System and method for realizing steady-state and dynamic simulation of the whole process of intelligent decision-making for heating system

Abstract

The present invention discloses a system for realizing steady-state and dynamic simulation of the entire process of intelligent decision-making for a heating system, comprising: multiple basic model libraries for storing relevant equipment components, equipment parameters, simulation parameters, and connection types; a simulation model establishment module for selecting and connecting graphical component modules from the model library, inputting equipment structure and characteristic data, and establishing steady-state simulation models and dynamic simulation models; a simulation verification module for verifying model accuracy using a control variable method and modifying corresponding structural parameters according to the error size to ensure that the error is within a reasonable range; a host computer module for real-time data transmission with the simulation unit, real-time display of simulation images, simulation process monitoring, and parameter adjustment; and a simulation application module for issuing simulation control instructions and simulation parameter data. The present invention meets the data requirements for refined analysis of intelligent decision-making such as operation scheduling, fault diagnosis, and planning and design, and improves simulation efficiency and accuracy.

Description

Technical Field

[0001] The present invention belongs to the field of smart heating technology, and specifically relates to a system and method for realizing steady-state and dynamic simulation of the entire intelligent decision-making process of a heating system. Background Art

[0002] With the continuous advancement of China's urbanization process, the scale of centralized heating in large and medium-sized cities in my country has continued to expand. The total heating area of ​​a single city has reached tens of millions or even hundreds of millions of square meters. Faced with a huge and complex heating system, heating companies need to balance the conflicting factors of heating safety, reliability, environmental protection, comfort and economy. This has posed a major challenge to the planning, design, scheduling and operation of the heating system. It is urgent to adopt modern, scientific and intelligent methods to improve the level of heating production technology at the system engineering level.

[0003] Due to the complex and diverse structures of heating systems, large differences in scale, many interfering operating factors, and unstable operating conditions, in order to efficiently and accurately obtain optimal scheduling, planning, design, diagnosis and other strategies for heating systems, it is necessary to use computer software to establish a simulation model of the heating system. By establishing equipment and pipeline operating condition models that conform to actual conditions, the real physical processes occurring in the actual system can be reproduced, and the state of the system in various scenarios can be simulated, providing safety verification and operation analysis for the heating system, which plays a key role in the development and maintenance of the heating system.

[0004] However, most current heating system simulation models can only achieve steady-state simulation of the system and cannot reflect the dynamic process when the operating conditions change. They are unable to meet the data requirements for refined analysis of intelligent decision-making such as operation scheduling, fault diagnosis, and planning and design, and the simulation efficiency and accuracy need to be improved.

[0005] Based on the above technical problems, it is necessary to design a new steady-state and dynamic simulation implementation system and method for the entire process of intelligent decision-making of the heating system. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology and provide a system and method for realizing steady-state and dynamic simulation of the entire process of intelligent decision-making of a heating system, so as to meet the data requirements for refined analysis of intelligent decision-making such as operation scheduling, fault diagnosis and planning and design, and improve the simulation efficiency and accuracy.

[0007] In order to solve the above technical problems, the technical solution of the present invention is:

[0008] The present invention provides a steady-state and dynamic simulation implementation system for the entire intelligent decision-making process of a heating system. The simulation implementation system comprises: a modeling unit, a simulation unit, and an application unit; the modeling unit comprises at least a plurality of basic model libraries and a simulation model establishment module; the simulation unit comprises at least a simulation calculation module and a simulation verification module; the application unit comprises at least a host computer module and a simulation application module;

[0009] The multiple basic model libraries are used to divide the whole process simulation of the heating system into multiple device-level modules and system-level modules, and store related device components, device parameters and simulation parameters, and connection types through the module libraries;

[0010] The simulation model establishment module includes a steady-state simulation module and a dynamic simulation module, which are used to select and connect corresponding graphical component modules from the basic model library, input the structure and characteristic data of the device, and establish a steady-state simulation model and a dynamic simulation model; the steady-state simulation model is established based on the characteristic equations of each component, indicating the input and output relationship of the system under steady state; the dynamic simulation model is obtained based on the steady-state simulation model, combined with the component characteristic equations and dynamic characteristic curve identification, and indicates the relationship between various parameters changing with time under unsteady state;

[0011] The simulation calculation module is used to simulate the heating system according to the issued simulation control instructions and simulation parameter data;

[0012] The simulation verification module is used to verify the accuracy of the model by using the control variable method. The model accuracy is verified by selecting data with complex working condition changes on a certain day as the boundary conditions of the model. The relative error of the model is calculated based on the comparison between the model verification data and the real data, and the corresponding structural parameters are modified according to the error size to ensure that the error is within a reasonable range. The dynamic simulation model simulates and verifies the dynamic characteristics of the system under various working conditions when different influencing parameters are disturbed.

[0013] The host computer module is used for real-time data transmission with the simulation unit, real-time display of simulation images, simulation process monitoring and parameter adjustment;

[0014] The simulation application module is used to issue simulation control instructions and simulation parameter data, and to modify the simulation control instructions based on the simulation results; and to apply the simulation model to diagnose, optimize scheduling, predict status, and dynamically preview the heating system.

[0015] Furthermore, the basic model library includes a thermal process module library, a control module library, an electrical system module library, a boundary condition module library, a simulation control module library, and a connection module library; the heating system is divided into heat sources, heat exchangers, radiators, enclosures, and pipe section components, and a heat capacity mass system method is used to establish mathematical models of the components of the heating system;

[0016] Among them, the thermal process module library at least includes connection points, pipelines, valves, heat exchangers, and heat exchanger modules; the control module library at least includes control and logic loop types, and actuator types; the electrical system module library at least includes switches, loads, lines, and buses; the boundary condition module library at least includes parameter maximum value, minimum value, average value, function, and value transfer; the simulation control module library is used to control the simulation speed and the time step of the calculation; the connection module library is used to establish the connection relationship between modules, and at least includes fluid connection type, analog signal connection type, digital signal connection type, pipeline connection type, and general connection type.

[0017] Furthermore, the dynamic simulation model is used to simulate and verify the dynamic characteristics of the heating system under different working conditions such as startup process, variable load, ambient temperature and flow disturbance; the simulation control instructions include simulation start and stop instructions and model parameter setting instructions; the simulation parameter data includes simulation initial condition data, boundary condition data and data involved in simulation calculation.

