficient. According to the historical operating parameter data, the historical energy consumption impact coefficient and the corresponding historical energy consumption data, the initial energy demand prediction model is trained to obtain the energy demand prediction model corresponding to the trained subsystem, and then the set operating parameter data, predicted weather data and planned production capacity data of the subsystems of the comprehensive industrial energy monitoring system in the preset time period are obtained, and the processed data are input into the energy demand prediction model to obtain the energy demand prediction data corresponding to the subsystem. At the same time, the real-time energy supply data of the subsystem is obtained, and the energy demand prediction data is combined with the energy demand prediction data for processing to obtain The supply and demand deviation rate of the subsystem is compared with the preset supply and demand deviation rate threshold to obtain the energy supply and demand status of the subsystem, including the supply and demand balance status or the supply and demand imbalance status. If it is the supply and demand imbalance status, adjust the set operating parameter data to optimize the energy supply and demand of the subsystem; then obtain the real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the subsystem, and process them in combination with the preset expected energy cost data to obtain the energy cost deviation rate of the subsystem, evaluate the energy cost of the subsystem, and process them in combination with the supply and demand deviation rate of the subsystem to obtain the subsystem operation evaluation index corresponding to the subsystem. The index is processed to obtain the system operation evaluation index of the comprehensive industrial energy monitoring system, which is used to evaluate the operation of the entire comprehensive industrial energy system. Finally, the system operation evaluation index is compared with the preset system operation evaluation threshold. The operation status of the comprehensive industrial energy monitoring system is determined according to the threshold comparison result, which is the normal operation status or the abnormal operation status. If it is the abnormal operation status, the energy supply between subsystems is optimized, thereby realizing the optimization of energy monitoring and control on the subsystem side and between subsystems, improving the autonomy and independence within the subsystem, and fully coordinating regional resources, realizing full resource utilization and reducing energy waste.

[0044] According to an embodiment of the present invention, the acquisition of historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of a subsystem of the comprehensive industrial energy monitoring system for a preset historical time period, and processing the historical weather data and the historical production capacity data to obtain the historical energy consumption impact coefficient include: The historical operation parameter data includes equipment historical operation parameter data and energy historical operation parameter data; The historical weather data includes historical temperature difference mean data and historical sunshine duration data; The historical production capacity data includes historical production data and historical single-unit energy consumption data; The historical temperature difference mean data and the historical sunshine duration data as well as the historical output data and the historical single-unit energy consumption data are input into a preset energy consumption impact coefficient evaluation model for processing to obtain a historical energy consumption impact coefficient.

[0045] It should be noted that the historical operating parameter data includes the historical operating parameter data of the equipment and the historical operating parameter data of the energy, wherein the historical operating parameter data of the equipment and the historical operating parameter data of the energy are specifically set by technical personnel in this field according to the subsystem, such as the real-time speed, rated voltage, rated current, efficiency and power factor of the subsystem motor, the real-time current, real-time voltage, frequency fluctuation value and harmonic content of the electric energy; the historical weather data includes the historical temperature difference mean data and the historical sunshine duration data, and the historical production capacity data includes the historical output data and the historical single-unit energy consumption data, wherein the historical temperature difference mean data refers to the average value of the difference between multiple historical temperatures and the preset standard temperature within a preset time period; the historical temperature difference mean data and the historical sunshine duration data as well as the historical output data and the historical single-unit energy consumption data are input into the preset energy consumption impact coefficient evaluation model for processing to obtain the historical energy consumption impact coefficient; The calculation formula of the historical energy consumption impact coefficient in the energy consumption impact coefficient evaluation model is: ; in, Historical energy consumption impact coefficient, , , , They are historical temperature difference average data, historical sunshine duration data, historical production data and historical single-unit energy consumption data. , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset comprehensive industrial energy monitoring platform).

