Enterprise energy consumption diagnosis method and device, storage medium and electronic equipment

Abstract

The invention relates to an enterprise energy consumption diagnosis method and device, a storage medium and electronic equipment. The enterprise energy consumption diagnosis method comprises the steps of obtaining a product production plan of a target enterprise in a future predetermined time based on a production plan scheduling system of the target enterprise; based on the production and manufacturing execution system of the target enterprise, obtaining the actual product production condition of the enterprise in the past preset time; performing energy consumption prediction on the product production plan of the target enterprise in the future predetermined time by combining the actual product production condition of the enterprise in the past predetermined time, the product production plan of the target enterprise in the future predetermined time and a pre-established energy consumption prediction model to obtain an energy consumption prediction result of the target enterprise in the future predetermined time; based on the energy consumption prediction result of the target enterprise in the future predetermined time, the energy supply of the energy supply system to the target enterprise in the future predetermined time is adjusted, so that the balance between supply and demand is better realized, and the effects of saving energy, improving efficiency and reducing cost are achieved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of integrated energy systems, and in particular to an enterprise energy consumption diagnosis method, an enterprise energy consumption diagnosis device, a storage medium and an electronic device. Background Art

[0002] As the country advocates the deepening of digitalization and intelligentization reform of enterprises and the increasing competition among enterprises, more and more enterprises are facing the transformation from traditional management mode to digital and intelligent management, and put forward many requirements in energy saving, efficiency improvement, cost reduction, safe production, etc. In this context, the refined management of enterprise energy consumption and the search for innovative and effective means of energy saving and efficiency improvement will become the general trend. Summary of the invention

[0003] In view of this, the embodiments of the present disclosure are intended to provide an enterprise energy usage diagnosis method, an enterprise energy usage diagnosis device, a storage medium, and an electronic device.

[0004] The technical solution of the present disclosure is achieved as follows:

[0005] In a first aspect, the present disclosure provides a method for diagnosing energy consumption of an enterprise.

[0006] The enterprise energy consumption diagnosis method provided by the embodiment of the present disclosure includes:

[0007] Based on the production planning and scheduling system of the target enterprise, obtain the product production plan of the target enterprise within the scheduled future time;

[0008] Based on the manufacturing execution system of the target enterprise, obtaining the actual product production status of the enterprise within a predetermined period of time in the past;

[0009] Based on the business service system, combined with the actual product production status of the enterprise within the past scheduled time, the product production plan of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model, the energy consumption prediction of the product production plan of the target enterprise within the future scheduled time is performed to obtain the energy consumption prediction result of the target enterprise within the future scheduled time;

[0010] Based on the energy consumption forecast result of the target enterprise within the predetermined future time, an energy supply diagnosis is performed on the energy supply system, and the energy supply of the energy supply system to the target enterprise within the predetermined future time is adjusted.

[0011] In some embodiments, the business service system combines the actual product production status of the enterprise within the past scheduled time, the product production plan of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model to perform energy consumption prediction on the product production plan of the target enterprise within the future scheduled time. Before obtaining the energy consumption prediction result of the target enterprise within the future scheduled time, the method includes:

[0012] Based on the regression analysis of the basic energy consumption per unit of production of each product of the target enterprise in each process space and various related factors, an energy consumption prediction model is established; wherein the various related factors include at least one of the following: product type, production space, production process, and production plan arrangement;

[0013] The energy consumption prediction model includes: the energy consumption of each product of the target enterprise during production in each process space.

[0014] In some embodiments, based on the energy consumption forecast result of the target enterprise within the future predetermined time, performing energy supply diagnosis on the energy supply system and adjusting the energy supply of the energy supply system to the target enterprise within the future predetermined time include:

[0015] Based on the energy consumption forecast results of the target enterprise within the predetermined future time, the start and stop schedules, operating parameters and required loads of each energy supply equipment in the energy supply system are adjusted.

[0016] In some embodiments, adjusting the start and stop schedule, operating parameters and required load of each energy supply device in the energy supply system based on the energy consumption forecast result of the target enterprise within the predetermined future time includes:

[0017] Based on the energy consumption forecast result of the target enterprise within the future scheduled time, an energy consumption forecast curve of the target enterprise within the future scheduled time is generated; wherein the energy consumption forecast curve represents the energy consumption of the target enterprise in production in each time period within the future scheduled time;

[0018] Based on the energy consumption forecast curve of the target enterprise within the predetermined future time, combined with the dispatchability of each energy supply equipment, dispatch instructions are issued to each energy supply equipment to adjust the start and stop, operating parameters and required load of each energy supply equipment.

[0019] In some embodiments, including:

[0020] Obtaining an actual energy consumption curve generated by the actual product production arrangement of the target enterprise within the predetermined future time;

[0021] Determining an energy usage deviation based on comparing the actual energy usage curve with the energy usage forecast curve;

[0022] Based on the energy usage deviation, the supply oversupply or shortage situation caused by the actual product production arrangement of the target enterprise is determined.

[0023] In a second aspect, the present disclosure provides an enterprise energy consumption diagnosis device, comprising:

[0024] Production planning and scheduling system, used to obtain the product production plan of the target enterprise within a predetermined time in the future;

[0025] Manufacturing execution system, used to obtain the actual product production status of the enterprise within the past scheduled time;

[0026] The business service system is used to combine the actual product production status of the enterprise within the past scheduled time, the product production plan of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model to perform energy consumption prediction on the product production plan of the target enterprise within the future scheduled time, and obtain the energy consumption prediction result of the target enterprise within the future scheduled time;

[0027] The energy supply system is used to perform energy supply diagnosis on the energy supply system based on the energy consumption forecast result of the target enterprise within the predetermined future time, and adjust the energy supply of the energy supply system to the target enterprise within the predetermined future time.

[0028] In some embodiments, including:

[0029] Big data service system, used to process energy metering data and real-time collected equipment operation data, and provide basic data support by connecting to energy consumption prediction models;

[0030] The device Internet of Things system is used to collect real-time data through data transmission, and is connected to the energy supply system. Through the equipment scheduling control module of the business service system, the start and stop of the supply equipment in the energy supply system and the adjustment of operating parameters are remotely controlled.