[0018] Furthermore, the simulation unit provides a parameter editing interface to the user, and the user can select the corresponding modules and parameters according to his actual needs; the simulation unit opens several data identification interfaces. When the characteristic values ​​of the system equipment cannot be obtained, the operation data input by the user is run through the data identification interface, and the simulation unit automatically extracts the characteristic values ​​of the equipment from it through the data identification method.

[0019] Furthermore, the mathematical model of the heat source is expressed as:

[0020]

[0021] Among them, C b is the heat capacity of the heat source; T b is the water supply temperature of the heat source; T r,1 is the return water temperature of the first-stage network; T m is the temperature of heat source feed water; U boiler is the control variable of the fuel input in the heat source; G fule.max is the rated fuel consumption of the heat source; h fule is the calorific value of the fuel; η boiler is the thermal efficiency of the heat source; c w is the specific heat capacity of water; G m is the water supply flow of the heat source; U dw G is the control variable of the circulating water flow rate of the heating pipe network, with a value of 0 to 1; dw is the flow rate of heat medium in the heat source;

[0022] The mathematical model of the heat exchanger is expressed as:

[0023]

[0024]

[0025] Where C1 is the total heat capacity of the first-level network side of the heat exchanger; C2 is the total heat capacity of the second-level network side of the heat exchanger; T s,1 T is the water supply temperature of the heat exchanger primary network; r,1 is the return water temperature of the first-stage network of the heat exchanger; T L is the logarithmic mean temperature difference of the heat exchanger; G1 is the mass flow rate of the heat medium of the first-level network of the heat exchanger; G2 is the mass flow rate of the heat medium of the second-level network; T s,2 T is the water supply temperature of the secondary network of the heat exchanger; r,2 is the return water temperature of the secondary network of the heat exchanger;

[0026] The mathematical model of the radiator is expressed as:

[0027]

[0028] Among them, C h is the heat capacity of the heat user radiator; T ave is the average temperature of the heat medium in the radiator; T s,2 is the water supply temperature of the secondary network; T r,2 is the secondary network return water temperature; K is the radiator heat transfer coefficient; c w is the specific heat capacity of water; A h is the heat dissipation area of ​​the radiator; G w is the mass flow rate of the radiator;

[0029] The mathematical model of the enclosure structure includes:

[0030] The heat balance equation of the outer surface of the enclosure structure with heat storage characteristics is expressed as:

[0031] A e,i h out (T w -T out,e )+2nK e,i A e,i (T1-T out,e )=0;

[0032] The heat balance equation between layers in the enclosure structure with heat storage characteristics is expressed as:

[0033]

[0034]

[0035]

[0036] The heat balance equation of the inner surface of the enclosure structure with heat storage characteristics is expressed as:

[0037] K e,i A e,i (T air -T in,e )+2nK e,i A e,i (T n -T in,e )=0;

[0038] Where n is the number of layers of the enclosure structure with heat storage characteristics; A e,i is the area of ​​the enclosure structure with heat storage characteristics; h out is the convection heat transfer coefficient between the outer surface of the enclosure structure with heat storage characteristics and the outdoor air; T w is the outdoor air temperature; T out,e is the surface temperature of the enclosure in contact with outdoor air; K e,i is the heat transfer coefficient of the enclosure structure with heat storage characteristics; A e,i is the area of ​​the enclosure structure with heat storage characteristics; δ is the thickness of each layer of the temperature node of the enclosure structure with heat storage characteristics; c q is the specific heat capacity of the enclosure material with heat storage characteristics; ρ q is the density of the enclosure material with heat storage characteristics; T i is the temperature of the i-th temperature node; T air is the indoor air temperature; T in,e is the temperature of the inner surface of the enclosure structure in contact with the indoor air;

[0039] The mathematical model of the pipe segment component is expressed as:

[0040]

[0041] Among them, C p is the heat capacity of the pipe; c w is the specific heat capacity of water; T k is the water temperature of a certain pipe section; T w is the outdoor air temperature; L is the length of the calculated pipe section; G p is the mass flow rate of the pipeline; K p is the heat loss per unit length of pipe.

[0042] Furthermore, a heat source simulation model is obtained by calling a corresponding module based on the mathematical model of the heat source, wherein the input port of the heat source simulation model includes the primary network return water temperature, the heat network feed water temperature, the fuel control variable and the circulating water volume control variable; and the output port of the heat source simulation model includes the heat source supply water temperature;

[0043] According to the mathematical model of the radiator and calling the corresponding module, a radiator simulation model is obtained, wherein the input port of the radiator simulation model includes the secondary network water supply temperature and the indoor temperature; the output port of the radiator simulation model includes the heat dissipation of the radiator average temperature mixed radiator;

[0044] According to the mathematical models on both sides of the heat exchanger and calling the corresponding modules, a heat exchanger simulation model is obtained, wherein the input port of the heat exchanger simulation model includes the primary network water supply temperature and the secondary network return water temperature; the output port of the heat exchanger simulation model includes the secondary network water supply temperature and the primary network return water temperature;

[0045] The enclosure structure simulation model is obtained by calling the corresponding module according to the mathematical model of the enclosure structure. The input port of the enclosure structure simulation model includes the outdoor temperature and the average temperature of the radiator; the output port of the enclosure structure simulation model includes the indoor air temperature, the average temperature of the radiator at the next moment, and the heat consumption; after the radiator is encapsulated in the enclosure structure, the input port of the enclosure structure simulation model includes the outdoor temperature and the radiator water supply temperature; the output port of the enclosure structure simulation model includes the indoor air temperature, the radiator return water temperature, and the heat consumption;

[0046] Based on the mathematical model of the pipe segment component and calling the corresponding module, different pipe segments are connected in sequence according to the topological structure of the actual pipe network to obtain a heat network simulation model. The input port of the pipe segment component includes the water temperature of the previous pipe segment and the outdoor ambient temperature; the output port of the pipe segment component includes the water temperature of the next pipe segment;

[0047] According to the actual connection form of the heating system, the simulation modules of different parts are called respectively, and then the modules are connected according to the connection sequence of the input ports and output ports to obtain the simulation model of the heating system.

