Digital twin city energy management system based on the Internet of Things

Abstract

The present invention discloses a digital twin city energy management system based on the Internet of Things, which relates to the technical field of intelligent management, including a construction module, an analysis module, and a management module, constructing a heating topology structure, constructing a node flow balance equation and a pressure difference balance equation, obtaining a heating flow equation group and a theoretical historical load value of a corresponding node, constructing a city heating digital twin model, comparing the theoretical current load value with the corresponding theoretical historical load value, executing a first operation, and sending a solution strategy. The present invention improves the efficiency and accuracy of energy management through digital twin technology, uses a digital twin model for virtual testing and verification, optimizes operation decisions and control strategies, reduces energy waste and operating costs through prediction and simulation, improves the dynamic adaptability of the system, and supports the sustainable development and environmental protection goals of the city by optimizing energy use.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management, and in particular to a digital twin city energy management system based on the Internet of Things. Background Art

[0002] In recent years, the digital twin city energy management technology based on the Internet of Things is developing rapidly, providing a strong impetus for the digital transformation of the energy industry and showing a wide range of application potential in many aspects. Emerging technologies such as cloud computing, artificial intelligence (AI), big data, and digital twins have provided new impetus for innovation and transformation in the energy industry and accelerated the digital transformation of energy systems. The digital twin technology system integrates Internet of Things technology, communication technology, big data analysis technology, high-performance computing technology, and advanced simulation analysis technology to provide a richer and more realistic model for smart energy systems and serve the operation and control of the system.

[0003] At present, a smart city energy management system and method are disclosed in a Chinese invention patent with publication number CN118278028A. The method manages, allocates and dispatches energy data through a management and control center module, and provides energy-saving suggestions and optimization measures. However, the related technology does not unify the discrete energy demand individuals in the city at a macro level, lacks management convenience and simplicity, and does not digitize and theorize municipal management through circuit thinking, which is not conducive to the scientificity and accuracy of management and has certain limitations. Summary of the invention

[0004] The technical problem solved by the present invention is that the related technology does not unify the discrete energy demand individuals in the city at a macro level, lacks the convenience and simplicity of management, does not digitize and theorize municipal management through circuit thinking, is not conducive to the scientificity and accuracy of management, and has certain limitations.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a digital twin city energy management system based on the Internet of Things, including a construction module, an analysis module and a management module;

[0006] The construction module sets each heating demand individual as a node, marks each node and the pipelines between each node, constructs a heating topology structure, and constructs a node flow balance equation according to Kirchhoff's law for the connection relationship between the node and the corresponding pipeline according to the first association matrix, and constructs a pressure difference balance equation according to Kirchhoff's pressure difference balance law, and obtains a heating flow equation group according to the node flow balance equation and the pressure difference balance equation;

[0007] The analysis module obtains historical operation data, obtains the topological loop where the node is located, and obtains the theoretical historical load value of the corresponding node according to the historical flow of other nodes in the topological loop and the heating flow equation group;

[0008] The management module obtains each theoretical load value and the corresponding historical operating conditions of the node, obtains the weight of the historical operating conditions for the theoretical historical load value based on multiple regression analysis, constructs a city heating digital twin model based on the weight, obtains current data, obtains the theoretical current load value based on the city heating digital twin model, compares the theoretical current load value with the corresponding theoretical historical load value, executes a first operation based on the comparison result, and sends a solution strategy based on the first operation.

[0009] As a preferred solution of the digital twin city energy management system based on the Internet of Things described in the present invention, wherein: the heating demand individuals are represented as residential houses, factories, office buildings and shopping malls;

[0010] Let the node be v N , N is a natural number, and the pipeline is recorded as e M , M is a natural number, the starting node or the end node forms a straight line structure with the pipeline, and the intermediate node and the pipeline form a closed loop structure to construct a heating topology structure.