[0046] According to an embodiment of the present invention, the step of obtaining the set operating parameter data, the forecast weather data and the planned production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset time period, and processing them in combination with the energy demand forecasting model to obtain the energy demand forecasting data corresponding to the subsystem includes: Obtain the set operating parameter data, forecast weather data and planned production capacity data for the pre-set time period of the integrated industrial energy monitoring system subsystem; The set operation parameter data includes equipment set operation parameter data and energy set operation parameter data; The predicted weather data includes predicted temperature difference mean data and predicted sunshine duration data; The planned production capacity data includes planned production data and calibrated single-unit energy consumption data; Input the predicted temperature difference mean data and the predicted sunshine duration data as well as the planned output data and the calibrated single-unit energy consumption data into a preset energy consumption impact coefficient evaluation model for processing to obtain a predicted energy consumption impact coefficient; The equipment setting operation parameter data, energy setting operation parameter data and predicted energy consumption impact coefficient are input into the energy consumption demand prediction model for processing to obtain the energy consumption demand prediction data corresponding to the subsystem.

[0047] It should be noted that in order to predict the energy demand of the subsystem, firstly, the set operating parameter data of the subsystem in the preset time period, including the set operating parameter data of the equipment and the set operating parameter data of the energy, including the predicted weather data of the predicted temperature difference mean data and the predicted light duration data, wherein the predicted temperature difference mean data refers to the average value of the difference between multiple predicted temperatures and the preset standard temperature in the preset time period, including the planned production data and the planned production capacity data of the calibrated single energy consumption data, the predicted temperature difference mean data and the predicted light duration data as well as the planned production data and the calibrated single energy consumption data are input into the preset energy consumption impact coefficient evaluation model for processing, and the predicted energy consumption impact coefficient is obtained; The calculation formula for predicting the energy impact coefficient in the energy impact coefficient evaluation model is: ; in, Predict the energy consumption impact coefficient, , , , They are the predicted temperature difference mean data, predicted sunshine duration data, planned output data and calibrated single unit energy consumption data. , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset comprehensive industrial energy monitoring platform); The equipment setting operating parameter data, energy setting operating parameter data and predicted energy consumption impact coefficient are input into the trained energy demand prediction model for processing to obtain the energy demand prediction data corresponding to the subsystem.

[0048] According to an embodiment of the present invention, the real-time energy supply data of the subsystem of the comprehensive industrial energy monitoring system is obtained, the real-time energy supply data and the energy demand forecast data are processed to obtain the supply and demand deviation rate of the subsystem, the supply and demand deviation rate is compared with a preset supply and demand deviation rate threshold, the energy supply and demand state of the subsystem is obtained, and the set operating parameter data is adjusted according to the energy supply state, including: Obtain real-time energy supply data of subsystems of the comprehensive industrial energy monitoring system; Processing the real-time energy supply data and the energy demand forecast data to obtain a supply-demand deviation rate of the subsystem; Comparing the supply-demand deviation rate with a preset supply-demand deviation rate threshold, obtaining the energy supply-demand state of the subsystem, including a supply-demand balance state or a supply-demand imbalance state; If the supply-demand deviation rate is less than or equal to a preset supply-demand deviation rate threshold, it is determined that the energy supply-demand state of the subsystem is a supply-demand balance state; If the supply-demand deviation rate is greater than a preset supply-demand deviation rate threshold, the energy supply-demand state of the subsystem is determined to be a supply-demand imbalance state, and the set operating parameter data is adjusted.