[0048] Furthermore, the steady-state simulation model and the dynamic simulation model use the same interface for data transmission, and the real-time operating status of the heating system is transmitted to the steady-state simulation model through the same interface for steady-state scheduling, and is simultaneously transmitted to the dynamic simulation model for dynamic preview.

[0049] Furthermore, the curve identification method of the dynamic characteristics includes interpolation method, neural network method, and regression analysis method; the simulation unit is also used to establish a simulation model for components with complex internal structure principles using a data-driven method, and select real data related to the component simulation model to train the selected deep learning or machine learning algorithm model in an external module; the simulation unit is provided with a module for interacting with external data, and the function of the simulation unit interacting with other external software is realized through a dynamic link library with a TCP protocol.

[0050] Furthermore, the modeling unit is also provided with a multi-time scale simulation mechanism for establishing a long-time simulation model, a medium-time simulation model and a short-time simulation model. The long-time simulation model optimizes the system design parameters by establishing a steady-state simulation model, with years as the time step; the medium-time simulation model indicates the supply and demand balance characteristics of the system and determines the appropriate operation strategy, with hours as the time step, and is applied to dynamic simulation models with slower dynamic response; the short-time simulation model analyzes the dynamic characteristics of the system by establishing a dynamic simulation model and importing real-time data, with seconds as the time step, and is applied to dynamic simulation models with faster dynamic response; the modeling unit is also provided with a multi-core parallel simulation mechanism, which is used to divide the entire heating system heat network into multiple sub-modules using a network partitioning algorithm, and each sub-module is simulated in parallel, and the large step size and the small step size are solved in parallel, and the interface is designed in a parallel manner to coordinate the timing of the interface to perform multi-time scale parallel simulation.

[0051] The present invention also provides a method for implementing steady-state and dynamic simulation of the entire intelligent decision-making process of a heating system, the simulation method comprising:

[0052] The whole process simulation of the heating system is divided into multiple equipment-level modules and system-level modules through the basic model library, and the relevant equipment components, equipment parameters and simulation parameters, and connection types are stored in the module library;

[0053] The simulation model building module selects and connects corresponding graphical component modules from the basic model library, inputs the structure and characteristic data of the equipment, and establishes a steady-state simulation model and a dynamic simulation model. The steady-state simulation model is established based on the characteristic equations of each component and shows the input and output relationship of the system under steady state. The dynamic simulation model is obtained based on the steady-state simulation model, combined with the component characteristic equations and dynamic characteristic curve identification, and shows the relationship between various parameters changing with time under unsteady state.

[0054] The heating system is simulated by the simulation calculation module according to the issued simulation control instructions and simulation parameter data; the simulation control instructions include simulation start and stop instructions and model parameter setting instructions; the simulation parameter data includes simulation initial condition data, boundary condition data and simulation calculation data;

[0055] The accuracy of the model is verified by using the control variable method through the simulation verification module. The data with complex working condition changes on a certain day is selected as the boundary condition of the model to verify the accuracy of the model. The relative error of the model is calculated based on the comparison between the model verification data and the real data. The corresponding structural parameters are modified according to the error size to ensure that the error is within a reasonable range. The dynamic simulation model simulates and verifies the dynamic characteristics of the system under various working conditions when different influencing parameters are disturbed.

[0056] Real-time data transmission, real-time display of simulation screen, simulation process monitoring and parameter adjustment through the host computer module;

[0057] The simulation control instructions and simulation parameter data are issued through the simulation application module, and the simulation control instructions are modified based on the simulation results; and the simulation model is used to diagnose, optimize scheduling, predict status, and dynamically rehearse the heating system.

[0058] The beneficial effects of the present invention are:

[0059] The present invention divides the heating system into multiple equipment components through the basic model library, and establishes a mathematical model of the heating system. According to the actual heating system, a graphical component model is used to establish a complete steady-state and dynamic simulation model of the heating system to achieve synchronous operation with the real system. Not only is the real-time operating status of the system transmitted to the steady-state simulation model, and a scheduling strategy is made based on the steady-state simulation, but it is also transmitted to the dynamic simulation model at the same time, so that the dynamic simulation can truly achieve a preview of the operation; in addition, the simulation model is verified and corrected to ensure the accuracy and safety of the simulation model. For the dynamic simulation model, the dynamic response of the heating system when different influencing parameters are disturbed is simulated to achieve dynamic characteristic verification of the system under various working conditions, reflect the dynamic process when the working conditions change, and meet the data requirements for system diagnosis, optimal scheduling and state prediction intelligent decision-making analysis. By establishing a steady-state simulation and dynamic simulation system for the entire process of the heating system, it is possible to realize intelligent decision-making application analysis such as optimal scheduling and diagnosis of the heating system based on the simulation model, thereby improving the accuracy of intelligent decision-making.

[0060] Other features and advantages will be described in the following description, and in part will become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 This is a schematic diagram of a steady-state and dynamic simulation system for the entire intelligent decision-making process of a heating system according to the present invention;

[0064] Figure 2 This is a schematic diagram of the simulation structure of a heating system of the present invention. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0066] Example 1

[0067] like Figure 1-2 As shown, this embodiment 1 provides a steady-state and dynamic simulation implementation system for the entire process of intelligent decision-making of a heating system. The simulation implementation system includes: a modeling unit, a simulation unit, and an application unit; the modeling unit includes at least a plurality of basic model libraries and a simulation model establishment module; the simulation unit includes at least a simulation calculation module and a simulation verification module; the application unit includes at least a host computer module and a simulation application module;

[0068] The multiple basic model libraries are used to divide the whole process simulation of the heating system into multiple device-level modules and system-level modules, and store related device components, device parameters and simulation parameters, and connection types through the module libraries;

[0069] The simulation model establishment module includes a steady-state simulation module and a dynamic simulation module, which are used to select and connect corresponding graphical component modules from the basic model library, input the structure and characteristic data of the device, and establish a steady-state simulation model and a dynamic simulation model; the steady-state simulation model is established based on the characteristic equations of each component, indicating the input and output relationship of the system under steady state; the dynamic simulation model is obtained based on the steady-state simulation model, combined with the component characteristic equations and dynamic characteristic curve identification, and indicates the relationship between various parameters changing with time under unsteady state;

[0070] The simulation calculation module is used to simulate the heating system according to the issued simulation control instructions and simulation parameter data;

[0071] The simulation verification module is used to verify the accuracy of the model by using the control variable method. The model accuracy is verified by selecting data with complex working condition changes on a certain day as the boundary conditions of the model. The relative error of the model is calculated based on the comparison between the model verification data and the real data, and the corresponding structural parameters are modified according to the error size to ensure that the error is within a reasonable range. The dynamic simulation model simulates and verifies the dynamic characteristics of the system under various working conditions when different influencing parameters are disturbed.