[0011] As a preferred solution of the digital twin city energy management system based on the Internet of Things described in the present invention, wherein: the connection relationship between the node and the corresponding pipeline is determined according to the first association matrix;

[0012] The expression of the first incidence matrix is:

[0013] ;

[0014] ;

[0015] in, is the first incidence matrix, It is the association relationship between the node and the pipeline.

[0016] As a preferred solution of the digital twin city energy management system based on the Internet of Things described in the present invention, wherein: a first association matrix is ​​obtained, the first association matrix is ​​a full-rank matrix, and according to graph theory, the heating topology structure includes M-N+1 basic loops, and the basic loop is represented as s K , where K is distributed in 1~M-N+1 and is a positive integer, a second association matrix of the basic loop and the pipeline is constructed, and a node flow balance equation is constructed according to the second association matrix;

[0017] The expression of the second incidence matrix is:

[0018] ;

[0019] ;

[0020] in, is the second incidence matrix, It is the relationship between pipelines and basic circuits;

[0021] The calculation expression of the node flow balance equation is:

[0022] ;

[0023] in, is the association relationship between each node in the first association matrix and any pipeline, Expressed as pipelines, of which , Expressed as The flow rate corresponding to each pipeline is For the The net flow of the node is expressed as The ingress traffic of each node;

[0024] The pressure drop of each pipeline is expressed as a pressure drop vector matrix, which is expressed as:

[0025] ;

[0026] The calculation expression of the pressure difference balance equation is:

[0027] ;

[0028] in, The transpose flag.

[0029] As a preferred solution of the digital twin city energy management system based on the Internet of Things described in the present invention, wherein: the heating flow equation group is obtained according to the node flow balance equation and the pressure difference balance equation, and the calculation expression of the heating flow equation group is:

[0030] ;

[0031] in, is the flow vector of each pipeline, is the net flow vector of each node, is the characteristic coefficient vector of each pipeline resistance, is the water pump vector corresponding to each pipeline, is the potential energy difference vector between the starting node and the ending node of each pipeline.

[0032] As a preferred solution of the digital twin city energy management system based on the Internet of Things described in the present invention, wherein: the historical operation data includes historical operating conditions, corresponding historical nodes, corresponding historical flow rates, corresponding historical outlet temperatures and historical inlet temperatures;

[0033] The historical operating conditions include circulating pump efficiency, valve opening and pump motor current frequency;

[0034] The circulating pump efficiency is expressed as the ratio of the pump motor output power to the circulating pump output power.

[0035] As a preferred solution of the digital twin city energy management system based on the Internet of Things described in the present invention, wherein: the topological loop where the node is located is obtained, the node number and the pipeline number contained in the topological loop are obtained, the historical operation database is retrieved, the node number and the pipeline number are input into the historical operation database, and the historical flow corresponding to each corresponding node is matched;

[0036] According to the historical flow of other nodes and the heating flow equation group, the theoretical historical flow of the corresponding node is obtained, which is recorded as the theoretical historical load value;

[0037] The historical traffic of the node includes historical import traffic and historical export traffic.

[0038] As a preferred solution of the digital twin city energy management system based on the Internet of Things described in the present invention, wherein: the current data includes the current operating conditions and the target node;

[0039] Retrieving a heat flow equation group, inputting the current operating condition into the heat flow equation group, obtaining a theoretical current load value corresponding to each node, and obtaining a theoretical current load value corresponding to a target node;

[0040] Get the theoretical historical load value of the current node;

[0041] The theoretical current load value is compared with the corresponding theoretical historical load value, a first operation is performed according to the comparison result, and a solution strategy is sent according to the first operation, wherein the first operation includes not performing simulation deduction and performing simulation deduction.