[0049] It should be noted that in order to evaluate the energy supply and demand of the subsystem, the real-time energy supply data of the subsystem is first obtained, and then processed with the obtained energy demand forecast data to obtain the supply and demand deviation rate of the subsystem. The supply and demand deviation rate refers to the ratio of the absolute value of the difference between the real-time energy supply data and the energy demand forecast data to the real-time energy supply data. For example, the real-time energy supply data is 10 and the energy demand forecast data is 8. The obtained supply and demand deviation rate is compared with the preset supply and demand deviation rate threshold to obtain the energy supply and demand state of the subsystem. In this embodiment, the supply and demand deviation rate threshold is set to (0, 0.25], (0.25, 1], corresponding to the supply and demand balance state and the supply and demand imbalance state, respectively. For example, if the obtained supply and demand deviation rate is 0.2, which is less than the preset supply and demand deviation rate threshold, the energy supply and demand state of the subsystem is determined to be the supply and demand balance state. If the obtained supply and demand deviation rate is 0.3, which is greater than the preset supply and demand deviation rate threshold, the energy supply and demand state of the subsystem is determined to be the supply and demand imbalance state, and the operation parameter data is adjusted and set according to the supply and demand deviation rate to balance the energy supply and demand of the subsystem.

[0050] According to an embodiment of the present invention, the real-time energy consumption data and energy price data of multiple energy sources in a preset time period of the subsystem of the comprehensive industrial energy monitoring system are obtained, and the energy cost deviation rate of the subsystem is obtained by processing the real-time energy consumption data and the energy price data in combination with the preset expected energy cost data, including: Obtain real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the comprehensive industrial energy monitoring system subsystem; The real-time energy consumption data is multiplied by the energy price data and the sum is processed to obtain the energy consumption cost data of the subsystem; The energy cost deviation rate of the subsystem is obtained by processing the energy cost data and the preset expected energy cost data.

[0051] It should be noted that in order to understand the energy usage cost, first obtain the real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the subsystem, multiply the real-time energy consumption data by the energy price data and sum them up to obtain the energy cost data of the subsystem; The energy cost data calculation formula is: ; in, For energy cost data, , are the real-time energy consumption data and energy price data of the i-th energy source; The energy cost deviation rate of the subsystem is obtained by processing the obtained energy cost data and the preset expected energy cost data; The energy cost deviation rate calculation formula is: .

[0052] in, is the energy cost deviation rate, , They are energy cost data and preset expected energy cost data respectively.

[0053] According to an embodiment of the present invention, the processing according to the supply-demand deviation rate and the energy cost deviation rate to obtain the sub-operation evaluation index corresponding to the subsystem, and the processing according to the sub-operation evaluation index to obtain the system operation evaluation index of the comprehensive industrial energy monitoring system include: Processing is performed according to the supply-demand deviation rate and the energy cost deviation rate to obtain a sub-operation evaluation index corresponding to the subsystem; Obtain type characteristic data of the subsystem, query a preset weight coefficient list according to the type characteristic data, and obtain the weight coefficient corresponding to the subsystem; The system operation evaluation index of the comprehensive industrial energy monitoring system is obtained by processing the plurality of sub-operation evaluation indexes and the corresponding weight coefficients.

[0054] It should be noted that the operation of the subsystem is comprehensively evaluated based on the energy supply and demand and cost usage of the subsystem. First, the obtained supply and demand deviation rate and energy cost deviation rate are processed to obtain the sub-operation evaluation index corresponding to the subsystem; The calculation formula of the sub-operation evaluation index is: ; in, is the sub-run evaluation index, , are the supply-demand deviation rate and the energy cost deviation rate, respectively. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset comprehensive industrial energy monitoring platform); Further obtain the type characteristic data of the subsystem, query the preset weight coefficient list according to the type characteristic data, and obtain the weight coefficient corresponding to the subsystem, wherein the preset weight coefficient list is obtained by querying the preset comprehensive industrial energy monitoring platform, and the system operation evaluation index of the comprehensive industrial energy monitoring system is obtained by processing according to the multiple sub-operation evaluation indexes and the corresponding weight coefficients; The calculation formula of the system operation evaluation index is: ; in, It is the system operation evaluation index. , are the sub-operation evaluation index and weight coefficient of the j-th subsystem, It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset comprehensive industrial energy monitoring platform).