[0072] The host computer module is used for real-time data transmission with the simulation unit, real-time display of simulation images, simulation process monitoring and parameter adjustment;

[0073] The simulation application module is used to issue simulation control instructions and simulation parameter data, and to modify the simulation control instructions based on the simulation results; and to apply the simulation model to diagnose, optimize scheduling, predict status, and dynamically preview the heating system.

[0074] In this embodiment, the basic model library includes a thermal process module library, a control module library, an electrical system module library, a boundary condition module library, a simulation control module library, and a connection module library; the heating system is divided into heat sources, heat exchangers, radiators, enclosures, and pipe components, and the heat capacity mass system method is used to establish mathematical models of the components of the heating system;

[0075] Among them, the thermal process module library at least includes connection points, pipelines, valves, heat exchangers, and heat exchanger modules; the control module library at least includes control and logic loop types, and actuator types; the electrical system module library at least includes switches, loads, lines, and buses; the boundary condition module library at least includes parameter maximum value, minimum value, average value, function, and value transfer; the simulation control module library is used to control the simulation speed and the time step of the calculation; the connection module library is used to establish the connection relationship between modules, and at least includes fluid connection type, analog signal connection type, digital signal connection type, pipeline connection type, and general connection type.

[0076] In this embodiment, the dynamic simulation model is used to simulate and verify the dynamic characteristics of the heating system under different working conditions such as startup process, variable load, ambient temperature and flow disturbance; the simulation control instructions include simulation start and stop instructions and model parameter setting instructions; the simulation parameter data includes simulation initial condition data, boundary condition data and data participating in simulation calculation.

[0077] In this embodiment, the simulation unit provides a parameter editing interface to the user, and the user can select the corresponding modules and parameters according to his actual needs; the simulation unit opens several data identification interfaces. When the characteristic values ​​of the system equipment cannot be obtained, the operation data input by the user is run through the data identification interface, and the simulation unit automatically extracts the characteristic values ​​of the equipment from it through the data identification method.

[0078] It should be noted that the heating system simulation implementation system opens the editing interface and data identification interface to users, supports users to edit and select modules, and inputs operating data from the outside for data identification and extraction of characteristic values, so as to facilitate model training using machine learning and data-driven methods for components with complex structures.

[0079] In this embodiment, the mathematical model of the heat source is expressed as:

[0080]

[0081] Among them, C b is the heat capacity of the heat source; T b is the water supply temperature of the heat source; T r,1 is the return water temperature of the first-stage network; T m is the temperature of heat source feed water; U boiler is the control variable of the fuel input in the heat source; G fule.max is the rated fuel consumption of the heat source; h fule is the calorific value of the fuel; η boiler is the thermal efficiency of the heat source; c w is the specific heat capacity of water; G m is the water supply flow of the heat source; U dw G is the control variable of the circulating water flow rate of the heating pipe network, with a value of 0 to 1; dw is the flow rate of heat medium in the heat source;

[0082] The mathematical model of the heat exchanger is expressed as:

[0083]

[0084]

[0085] Where C1 is the total heat capacity of the first-level network side of the heat exchanger; C2 is the total heat capacity of the second-level network side of the heat exchanger; T s,1 T is the water supply temperature of the first-level network of the heat exchanger; r,1 is the return water temperature of the first-stage network of the heat exchanger; T L is the logarithmic mean temperature difference of the heat exchanger; G1 is the mass flow rate of the heat medium of the first-level network of the heat exchanger; G2 is the mass flow rate of the heat medium of the second-level network; T s,2 T is the water supply temperature of the secondary network of the heat exchanger; r,2 is the return water temperature of the secondary network of the heat exchanger;

[0086] The mathematical model of the radiator is expressed as:

[0087]

[0088] Among them, C h is the heat capacity of the heat user radiator; T ave is the average temperature of the heat medium in the radiator; T s,2 is the water supply temperature of the secondary network; T r,2 is the secondary network return water temperature; K is the radiator heat transfer coefficient; c w is the specific heat capacity of water; A h is the heat dissipation area of ​​the radiator; G wis the mass flow rate of the radiator;

[0089] The mathematical model of the enclosure structure includes:

[0090] The heat balance equation of the outer surface of the enclosure structure with heat storage characteristics is expressed as:

[0091] A e,i h out (T w -T out,e )+2nK e,i A e,i (T1-T out,e )=0;

[0092] The heat balance equation between layers in the enclosure structure with heat storage characteristics is expressed as:

[0093]

[0094]

[0095]

[0096] The heat balance equation of the inner surface of the enclosure structure with heat storage characteristics is expressed as:

[0097] K e,i A e,i (T air -T in,e )+2nK e,i A e,i (T n -T in,e )=0;

[0098] Where n is the number of layers of the enclosure structure with heat storage characteristics; A e,i is the area of ​​the enclosure structure with heat storage characteristics; h out is the convection heat transfer coefficient between the outer surface of the enclosure structure with heat storage characteristics and the outdoor air; T w is the outdoor air temperature; T out,e is the surface temperature of the enclosure in contact with outdoor air; K e,i is the heat transfer coefficient of the enclosure structure with heat storage characteristics; A e,i is the area of ​​the enclosure structure with heat storage characteristics; δ is the thickness of each layer of the temperature node of the enclosure structure with heat storage characteristics; c q is the specific heat capacity of the enclosure material with heat storage characteristics; ρ q is the density of the enclosure material with heat storage characteristics; T i is the temperature of the ith temperature node; T air is the indoor air temperature; T in,e The temperature of the inner surface of the enclosure structure in contact with the indoor air;

[0099] The mathematical model of the pipe segment component is expressed as:

[0100]

[0101] Among them, C p is the heat capacity of the pipe; c w is the specific heat capacity of water; T k is the water temperature of a certain pipe section; T w is the outdoor air temperature; L is the length of the calculated pipe section; G p is the mass flow rate of the pipeline; K p is the heat loss per unit length of pipe.