[0042] As a preferred solution of the digital twin city energy management system based on the Internet of Things described in the present invention, when the theoretical current load value is equal to the corresponding theoretical historical load value, the first operation is set to not perform simulation deduction;

[0043] When the theoretical current load value is not equal to the corresponding theoretical historical load value, the first operation is set to perform simulation deduction;

[0044] When the first operation is to perform simulation deduction, the first value, the second value and the third value are respectively set as the change in the circulation pump efficiency, the change in the valve opening and the change in the pump motor current frequency, and the first change is first made to the circulation pump efficiency through the first value through the control variable method, and the theoretical current load value after the first change is obtained, and the theoretical current load value after the first change is subtracted from the theoretical historical load value to obtain a first difference, and the fourth value is set as the difference threshold, and when the first difference is less than the fourth value, the deduction is stopped, the corresponding total change in the circulation pump efficiency is obtained and the first solution strategy is output, and the first solution strategy is represented by continuously increasing the total change in the circulation pump efficiency, or continuously reducing the total change in the circulation pump efficiency;

[0045] The first change, the second change and the third change are represented as a continuous increase or a continuous decrease.

[0046] As a preferred solution of the digital twin city energy management system based on the Internet of Things described in the present invention, when the first difference is greater than or equal to the fourth value, the valve opening is changed for the second time by the second value, and the theoretical current load value after the second change is obtained, and the theoretical current load value after the second change is subtracted from the fourth value to obtain the second difference. When the second difference is less than the fourth value, the deduction is stopped, the corresponding total change of the valve opening is obtained, and the second solution strategy is output, and the second solution strategy is represented by the first solution strategy and the total change of the valve opening is continuously increased, or the first solution strategy and the total change of the valve opening is continuously reduced;

[0047] When the second difference is greater than or equal to the fourth value, the third change is made to the pump motor current frequency through the third value, and the theoretical current load value after the third change is obtained, and the third difference is obtained by subtracting the theoretical current load value after the third change from the fourth value. When the third difference is less than the fourth value, the deduction is stopped, and the corresponding total change of the pump motor current frequency is obtained, and the third solution strategy is output. The third solution strategy is expressed as the first solution measurement and the second solution strategy and continuously increases the total change of the pump motor current frequency, or the first solution measurement and the second solution strategy and continuously decreases the total change of the pump motor current frequency.

[0048] The beneficial effects of the present invention are as follows: Through digital twin technology, urban energy systems can be monitored and analyzed more accurately, the efficiency and accuracy of energy management can be improved, and digital twin models can be used for virtual testing and verification. Without affecting the actual system, operational decisions and control strategies can be optimized. Through prediction and simulation, energy waste can be reduced and operating costs can be reduced. Digital twin technology enables energy systems to adapt to changes in structure and parameters, improves the dynamic adaptability of the system, and supports the city's sustainable development and environmental protection goals by optimizing energy use. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of the basic process of a digital twin city energy management system based on the Internet of Things provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0051] Example, see Figure 1 , is an embodiment of the present invention, and provides a digital twin city energy management system based on the Internet of Things, including a construction module, an analysis module, and a management module;

[0052] The construction module sets each heating demand individual as a node, marks each node and the pipelines between each node, constructs a heating topology structure, and constructs a node flow balance equation according to Kirchhoff's law for the connection relationship between the node and the corresponding pipeline according to the first association matrix, and constructs a pressure difference balance equation according to Kirchhoff's pressure difference balance law, and obtains a heating flow equation group according to the node flow balance equation and the pressure difference balance equation;

[0053] The analysis module obtains historical operation data, obtains the topological loop where the node is located, and obtains the theoretical historical load value of the corresponding node according to the historical flow of other nodes in the topological loop and the heating flow equation group;

[0054] The management module obtains each theoretical load value and the corresponding historical operating conditions of the node, obtains the weight of the historical operating conditions for the theoretical historical load value based on multiple regression analysis, constructs a city heating digital twin model based on the weight, obtains current data, obtains the theoretical current load value based on the city heating digital twin model, compares the theoretical current load value with the corresponding theoretical historical load value, executes a first operation based on the comparison result, and sends a solution strategy based on the first operation.