[0031] In some embodiments, the business service system includes:

[0032] A product data acquisition module is used to acquire basic data information of a product; the basic data information of a product includes at least one of the following: product catalog, product model specifications, production space, process flow, energy system required for each process flow, and basic measurement unit;

[0033] The equipment data acquisition module is used to obtain basic data information of the dispatchable equipment; the basic data information of the dispatchable equipment includes at least one of the following: the name of the equipment system, the cold start time, the energy supply type, the basic capacity, the operating parameters, the adjustable load and other information;

[0034] A production information acquisition module is used to acquire production information related to the actual production situation of the target enterprise; the production information related to the actual production situation includes at least one of the following: production product name, production period, production space, production process, and product output;

[0035] The energy consumption prediction module is used to establish an energy consumption prediction model based on the regression analysis of the basic energy consumption per unit of production of each product of the target enterprise in each process space and various related factors; wherein the various related factors include at least one of the following: product type, production space, production process, and production plan arrangement; wherein the energy consumption prediction model includes: the energy consumption of each product of the target enterprise during production in each process space; and

[0036] Combined with the product production information in the actual product production arrangement of the target enterprise within the future scheduled time and the energy consumption prediction model, energy consumption prediction is performed on the actual product production arrangement of the target enterprise within the future scheduled time to obtain the energy consumption prediction result of the target enterprise within the future scheduled time;

[0037] The energy diagnosis module is used to determine the supply based on the forecast of energy demand during the production period, define energy demand as the basic requirement for the enterprise's energy supply, and determine the start and stop schedule, operating parameters and required load of the supply equipment in the energy supply system in combination with the basic energy supply capacity of the target enterprise's existing energy supply system;

[0038] The equipment scheduling control module is used to adjust the start and stop schedules, operating parameters and required loads of each energy supply equipment in the energy supply system.

[0039] In a third aspect, the present disclosure provides a computer-readable storage medium having stored thereon an enterprise energy usage diagnostic program, which, when executed by a processor, implements the enterprise energy usage diagnostic method described in the first aspect.

[0040] In a fourth aspect, the present disclosure provides an electronic device comprising a memory, a processor, and an enterprise energy usage diagnostic program stored in the memory and executable on the processor, wherein when the processor executes the enterprise energy usage diagnostic program, the enterprise energy usage diagnostic method described in the first aspect is implemented.

[0041] According to the enterprise energy consumption diagnosis method of the embodiment of the present disclosure, it includes obtaining the product production plan of the target enterprise within the future scheduled time based on the production planning and scheduling system of the target enterprise; obtaining the actual product production status of the enterprise within the past scheduled time based on the production and manufacturing execution system of the target enterprise; combining the actual product production status of the enterprise within the past scheduled time, the product production plan of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model, performing energy consumption prediction on the product production plan of the target enterprise within the future scheduled time, and obtaining the energy consumption prediction result of the target enterprise within the future scheduled time; based on the energy consumption prediction result of the target enterprise within the future scheduled time, adjusting the energy supply system to the target enterprise within the future scheduled time. In the present application, the energy consumption prediction model is combined with the actual product production status of the enterprise within the past scheduled time and the product production plan of the target enterprise within the future scheduled time to perform energy consumption prediction on the product production plan of the target enterprise within the future scheduled time, and obtain more accurate prediction results, and then the energy consumption prediction results are used to adjust the energy supply system to the target enterprise within the future scheduled time, which is conducive to better achieving supply and demand balance and achieving energy saving, efficiency improvement, and cost reduction.

[0042] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of a method for diagnosing energy consumption in an enterprise according to an exemplary embodiment;

[0044] Figure 2 The structure of an enterprise energy consumption diagnosis device is shown according to an exemplary embodiment. Figure 1 ;

[0045] Figure 3 The structure of an enterprise energy consumption diagnosis device is shown according to an exemplary embodiment. Figure 2 . DETAILED DESCRIPTION

[0046] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0047] As the country advocates the deepening of digitalization and intelligentization reform of enterprises and the increasing competition among enterprises, more and more enterprises are facing the transformation from traditional management mode to digital and intelligent management, and put forward many requirements in energy saving, efficiency improvement, cost reduction, safe production, etc. In this context, the refined management of enterprise energy consumption and the search for innovative and effective means of energy saving and efficiency improvement will become the general trend.

[0048] In view of the above situation, the present disclosure provides a method for diagnosing energy consumption in an enterprise. Figure 1 FIG. 1 is a flow chart of a method for diagnosing energy consumption in an enterprise according to an exemplary embodiment. Figure 1 As shown, the energy diagnosis method of the enterprise includes:

[0049] Step 10: Based on the production planning and scheduling system of the target enterprise, obtain the product production plan of the target enterprise within a predetermined time in the future;

[0050] Step 11: Based on the manufacturing execution system of the target enterprise, obtain the actual product production status of the enterprise within the past predetermined time;

[0051] Step 12: Based on the business service system, in combination with the actual product production status of the enterprise within the past scheduled time, the product production plan of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model, an energy consumption prediction is performed on the product production plan of the target enterprise within the future scheduled time to obtain the energy consumption prediction result of the target enterprise within the future scheduled time;

[0052] Step 13: Based on the energy consumption forecast result of the target enterprise within the predetermined future time, perform energy supply diagnosis on the energy supply system, and adjust the energy supply of the energy supply system to the target enterprise within the predetermined future time.

[0053] In this exemplary embodiment, the energy consumption prediction model can be trained based on past energy consumption and the production situation of the target enterprise. The energy consumption prediction model can predict future energy consumption results or trends based on historical data or known actual product production arrangements.

[0054] In this exemplary embodiment, after obtaining the energy consumption forecast results of the target enterprise within the future scheduled time corresponding to the actual product production arrangement, the energy supply system can be diagnosed based on the energy consumption forecast results of the target enterprise within the future scheduled time, and the energy supply system can be adjusted to supply energy to the target enterprise within the future scheduled time.

[0055] According to the enterprise energy consumption diagnosis method of the embodiment of the present disclosure, it includes obtaining the product production plan of the target enterprise within the future scheduled time based on the production planning and scheduling system of the target enterprise; obtaining the actual product production status of the enterprise within the past scheduled time based on the production and manufacturing execution system of the target enterprise; combining the actual product production status of the enterprise within the past scheduled time, the product production plan of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model, performing energy consumption prediction on the product production plan of the target enterprise within the future scheduled time, and obtaining the energy consumption prediction result of the target enterprise within the future scheduled time; based on the energy consumption prediction result of the target enterprise within the future scheduled time, adjusting the energy supply system to the target enterprise within the future scheduled time. In the present application, the energy consumption prediction model is combined with the actual product production status of the enterprise within the past scheduled time and the product production plan of the target enterprise within the future scheduled time, and a more accurate prediction result is obtained. Then, the energy supply of the energy supply system to the target enterprise within the future scheduled time is adjusted according to the energy consumption prediction result, which is conducive to better achieving supply and demand balance and achieving the effects of energy saving, efficiency improvement, and cost reduction.