[0055] According to an embodiment of the present invention, the system operation evaluation index is compared with a preset system operation evaluation threshold, the operation state of the comprehensive industrial energy monitoring system is determined according to the threshold comparison result, and the energy supply between subsystems is optimized according to the operation state, including: Processing the system operation evaluation index with a preset system operation evaluation benchmark index to obtain an operation evaluation relative value; Comparing the operation evaluation relative value with a preset system operation evaluation threshold value to obtain the operation status of the comprehensive industrial energy monitoring system, including a normal operation status or an abnormal operation status; If the operation evaluation relative value is less than or equal to the preset system operation evaluation threshold, the operation state of the comprehensive industrial energy monitoring system is determined to be an abnormal operation state, and the energy supply between subsystems is optimized according to the operation state; If the operation evaluation relative value is greater than a preset system operation evaluation threshold, the operation state of the comprehensive industrial energy monitoring system is determined to be a normal operation state.

[0056] It should be noted that the obtained system operation evaluation index is processed with the preset system operation evaluation benchmark index to obtain an operation evaluation relative value. For example, if the system operation evaluation index is 7 and the preset system operation evaluation benchmark index is 10, then 7 / 10=0.7 is the operation evaluation relative value. Then, the operation evaluation relative value is compared with the preset system operation evaluation threshold to obtain the operation status of the comprehensive industrial energy monitoring system. In this embodiment, the preset system operation evaluation threshold is set to (0, 0.75], (0.75, 1], corresponding to the normal operation status and the abnormal operation status, respectively. For example, if the obtained operation evaluation relative value is 0.7, which is less than the preset system operation evaluation threshold, then the operation status of the comprehensive industrial energy monitoring system is determined to be an abnormal operation status, and the energy supply between subsystems is optimized according to the operation status; if the obtained operation evaluation relative value is 0.9, which is greater than the preset system operation evaluation threshold, then the operation status of the comprehensive industrial energy monitoring system is determined to be a normal operation status.

[0057] It is worth mentioning that according to an embodiment of the present invention, it also includes: Obtain energy consumption time data, total self-supplied energy data and total energy demand data from the comprehensive industrial energy monitoring system; Comparing the total self-supplied energy data with the total energy demand data; If the self-supplied energy total amount data is greater than the total energy demand data, the self-supplied energy total amount data and the total energy demand data are processed to obtain excess energy supply data; Query a preset energy price list according to the energy consumption time data to obtain real-time energy prices; Calculate the excess energy value data based on the excess energy supply data and the real-time energy price; Compare the excess energy value data with a preset energy value threshold, and determine an excess energy disposal plan based on the threshold comparison result; If it is greater than the preset energy value threshold, it will be sold on the grid; If it is less than or equal to the preset energy value threshold, the energy storage device energy storage response is initiated.

[0058] It should be noted that in order to reduce energy operating costs and maximize the advantages of the company's self-supply, the energy consumption time data, total self-supply data and total energy demand data of the comprehensive industrial energy monitoring system are obtained. First, the total self-supply data and the total energy demand data are compared. If the total self-supply data is greater than the total energy demand data, the difference between the total self-supply data and the total energy demand data is the excess energy supply data, indicating that the company's self-supply has a surplus and needs to be arranged reasonably. Further, the preset energy price list is queried according to the energy consumption time data to obtain the real-time energy price, and then the excess energy supply data and the real-time energy price are calculated. The excess energy value data is calculated based on the grid to obtain the excess energy value data. For example, the excess energy supply data is 100, and the real-time energy price is 0.55, then 100*0.55=55 is the excess energy value data; finally, the excess energy value data is compared with the preset energy value threshold, and the excess energy disposal plan is determined according to the threshold comparison result. In this embodiment, the energy value threshold is set to 100. For example, if the excess energy value data obtained is 55, which is less than the preset energy value threshold, the energy storage response of the energy storage device is started. If the excess energy value data obtained is 101, it is sold on the grid to improve profitability.

