Smart factory energy efficiency optimization system and method based on digital twin technology

Abstract

A smart factory energy efficiency optimization system and method based on digital twin technology. The system comprises a smart factory visualization module, an energy consumption preprocessing module, an energy consumption prediction module, and energy efficiency control module. A predicted operating time of a device for the day is obtained on the basis of a production speed of the device and a production quantity for the day in a real-time production schedule plan; recovered energy of the device for the day is predicted on the basis of the predicted operating time; the recovered energy for the day is then added to original stored energy in an energy recovery system to obtain actual available energy that the energy recovery system can provide for the day; the actual available energy is compared with tiered energy consumption of tiered devices for the day; and corresponding control signals are obtained on the basis of a comparison result. Therefore, recovered energy can be supplied to the tiered devices, maximizing energy utilization efficiency while minimizing the number of times of switching an energy supply mode, thus ensuring the stability of energy supply during operation of a production device.

Description

Intelligent factory energy efficiency optimization system and method based on digital twinning technology TECHNICAL FIELD

[0001] The present application relates to the technical field of factory energy management, in particular to an intelligent factory energy efficiency optimization system and method based on digital twinning technology. BACKGROUND

[0002] The digital twinning visualization platform is an important monitoring and management platform in the manufacturing production process, directly determines the quality and efficiency of products in production, and is the key to improving industry competitiveness and reducing costs and increasing efficiency.

[0003] The prior art CN115481894A discloses an intelligent energy management system and method based on a green factory, which includes an intelligent energy management center, a data processing system and an energy system. The intelligent energy management center is connected to the energy system through the data processing system. The intelligent energy management center is used to establish and issue control strategies according to the characteristic models of different components and devices in the energy system and the energy demand of the production and operation process. The energy system manages the energy use of different devices in the production and operation process according to the control strategy.

[0004] However, when the production equipment in the factory consumes energy, the energy used cannot be completely converted into useful work. At this time, the energy will be partially lost during the operation of the production equipment. Since the equipment in the factory runs for a long time and the number of devices is large, the energy loss during the operation of the equipment will increase the energy consumption loss of the factory. SUMMARY

[0005] The purpose of the present application is to solve the problems in the background art, and an intelligent factory energy efficiency optimization system and method based on digital twinning technology are proposed.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme:

[0007] The intelligent factory energy efficiency optimization system based on digital twinning technology comprises:

[0008] The intelligent factory visualization module is based on digital twinning technology, and a visualization module is constructed for the production factory. According to the rated power of the energy-consuming equipment in the production factory, the energy-consuming equipment is divided into equipment grades, including primary equipment, secondary equipment and tertiary equipment.

[0009] The energy consumption preprocessing module is based on the device function of the energy-consuming equipment in the factory, and the energy-consuming equipment is divided into operating equipment and auxiliary equipment. Then, according to the historical production quantity and daily operation record, the device production speed of the operating equipment is calculated, and according to the actual power and rated power of the operating equipment, the energy consumption recovery coefficient of the operating equipment is calculated. Then the device production speed and energy consumption recovery coefficient are transmitted to the energy consumption prediction module by the energy consumption preprocessing module.

[0010] a real-time production module, which arranges the production amount of products in a day based on system orders and production inventory, obtains a real-time production scheduling plan, and transmits the real-time production scheduling plan to the energy consumption prediction module;

[0011] the energy consumption prediction module, which calculates the predicted working time length of the operating equipment based on the production speed of the equipment, predicts the recovered energy of the operating equipment in a day according to the predicted working time length, adds the recovered energy in a day to the original stored energy in the energy recovery system to obtain the real supply energy in the energy recovery system in a day, then calculates the grade energy consumption according to the equipment grade of the operating equipment, compares the grade energy consumption with the real supply energy, obtains a control signal according to the comparison result, and transmits the control signal to the energy efficiency control module;

[0012] the energy efficiency control module, which switches the energy supply mode of the energy-consuming equipment based on the received control signal, wherein the energy supply mode includes direct energy supply and recovered energy supply.