[0056] In some embodiments, the business service system combines the actual product production status of the enterprise within the past scheduled time, the product production plan of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model to perform energy consumption prediction on the product production plan of the target enterprise within the future scheduled time. Before obtaining the energy consumption prediction result of the target enterprise within the future scheduled time, the method includes:

[0057] Based on the regression analysis of the basic energy consumption per unit of production of each product of the target enterprise in each process space and various related factors, an energy consumption prediction model is established; wherein the various related factors include at least one of the following: product type, production space, production process, and production plan arrangement;

[0058] The energy consumption prediction model includes: the energy consumption of each product of the target enterprise during production in each process space. The energy consumption prediction model may be a regression analysis model.

[0059] In this exemplary embodiment, based on the business service system, combined with the product production information in the actual product production arrangement of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model, the actual product production arrangement of the target enterprise within the future scheduled time is predicted to use energy. Before obtaining the energy consumption prediction result of the target enterprise within the future scheduled time, it is necessary to train the model based on the energy consumption data corresponding to the historical production of the target enterprise to obtain the energy consumption prediction model. For example, the energy consumption prediction model is established by performing regression analysis on the production basic energy unit consumption of each product of the target enterprise under each process space and related factors such as product type, production space, production process, and production plan arrangement. The energy consumption prediction model may include the energy consumption of each product of the target enterprise when it is produced under each process space. Then, according to the actual product production arrangement of the target enterprise within the future scheduled time and the energy consumption of each product of the target enterprise when it is produced under each process space, the energy consumption prediction result of the target enterprise within the future scheduled time can be predicted. Finally, according to the energy consumption prediction result of the target enterprise within the future scheduled time, the energy supply system can be adjusted to supply energy to the target enterprise within the future scheduled time. Since the energy supply system's current energy supply to the target enterprise in the future scheduled time may not match the current energy supply, the energy supply system's energy supply to the target enterprise in the future scheduled time can be adjusted according to the energy consumption forecast result of the target enterprise in the future scheduled time. This is conducive to minimizing energy waste while meeting the energy needs of the target enterprise.

[0060] In some embodiments, based on the energy consumption forecast result of the target enterprise within the future predetermined time, performing energy supply diagnosis on the energy supply system and adjusting the energy supply of the energy supply system to the target enterprise within the future predetermined time include:

[0061] Based on the energy consumption forecast results of the target enterprise within the predetermined future time, the start and stop schedules, operating parameters and required loads of each energy supply equipment in the energy supply system are adjusted.

[0062] In this exemplary embodiment, the energy supply amount and energy supply time of each energy supply device in the energy supply system can be controlled by adjusting the start and stop schedule, operating parameters and required load of each energy supply device in the energy supply system, so as to meet the energy demand of the target enterprise within a predetermined time in the future.

[0063] In some embodiments, adjusting the start and stop schedule, operating parameters and required load of each energy supply device in the energy supply system based on the energy consumption forecast result of the target enterprise within the predetermined future time includes:

[0064] Based on the energy consumption forecast result of the target enterprise within the future scheduled time, an energy consumption forecast curve of the target enterprise within the future scheduled time is generated; wherein the energy consumption forecast curve represents the energy consumption of the target enterprise in production in each time period within the future scheduled time;

[0065] Based on the energy consumption forecast curve of the target enterprise within the predetermined future time, combined with the dispatchability of each energy supply equipment, dispatch instructions are issued to each energy supply equipment to adjust the start and stop, operating parameters and required load of each energy supply equipment.

[0066] In this exemplary embodiment, in order to better characterize the energy consumption of the target enterprise in production in each time period within the future scheduled time, an energy consumption forecast curve of the target enterprise in the future scheduled time can be generated based on the energy consumption forecast result of the target enterprise in the future scheduled time. The energy consumption forecast curve of the target enterprise in the future scheduled time is used to better reflect the energy consumption of the target enterprise in production in each time period within the future scheduled time. Then, based on the energy consumption forecast curve of the target enterprise in the future scheduled time, combined with the dispatchability of each energy supply equipment, a dispatch instruction is issued to each energy supply equipment to start and stop each energy supply equipment, adjust the operating parameters and required load, and control the energy supply amount and energy supply time of each energy supply equipment in the energy supply system, so as to meet the energy demand of the target enterprise in the future scheduled time.

[0067] In some embodiments, including:

[0068] Obtaining an actual energy consumption curve generated by the actual product production arrangement of the target enterprise within the predetermined future time;

[0069] Determining an energy usage deviation based on comparing the actual energy usage curve with the energy usage forecast curve;

[0070] Based on the energy usage deviation, the supply oversupply or shortage situation caused by the actual product production arrangement of the target enterprise is determined.

[0071] In this exemplary embodiment, as time goes by, the actual actual energy usage curve can be compared with the predicted energy usage curve. The resulting energy usage deviation can assist the enterprise production to find the oversupply / shortage of energy equipment, to determine whether the energy usage deviation affects production, and to seek more business goals such as energy saving points.

[0072] In this exemplary embodiment, Table 1 is an example table of production plan data. The production planning and scheduling system APS can be used to provide enterprises with production plan arrangements as shown in Table 1. The production plan arrangement directly affects the production energy consumption of the enterprise in the future period. Among them, Table 1 contains product name, production space, production process, planned production period and planned production product output. That is, if there is a production plan, there will be a corresponding energy demand (i.e., production energy demand). The prediction of this energy demand can be calculated by a commonly used prediction model.

[0073] Product Name Production Space Production process Planned production period Planned production output aProduct aRoom1 aProcess1 08:00～16:00 a1PlanCount1 aProduct aRoom2 aProcess1 08:00～16:00 a1PlanCount2 aProduct aRoom3 aProcess2 10:00～18:00 a2PlanCount1 bProduct bRoom1 bProcess1 08:00～16:00 b1PlanCount1 bProduct bRoom2 bProcess1 10:00～18:00 b1PlanCount2

[0074] Table 1 Production plan arrangement data example table

[0075] The MES system is used to provide the actual production situation of the enterprise, which reflects the past production performance and indirectly affects the future short-term energy demand of the enterprise. In this application, the energy consumption prediction model can combine the production performance and production plan at the same time to make timely adjustments to the short-term energy demand.