[0059] It is worth mentioning that according to an embodiment of the present invention, it also includes: If the self-supplied energy total amount data is less than the total energy demand data, the self-supplied energy total amount data and the total energy demand data are processed to obtain energy supply difference data; Performing a threshold comparison between the energy supply difference data and a preset difference threshold, and determining an energy supply adjustment plan according to the threshold comparison result; Extracting a first preset difference threshold and a second preset difference threshold according to the preset difference threshold, wherein the first preset difference threshold is smaller than the second preset difference threshold; If the energy supply difference data is less than or equal to a first preset difference threshold, adjusting the setting parameters of the self-powered device; If the energy supply difference data is greater than the first preset difference threshold and less than or equal to the second preset difference threshold, activating the external network energy supply response; If the energy supply difference data is greater than the second preset difference threshold, the setting parameters of the self-powered device are adjusted, and the external network energy supply response is activated.

[0060] It should be noted that if the total self-supplied energy data is less than the total energy demand data, it means that the current self-supplied energy is insufficient and the energy supply capacity needs to be improved. In order to reduce the energy cost, the energy supply needs to be adjusted according to the specific situation. First, the total energy demand data is subtracted from the total self-supplied energy data to obtain the energy supply difference data. For example, if the total energy demand data is 100 and the total self-supplied energy data is 80, then 100-80=20 is the energy supply difference data. Then, the energy supply difference data is compared with the preset difference threshold. The energy supply adjustment plan is determined according to the threshold comparison result. The preset difference threshold is used to adjust the energy supply. The value includes a first preset difference threshold and a second preset difference threshold. In this embodiment, the first preset difference threshold is set to 25, and the second preset difference threshold is set to 30. For example, if the obtained energy supply difference data is 20, which is less than the first preset difference threshold, the setting parameters of the self-energy supply device are adjusted; if the obtained energy supply difference data is 28, which is greater than the first preset difference threshold and less than the second preset difference threshold, the external network energy supply response is activated; if the obtained energy supply difference data is 35, which is greater than the second preset difference threshold, the setting parameters of the self-energy supply device are adjusted, and the external network energy supply response is activated.

[0061] It is worth mentioning that according to an embodiment of the present invention, if the energy supply difference data is greater than the second preset difference threshold, the setting parameters of the self-powered device are adjusted, and the external network energy supply response is activated, which also includes: Obtain energy usage information of subsystems of the integrated industrial energy monitoring system; According to the energy consumption information, a preset energy consumption priority list is searched to obtain the energy consumption priority corresponding to the subsystem; Sort the energy usage priorities from high to low to obtain a subsystem energy usage sequence table; The energy allocation plan is adjusted according to the energy usage sequence table.

[0062] It should be noted that if self-supplied energy is insufficient, the energy supply of important equipment should be guaranteed first. First, the energy consumption information of the subsystem should be obtained, and the preset energy consumption priority list should be queried based on the energy consumption information to obtain the energy consumption priority corresponding to the subsystem. The preset energy consumption priority list is obtained by querying the preset comprehensive industrial energy monitoring platform, and then the energy consumption priorities are sorted from high to low to obtain the subsystem energy consumption sequence table. Finally, the energy allocation plan is adjusted according to the energy consumption sequence table to give priority to ensuring the energy consumption of subsystems with high energy consumption priorities.

[0063] The third aspect of the present invention provides a readable storage medium, which stores a control optimization method program based on a comprehensive industrial energy monitoring system. When the control optimization method program based on the comprehensive industrial energy monitoring system is executed by a processor, the steps of the control optimization method based on the comprehensive industrial energy monitoring system as described in any one of the above items are implemented.