[0013] As a further scheme of the present application, the device grade division method comprises:

[0014] According to the physical model of the production plant, all energy-consuming equipment in the production plant is obtained, the energy-consuming equipment refers to the equipment consuming energy, and the physical model refers to the field distribution structure diagram of the production plant;

[0015] The rated power of the energy-consuming equipment is identified, and the energy-consuming equipment is divided into device grades according to the rated power, wherein the device grades include first-level devices, second-level devices and third-level devices, the first-level devices refer to the rated power of the energy-consuming equipment being greater than or equal to A1, the second-level devices refer to the rated power of the energy-consuming equipment being less than A1 and greater than or equal to A2, and the third-level devices refer to the rated power of the energy-consuming equipment being less than A2, A1 and A2 are power threshold values.

[0016] As a further scheme of the present application, the specific acquisition method of the device production speed of the operating equipment according to the historical production scheduling amount and the daily operation record comprises:

[0017] According to the device function of the energy-consuming equipment in the smart factory, the energy-consuming equipment is divided into operating equipment and auxiliary equipment, wherein the operating equipment refers to the equipment used for production and processing in the factory, and the auxiliary equipment refers to the equipment not directly used for production and processing in the production process;

[0018] According to the historical production scheduling amount in the production information and the daily operation record in the device information, the daily operation record of the operating equipment in a previous period of time is obtained first, and the daily production scheduling amount in the historical production scheduling amount is corresponded to the daily operation record of the operating equipment according to the time date;

[0019] Wherein, the daily operation record is the total length of daily work of the operating equipment, the historical production quantity is the daily product production quantity in a period of time obtained from the current time as a node, and the period of time is the threshold value;

[0020] The daily production speed is obtained by dividing the daily production quantity by the daily operation record of the corresponding operating equipment, and then the daily production speed of the operating equipment in the period of time is averaged to obtain the equipment production speed Vs, and s represents different operating equipment.

[0021] As a further scheme of the present application, the calculation method of the real energy supply of the day according to the predicted working time length comprises:

[0022] The daily production quantity in the real-time production scheduling plan is extracted, and the daily production quantity is multiplied by the equipment production speed to obtain the predicted working time length TDs of the operating equipment of the day;

[0023] The distance loss value DCs of the operating equipment s is obtained by adopting The daily recovered energy NX is obtained, DCs represents the distance loss value of the operating equipment s, Xh is the conversion loss coefficient of the energy recovery system, WSs represents the actual power of the operating equipment s, Hs represents the energy consumption recovery coefficient of the operating equipment s, and n represents the total number of operating equipment in the production plant;

[0024] The calculation method of the distance loss value DCs is: DCs = WSs × TDs × Hs × CL × DLs, CL is the transportation loss coefficient in the energy recovery system, and DLs is the transmission distance between the operating equipment s and the energy recovery system;

[0025] Then the original storage energy NY in the energy recovery system is obtained, and the original storage energy NY is added to the daily recovered energy NX to obtain the real energy supply NG, wherein the original storage energy NY refers to the energy value originally stored in the energy recovery system.

[0026] As a further scheme of the present application, the calculation method of the energy consumption recovery coefficient comprises:

[0027] The rated power WEs and the actual power WSs during operation of the operating equipment are obtained respectively, the efficiency conversion coefficient X1s of the equipment is obtained by adopting WSs ÷ WEs = X1s, and the energy consumption recovery coefficient Hs is obtained by adopting Hs = (1-X1s) × e -Ks , wherein Ks is the additional loss coefficient corresponding to the operating equipment s.

[0028] As a further scheme of the present application, the obtaining method of the grade energy consumption comprises:

[0029] According to the equipment grade, the total energy consumption NH1 of the operating equipment in the first-level equipment is calculated first, and the calculation method is a represents the operating equipment in the primary equipment, the total energy consumption of the operating equipment in the secondary equipment and the tertiary equipment is calculated according to the above formula, and the calculation formula is b represents the operating equipment in the secondary equipment, c represents the operating equipment in the tertiary equipment, NH2 represents the total energy consumption of the operating equipment in the primary equipment, NH3 represents the total energy consumption of the operating equipment in the tertiary equipment, and a, b and c all belong to s, m1 is the total number of the operating equipment in the primary equipment, m2 is the total number of the operating equipment in the secondary equipment, m3 is the total number of the operating equipment in the tertiary equipment, and m1+m2+m3=n;