[0055] The present invention uses digital twin technology to more accurately monitor and analyze urban energy systems, improve the efficiency and accuracy of energy management, and use digital twin models for virtual testing and verification. Without affecting the actual system, it optimizes operational decisions and control strategies, reduces energy waste and lowers operating costs through prediction and simulation. Digital twin technology enables the energy system to adapt to changes in structure and parameters, improves the system's dynamic adaptability, and supports the city's sustainable development and environmental protection goals by optimizing energy use.

[0056] The heating demand individuals are represented by residential buildings, factories, office buildings and shopping malls;

[0057] Let the node be v N , N is a natural number, and the pipeline is recorded as e M , M is a natural number, the starting node or the end node forms a straight line structure with the pipeline, and the intermediate node and the pipeline form a closed loop structure to construct a heating topology structure.

[0058] Connecting the nodes with the corresponding pipelines according to the first association matrix;

[0059] The expression of the first incidence matrix is:

[0060] ;

[0061] ;

[0062] in, is the first incidence matrix, It is the association relationship between the node and the pipeline.

[0063] Obtain a first correlation matrix, which is a full-rank matrix. According to graph theory, the heating topology structure includes M-N+1 basic loops, and the basic loop is represented by s K , where K is distributed in 1~M-N+1 and is a positive integer, a second association matrix of the basic loop and the pipeline is constructed, and a node flow balance equation is constructed according to the second association matrix;

[0064] The expression of the second incidence matrix is:

[0065] ;

[0066] ;

[0067] in, is the second incidence matrix, It is the relationship between pipelines and basic circuits;

[0068] The calculation expression of the node flow balance equation is:

[0069] ;

[0070] in, is the association relationship between each node in the first association matrix and any pipeline, Expressed as pipelines, of which , Expressed as The flow rate corresponding to each pipeline is For the The net flow of the node is expressed as The ingress traffic of each node;

[0071] The pressure drop of each pipeline is expressed as a pressure drop vector matrix, which is expressed as:

[0072] ;

[0073] The calculation expression of the pressure difference balance equation is:

[0074] ;

[0075] in, The transpose flag.

[0076] In the specific implementation, by constructing the first correlation matrix and the second correlation matrix, the system can accurately calculate the flow balance of each node in the heating network and the pressure difference balance of the pipeline. This precise calculation capability is the basis for optimizing and efficiently managing the heating system. Through accurate flow and pressure difference calculations, it ensures that each node and pipeline in the heating system operates in the best state, thereby improving the reliability and stability of the entire heating system. According to the theoretical load value and historical operating conditions, weights are obtained through multivariate regression analysis, and a digital twin model is constructed to optimize energy distribution, reduce energy waste, and improve energy efficiency.

[0077] The heating flow equation group is obtained according to the node flow balance equation and the pressure difference balance equation. The calculation expression of the heating flow equation group is:

[0078] ;

[0079] in, is the flow vector of each pipeline, is the net flow vector of each node, is the characteristic coefficient vector of each pipeline resistance, is the water pump vector corresponding to each pipeline, is the potential energy difference vector between the starting node and the ending node of each pipeline.

[0080] In specific implementation, through the analysis of historical data and real-time monitoring of current data, the system predicts potential failures and performance degradation, realizes predictive maintenance, reduces unexpected downtime, and reduces maintenance costs. The digital twin model is dynamically adjusted according to real-time data and historical data, so that the heating system can adapt to different operating conditions and demand changes, improve the adaptability and flexibility of the system, and through the simulation and analysis of the digital twin model, managers can better understand the operating status of the heating system and provide scientific decision-making support for the operation and maintenance of the system.

[0081] The historical operation data includes historical operation conditions, corresponding historical nodes, corresponding historical flow rates, corresponding historical outlet temperatures and historical inlet temperatures;

[0082] The historical operating conditions include circulating pump efficiency, valve opening and pump motor current frequency;

[0083] The circulating pump efficiency is expressed as the ratio of the pump motor output power to the circulating pump output power.