[0064] The control optimization method and system based on the comprehensive industrial energy monitoring system disclosed in the present invention obtains the historical weather data and historical production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset time period for processing, obtains the historical energy consumption impact coefficient, combines the historical operating parameter data and the corresponding historical energy consumption data for training, obtains the energy demand prediction model corresponding to the subsystem, obtains the set operating parameter data, predicted weather data and planned production capacity data of the subsystem for a preset time period, combines the energy demand prediction model for processing, obtains the energy demand prediction data corresponding to the subsystem, obtains the real-time energy supply data of the subsystem, combines the energy demand prediction data for processing, and obtains the energy demand prediction data of the subsystem. The supply and demand deviation rate is used to obtain the energy supply and demand status of the subsystem through threshold comparison, and the operating parameter data is set according to the energy supply status. The real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the subsystem are obtained and processed in combination with the preset expected energy cost data to obtain the energy cost deviation rate of the subsystem. Combined with the supply and demand deviation rate, the subsystem operation evaluation index is obtained. After further processing, the system operation evaluation index of the comprehensive industrial energy monitoring system is obtained. Finally, the operation status of the comprehensive industrial energy monitoring system is determined through the threshold comparison results, and the energy supply between subsystems is optimized according to the operation status, thereby realizing the control optimization of energy within and between subsystems.

[0065] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0066] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0067] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0068] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0069] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A control optimization method based on a comprehensive industrial energy monitoring system, characterized in that: The following steps are involved: Obtain historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset historical period of time, and process the historical weather data and historical production capacity data to obtain the historical energy consumption impact coefficient; Training is performed based on the historical operating parameter data, the historical energy consumption impact coefficient, and the corresponding historical energy consumption data to obtain an energy consumption demand prediction model corresponding to the subsystem; Obtaining the set operating parameter data, forecast weather data and planned production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset time period, and processing them in combination with the energy demand forecasting model to obtain the energy demand forecasting data corresponding to the subsystem; Acquire real-time energy supply data of the subsystem of the comprehensive industrial energy monitoring system, process the real-time energy supply data and the energy demand forecast data to obtain the supply and demand deviation rate of the subsystem, compare the supply and demand deviation rate with a preset supply and demand deviation rate threshold, obtain the energy supply and demand status of the subsystem, and adjust the set operating parameter data according to the energy supply status; Obtain the real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the comprehensive industrial energy monitoring system subsystem, and process the real-time energy consumption data and energy price data in combination with the preset expected energy cost data to obtain the energy cost deviation rate of the subsystem; Processing is performed according to the supply-demand deviation rate and the energy cost deviation rate to obtain a sub-operation evaluation index corresponding to the subsystem, and processing is performed according to the sub-operation evaluation index to obtain a system operation evaluation index of the comprehensive industrial energy monitoring system; The system operation evaluation index is compared with a preset system operation evaluation threshold, the operation status of the comprehensive industrial energy monitoring system is determined according to the threshold comparison result, and the energy supply between subsystems is optimized according to the operation status.

2. The control optimization method based on the comprehensive industrial energy monitoring system according to claim 1 is characterized in that: The acquisition of historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a historical preset time period, and processing according to the historical weather data and historical production capacity data to obtain the historical energy consumption impact coefficient includes: The historical operation parameter data includes equipment historical operation parameter data and energy historical operation parameter data; The historical weather data includes historical temperature difference mean data and historical sunshine duration data; The historical production capacity data includes historical production data and historical single-unit energy consumption data; The historical temperature difference mean data and the historical sunshine duration data as well as the historical output data and the historical single-unit energy consumption data are input into a preset energy consumption impact coefficient evaluation model for processing to obtain a historical energy consumption impact coefficient.

3. The control optimization method based on the comprehensive industrial energy monitoring system according to claim 2 is characterized in that: The obtaining of the set operating parameter data, the forecasted weather data and the planned production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset time period, and processing in combination with the energy demand forecasting model to obtain the energy demand forecasting data corresponding to the subsystem, includes: Obtain the set operating parameter data, forecast weather data and planned production capacity data for the pre-set time period of the integrated industrial energy monitoring system subsystem; The set operation parameter data includes equipment set operation parameter data and energy set operation parameter data; The predicted weather data includes predicted temperature difference mean data and predicted sunshine duration data; The planned production capacity data includes planned production data and calibrated single-unit energy consumption data; Input the predicted temperature difference mean data and the predicted sunshine duration data as well as the planned output data and the calibrated single-unit energy consumption data into a preset energy consumption impact coefficient evaluation model for processing to obtain a predicted energy consumption impact coefficient; The equipment setting operation parameter data, energy setting operation parameter data and predicted energy consumption impact coefficient are input into the energy consumption demand prediction model for processing to obtain the energy consumption demand prediction data corresponding to the subsystem.