[0102] In this embodiment, a heat source simulation model is obtained by calling the corresponding module based on the mathematical model of the heat source. The input port of the heat source simulation model includes the primary network return water temperature, the heating network feed water temperature, the fuel control variable and the circulating water control variable; the output port of the heat source simulation model includes the heat source supply water temperature;

[0103] According to the mathematical model of the radiator and calling the corresponding module, a radiator simulation model is obtained, wherein the input port of the radiator simulation model includes the secondary network water supply temperature and the indoor temperature; the output port of the radiator simulation model includes the heat dissipation of the radiator average temperature mixed radiator;

[0104] According to the mathematical models on both sides of the heat exchanger and calling the corresponding modules, a heat exchanger simulation model is obtained, wherein the input port of the heat exchanger simulation model includes the primary network water supply temperature and the secondary network return water temperature; the output port of the heat exchanger simulation model includes the secondary network water supply temperature and the primary network return water temperature;

[0105] The enclosure structure simulation model is obtained by calling the corresponding module according to the mathematical model of the enclosure structure. The input port of the enclosure structure simulation model includes the outdoor temperature and the average temperature of the radiator; the output port of the enclosure structure simulation model includes the indoor air temperature, the average temperature of the radiator at the next moment, and the heat consumption; after the radiator is encapsulated in the enclosure structure, the input port of the enclosure structure simulation model includes the outdoor temperature and the radiator water supply temperature; the output port of the enclosure structure simulation model includes the indoor air temperature, the radiator return water temperature, and the heat consumption;

[0106] Based on the mathematical model of the pipe segment component and calling the corresponding module, different pipe segments are connected in sequence according to the topological structure of the actual pipe network to obtain a heat network simulation model. The input port of the pipe segment component includes the water temperature of the previous pipe segment and the outdoor ambient temperature; the output port of the pipe segment component includes the water temperature of the next pipe segment;

[0107] According to the actual connection form of the heating system, the simulation modules of different parts are called respectively, and then the modules are connected according to the connection sequence of the input ports and output ports to obtain the simulation model of the heating system.

[0108] In this embodiment, the steady-state simulation model and the dynamic simulation model use the same interface for data transmission. The real-time operating status of the heating system is transmitted to the steady-state simulation model through the same interface for steady-state scheduling, and is simultaneously transmitted to the dynamic simulation model for dynamic preview.

[0109] In this embodiment, the curve identification method of the dynamic characteristics includes interpolation method, neural network method, and regression analysis method; the simulation unit is also used to establish a simulation model for components with complex internal structure principles using a data-driven method, and select real data related to the component simulation model to train the selected deep learning or machine learning algorithm model in an external module; the simulation unit is provided with a module for interacting with external data, and the function of the simulation unit interacting with other external software is realized through a dynamic link library with a TCP protocol.

[0110] In practical applications, there are mainly the following methods for processing characteristic curves:

[0111] ① Interpolation method: Use a grid to discretize the characteristic curve, and then interpolate the values ​​of other points based on the discrete points to obtain the values ​​under other working conditions. For values ​​on non-network nodes, the accuracy will be reduced after multiple interpolations.

[0112] ② Neural network method: Neural network method has the characteristics of highly nonlinear mapping. It has high fitting accuracy for nonlinear mathematical models. However, because its principle adopts the internal implicit learning method, it lacks good explanation for the relationship between input and output. At the same time, its iterative calculation method affects its convergence speed, making it difficult to use for real-time calculation.

[0113] ③ Regression Analysis: Regression analysis is a statistical analysis method that determines the relationship between two or more variables. Depending on the number of independent variables, regression analysis can be divided into simple and multiple regression analysis; depending on the type of relationship between the dependent and independent variables, it can be divided into linear and nonlinear regression. Regression analysis fits data to obtain a functional relationship between the dependent and independent variables and is currently widely used in data analysis across various fields.

[0114] Multiple regression analysis is used to establish the operating characteristic curve. This involves determining the operating characteristic variables, converting the component mathematical model from a nonlinear model to a linear one, and analyzing it using multiple linear regression. Once the regression model is obtained, the variables in the regression model are replaced with the dimensionless original component variables to obtain the component characteristic regression equation. The regression model is then validated, demonstrating that the individual variables in the regression model are highly linearly correlated with the dependent variable, thus demonstrating a significant effect. Once the regression equation for the operating characteristic curve is obtained, combined with its steady-state characteristic equation, the component dynamic characteristic equation under different operating conditions can be derived.

[0115] In this embodiment, the modeling unit is also provided with a multi-time scale simulation mechanism for establishing a long-time simulation model, a medium-time simulation model and a short-time simulation model. The long-time simulation model optimizes the system design parameters by establishing a steady-state simulation model, with years as the time step; the medium-time simulation model indicates the supply and demand balance characteristics of the system and determines the appropriate operation strategy, with hours as the time step, and is applied to dynamic simulation models with slower dynamic response; the short-time simulation model analyzes the dynamic characteristics of the system by establishing a dynamic simulation model and importing real-time data, with seconds as the time step, and is applied to dynamic simulation models with faster dynamic response; the modeling unit is also provided with a multi-core parallel simulation mechanism for dividing the entire heating system heat network into multiple sub-modules using a network partitioning algorithm, and each sub-module is simulated in parallel, and the large step size and the small step size are solved in parallel, and the interface is designed in a parallel manner to coordinate the timing of the interface to perform multi-time scale parallel simulation.

[0116] It should be noted that by setting up a multi-time scale simulation mechanism, the dynamic characteristics of the simulation model at multiple time scales can be accurately reflected, and long-time, medium-time and short-time simulation models can be established based on different time steps to improve the simulation accuracy and speed; in addition, the use of a multi-core parallel simulation mechanism can enable parallel simulation and speed up the simulation speed.