[0084] Obtain the topological loop where the node is located, obtain the node number and pipeline number contained in the topological loop, call up a historical operation database, input the node number and pipeline number into the historical operation database, and match the historical flow corresponding to each corresponding node;

[0085] According to the historical flow of other nodes and the heating flow equation group, the theoretical historical flow of the corresponding node is obtained, which is recorded as the theoretical historical load value;

[0086] The historical traffic of the node includes historical import traffic and historical export traffic.

[0087] In the specific implementation, by preprocessing the historical heating data, the data quality is improved, laying a solid foundation for subsequent data analysis and mining, and obtaining more accurate and reliable conclusions. Targeted data processing methods are proposed for heating data of different natures to make the preprocessing effect better. The sequence data after preprocessing is closer to the true value. The historical flow data and the heating flow equation group are used to more accurately predict the load demand of the heating system, thereby optimizing the operation and regulation of the heating system.

[0088] The current data includes the current operating condition and the target node;

[0089] Retrieving a heat flow equation group, inputting the current operating condition into the heat flow equation group, obtaining a theoretical current load value corresponding to each node, and obtaining a theoretical current load value corresponding to a target node;

[0090] Get the theoretical historical load value of the current node;

[0091] The theoretical current load value is compared with the corresponding theoretical historical load value, a first operation is performed according to the comparison result, and a solution strategy is sent according to the first operation, wherein the first operation includes not performing simulation deduction and performing simulation deduction.

[0092] In specific implementation, by acquiring data on current operating conditions and target nodes in real time, the system can respond promptly to changes in the heating system and improve the response speed and flexibility of the system. By comparing the theoretical current load value with the theoretical historical load value, the system identifies the changing trend of energy demand, thereby optimizing energy allocation and reducing energy waste. By analyzing changes in load values, the system adjusts heating parameters such as flow rate and pressure to improve the energy efficiency of the entire heating system.

[0093] When the theoretical current load value is equal to the corresponding theoretical historical load value, the first operation is set to not perform simulation deduction;

[0094] When the theoretical current load value is not equal to the corresponding theoretical historical load value, the first operation is set to perform simulation deduction;

[0095] When the first operation is to perform simulation deduction, the first value, the second value and the third value are respectively set as the change in the circulation pump efficiency, the change in the valve opening and the change in the pump motor current frequency, and the first change is first made to the circulation pump efficiency through the first value through the control variable method, and the theoretical current load value after the first change is obtained, and the theoretical current load value after the first change is subtracted from the theoretical historical load value to obtain a first difference, and the fourth value is set as the difference threshold, and when the first difference is less than the fourth value, the deduction is stopped, the corresponding total change in the circulation pump efficiency is obtained and the first solution strategy is output, and the first solution strategy is represented by continuously increasing the total change in the circulation pump efficiency, or continuously reducing the total change in the circulation pump efficiency;

[0096] The first change, the second change and the third change are represented as a continuous increase or a continuous decrease.

[0097] In specific implementation, by comparing the theoretical current load value with the theoretical historical load value, the system can accurately identify the parameters that need to be adjusted and achieve precise control of the heating system. Through simulation and deduction, the system can evaluate the impact of different operations on the heating system, thereby conducting risk management and emergency preparedness.

[0098] When the first difference is greater than or equal to the fourth value, the valve opening is changed for the second time by the second value, and the theoretical current load value after the second change is obtained, and the theoretical current load value after the second change is subtracted from the fourth value to obtain the second difference. When the second difference is less than the fourth value, the deduction is stopped, the corresponding total change of the valve opening is obtained, and a second solution strategy is output, wherein the second solution strategy is represented by the first solution strategy and the total change of the valve opening is continuously increased, or the first solution strategy and the total change of the valve opening is continuously reduced;

[0099] When the second difference is greater than or equal to the fourth value, the third change is made to the pump motor current frequency through the third value, and the theoretical current load value after the third change is obtained, and the third difference is obtained by subtracting the theoretical current load value after the third change from the fourth value. When the third difference is less than the fourth value, the deduction is stopped, and the corresponding total change of the pump motor current frequency is obtained, and the third solution strategy is output. The third solution strategy is expressed as the first solution measurement and the second solution strategy and continuously increases the total change of the pump motor current frequency, or the first solution measurement and the second solution strategy and continuously decreases the total change of the pump motor current frequency.