[0030] The level energy consumption includes the primary energy consumption, the secondary energy consumption and the tertiary energy consumption;

[0031] The total energy consumption of the operating equipment in the primary equipment, the secondary equipment and the tertiary equipment is marked as the primary energy consumption EB1, EB1=NH1+NH2+NH3, the total energy consumption of the operating equipment in the secondary equipment and the tertiary equipment is marked as the secondary energy consumption EB2, EB2=NH2+NH3, and the total energy consumption of the operating equipment in the tertiary equipment alone is marked as the tertiary energy consumption EB3, EB3=NH3.

[0032] As a further scheme of the present application, the method for obtaining the control signal comprises:

[0033] The control signal comprises a first energy supply signal, a second energy supply signal, a third energy supply signal and an auxiliary energy supply signal;

[0034] When NG>EB1, the first energy supply signal is generated, when EB1≥NG>EB2, the second energy supply signal is generated, when EB2≥NG>EB3, the third energy supply signal is generated, and when NG≤EB3, the auxiliary energy supply signal is generated.

[0035] As a further scheme of the present application, the method for switching the energy supply mode of the energy-consuming equipment according to the control signal comprises:

[0036] The control signal is identified, if the control signal is the first energy supply signal, all the energy-consuming equipment adopts the recycled energy supply mode, if the control signal is the second energy supply signal, the primary equipment adopts the direct energy supply mode, and the secondary equipment and the tertiary equipment adopt the recycled energy supply mode, if the control signal is the third energy supply signal, the primary equipment and the secondary equipment adopt the direct energy supply mode, and the tertiary equipment adopts the recycled energy supply mode, and if the control signal is the auxiliary energy supply signal, all the operating equipment adopts the direct energy supply mode, and the auxiliary equipment adopts the recycled energy supply mode.

[0037] As a further scheme of the present application, an integrated storage module is further included;

[0038] The integrated storage module stores production information and equipment information based on production information and equipment information of a production factory, wherein the production information refers to information related to factory production, and the equipment information refers to information related to equipment existing in the production factory;

[0039] The intelligent factory energy efficiency optimization method based on the digital twin technology specifically comprises the following steps:

[0040] Step one: based on the digital twin technology, a visual module of the production factory is constructed according to a physical model of the production factory, and meanwhile, energy-consuming equipment is divided into equipment grades according to the rated power of the energy-consuming equipment in the production factory, wherein the equipment grades include first-grade equipment, second-grade equipment and third-grade equipment;

[0041] Step two: based on historical big data, the equipment production speed of operating equipment is calculated according to historical production quantity and daily operation records, and the energy consumption recovery coefficient of the operating equipment is calculated according to the actual power and the rated power of the operating equipment;

[0042] Step three: the expected working time of the operating equipment is obtained by calculating the daily production quantity arranged in the real-time production scheduling and the equipment production speed of the operating equipment, and then the daily recovered energy is calculated according to the expected working time of the operating equipment, and the real supply energy on the day is obtained by adding the daily recovered energy and the original storage energy in the energy recovery system;

[0043] Step four: then, the grade energy consumption is calculated according to the equipment grade of the operating equipment, and the grade energy consumption is compared with the real supply energy, and a control signal is obtained according to the comparison result, and then the energy supply mode of the energy-consuming equipment is switched by the energy efficiency control module according to the control signal, wherein the energy supply mode includes direct energy supply and recovered energy supply.