[0076] The business service system is the core unit of this application. Figure 3 The structure of an enterprise energy consumption diagnosis device is shown according to an exemplary embodiment. Figure 2 .like Figure 3 As shown, the enterprise energy consumption diagnosis device includes a production planning and scheduling system 20, a production and manufacturing execution system 21, a business service system 22, a big data service system 24, and an equipment Internet of Things system 25; wherein the business service system 22 includes at least Figure 3 The six sub-modules listed are: product information acquisition module 201, equipment information acquisition module 202, production information acquisition module 203, energy consumption prediction module 204, energy consumption diagnosis module 205, and equipment scheduling control module 206.

[0077] Among them, the product information acquisition module 201: manages the basic data information of the product, including product catalog, product model specifications, production space, process flow, energy system (energy supply end) required for each process flow, basic measurement units and other information. The basic data information of the product is output to the energy consumption prediction model, and the production plan arrangement provided by the production planning and scheduling system APS jointly supports the logical processing of the enterprise production energy consumption prediction. The product data model information can form a basic data matrix. Table 2 is an example table of product data module data. The example is shown in Table 2 below, where Table 2 contains product name, production space, production process, energy unit consumption (steam), energy unit consumption (compressed air), etc.

[0078] Product Name Production Space Production process Energy consumption per unit (steam) Energy consumption (compressed air) aProduct aRoom1 aProcess1 a1Energy1 a2Energy1 aProduct aRoom2 aProcess1 a1Energy2 a2Energy2 aProduct ARoom3 aProcess2 a1Energy3 a2Energy3 bProduct bRoom1 bProcess1 b1Energy1 b2Energy1 bProduct bRoom2 bProcess1 b1Energy2 b2Energy2 bProduct bRoom3 bProcess2 b1Energy3 b2Energy3 bProduct bRoom4 bProcess2 b1Energy4 b2Energy4 bProduct bRoom5 bProcess2 b1Energy5 b2Energy5 …… …… …… …… …… bProduct bRoomN bProcessN b1EnergyN b2EnergyN

[0079] Table 2 Product information module data example table

[0080] Equipment data acquisition module 202: manages the basic data information of dispatchable / controllable equipment, including the name of the equipment system, cold start duration, energy supply type, basic production capacity, operating parameters, adjustable load and other information. The basic data information of the control equipment is output to the energy consumption prediction model & algorithm module. Based on the enterprise's production energy demand and time forecast, the algorithm is combined to calculate the equipment-level scheduling arrangements such as the start and stop time and operating load of the equipment system. Table 3 is an example table of equipment data module data. The equipment data module information can form a basic data matrix, and an example is shown in Table 3 below. Among them, Table 3 contains system name, energy supply type, basic production capacity, cold start duration and adjustable load, etc.

[0081] System Name Energy supply type Basic production capacity Cold start time Adjustable load Equipment steam 16t / h 30min 20％～80％ Equipment steam 16t / h 30min 20％～80％ Equipment Compressed air <![CDATA[18Nm 3 / min]]> 10min 0％、100％ dEquipment Compressed air <![CDATA[18Nm 3 / min]]> 10min 0％、100％ eEquipment Compressed air <![CDATA[18Nm 3 / min]]> 10min 0％、100％

[0082] Table 3 Equipment information module data example table

[0083] Production information acquisition module 203: used to manage the actual production situation of the enterprise, including: production product name, production period, production space, production process, product output and other information, usually achieved by docking with the MES system. This information is output to the energy consumption prediction model. Based on the enterprise's production energy demand and time forecast, combined with the actual production situation provided by the MES system, a secondary optimization and adjustment forecast is made for the enterprise's future short-term production energy demand. Table 4 is an example table of production information module data. Production information docking can form a basic data matrix, and example Table 4 is as follows. Among them, Table 4 contains product name, production space, production process, production period and product output, etc.

[0084] Product Name Production Space Production process Production period Product Output aProduct aRoom1 aProcess1 08:00～16:00 a1Count1 aProduct aRoom2 aProcess1 08:00～16:00 a1Count2 aProduct aRoom3 aProcess2 10:00～18:00 a2Count1 bProduct bRoom1 bProcess1 08:00～16:00 b1Count1 bProduct bRoom2 bProcess1 10:00～18:00 b1Count2

[0085] Table 4 Production Information Module Data Example Energy Consumption Forecasting Module 204: Based on the production plan arrangement of the enterprise and the maintenance of the product information module data, the energy consumption data of the overall production energy consumption period and each period are obtained through the energy prediction model. Among them, the constraint condition: depends on the input of the APS system and the product information module.

[0086] Initially, the energy forecasting model based on regression analysis is combined with intelligent algorithms to forecast energy demand; for example, a mathematical model is established based on the basic energy consumption of each product / processed product in each process space of the enterprise, and an energy forecasting model for energy demand is established by performing regression analysis on the historical product energy consumption and various related factors (product / processed product type, production space, production process, production plan, etc.). The energy forecasting model may include the following:

[0087] Total production energy forecast (steam) = a1PlanCount1*a1Energy1+a1PlanCount1*a1Energy2+a2PlanCount1*a1Energy3+b1PlanCount1*b1Energy1+b1PlanCount2*b1Energy2;

[0088] Energy consumption forecast for each production period (steam) = [00:00-08:00]: 0;

[0089] [08:00~09:00] is: a1PlanCount1 / 8*a1Energy1+a1PlanCount2 / 8*a1Energy2+b1PlanCount1 / 8*b1Energy1;

[0090] [09:00~10:00] is: a1PlanCount1 / 8*a1Energy1+a1PlanCount2 / 8*a1Energy2+b1PlanCount1 / 8*b1Energy1;

[0091] 【10:00~11:00】is:a1PlanCount1 / 8*a1Energy1+a1PlanCount2 / 8*a1Energy2+a2PlanCount1 / 8*a1Energy3+b1PlanCount1 / 8*b1Energy1+b1PlanCount2 / 8*b1Energy2;

[0092] 【11:00~12:00】is:a1PlanCount1 / 8*a1Energy1+a1PlanCount2 / 8*a1Energy2+a2PlanCount1 / 8*a1Energy3+b1PlanCount1 / 8*b1Energy1+b1PlanCount2 / 8*b1Energy2;

[0093] 【12:00~13:00】is:a1PlanCount1 / 8*a1Energy1+a1PlanCount2 / 8*a1Energy2+a2PlanCount1 / 8*a1Energy3+b1PlanCount1 / 8*b1Energy1+b1PlanCount2 / 8*b1Energy2;

[0094] 【13:00~14:00】is:a1PlanCount1 / 8*a1Energy1+a1PlanCount2 / 8*a1Energy2+a2PlanCount1 / 8*a1Energy3+b1PlanCount1 / 8*b1Energy1+b1PlanCount2 / 8*b1Energy2;