4. The control optimization method based on the comprehensive industrial energy monitoring system according to claim 3 is characterized in that: The real-time energy supply data of the subsystem of the comprehensive industrial energy monitoring system is obtained, and the real-time energy supply data and the energy demand forecast data are processed to obtain the supply and demand deviation rate of the subsystem, and the supply and demand deviation rate is compared with a preset supply and demand deviation rate threshold to obtain the energy supply and demand state of the subsystem, and the set operation parameter data is adjusted according to the energy supply state, including: Obtain real-time energy supply data of subsystems of the comprehensive industrial energy monitoring system; Processing the real-time energy supply data and the energy demand forecast data to obtain a supply-demand deviation rate of the subsystem; Comparing the supply-demand deviation rate with a preset supply-demand deviation rate threshold, obtaining the energy supply-demand state of the subsystem, including a supply-demand balance state or a supply-demand imbalance state; If the supply-demand deviation rate is less than or equal to a preset supply-demand deviation rate threshold, it is determined that the energy supply-demand state of the subsystem is a supply-demand balance state; If the supply-demand deviation rate is greater than a preset supply-demand deviation rate threshold, the energy supply-demand state of the subsystem is determined to be a supply-demand imbalance state, and the set operating parameter data is adjusted.

5. The control optimization method based on the comprehensive industrial energy monitoring system according to claim 4 is characterized in that: The method of obtaining real-time energy consumption data and energy price data of multiple energy sources in a preset time period of the subsystem of the comprehensive industrial energy monitoring system, and processing the real-time energy consumption data and energy price data in combination with preset expected energy cost data to obtain the energy cost deviation rate of the subsystem includes: Obtain real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the comprehensive industrial energy monitoring system subsystem; The real-time energy consumption data is multiplied by the energy price data and the sum is processed to obtain the energy consumption cost data of the subsystem; The energy cost deviation rate of the subsystem is obtained by processing the energy cost data and the preset expected energy cost data.

6. The control optimization method based on the comprehensive industrial energy monitoring system according to claim 5 is characterized in that: The processing according to the supply-demand deviation rate and the energy cost deviation rate to obtain the sub-operation evaluation index corresponding to the subsystem, and the processing according to the sub-operation evaluation index to obtain the system operation evaluation index of the comprehensive industrial energy monitoring system, including: Processing is performed according to the supply-demand deviation rate and the energy cost deviation rate to obtain a sub-operation evaluation index corresponding to the subsystem; Obtain type characteristic data of the subsystem, query a preset weight coefficient list according to the type characteristic data, and obtain the weight coefficient corresponding to the subsystem; The system operation evaluation index of the comprehensive industrial energy monitoring system is obtained by processing the plurality of sub-operation evaluation indexes and the corresponding weight coefficients.

7. The control optimization method based on the comprehensive industrial energy monitoring system according to claim 6 is characterized in that: The step of comparing the system operation evaluation index with a preset system operation evaluation threshold, determining the operation status of the comprehensive industrial energy monitoring system according to the threshold comparison result, and optimizing the energy supply between subsystems according to the operation status includes: Processing the system operation evaluation index with a preset system operation evaluation benchmark index to obtain an operation evaluation relative value; Comparing the operation evaluation relative value with a preset system operation evaluation threshold value to obtain the operation status of the comprehensive industrial energy monitoring system, including a normal operation status or an abnormal operation status; If the operation evaluation relative value is less than or equal to the preset system operation evaluation threshold, the operation state of the comprehensive industrial energy monitoring system is determined to be an abnormal operation state, and the energy supply between subsystems is optimized according to the operation state; If the operation evaluation relative value is greater than a preset system operation evaluation threshold, the operation state of the comprehensive industrial energy monitoring system is determined to be a normal operation state.