[0100] In specific implementation, by gradually adjusting the circulation pump efficiency, valve opening and pump motor current frequency, the system can carefully optimize the operating parameters of the heating system to achieve optimal performance. By optimizing the circulation pump efficiency and valve opening, the system can reduce energy waste and improve energy utilization efficiency. Through gradual adjustment, the system can avoid instability caused by excessive parameter changes and ensure stable operation of the heating system.

[0101] The present invention uses digital twin technology to more accurately monitor and analyze urban energy systems, improve the efficiency and accuracy of energy management, and use digital twin models for virtual testing and verification. Without affecting the actual system, it optimizes operational decisions and control strategies, reduces energy waste and lowers operating costs through prediction and simulation. Digital twin technology enables the energy system to adapt to changes in structure and parameters, improves the system's dynamic adaptability, and supports the city's sustainable development and environmental protection goals by optimizing energy use.

[0102] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Wherein, the storage medium is implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions of the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims

1. The digital twin city energy management system based on the Internet of Things is characterized by: Includes construction module, analysis module and management module; The construction module sets each heating demand individual as a node, marks each node and the pipelines between each node, constructs a heating topology structure, and constructs a node flow balance equation according to Kirchhoff's law for the connection relationship between the node and the corresponding pipeline according to the first association matrix, and constructs a pressure difference balance equation according to Kirchhoff's pressure difference balance law, and obtains a heating flow equation group according to the node flow balance equation and the pressure difference balance equation; The analysis module obtains historical operation data, obtains the topological loop where the node is located, and obtains the theoretical historical load value of the corresponding node according to the historical flow of other nodes in the topological loop and the heating flow equation group; The management module obtains each theoretical load value and the corresponding historical operating condition of the node, obtains the weight of the historical operating condition for the theoretical historical load value according to the multivariate regression analysis, constructs a city heating digital twin model according to the weight, obtains current data, obtains the theoretical current load value according to the city heating digital twin model, compares the theoretical current load value with the corresponding theoretical historical load value, executes a first operation according to the comparison result, and sends a solution strategy according to the first operation; Let the node be v N , N is a natural number, and the pipeline is recorded as e M , M is a natural number, the starting node or the end node forms a straight line structure with the pipeline, and the intermediate node and the pipeline form a closed loop structure to construct a heating topology structure; The expression of the first incidence matrix is: ； ； in, is the first incidence matrix, is the association relationship between the node and the pipeline; The first association matrix is ​​a full-rank matrix. According to graph theory, the heating topology structure includes M-N+1 basic loops, and the basic loop is represented by s K , where K is distributed in 1~M-N+1 and is a positive integer, a second association matrix of the basic loop and the pipeline is constructed, and a node flow balance equation is constructed according to the second association matrix; The expression of the second incidence matrix is: ； ； in, is the second incidence matrix, It is the relationship between pipelines and basic circuits; The calculation expression of the node flow balance equation is: ； in, is the association relationship between each node in the first association matrix and any pipeline, Expressed as pipelines, of which , Expressed as The flow rate corresponding to each pipeline is For the The net flow of the node is expressed as The ingress traffic of each node; The pressure drop of each pipeline is expressed as a pressure drop vector matrix, and the pressure drop vector matrix is ​​expressed as ; The calculation expression of the pressure difference balance equation is: ； in, is the transposition sign; The calculation expression of the heating flow equation group is: ； in, is the flow vector of each pipeline, is the net flow vector of each node, is the characteristic coefficient vector of each pipeline resistance, is the water pump vector corresponding to each pipeline, is the potential energy difference vector between the starting node and the ending node of each pipeline.