[0095] 【14:00~15:00】is:a1PlanCount1 / 8*a1Energy1+a1PlanCount2 / 8*a1Energy2+a2PlanCount1 / 8*a1Energy3+b1PlanCount1 / 8*b1Energy1+b1PlanCount2 / 8*b1Energy2;

[0096] 【15:00~16:00】is:a1PlanCount1 / 8*a1Energy1+a1PlanCount2 / 8*a1Energy2+a2PlanCount1 / 8*a1Energy3+b1PlanCount1 / 8*b1Energy1+b1PlanCount2 / 8*b1Energy2;

[0097] [16:00~17:00] is: a2PlanCount1 / 8*a1Energy3+b1PlanCount2 / 8*b1Energy2;

[0098] [17:00~18:00] is: a2PlanCount1 / 8*a1Energy3+b1PlanCount2 / 8*b1Energy2;

[0099] 【18:00~24:00】is: Total production energy forecast (compressed air) =

[0100] a1PlanCount1*a2Energy1+a1PlanCount1*a2Energy2+a2PlanCount1*a2Energy3+b1PlanCount1*b2Energy1+b1PlanCount2*b2Energy2;

[0101] Energy consumption forecast for each production period (compressed air) = [00:00-08:00]: 0;

[0102] [08:00~09:00] is: a1PlanCount1 / 8*a2Energy1+a1PlanCount2 / 8*a2Energy2+b1PlanCount1 / 8*b2Energy1;

[0103] [09:00~10:00] is: a1PlanCount1 / 8*a2Energy1+a1PlanCount2 / 8*a2Energy2+b1PlanCount1 / 8*b2Energy1;

[0104] 【10:00~11:00】is:a1PlanCount1 / 8*a2Energy1+a1PlanCount2 / 8*a2Energy2+a2PlanCount1 / 8*a2Energy3+b1PlanCount1 / 8*b2Energy1+b1PlanCount2 / 8*b2Energy2;

[0105] 【11:00~12:00】is:a1PlanCount1 / 8*a2Energy1+a1PlanCount2 / 8*a2Energy2+a2PlanCount1 / 8*a2Energy3+b1PlanCount1 / 8*b2Energy1+b1PlanCount2 / 8*b2Energy2;

[0106] 【12:00~13:00】is:a1PlanCount1 / 8*a2Energy1+a1PlanCount2 / 8*a2Energy2+a2PlanCount1 / 8*a2Energy3+b1PlanCount1 / 8*b2Energy1+b1PlanCount2 / 8*b2Energy2;

[0107] 【13:00~14:00】is:a1PlanCount1 / 8*a2Energy1+a1PlanCount2 / 8*a2Energy2+a2PlanCount1 / 8*a2Energy3+b1PlanCount1 / 8*b2Energy1+b1PlanCount2 / 8*b2Energy2;

[0108] 【14:00~15:00】is:a1PlanCount1 / 8*a2Energy1+a1PlanCount2 / 8*a2Energy2+a2PlanCount1 / 8*a2Energy3+b1PlanCount1 / 8*b2Energy1+b1PlanCount2 / 8*b2Energy2;

[0109] 【15:00~16:00】is:a1PlanCount1 / 8*a2Energy1+a1PlanCount2 / 8*a2Energy2+a2PlanCount1 / 8*a2Energy3+b1PlanCount1 / 8*b2Energy1+b1PlanCount2 / 8*b2Energy2;

[0110] [16:00~17:00] is: a2PlanCount1 / 8*a2Energy3+b1PlanCount2 / 8*b2Energy2;

[0111] 【17:00~18:00】is: a2PlanCount1 / 8*a2Energy3+b1PlanCount2 / 8*b2Energy2.

[0112] As enterprise data gradually accumulates, the system can introduce more energy consumption prediction model algorithms, and seek the optimal solution by adjusting parameters (such as production season, production scheduling, production equipment performance, etc.) and feedback of results. Similar mature solution models are already available on the market and can be directly referenced, so I will not go into details here.

[0113] Energy consumption diagnosis module 205: Based on the energy demand forecast during the production period, supply is determined according to demand, and the demand is defined as the basic requirement for the enterprise's energy supply. Combined with the basic energy supply capacity of the enterprise's existing energy supply system / equipment, the start and stop schedule, operating parameters and required load size of the corresponding energy supply system / equipment are calculated; among which, the constraints: rely on the production energy consumption forecast results and the input of the equipment data module.

[0114] Based on the energy forecasting model, the energy consumption forecast for each production period is obtained, taking steam as an example:

[0115] 【10:00~11:00】a1PlanCount1 / 8*a1Energy1+a1PlanCount2 / 8*a1Energy2+a2PlanCount1 / 8*a1Energy3+b1PlanCount1 / 8*b1Energy1+b1PlanCount2 / 8*b1Energy2. Denoted as Q_steam;

[0116] If Q_steam < equipment basic capacity (16t / h*80%), then aEquipment can be turned on, and the adjustable load parameter is set to Q-steam / 16. The equipment start time is 30 minutes before the energy demand time, that is, 09:30;

[0117] If Q_steam>equipment basic capacity (16t / h*80%), both aEquipment and bEquipment will be turned on, and the adjustable load parameter of aEquipment will be set to 80%, and the adjustable load parameter of bEquipment will be set to (Q_steam-16*80%) / 16. The equipment start time will be 30 minutes before the energy demand time, that is, 09:30.

[0118] Based on the above algorithm logic, the dispatch curve or dispatch manual of the start and stop and load parameters of the equipment in each time period (hour) can be obtained.

[0119] When the production information acquisition module inputs the actual production performance of the enterprise into the energy consumption diagnosis module through the prediction of the energy consumption prediction model, it will affect the start and stop of existing equipment and the parameter scheduling, forming a new scheduling curve / scheduling manual, which will be updated with low latency along with the actual production performance.

[0120] The equipment scheduling control module 206 issues scheduling instructions to each system based on the scheduling curve / manual of the energy diagnosis module and the scheduling availability of each equipment system, and adjusts the start and stop and operating parameters of specific equipment.

[0121] The big data service system 24 is used to process energy metering data and real-time collected equipment operation data and other information, and provides basic data support by connecting to the energy consumption prediction model.

[0122] The device IoT system 25, on the one hand, realizes the collection of real-time data information through data transmission, and on the other hand, remotely completes the automatic control action of equipment start / stop / operation parameter adjustment through the device scheduling control module of the business service system.