[0117] Example 2

[0118] This embodiment 2 provides a method for implementing steady-state and dynamic simulation of the entire intelligent decision-making process of a heating system. The simulation method includes:

[0119] The whole process simulation of the heating system is divided into multiple equipment-level modules and system-level modules through the basic model library, and the relevant equipment components, equipment parameters and simulation parameters, and connection types are stored in the module library;

[0120] The simulation model building module selects and connects corresponding graphical component modules from the basic model library, inputs the structure and characteristic data of the equipment, and establishes a steady-state simulation model and a dynamic simulation model. The steady-state simulation model is established based on the characteristic equations of each component and shows the input and output relationship of the system under steady state. The dynamic simulation model is obtained based on the steady-state simulation model, combined with the component characteristic equations and dynamic characteristic curve identification, and shows the relationship between various parameters changing with time under unsteady state.

[0121] The heating system is simulated by the simulation calculation module according to the issued simulation control instructions and simulation parameter data; the simulation control instructions include simulation start and stop instructions and model parameter setting instructions; the simulation parameter data includes simulation initial condition data, boundary condition data and simulation calculation data;

[0122] The accuracy of the model is verified by using the control variable method through the simulation verification module. The data with complex working condition changes on a certain day is selected as the boundary condition of the model to verify the accuracy of the model. The relative error of the model is calculated based on the comparison between the model verification data and the real data. The corresponding structural parameters are modified according to the error size to ensure that the error is within a reasonable range. The dynamic simulation model simulates and verifies the dynamic characteristics of the system under various working conditions when different influencing parameters are disturbed.

[0123] Real-time data transmission, real-time display of simulation screen, simulation process monitoring and parameter adjustment through the host computer module;

[0124] The simulation control instructions and simulation parameter data are issued through the simulation application module, and the simulation control instructions are modified based on the simulation results; and the simulation model is used to diagnose, optimize scheduling, predict status, and dynamically rehearse the heating system.

[0125] In practical applications, the application of simulation models for intelligent decision-making includes at least:

[0126] (1) Operation training: Through the integrated functions of the simulation platform, the basic functions of the system from cold state to hot state operation and shutdown are realized, meeting the virtual reality simulation training process of the operators; at the same time, correct fault and accident phenomenon simulation is provided to improve the crew's emergency judgment and handling of various faults and accidents, as well as the comprehensive analysis of the unit operation, and provide means for further improving the operation mode and formulating anti-accident countermeasures; through the simulation training system, the DCS configuration environment and debugging methods are familiarized, and reference information is provided for control system optimization experiments and design scheme improvements; through the simulation system, the influence of the unit system equipment changes and equipment performance changes on the unit performance is studied, providing a reference basis for the unit design scheme; the control strategy comparison, control parameter adjustment and optimization are carried out on the simulation system, from the perspectives of safety and economy, so as to achieve the optimal control scheme.

[0127] (2) Prediction of unit operating status: By studying the dynamic characteristics of the operating unit, the dynamic behavior of the relevant system after the disturbance occurs can be predicted, the characteristics of the research object can be analyzed, and the unit operator can be guided in designing and optimizing the control strategy of the relevant system. By acquiring field data and importing it into the simulation model in real time, and utilizing the acceleration function of the simulation platform, the system model can be accelerated to achieve advance prediction of the unit, thereby providing assistance to the on-site operating personnel in making advance predictions of the unit operating status and fault response strategies, thus achieving the purpose of guiding the site.

[0128] (3) Optimization of unit operation status: Cogeneration of multiple units and multiple boilers, reasonable distribution of loads, and rational use of energy are conducive to improving energy utilization. Genetic algorithms are used to build a black box model between the unit coal supply and process variables under the given conditions of unit power generation and steam supply, with the minimum coal supply as the goal and the important operating parameters of the unit as process variables, including main steam temperature, main steam pressure, main steam flow, superheated steam temperature, superheated steam pressure, feed water temperature, feed water flow, primary air volume, secondary air volume, flue gas oxygen content, exhaust temperature, load, and steam extraction volume, using neural networks.

[0129] The specific process is divided into: neural network prediction and genetic algorithm optimization process. In the neural network prediction modeling stage, the actual historical data of heating under different working conditions under normal conditions are selected and divided into two parts. One part is used for modeling to establish the functional relationship between power generation and steam supply and process variables, and then the other part of the data is used for testing and verification. Modeling process: The data first undergoes a data normalization preprocessing process, and then is input into the neural network model for training. The model is trained with the minimum mean square error (MSE) as the goal, and the model is saved after training. Prediction process: The new data undergoes the same normalization preprocessing process as the training data, and then is input into the saved model to calculate the predicted coal supply, which is compared with the actual coal supply to judge the accuracy of the model.

[0130] Genetic Algorithm Optimization Phase: A genetic algorithm is a highly parallel, global, randomized, and adaptive search algorithm that draws on the mechanisms of natural selection and natural inheritance in the biological world. When using a genetic algorithm to solve a problem, the objective function and variables must first be determined, and then the variables must be encoded.

[0131] Under the conditions of optimal load and a given steam supply, a single-objective optimization model was established with the goal of minimizing coal feed rate and the constraints of process variables remaining within a reasonable range. The coal feed rate encompassed the feed rates for both boilers, and a genetic algorithm was used to determine the coal feed ratio and associated process variable parameter values ​​under different operating conditions.

[0132] The optimized coal feed rate and process variable parameters are input into a dynamic cogeneration simulation model for verification. The model is tested under the lowest coal feed rate to verify that the set load and steam extraction rates are achieved. Multiple verifications using the dynamic simulation model stabilize the load and steam extraction rates. Once steady state is achieved, the optimized coal feed rate and process parameters are fed back to the actual power plant to guide production, reduce coal feed rates, and ensure economical operation. Furthermore, the optimal process parameters can be found for each operating condition, enabling economical operation.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the systems, methods, and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a portion of code, and the module, program segment, or a portion of code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or actions, or can be implemented using a combination of dedicated hardware and computer instructions.

[0134] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0135] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0136] With the above-described preferred embodiments of the present invention as a guide, and with reference to the above description, relevant personnel are fully capable of making various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification and must be determined according to the scope of the claims.