[0044] Compared with the prior art, the present application has the following advantages:

[0045] By obtaining the operating speed of the equipment in the production factory, the expected working time of the equipment on the day is obtained according to the daily production quantity in the real-time production scheduling, and the daily recovered energy of the equipment is predicted according to the expected working time, and the real supply energy provided by the energy recovery system on the day is obtained by adding the daily recovered energy and the original storage energy in the energy recovery system, and the real supply energy is compared with the grade energy consumption of the grade equipment on the day, and a corresponding control signal is obtained according to the comparison result, so that the recovered energy is supplied to the grade equipment, on the one hand, the energy utilization efficiency is maximized, and on the other hand, the energy supply mode is switched frequently, thereby ensuring the stability of the energy supply of the production equipment during operation;

[0046] The application is based on digital twin technology, a visual module is constructed for a factory, and the production factory is converted into a smart factory, so that the data information in the equipment operation process is interconnected and communicated when the factory carries out energy optimization management, and the real-time and continuity of the factory information management are further improved. BRIEF DESCRIPTION OF DRAWINGS

[0047] Fig. 1 is a schematic diagram of the system structure of the application;

[0048] Fig. 2 is a schematic diagram of the flow structure of the application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all.

[0050] In embodiment one, referring to Figs. 1 and 2, the energy efficiency optimization system of the smart factory based on the digital twin technology comprises an integrated storage module, a smart factory visual module, a real-time production module, an energy consumption preprocessing module, an energy consumption prediction module and an energy efficiency control module.

[0051] The integrated storage module stores production information and equipment information based on the production information and the equipment information of the production factory, wherein the production information refers to information related to the production of the factory, including production yield, etc., and the equipment information refers to information related to the equipment existing in the production factory, including the rated power of the equipment and the equipment operation data, etc., and the integrated storage module is bidirectionally electrically connected with the smart factory visual module.

[0052] The smart factory visual module is based on the digital twin technology, and a visual module is constructed for the production factory according to the physical model of the production factory, wherein the physical model of the production factory refers to the field distribution structure diagram of the production factory, and the factory model constructed in the visual module is a three-dimensional space model diagram, in which the overall equipment of the factory is interconnected, and the production factory is converted into a smart factory.

[0053] In another embodiment of the application, according to the equipment information stored in the integrated storage module, the energy-consuming equipment in the production factory is divided into equipment grades when the visual module is constructed for the production factory, and the specific method for dividing the equipment grades comprises:

[0054] According to the physical model of the production factory, all energy-consuming equipment in the production factory is obtained, wherein the energy-consuming equipment refers to equipment consuming energy, including production equipment, signal acquisition equipment and lighting equipment, etc.

[0055] extracting device information of the energy-consuming device, and identifying a rated power of the energy-consuming device, and dividing the energy-consuming device into device levels according to the rated power, wherein the device levels include a first-level device, a second-level device and a third-level device, the first-level device refers to the rated power of the energy-consuming device being greater than or equal to A1, the second-level device refers to the rated power of the energy-consuming device being less than A1 and greater than or equal to A2, and the third-level device refers to the rated power of the energy-consuming device being less than A2, A1 and A2 are power thresholds, and specific values thereof are determined by a person skilled in the art according to actual conditions of the production plant;

[0056] Then, the device levels of the energy-consuming device are labeled in the smart factory visual module, and the smart factory visual module is bidirectionally electrically connected with the energy consumption preprocessing module.

[0057] The energy consumption preprocessing module performs data dynamic analysis on the energy-consuming device in the smart factory based on historical big data, and specific data dynamic analysis methods include:

[0058] S1: According to the device function of the energy-consuming device in the smart factory, the energy-consuming device is divided into operating equipment and auxiliary equipment, wherein the operating equipment refers to equipment used for production and processing in the factory, and the auxiliary equipment refers to equipment not directly used for production and processing in the production process, such as lighting equipment and air conditioners, etc.

[0059] S2: According to the historical production quantity in the production information and the daily operation record in the device information, the production relationship between the historical production quantity and the operating equipment is obtained, wherein the historical production quantity refers to the production quantity of products per day within a cycle time from the current time as a node, and the cycle time is a threshold, and the production quantity in the current time is not included in the historical production quantity, and specific methods for obtaining the production relationship between the historical production quantity and the operating equipment include:

[0060] S21: First, the daily operation record of the operating equipment within the previous cycle time is obtained, and the daily production quantity in the historical production quantity is corresponded to the daily operation record of the operating equipment according to the time date, and the daily operation record is the total length of the operating equipment working per day;

[0061] S22: The daily production quantity is divided by the corresponding daily operation record of the operating equipment to obtain the daily production speed, and then the daily production speed of the operating equipment within the cycle time is averaged to obtain the device production speed Vs, s represents different operating equipment.