[0123] The present disclosure provides an enterprise energy consumption diagnosis device. Figure 2 The structure of an enterprise energy consumption diagnosis device is shown according to an exemplary embodiment. Figure 1 .like Figure 2 As shown, the enterprise energy consumption diagnosis device includes:

[0124] The production planning and scheduling system 20 is used to obtain the product production plan of the target enterprise within a predetermined time in the future;

[0125] Manufacturing execution system 21, used to obtain the actual product production status of the enterprise within the past predetermined time;

[0126] The business service system 22 is used to combine the actual product production status of the enterprise within the past scheduled time, the product production plan of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model to perform energy consumption prediction on the product production plan of the target enterprise within the future scheduled time, and obtain the energy consumption prediction result of the target enterprise within the future scheduled time;

[0127] The energy supply system 23 is used to perform energy supply diagnosis on the energy supply system based on the energy consumption forecast result of the target enterprise within the future scheduled time, and adjust the energy supply of the energy supply system to the target enterprise within the future scheduled time.

[0128] In this exemplary embodiment, the energy consumption prediction model can be trained and obtained based on the past energy consumption and the production situation of the target enterprise. The energy consumption prediction model can predict future energy consumption results or trends based on historical data or known actual product production arrangements. The enterprise energy consumption diagnosis device may include a production planning and scheduling system 20, a production and manufacturing execution system 21, a business service system 22, and an energy supply system 23.

[0129] In this exemplary embodiment, after obtaining the energy consumption forecast results of the target enterprise within the future scheduled time corresponding to the actual product production arrangement, the energy supply system can be diagnosed based on the energy consumption forecast results of the target enterprise within the future scheduled time, and the energy supply system can be adjusted to supply energy to the target enterprise within the future scheduled time.

[0130] According to the enterprise energy consumption diagnosis device of the embodiment of the present disclosure, it is used to obtain the product production plan of the target enterprise within the future scheduled time based on the production planning and scheduling system of the target enterprise; obtain the actual product production status of the enterprise within the past scheduled time based on the production and manufacturing execution system of the target enterprise; combine the actual product production status of the enterprise within the past scheduled time, the product production plan of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model to perform energy consumption prediction on the product production plan of the target enterprise within the future scheduled time, and obtain the energy consumption prediction result of the target enterprise within the future scheduled time; based on the energy consumption prediction result of the target enterprise within the future scheduled time, adjust the energy supply system to the target enterprise within the future scheduled time. In this application, the energy consumption prediction model is combined with the actual product production status of the enterprise within the past scheduled time and the product production plan of the target enterprise within the future scheduled time to perform energy consumption prediction on the product production plan of the target enterprise within the future scheduled time, and obtain a more accurate prediction result, and then adjust the energy supply of the energy supply system to the target enterprise within the future scheduled time according to the energy consumption prediction result, which is conducive to better achieving supply and demand balance and achieving energy saving, efficiency improvement, and cost reduction.

[0131] In some embodiments, including:

[0132] Big data service system 24, used to process energy metering data and real-time collected equipment operation data, and provide basic data support by connecting to energy consumption prediction models;

[0133] The device IoT system 25 is used to collect real-time data through data transmission, and is connected to the energy supply system. Through the device scheduling control module of the business service system, it remotely controls the start and stop of the supply equipment in the energy supply system and adjusts the operating parameters.

[0134] In this exemplary embodiment, the energy supply amount and energy supply time of each energy supply device in the energy supply system can be controlled by adjusting the start and stop schedule, operating parameters and required load of each energy supply device in the energy supply system, so as to meet the energy demand of the target enterprise within a predetermined time in the future.

[0135] In some embodiments, the business service system includes:

[0136] A product data acquisition module is used to acquire basic data information of a product; the basic data information of a product includes at least one of the following: product catalog, product model specifications, production space, process flow, energy system required for each process flow, and basic measurement unit;

[0137] The equipment data acquisition module is used to obtain basic data information of the dispatchable equipment; the basic data information of the dispatchable equipment includes at least one of the following: the name of the equipment system, the cold start time, the energy supply type, the basic capacity, the operating parameters, the adjustable load and other information;

[0138] A production information acquisition module is used to acquire production information related to the actual production situation of the target enterprise; the production information related to the actual production situation includes at least one of the following: production product name, production period, production space, production process, and product output;

[0139] The energy consumption prediction module is used to establish an energy consumption prediction model based on the regression analysis of the basic energy consumption per unit of production of each product of the target enterprise in each process space and various related factors; wherein the various related factors include at least one of the following: product type, production space, production process, and production plan arrangement; wherein the energy consumption prediction model includes: the energy consumption of each product of the target enterprise during production in each process space; and

[0140] Combined with the product production information in the actual product production arrangement of the target enterprise within the future scheduled time and the energy consumption prediction model, energy consumption prediction is performed on the actual product production arrangement of the target enterprise within the future scheduled time to obtain the energy consumption prediction result of the target enterprise within the future scheduled time;

[0141] The energy diagnosis module is used to determine the supply based on the forecast of energy demand during the production period, define energy demand as the basic requirement for the enterprise's energy supply, and determine the start and stop schedule, operating parameters and required load of the supply equipment in the energy supply system in combination with the basic energy supply capacity of the target enterprise's existing energy supply system;

[0142] The equipment scheduling control module is used to adjust the start and stop schedules, operating parameters and required loads of each energy supply equipment in the energy supply system.

[0143] In this exemplary embodiment, as time goes by, the actual actual energy usage curve can be compared with the predicted energy usage curve. The resulting energy usage deviation can assist the enterprise production to find the oversupply / shortage of energy equipment, to determine whether the energy usage deviation affects production, and to seek more business goals such as energy saving points.

[0144] In this exemplary embodiment, the production planning and scheduling system APS can be used to provide the enterprise with a production plan arrangement as shown in Table 1. The production plan arrangement directly affects the production energy consumption of the enterprise in the future period. That is, if there is a production plan, there will be a corresponding energy demand (i.e., production energy demand). The prediction of this energy demand can be calculated through a commonly used prediction model. The MES system is used to provide the actual production situation of the enterprise. The actual production situation reflects the past production performance and indirectly affects the future short-term energy demand of the enterprise. In this application, the energy consumption prediction model can combine production performance and production plan at the same time to make timely adjustments to short-term energy demand. The business service system is the core unit of this application.