2. The digital twin city energy management system based on the Internet of Things as claimed in claim 1, characterized in that: The heating demand entities are represented by residential buildings, factories, office buildings and shopping malls.

3. The digital twin city energy management system based on the Internet of Things as claimed in claim 1, characterized in that: The historical operation data includes historical operation conditions, corresponding historical nodes, corresponding historical flow rates, corresponding historical outlet temperatures and historical inlet temperatures; The historical operating conditions include circulating pump efficiency, valve opening and pump motor current frequency; The circulating pump efficiency is expressed as the ratio of the pump motor output power to the circulating pump output power.

4. The digital twin city energy management system based on the Internet of Things as claimed in claim 1, characterized in that: Obtain the topological loop where the node is located, obtain the node number and pipeline number contained in the topological loop, call up a historical operation database, input the node number and pipeline number into the historical operation database, and match the historical flow corresponding to each corresponding node; According to the historical flow of other nodes and the heating flow equation group, the theoretical historical flow of the corresponding node is obtained, which is recorded as the theoretical historical load value; The historical traffic of the node includes historical import traffic and historical export traffic.

5. The digital twin city energy management system based on the Internet of Things as claimed in claim 1, characterized in that: The current data includes the current operating condition and the target node; Retrieving a heat flow equation group, inputting the current operating condition into the heat flow equation group, obtaining a theoretical current load value corresponding to each node, and obtaining a theoretical current load value corresponding to a target node; Get the theoretical historical load value of the current node; The theoretical current load value is compared with the corresponding theoretical historical load value, a first operation is performed according to the comparison result, and a solution strategy is sent according to the first operation, wherein the first operation includes not performing simulation deduction and performing simulation deduction.

6. The digital twin city energy management system based on the Internet of Things as claimed in claim 1, characterized in that: When the theoretical current load value is equal to the corresponding theoretical historical load value, the first operation is set to not perform simulation deduction; When the theoretical current load value is not equal to the corresponding theoretical historical load value, the first operation is set to perform simulation deduction; When the first operation is to perform simulation deduction, the first value, the second value and the third value are respectively set as the change in the circulation pump efficiency, the change in the valve opening and the change in the pump motor current frequency, and the first change is first made to the circulation pump efficiency through the first value through the control variable method, and the theoretical current load value after the first change is obtained, and the theoretical current load value after the first change is subtracted from the theoretical historical load value to obtain a first difference, and the fourth value is set as the difference threshold, and when the first difference is less than the fourth value, the deduction is stopped, the corresponding total change in the circulation pump efficiency is obtained and the first solution strategy is output, and the first solution strategy is represented by continuously increasing the total change in the circulation pump efficiency, or continuously reducing the total change in the circulation pump efficiency; The first change, the second change and the third change are represented as a continuous increase or a continuous decrease.

7. The digital twin city energy management system based on the Internet of Things as claimed in claim 1, characterized in that: When the first difference is greater than or equal to the fourth value, the valve opening is changed for the second time by the second value, and the theoretical current load value after the second change is obtained, and the theoretical current load value after the second change is subtracted from the fourth value to obtain the second difference. When the second difference is less than the fourth value, the deduction is stopped, the corresponding total change of the valve opening is obtained, and a second solution strategy is output, wherein the second solution strategy is represented by the first solution strategy and the total change of the valve opening is continuously increased, or the first solution strategy and the total change of the valve opening is continuously reduced; When the second difference is greater than or equal to the fourth value, the third change is made to the pump motor current frequency through the third value, and the theoretical current load value after the third change is obtained, and the third difference is obtained by subtracting the theoretical current load value after the third change from the fourth value. When the third difference is less than the fourth value, the deduction is stopped, and the corresponding total change of the pump motor current frequency is obtained, and the third solution strategy is output. The third solution strategy is expressed as the first solution measurement and the second solution strategy and continuously increases the total change of the pump motor current frequency, or the first solution measurement and the second solution strategy and continuously decreases the total change of the pump motor current frequency.

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