[0062] It should be further explained that the daily operation record of the operating equipment takes each hour as a time unit, and the daily production speed is averaged.

[0063] S23: After obtaining the rated power WEsof the operating equipment and the actual power WSsthat is running, the efficiency conversion coefficient X1sof the equipment is obtained by WSs÷WEs=X1s, and the energy consumption recovery coefficient Hs is obtained by Hs=(1-X1s)×e -Ks The energy consumption recovery coefficient Hs is obtained, where Ks is the additional loss coefficient corresponding to the operating equipment s, and the specific value is obtained by technicians in the art through multiple experimental simulation;

[0064] The energy pretreatment module transmits the energy consumption recovery coefficient of the operating equipment and the equipment production speed to the energy consumption prediction module;

[0065] The real-time production module is used to arrange the production quantity of the product of the day according to the system order and the production inventory, and obtain the real-time production scheduling plan. Then the real-time production module transmits the real-time production scheduling plan to the energy consumption prediction module, where the system order and the production inventory are exported from the production management system. In this embodiment, the production management system includes WMS and B2B system;

[0066] The energy consumption prediction module predicts and analyzes the energy consumption value of the energy-consuming equipment based on the real-time production scheduling plan. The specific prediction and analysis method includes:

[0067] The daily production quantity in the real-time production scheduling plan is extracted, and the daily production quantity is multiplied by the equipment production speed to obtain the expected working time TDsof the operating equipment of the day;

[0068] Then, the expected working time TDsof the operating equipment of the day is obtained by The recovered energy NXof the day is obtained, where DCs represents the distance loss value of the operating equipment s, Xh is the conversion loss coefficient of the energy recovery system, and the specific value is determined according to the actual energy recovery system, and n represents the total number of operating equipment in the production plant;

[0069] The calculation method of the distance loss value DCs is: DCs=WSs×TDs×Hs×CL×DLs, CL is the transportation loss coefficient in the energy recovery system, and the specific value is determined by the heat preservation material and the transmission medium, and DLs is the transmission distance between the operating equipment s and the energy recovery system;

[0070] Then the original storage energy NYin the energy recovery system is obtained, and the original storage energy NYis added to the recovered energy NXof the day to obtain the actual supply energy NG, where the original storage energy NYrepresents the energy value originally stored in the energy recovery system;

[0071] Then, according to the equipment level, the expected consumption energy of the first-level equipment, the second-level equipment and the third-level equipment of the day is obtained respectively. The specific calculation method of the expected consumption energy includes:

[0072] According to the equipment level, the total energy consumption NH1 of the operating equipment in the first-level equipment is calculated first, and the calculation method is a represents the operating equipment in the first-level equipment, and according to the above formula, the total energy consumption of the operating equipment in the second-level equipment and the third-level equipment is calculated respectively, and the calculation formulas are b represents the operating equipment in the second-level equipment, c represents the operating equipment in the third-level equipment, NH2 represents the total energy consumption of the operating equipment in the first-level equipment, NH3 represents the total energy consumption of the operating equipment in the third-level equipment, and a, b, and c all belong to s, m1 is the total number of operating equipment in the first-level equipment, m2 is the total number of operating equipment in the second-level equipment, and m3 is the total number of operating equipment in the third-level equipment, and m1+m2+m3=n;

[0073] The level energy consumption includes the first-level energy consumption, the second-level energy consumption, and the third-level energy consumption;

[0074] Among them, the sum of the energy consumption of the operating equipment in the first-level equipment, the second-level equipment and the third-level equipment is marked as the first-level energy consumption EB1, EB1=NH1+NH2+NH3, the sum of the energy consumption of the operating equipment in the second-level equipment and the third-level equipment is marked as the second-level energy consumption EB2, EB2=NH2+NH3, and the sum of the energy consumption of the operating equipment in the third-level equipment alone is marked as the third-level energy consumption EB3, EB3=NH3;

[0075] The level energy consumption is compared with the actual energy supply NG, and a control signal is generated according to the comparison result, wherein the control signal includes a first energy supply signal, a second energy supply signal, a third energy supply signal, and an auxiliary energy supply signal;