[0145] Figure 3 The structure of an enterprise energy consumption diagnosis device is shown according to an exemplary embodiment. Figure 2 .like Figure 3 As shown, the enterprise energy consumption diagnosis device includes a production planning and scheduling system 20, a production and manufacturing execution system 21, a business service system 22, a big data service system 24, and an equipment Internet of Things system 25; wherein the business service system 22 includes at least Figure 3 The six submodules listed are: product data acquisition module 201, equipment data acquisition module 202, production information acquisition module 203, energy consumption prediction module 204, energy consumption diagnosis module 205, and equipment scheduling control module 206. Among them, product data acquisition module 201: manages the basic data information of the product, including product catalog, product model specifications, production space, process flow, energy system (energy supply end) required for each process flow, basic measurement unit and other information. The basic data information of the product is output to the energy consumption prediction model, and together with the production plan arrangement provided by the production planning and scheduling system APS, it supports the logical processing of the enterprise production energy consumption prediction. The product data model information can form a basic data matrix.

[0146] Equipment data acquisition module 202: manages the basic data information of dispatchable / controllable equipment, including the name of the equipment system, cold start duration, energy supply type, basic production capacity, operating parameters, adjustable load and other information. The basic data information of the control equipment is output to the energy consumption prediction model & algorithm module. Based on the enterprise's production energy demand and time forecast, the algorithm is combined to calculate the equipment system's start and stop time, operating load and other equipment-level scheduling arrangements. The equipment data module information can form a basic data matrix, an example of which is shown in Table 3.

[0147] Production information acquisition module 203: used to manage the actual production situation of the enterprise, including: product name, production period, production space, production process, product output and other information, usually achieved by docking with the MES system. This information is output to the energy consumption forecasting model. Based on the enterprise's production energy demand and time forecast, combined with the actual production situation provided by the MES system, a secondary optimization and adjustment forecast is made for the enterprise's future short-term production energy demand. The production information docking can form a basic data matrix, as shown in Table 4.

[0148] Energy consumption forecasting module 204: Based on the production plan arrangement of the enterprise and the maintenance of the product data module data, the energy consumption data of the overall production energy consumption period and each period are obtained through the energy forecasting model. Among them, the constraint condition: depends on the input of the APS system and the product data module.

[0149] In the initial stage, the energy forecasting model based on regression analysis is combined with intelligent algorithms to predict energy demand. For example, a mathematical model is established based on the basic energy consumption of each product / processed product in each process space of the enterprise, and an energy demand forecasting model is established by regressing the historical product energy consumption with relevant factors (product / processed product type, production space, production process, production plan arrangement, etc.).

[0150] As enterprise data gradually accumulates, the system can introduce more energy consumption prediction model algorithms, and seek the optimal solution by adjusting parameters (such as production season, production scheduling, production equipment performance, etc.) and feedback of results. Similar mature solution models are already available on the market and can be directly referenced, so I will not go into details here.

[0151] Energy consumption diagnosis module 205: Based on the energy demand forecast during the production period, supply is determined based on demand, and the demand is defined as the basic requirement for the enterprise's energy supply. Combined with the basic energy supply capacity of the enterprise's existing energy supply system / equipment, the start and stop schedule, operating parameters and required load size of the corresponding energy supply system / equipment are calculated.

[0152] Constraints: Depends on production energy consumption forecast results and input from the equipment data module.

[0153] Based on the energy forecasting model, the energy consumption forecast for each production period is obtained, taking steam as an example:

[0154] 【10:00~11:00】a1PlanCount1 / 8*a1Energy1+a1PlanCount2 / 8*a1Energy2+a2PlanCount1 / 8*a1Energy3+b1PlanCount1 / 8*b1Energy1+b1PlanCount2 / 8*b1Energy2. Denoted as Q_steam;

[0155] If Q_steam < equipment basic capacity (16t / h*80%), then aEquipment can be turned on, and the adjustable load parameter is set to Q-steam / 16. The equipment start time is 30 minutes before the energy demand time, that is, 09:30;

[0156] If Q_steam>equipment basic capacity (16t / h*80%), both aEquipment and bEquipment will be turned on, and the adjustable load parameter of aEquipment will be set to 80%, and the adjustable load parameter of bEquipment will be set to (Q_steam-16*80%) / 16. The equipment start time will be 30 minutes before the energy demand time, that is, 09:30.

[0157] Based on the above algorithm logic, the dispatch curve or dispatch manual of the start and stop and load parameters of the equipment in each time period (hour) can be obtained.

[0158] When the production information acquisition module inputs the actual production performance of the enterprise into the energy consumption diagnosis module through the prediction of the energy consumption prediction model, it will affect the start and stop of existing equipment and the parameter scheduling, forming a new scheduling curve / scheduling manual, which will be updated with low latency along with the actual production performance.

[0159] The equipment scheduling control module 206 issues scheduling instructions to each system based on the scheduling curve / manual of the energy diagnosis module and the scheduling availability of each equipment system, and adjusts the start and stop and operating parameters of specific equipment.

[0160] The big data service system 24 is used to process energy metering data and real-time collected equipment operation data and other information, and provides basic data support by connecting to the energy consumption prediction model.

[0161] The device IoT system 25, on the one hand, realizes the collection of real-time data information through data transmission, and on the other hand, remotely completes the automatic control action of equipment start / stop / operation parameter adjustment through the device scheduling control module of the business service system.

[0162] The present disclosure provides a computer-readable storage medium on which an enterprise energy usage diagnosis program is stored. When the enterprise energy usage diagnosis program is executed by a processor, the enterprise energy usage diagnosis method described in the above embodiments is implemented.

[0163] The present disclosure provides an electronic device, including a memory, a processor, and an enterprise energy usage diagnostic program stored in the memory and executable on the processor. When the processor executes the enterprise energy usage diagnostic program, the enterprise energy usage diagnostic method described in the above embodiments is implemented.

[0164] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0165] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0166] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0167] In the description of the present disclosure, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present disclosure.

[0168] In addition, the terms "first", "second", etc. used in the embodiments of the present disclosure are only used for descriptive purposes and should not be understood as indicating or implying relative importance, or implicitly indicating the number of technical features indicated in the embodiments. Therefore, the features defined by the terms "first", "second", etc. in the embodiments of the present disclosure may explicitly or implicitly indicate that at least one of the features is included in the embodiment. In the description of the present disclosure, the word "multiple" means at least two or two or more, such as two, three, four, etc., unless otherwise clearly and specifically defined in the embodiments.

[0169] In the present disclosure, unless otherwise clearly specified or limited in the embodiments, the terms "installed", "connected", "connected" and "fixed" etc. appearing in the embodiments should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integrated connection. It can be understood that it can also be a mechanical connection, an electrical connection, etc.; of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal connection of two elements, or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present disclosure can be understood according to the specific implementation situation.