8. A control optimization system based on an integrated industrial energy monitoring system, characterized in that: The system comprises a memory and a processor, wherein the memory comprises a program of a control optimization method based on an integrated industrial energy monitoring system, and when the program of the control optimization method based on an integrated industrial energy monitoring system is executed by the processor, the following steps are implemented: Obtain historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset historical period of time, and process the historical weather data and historical production capacity data to obtain the historical energy consumption impact coefficient; Training is performed based on the historical operating parameter data, the historical energy consumption impact coefficient, and the corresponding historical energy consumption data to obtain an energy consumption demand prediction model corresponding to the subsystem; Obtaining the set operating parameter data, forecast weather data and planned production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset time period, and processing them in combination with the energy demand forecasting model to obtain the energy demand forecasting data corresponding to the subsystem; Acquire real-time energy supply data of the subsystem of the comprehensive industrial energy monitoring system, process the real-time energy supply data and the energy demand forecast data to obtain the supply and demand deviation rate of the subsystem, compare the supply and demand deviation rate with a preset supply and demand deviation rate threshold, obtain the energy supply and demand status of the subsystem, and adjust the set operating parameter data according to the energy supply status; Obtain the real-time energy consumption data and energy price data of multiple energy sources in the preset time period of the comprehensive industrial energy monitoring system subsystem, and process the real-time energy consumption data and energy price data in combination with the preset expected energy cost data to obtain the energy cost deviation rate of the subsystem; Processing is performed according to the supply-demand deviation rate and the energy cost deviation rate to obtain a sub-operation evaluation index corresponding to the subsystem, and processing is performed according to the sub-operation evaluation index to obtain a system operation evaluation index of the comprehensive industrial energy monitoring system; The system operation evaluation index is compared with a preset system operation evaluation threshold, the operation status of the comprehensive industrial energy monitoring system is determined according to the threshold comparison result, and the energy supply between subsystems is optimized according to the operation status.

9. The control optimization system based on the comprehensive industrial energy monitoring system according to claim 8 is characterized in that: The acquisition of historical operating parameter data, historical energy consumption data, historical weather data and historical production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a historical preset time period, and processing according to the historical weather data and historical production capacity data to obtain the historical energy consumption impact coefficient includes: The historical operation parameter data includes equipment historical operation parameter data and energy historical operation parameter data; The historical weather data includes historical temperature difference mean data and historical sunshine duration data; The historical production capacity data includes historical production data and historical single-unit energy consumption data; The historical temperature difference mean data and the historical sunshine duration data as well as the historical output data and the historical single-unit energy consumption data are input into a preset energy consumption impact coefficient evaluation model for processing to obtain a historical energy consumption impact coefficient.

10. The control optimization system based on the comprehensive industrial energy monitoring system according to claim 9 is characterized in that: The obtaining of the set operating parameter data, the forecasted weather data and the planned production capacity data of the subsystem of the comprehensive industrial energy monitoring system for a preset time period, and processing in combination with the energy demand forecasting model to obtain the energy demand forecasting data corresponding to the subsystem, includes: Obtain the set operating parameter data, forecast weather data and planned production capacity data for the pre-set time period of the integrated industrial energy monitoring system subsystem; The set operation parameter data includes equipment set operation parameter data and energy set operation parameter data; The predicted weather data includes predicted temperature difference mean data and predicted sunshine duration data; The planned production capacity data includes planned production data and calibrated single-unit energy consumption data; Input the predicted temperature difference mean data and the predicted sunshine duration data as well as the planned output data and the calibrated single-unit energy consumption data into a preset energy consumption impact coefficient evaluation model for processing to obtain a predicted energy consumption impact coefficient; The equipment setting operation parameter data, energy setting operation parameter data and predicted energy consumption impact coefficient are input into the energy consumption demand prediction model for processing to obtain the energy consumption demand prediction data corresponding to the subsystem.

Images