[0076] Among them, when NG>EB1, the first energy supply signal is generated, when EB1≥NG>EB2, the second energy supply signal is generated, when EB2≥NG>EB3, the third energy supply signal is generated, and when NG≤EB3, the auxiliary energy supply signal is generated;

[0077] Then the energy consumption prediction module transmits the control signal to the energy efficiency control module;

[0078] The energy efficiency control module switches the energy supply mode of the energy-consuming equipment according to the received control signal, and the energy supply mode includes direct energy supply and recycled energy supply, the direct energy supply means that the energy-consuming equipment uses the power transmitted by the power grid to maintain the operating state of the equipment, and the recycled energy supply means that the energy-consuming equipment uses the recycled energy in the energy recovery system to maintain the operating state of the equipment, and the specific switching method includes:

[0079] The control signal received by the energy efficiency control module is identified. If the control signal is a first energy supply signal, all energy-consuming devices in the production plant are in the recovered energy supply mode at this time. If the control signal is a second energy supply signal, the first-level device is in the direct energy supply mode, and the second-level device and the third-level device are in the recovered energy supply mode. If the control signal is a third energy supply signal, the first-level device and the second-level device are in the direct energy supply mode, and the third-level device is in the recovered energy supply mode. If the control signal is an auxiliary energy supply signal, all operating devices are in the direct energy supply mode, and the auxiliary device is in the recovered energy supply mode, thereby ensuring the stability of the operating device during operation.

[0080] Then the energy supply mode of the energy-consuming device is transmitted to the intelligent factory visual module by the energy efficiency control module, so that the management personnel can understand the control mode of the production plant.

[0081] Embodiment two, the intelligent factory energy efficiency optimization method based on digital twin technology, which specifically comprises the following steps:

[0082] Step one: based on digital twin technology, according to the physical model of the production plant, a visual module of the production plant is constructed, and at the same time, according to the rated power of the energy-consuming device in the production plant, the energy-consuming device is divided into device levels, including first-level device, second-level device and third-level device;

[0083] Step two: based on historical big data, according to the historical production quantity and daily operation record, the device production speed of the operating device is calculated, and at the same time, according to the actual power and rated power of the operating device, the energy consumption recovery coefficient of the operating device is calculated;

[0084] Step three: according to the daily production quantity arranged in the real-time production plan, the daily production quantity and the device production speed of the operating device are calculated to obtain the expected working time of the operating device, and then the daily recovered energy is calculated according to the expected working time of the operating device, and the daily recovered energy and the original storage energy in the energy recovery system are added to obtain the real energy supply of the day;

[0085] Step four: then according to the device level of the operating device, the level energy consumption is calculated, and the level energy consumption is compared with the real energy supply, and the control signal is obtained according to the comparison result, and then the energy efficiency control module switches the energy supply mode of the energy-consuming device according to the control signal.

[0086] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited to this. Any skilled person in the art can make equivalent replacement or change according to the technical solution and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A smart factory energy efficiency optimization system based on digital twin technology, characterized in that, The application relates to a smart factory energy efficiency control method and device. The smart factory visual module is based on digital twin technology and constructs a visual module for a production factory; according to the rated power of energy-consuming equipment in the production factory, the energy-consuming equipment is divided into equipment grades, and the equipment grades include primary equipment, secondary equipment and tertiary equipment. The energy consumption preprocessing module is based on the equipment functions of the energy-consuming equipment in the factory, and the energy-consuming equipment is divided into operating equipment and auxiliary equipment; then, according to historical production quantities and daily operation records, the equipment production speed of the operating equipment is calculated, and according to the actual power and the rated power of the operating equipment, the energy consumption recovery coefficient of the operating equipment is calculated; then, the equipment production speed and the energy consumption recovery coefficient are transmitted to the energy consumption prediction module. The real-time production module is based on system orders and production inventories, arranges the production quantity of products on the day, obtains a real-time production scheduling plan, and transmits the real-time production scheduling plan to the energy consumption prediction module. The energy consumption prediction module is based on the equipment production speed, calculates the predicted working time length of the operating equipment, and predicts the daily recovered energy of the operating equipment according to the predicted working time length; the daily recovered energy and the original storage energy in the energy recovery system are added to obtain the real supply energy in the energy recovery system on the day; then, according to the equipment grades of the operating equipment, the grade energy consumption is calculated; the grade energy consumption is compared with the real supply energy; according to the comparison result, a control signal is obtained and transmitted to the energy efficiency control module. The energy efficiency control module is based on the received control signal, and mode switching is performed on the energy supply mode of the energy-consuming equipment, wherein the energy supply mode includes direct energy supply and recovered energy supply.