[0170] In the present disclosure, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0171] Although the embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A method for diagnosing energy consumption in an enterprise, characterized in that: include: Based on the production planning and scheduling system of the target enterprise, obtain the product production plan of the target enterprise within the scheduled future time; Based on the manufacturing execution system of the target enterprise, obtaining the actual product production status of the enterprise within a predetermined period of time in the past; Based on the business service system, combined with the actual product production status of the enterprise within the past scheduled time, the product production plan of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model, the energy consumption prediction of the product production plan of the target enterprise within the future scheduled time is performed to obtain the energy consumption prediction result of the target enterprise within the future scheduled time; Based on the energy consumption forecast result of the target enterprise within the predetermined future time, an energy supply diagnosis is performed on the energy supply system, and the energy supply of the energy supply system to the target enterprise within the predetermined future time is adjusted.

2. The enterprise energy consumption diagnosis method according to claim 1, characterized in that: The business service system is based on the actual product production status of the enterprise within the past scheduled time, the product production plan of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model, and the energy consumption prediction of the product production plan of the target enterprise within the future scheduled time is performed. Before obtaining the energy consumption prediction result of the target enterprise within the future scheduled time, the method includes: Based on the regression analysis of the basic energy consumption per unit of production of each product of the target enterprise in each process space and various related factors, an energy consumption prediction model is established; wherein the various related factors include at least one of the following: product type, production space, production process, and production plan arrangement; The energy consumption prediction model includes: the energy consumption of each product of the target enterprise during production in each process space.

3. The enterprise energy consumption diagnosis method according to claim 2, characterized in that: The energy supply diagnosis of the energy supply system based on the energy consumption forecast result of the target enterprise within the predetermined future time and adjusting the energy supply of the energy supply system to the target enterprise within the predetermined future time include: Based on the energy consumption forecast results of the target enterprise within the predetermined future time, the start and stop schedules, operating parameters and required loads of each energy supply equipment in the energy supply system are adjusted.

4. The enterprise energy consumption diagnosis method according to claim 3 is characterized in that: The adjusting of the start and stop schedule, operating parameters and required load of each energy supply equipment in the energy supply system based on the energy consumption forecast result of the target enterprise within the predetermined future time includes: Based on the energy consumption forecast result of the target enterprise within the future scheduled time, an energy consumption forecast curve of the target enterprise within the future scheduled time is generated; wherein the energy consumption forecast curve represents the energy consumption of the target enterprise in production in each time period within the future scheduled time; Based on the energy consumption forecast curve of the target enterprise within the predetermined future time, combined with the dispatchability of each energy supply equipment, dispatch instructions are issued to each energy supply equipment to adjust the start and stop, operating parameters and required load of each energy supply equipment.

5. The enterprise energy consumption diagnosis method according to claim 4, characterized in that: include: Obtaining an actual energy consumption curve generated by the actual product production arrangement of the target enterprise within the predetermined future time; Determining an energy usage deviation based on comparing the actual energy usage curve with the energy usage forecast curve; Based on the energy usage deviation, the supply oversupply or shortage situation caused by the actual product production arrangement of the target enterprise is determined.

6. An enterprise energy consumption diagnosis device, characterized in that: include: Production planning and scheduling system, used to obtain the product production plan of the target enterprise within a predetermined time in the future; Manufacturing execution system, used to obtain the actual product production status of the enterprise within the past scheduled time; The business service system is used to combine the actual product production status of the enterprise within the past scheduled time, the product production plan of the target enterprise within the future scheduled time, and the pre-established energy consumption prediction model to perform energy consumption prediction on the product production plan of the target enterprise within the future scheduled time, and obtain the energy consumption prediction result of the target enterprise within the future scheduled time; The energy supply system is used to perform energy supply diagnosis on the energy supply system based on the energy consumption forecast result of the target enterprise within the predetermined future time, and adjust the energy supply of the energy supply system to the target enterprise within the predetermined future time.

7. The enterprise energy consumption diagnosis device according to claim 6, characterized in that: include: Big data service system, used to process energy metering data and real-time collected equipment operation data, and provide basic data support by connecting to energy consumption prediction models; The device Internet of Things system is used to collect real-time data through data transmission, and is connected to the energy supply system. Through the equipment scheduling control module of the business service system, the start and stop of the supply equipment in the energy supply system and the adjustment of operating parameters are remotely controlled.

8. The enterprise energy consumption diagnosis device according to claim 6, characterized in that: The business service system comprises: A product data acquisition module is used to acquire basic data information of a product; the basic data information of a product includes at least one of the following: product catalog, product model specifications, production space, process flow, energy system required for each process flow, and basic measurement unit; The equipment data acquisition module is used to obtain basic data information of the dispatchable equipment; the basic data information of the dispatchable equipment includes at least one of the following: the name of the equipment system, the cold start time, the energy supply type, the basic capacity, the operating parameters, the adjustable load and other information; A production information acquisition module is used to acquire production information related to the actual production situation of the target enterprise; the production information related to the actual production situation includes at least one of the following: production product name, production period, production space, production process, and product output; The energy consumption prediction module is used to establish an energy consumption prediction model based on the regression analysis of the basic energy consumption per unit of production of each product of the target enterprise in each process space and various related factors; wherein the various related factors include at least one of the following: product type, production space, production process, and production plan arrangement; wherein the energy consumption prediction model includes: the energy consumption of each product of the target enterprise during production in each process space; and Combined with the product production information in the actual product production arrangement of the target enterprise within the future scheduled time and the energy consumption prediction model, energy consumption prediction is performed on the actual product production arrangement of the target enterprise within the future scheduled time to obtain the energy consumption prediction result of the target enterprise within the future scheduled time; The energy diagnosis module is used to determine the supply based on the forecast of energy demand during the production period, define energy demand as the basic requirement for the enterprise's energy supply, and determine the start and stop schedule, operating parameters and required load of the supply equipment in the energy supply system in combination with the basic energy supply capacity of the target enterprise's existing energy supply system; The equipment scheduling control module is used to adjust the start and stop schedules, operating parameters and required loads of each energy supply equipment in the energy supply system.

9. A computer-readable storage medium, characterized in that: An enterprise energy consumption diagnosis program is stored thereon, and when the enterprise energy consumption diagnosis program is executed by a processor, the enterprise energy consumption diagnosis method described in any one of claims 1-5 is implemented.

10. An electronic device, characterized in that: The invention comprises a memory, a processor and an enterprise energy usage diagnostic program stored in the memory and executable on the processor. When the processor executes the enterprise energy usage diagnostic program, the enterprise energy usage diagnostic method described in any one of claims 1 to 5 is implemented.

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