Claims

1. A system for realizing steady-state and dynamic simulation of the entire intelligent decision-making process of a heating system, characterized by: It includes modeling unit, simulation unit and application unit; The modeling unit at least includes a plurality of basic model libraries and a simulation model building module; The simulation unit at least includes a simulation calculation module and a simulation verification module; The application unit at least includes a host computer module and a simulation application module; The basic model library is used to divide the whole process simulation of the heating system into multiple device-level modules and system-level modules, and store related device components, device parameters and simulation parameters, and connection types through the module library; The simulation model building module includes a steady-state simulation module and a dynamic simulation module, which are used to select and connect corresponding graphical component modules from the basic model library, input the structure and characteristic data of the device, and build a steady-state simulation model and a dynamic simulation model; the steady-state simulation model is built based on the characteristic equations of each component, indicating the input and output relationship of the system in steady state; The dynamic simulation model is based on the steady-state simulation model and is obtained by combining the component characteristic equation and the dynamic characteristic curve identification, which shows the relationship between the parameters and time changes in the unsteady state; The simulation calculation module is used to simulate the heating system according to the issued simulation control instructions and simulation parameter data; The simulation verification module is used to verify the accuracy of the model by using the control variable method. By selecting data with complex working condition changes on a certain day as the boundary conditions of the model, the accuracy of the model is verified. The relative error of the model is calculated based on the comparison between the model verification data and the real data, and the corresponding structural parameters are modified according to the error size to ensure that the error is within a reasonable range. The dynamic simulation model simulates and verifies the dynamic characteristics of the system under various working conditions when different influencing parameters are disturbed. The host computer module is used for real-time data transmission with the simulation unit, real-time display of simulation screen, simulation process monitoring and parameter adjustment; The simulation application module is used to issue simulation control instructions and simulation parameter data, and to modify the simulation control instructions based on the simulation results, and to apply the simulation model to diagnose, optimize scheduling, predict status, and dynamically preview the heating system.

2. The simulation implementation system according to claim 1, characterized in that: The basic model library includes a thermal process module library, a control module library, an electrical system module library, a boundary condition module library, a simulation control module library, and a connection module library; the heating system is divided into heat sources, heat exchangers, radiators, enclosures, and pipe section components, and the heat capacity mass system method is used to establish mathematical models of the various components of the heating system; The thermal process module library at least includes connection points, pipelines, valves, heat exchangers, and heat exchanger modules; The control module library includes at least control and logic circuit types and actuator types; The electrical system module library at least includes switches, loads, lines, and buses; The boundary condition module library at least includes parameter maximum value, minimum value, average value, function and value transfer; The simulation control module library is used to control the simulation speed and the time step of the calculation; The connection module library is used to establish connection relationships between modules, and the connection module library at least includes fluid connection types, analog signal connection types, digital signal connection types, pipeline connection types and universal connection types.

3. The simulation implementation system according to claim 1, wherein: The dynamic simulation model is used to simulate and verify the dynamic characteristics of the heating system under different working conditions such as startup process, variable load, ambient temperature and flow disturbance; The simulation control instructions include simulation start and stop instructions and model parameter setting instructions; The simulation parameter data includes simulation initial condition data, boundary condition data and data involved in simulation calculation.

4. The simulation implementation system according to claim 1, wherein: The simulation unit is used to provide a parameter editing interface to the user, and the user selects the corresponding module and parameters according to his actual needs; The simulation unit opens several data identification interfaces. When the characteristic values ​​of system devices cannot be obtained, the operation data input by the user is run through the data identification interface, and the simulation unit automatically extracts the characteristic values ​​of the devices from the data through the data identification method.

5. The simulation implementation system according to claim 2, wherein: The mathematical model of the heat source is expressed as: Among them, C b is the heat capacity of the heat source; T b is the water supply temperature of the heat source; T r,1 is the return water temperature of the first-level network; T m is the temperature of heat source feed water; U boiler is the control variable of the fuel input in the heat source; G fule.max is the rated fuel consumption of the heat source; h fule is the calorific value of the fuel; η boiler is the thermal efficiency of the heat source; c w is the specific heat capacity of water; G m is the water supply flow of the heat source; U dw G is the control variable of the circulating water flow rate of the heating pipe network, with a value of 0 to 1; dw is the flow rate of heat medium in the heat source; The mathematical model of the heat exchanger is expressed as: Where C1 is the total heat capacity of the first-level network side of the heat exchanger; C2 is the total heat capacity of the second-level network side of the heat exchanger; T s,1 T is the water supply temperature of the heat exchanger primary network; r,1 is the return water temperature of the first-stage network of the heat exchanger; T L is the logarithmic mean temperature difference of the heat exchanger; G1 is the mass flow rate of the heat medium of the first-level network of the heat exchanger; G2 is the mass flow rate of the heat medium of the second-level network; T s,2 T is the water supply temperature of the secondary network of the heat exchanger; r,2 is the return water temperature of the secondary network of the heat exchanger; The mathematical model of the radiator is expressed as: Among them, C h is the heat capacity of the heat user radiator; T ave is the average temperature of the heat medium in the radiator; T s,2 is the water supply temperature of the secondary network; T r,2 is the secondary network return water temperature; K is the radiator heat transfer coefficient; c w is the specific heat capacity of water; A h is the heat dissipation area of ​​the radiator; G w is the mass flow rate of the radiator; The mathematical model of the enclosure structure includes: The heat balance equation of the outer surface of the enclosure structure with heat storage characteristics is expressed as: A e,i h out (T w -T out,e )+2nK e,i A e,i (T1-T out,e )=0; The heat balance equation between layers in the enclosure structure with heat storage characteristics is expressed as: The heat balance equation of the inner surface of the enclosure structure with heat storage characteristics is expressed as: K e,i A e,i (T air -T in,e )+2nK e,i A e,i (T n -T in,e )=0; Where n is the number of layers of the enclosure structure with heat storage characteristics; A e,i is the area of ​​the enclosure structure with heat storage characteristics; h out is the convection heat transfer coefficient between the outer surface of the enclosure structure with heat storage characteristics and the outdoor air; T w is the outdoor air temperature; T out,e is the surface temperature of the enclosure in contact with outdoor air; K e,i is the heat transfer coefficient of the enclosure structure with heat storage characteristics; A e,i is the area of ​​the enclosure structure with heat storage characteristics; δ is the thickness of each layer of the temperature node of the enclosure structure with heat storage characteristics; c q is the specific heat capacity of the enclosure material with heat storage characteristics; ρ q is the density of the enclosure material with heat storage characteristics; T i is the temperature of the ith temperature node; T air is the indoor air temperature; T in,e The temperature of the inner surface of the enclosure structure in contact with the indoor air; The mathematical model of the pipe segment component is expressed as: Among them, C p is the heat capacity of the pipe; c w is the specific heat capacity of water; T k is the water temperature of a certain pipe section; T w is the outdoor air temperature; L is the length of the calculated pipe section; G p is the mass flow rate of the pipeline; K p is the heat loss per unit length of pipe.