2. The smart factory energy efficiency optimization system based on digital twin technology according to claim 1, wherein, The method for dividing the equipment grades comprises the following steps: According to the physical model of the production factory, all the energy-consuming equipment in the production factory is obtained, the energy-consuming equipment refers to the equipment consuming energy, and the physical model refers to the field distribution structure diagram of the production factory. The rated power of the energy-consuming equipment is identified, and the energy-consuming equipment is divided into equipment grades according to the rated power, wherein the equipment grades include primary equipment, secondary equipment and tertiary equipment; the primary equipment refers to the energy-consuming equipment with a rated power greater than or equal to A1; the secondary equipment refers to the energy-consuming equipment with a rated power less than A1 and greater than or equal to A2; the tertiary equipment refers to the energy-consuming equipment with a rated power less than A2; A1 and A2 are power threshold values. 3.The smart factory energy efficiency optimization system based on digital twin technology of claim 1, wherein, The specific method for calculating the equipment production speed of the operating equipment according to the historical production quantities and the daily operation records comprises the following steps: According to the equipment functions of the energy-consuming equipment in the smart factory, the energy-consuming equipment is divided into operating equipment and auxiliary equipment, wherein the operating equipment refers to the equipment used for factory production and processing, and the auxiliary equipment refers to the equipment not directly used for production and processing in the production process; According to the historical production quantities in the production information and the daily operation records in the equipment information, the daily operation records of the operating equipment in a previous period of time are obtained, and the daily production quantities in the historical production quantities are matched with the daily operation records of the operating equipment according to time and date; The daily operation record is the total working time length of the operating equipment per day, and the historical production quantity refers to the production quantity of products per day in a period of time obtained from the current time as a node, and the period of time is a threshold value. The daily production speed is obtained by dividing the daily production output by the daily operation record of the corresponding operating equipment, and then the daily production speed of the operating equipment in the cycle time is averaged to obtain the equipment production speed Vs, and s represents different operating equipment.

4. The smart factory energy efficiency optimization system based on digital twin technology according to claim 1, wherein, The calculation method of the actual energy supply of the day according to the estimated working time includes: The daily production of the real-time production planning is extracted, and the daily production is multiplied by the equipment production speed to obtain the estimated working time TDs of the operating equipment of the day; Adopting The daily recovered energy NX is obtained, DCs represents the distance loss value of the operating equipment s, Xh is the conversion loss coefficient of the energy recovery system, WSs represents the actual power of the operating equipment s, Hs represents the energy recovery coefficient of the operating equipment s, and n represents the total number of operating equipment in the production plant; The calculation method of the distance loss value DCs is: DCs=WSs×TDs×Hs×CL×DLs, CL is the transportation loss coefficient in the energy recovery system, and DLs is the transmission distance between the operating equipment s and the energy recovery system; Then the original storage energy NY in the energy recovery system is obtained, and the original storage energy NY is added to the daily recovered energy NX to obtain the actual energy supply NG, wherein the original storage energy NY refers to the original storage energy value in the energy recovery system.

5. The digital twin technology based smart factory energy efficiency optimization system of claim 4, wherein, The calculation method of the energy recovery coefficient includes: The rated power WEsof the operating equipment and the actual power WSswhen operating are obtained respectively, the efficiency conversion coefficient X1sof the equipment is obtained by using WSs÷WEs=X1s, and the energy consumption recovery coefficient Hs is obtained by using Hs=(1-X1s)×e -Ks The energy consumption recovery coefficient Hs is obtained, wherein Ks is the additional loss coefficient corresponding to the operating equipment s.

6. The smart factory energy efficiency optimization system based on digital twin technology according to claim 5, wherein, The acquisition method of the level energy consumption includes: According to the equipment level, the total energy consumption NH1 of the operating equipment in the first-level equipment is calculated first, and the calculation method is a represents the operating equipment in the primary equipment, and the total energy consumption of the operating equipment in the secondary equipment and the tertiary equipment is calculated according to the above formula, and the calculation formula is b represents the operating equipment in the second-level equipment, c represents the operating equipment in the third-level equipment, NH2 represents the total energy consumption of the operating equipment in the first-level equipment, NH3 represents the total energy consumption of the operating equipment in the third-level equipment, and a, b, and c all belong to s, m1 is the total number of operating equipment in the first-level equipment, m2 is the total number of operating equipment in the second-level equipment, m3 is the total number of operating equipment in the third-level equipment, and m1+m2+m3=n; The level energy consumption includes first-level energy consumption, second-level energy consumption and third-level energy consumption; Among them, the sum of the energy consumption of the operating equipment in the first-level equipment, the second-level equipment and the third-level equipment is marked as the first-level energy consumption EB1, EB1=NH1+NH2+NH3, the sum of the energy consumption of the operating equipment in the second-level equipment and the third-level equipment is marked as the second-level energy consumption EB2, EB2=NH2+NH3, and the sum of the energy consumption of the operating equipment in the third-level equipment alone is marked as the third-level energy consumption EB3, EB3=NH3.

7. The digital twin technology based smart factory energy efficiency optimization system according to claim 6, wherein, The acquisition method of the control signal includes: The control signal includes a first energy supply signal, a second energy supply signal, a third energy supply signal and an auxiliary energy supply signal; Among them, when NG>EB1, the first energy supply signal is generated, when EB1≥NG>EB2, the second energy supply signal is generated, when EB2≥NG>EB3, the third energy supply signal is generated, and when NG≤EB3, the auxiliary energy supply signal is generated.

8. The smart factory energy efficiency optimization system based on digital twin technology according to claim 7, characterized in that, The method for switching the energy supply mode of the energy-consuming equipment according to the control signal includes: The control signal is identified. If the control signal is a first energy supply signal, all energy-consuming devices are supplied with recycled energy. If the control signal is a second energy supply signal, the first-level device is supplied with direct energy, the second-level device and the third-level device are supplied with recycled energy. If the control signal is a third energy supply signal, the first-level device and the second-level device are supplied with direct energy, and the third-level device is supplied with recycled energy. If the control signal is an auxiliary energy supply signal, all operating devices are supplied with direct energy, and the auxiliary device is supplied with recycled energy.

9. The digital twin technology based smart factory energy efficiency optimization system according to claim 1, wherein, The integrated storage module is also included. The integrated storage module stores production information and device information based on the production information and device information of the production plant, wherein the production information refers to information related to the production of the plant, and the device information refers to information related to the devices present in the production plant.

10. The method of energy efficiency optimization for smart factory based on digital twin technology, which is applied to the energy efficiency optimization system of any one of claims 1-9, characterized in that, The method specifically includes the following steps: Step 1: Based on digital twinning technology, a visual module is constructed for the production plant according to the physical model of the production plant. At the same time, the energy-consuming devices are divided into device levels according to their rated power, including first-level devices, second-level devices, and third-level devices. Step 2: Based on historical big data, the device production speed of the operating device is calculated according to the historical production volume and daily operation records. The energy consumption recovery coefficient of the operating device is calculated according to the actual power and rated power of the operating device. Step 3: According to the daily production volume arranged in the real-time production scheduling plan, the daily production volume is calculated with the device production speed of the operating device to obtain the expected working time of the operating device. Then, the real-time energy recovery is calculated according to the expected working time of the operating device. The real-time energy recovery is added to the original storage energy in the energy recovery system to obtain the real-time energy supply. Step 4: Then, the level energy consumption is calculated according to the device level of the operating device. The level energy consumption is compared with the real-time energy supply, and the control signal is obtained according to the comparison result. The energy supply mode of the energy-consuming device is switched by the energy efficiency control module according to the control signal. The energy supply mode includes direct energy supply and recycled energy supply.

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