6. The simulation implementation system according to claim 5, characterized in that: According to the mathematical model of the heat source and calling the corresponding module, a heat source simulation model is obtained, wherein the input port of the heat source simulation model includes the primary network return water temperature, the heat network feed water temperature, the fuel control variable and the circulating water volume control variable; The output port of the heat source simulation model includes the heat source water supply temperature; According to the mathematical model of the radiator and calling the corresponding module, a radiator simulation model is obtained, wherein the input port of the radiator simulation model includes the secondary network water supply temperature and the indoor temperature; the output port of the radiator simulation model includes the heat dissipation of the radiator average temperature mixed radiator; According to the mathematical models on both sides of the heat exchanger and calling the corresponding modules, a heat exchanger simulation model is obtained, wherein the input port of the heat exchanger simulation model includes the primary network water supply temperature and the secondary network return water temperature; the output port of the heat exchanger simulation model includes the secondary network water supply temperature and the primary network return water temperature; According to the mathematical model of the enclosure structure, the corresponding module is called to obtain an enclosure structure simulation model, wherein the input port of the enclosure structure simulation model includes the outdoor temperature and the average temperature of the radiator; the output port of the enclosure structure simulation model includes the indoor air temperature, the average temperature of the radiator at the next moment, and the heat consumption; After the radiator is encapsulated in the enclosure structure, the input ports of the enclosure structure simulation model include the outdoor temperature and the radiator water supply temperature; The output ports of the enclosure simulation model include indoor air temperature, radiator return water temperature and heat consumption; Based on the mathematical model of the pipe segment component and calling the corresponding module, different pipe segments are connected in sequence according to the topological structure of the actual pipe network to obtain a heat network simulation model. The input port of the pipe segment component includes the water temperature of the previous pipe segment and the outdoor ambient temperature; the output port of the pipe segment component includes the water temperature of the next pipe segment; According to the actual connection form of the heating system, the simulation modules of different parts are called respectively, and then the modules are connected according to the connection sequence of the input ports and output ports to obtain the simulation model of the heating system.

7. The simulation implementation system according to claim 1, characterized in that: The steady-state simulation model and the dynamic simulation model use the same interface for data transmission. The real-time operating status of the heating system is transmitted to the steady-state simulation model through the same interface for steady-state scheduling, and is simultaneously transmitted to the dynamic simulation model for dynamic preview.

8. The simulation implementation system according to claim 1, characterized in that: The curve identification methods of the dynamic characteristics include interpolation method, neural network method, and regression analysis method; the simulation unit is also used to establish a simulation model for components with complex internal structure principles using a data-driven method, and select real data related to the component simulation model to train the selected deep learning or machine learning algorithm model in an external module; the simulation unit is provided with a module for interacting with external data, and the function of the simulation unit interacting with other external software is realized through a dynamic link library with a TCP protocol.

9. The simulation implementation system according to claim 1, characterized in that: The modeling unit is also provided with a multi-time scale simulation mechanism for establishing a long-time simulation model, a medium-time simulation model and a short-time simulation model; The long-term simulation model is to optimize the system design parameters by establishing a steady-state simulation model, with a time step of one year; The medium-time simulation model is used to indicate the supply and demand balance characteristics of the system and determine the appropriate operation strategy. It uses hours as the time step and is applied to dynamic simulation models with slow dynamic response. The short-time simulation model analyzes the dynamic characteristics of the system by establishing a dynamic simulation model and importing real-time data, and is applied to a dynamic simulation model with faster dynamic response with a time step of seconds; The modeling unit is also provided with a multi-core parallel simulation mechanism, which is used to divide the entire heating system heat network into multiple sub-modules using a network partitioning algorithm. Each sub-module is simulated in parallel, and large step sizes and small step sizes are solved in parallel. The interface is designed in a parallel manner to coordinate the timing of the interface and perform multi-time scale parallel simulation.

10. A method for realizing steady-state and dynamic simulation of the whole process of intelligent decision-making of a heating system, characterized in that: The simulation implementation method includes: The whole process simulation of the heating system is divided into multiple equipment-level modules and system-level modules through the basic model library, and the relevant equipment components, equipment parameters and simulation parameters, and connection types are stored in the module library; The simulation model building module selects and connects corresponding graphical component modules from the basic model library, inputs the structure and characteristic data of the equipment, and establishes a steady-state simulation model and a dynamic simulation model. The steady-state simulation model is established based on the characteristic equations of each component and shows the input and output relationship of the system under steady state. The dynamic simulation model is obtained based on the steady-state simulation model, combined with the component characteristic equations and dynamic characteristic curve identification, and shows the relationship between various parameters changing with time under unsteady state. The heating system is simulated by the simulation calculation module according to the issued simulation control instructions and simulation parameter data; the simulation control instructions include simulation start and stop instructions and model parameter setting instructions; the simulation parameter data includes simulation initial condition data, boundary condition data and simulation calculation data; The accuracy of the model is verified by using the control variable method through the simulation verification module. The data with complex working condition changes on a certain day is selected as the boundary condition of the model to verify the accuracy of the model. The relative error of the model is calculated based on the comparison between the model verification data and the real data. The corresponding structural parameters are modified according to the error size to ensure that the error is within a reasonable range. The dynamic simulation model simulates and verifies the dynamic characteristics of the system under various working conditions when different influencing parameters are disturbed. Real-time data transmission, real-time display of simulation screen, simulation process monitoring and parameter adjustment through the host computer module; The simulation control instructions and simulation parameter data are issued through the simulation application module, and the simulation control instructions are modified based on the simulation results. The simulation model is used to diagnose, optimize scheduling, predict status, and dynamically preview the heating system.

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