Heating ventilation air conditioner modeling simulation method and system

Abstract

The invention discloses a heating ventilation air conditioner modeling simulation method and system. The method comprises the steps that a heat conduction and airflow flowing coupling model of heating ventilation air conditioner equipment is established; the method comprises the following steps: decomposing heating ventilation air conditioning equipment into a plurality of key components, and establishing an independent dynamic model for each key component; establishing a dynamic behavior model for describing dynamic response characteristics of the heating, ventilating and air conditioning equipment under different working conditions and a nonlinear dynamic model for describing nonlinear characteristics of the heating, ventilating and air conditioning equipment in actual operation; combining a coupling model, a dynamic model, a dynamic behavior model and the nonlinear dynamic model to generate a unified equipment mechanism model of the heating ventilation air conditioner; and converting the mechanism model into a computable form, carrying out on-line simulation aiming at different working conditions, and carrying out visual display through an interactive interface. According to the embodiment of the invention, the quantum processing can be combined with the chaos theory, so that the security and confidentiality of the transmitted data are improved, the accuracy and reliability of the design and operation of the heating ventilation and air conditioning system are improved, and more effective support can be provided for building energy efficiency optimization and environment quality improvement.

Description

Technical Field

[0001] The present invention belongs to the technical field of analysis and measurement control modeling simulation communication, and in particular to a heating, ventilation and air conditioning modeling simulation method and system. Background Art

[0002] With the development of economy and technology, and with the rapid development of information technology, big data has become a key resource for the operation of modern society. The high-speed computing and transmission capabilities of big data are crucial to many fields such as business analysis, healthcare, intelligent transportation, and national security. As the amount of data grows, the security requirements for data transmission are becoming more stringent. Confidential communication technology, especially when it comes to HVAC modeling and simulation technology, has become particularly important. It is clearly required to improve the statistical monitoring and metering system of energy consumption in the building field, and to strengthen the construction of online monitoring systems for energy consumption of key energy-consuming units, which has become a key development direction. The HVAC system plays a vital role in modern buildings. Its main function is to provide a comfortable indoor environment, ensure air quality, and effectively control temperature and humidity. With the increasingly stringent building design and energy efficiency standards, the design, operation and management of HVAC systems face higher and higher requirements. Traditional HVAC system design usually relies on empirical rules and simple calculation models, which are insufficient when dealing with complex indoor environments and changing operating conditions. Due to the diversity of building usage, climatic conditions and user needs, simple linear models often cannot accurately reflect the actual performance and dynamic behavior of HVAC systems.

[0003] As new-generation information technologies such as 5G, cloud computing, blockchain, artificial intelligence, digital twins, and Beidou communications gradually mature, they can effectively promote the digital transformation of products and services, improve the digital level of product and service planning, implementation, and optimization processes, and create differentiated, scenario-based, and intelligent digital products and services. Develop intelligent products and services with modeling perception, interactive simulation, self-learning, and decision-making support functions to better meet and guide user needs.

[0004] Existing confidential communication technologies mainly rely on classical encryption algorithms, such as symmetric encryption algorithms and asymmetric encryption algorithms. Symmetric encryption algorithms are widely used in big data communications due to their high computational efficiency and ability to adapt to large amounts of data. However, the key distribution and management issues of symmetric encryption have always been a challenge, especially in large-scale systems. Although asymmetric encryption has advantages in key management, it has high computational complexity and is not suitable for real-time or near-real-time transmission of large amounts of data. Summary of the invention

[0005] The purpose of the present invention is to provide a HVAC modeling simulation method and system to address the deficiencies in the prior art, and to combine quantum processing with chaos theory to improve the security and confidentiality of transmitted data, improve the accuracy and reliability of HVAC system design and operation, and provide more effective support for building energy efficiency optimization and environmental quality improvement.

[0006] An embodiment of the present application provides a HVAC modeling and simulation method, the method comprising: Establish a coupled model of heat conduction and air flow in HVAC equipment; Decompose the HVAC equipment into several key components, establish an independent dynamic model for each key component, where the dynamic model of each component is represented by a transfer function or a state space model, and establish a system dynamic coupling relationship between the components; Establish a dynamic behavior model to describe the dynamic response characteristics of HVAC equipment under different working conditions, and a nonlinear dynamic model to describe the nonlinear characteristics of HVAC equipment in actual operation; Combining the coupling model, the dynamic model, the dynamic behavior model and the nonlinear dynamic model to generate a unified HVAC equipment mechanism model; The mechanism model is converted into a computable form, and online simulation is performed for different working conditions and visualized through an interactive interface to cover all operating ranges of HVAC equipment.

[0007] Optionally, the method further includes: Based on the mechanism model, design simulation, load simulation, online playback of historical data or one-click import of large-scale data for simulation are performed.

[0008] Optionally, the step of establishing a coupled model of heat conduction and air flow of HVAC equipment includes: According to the one-dimensional heat conduction equation describing the heat transfer process in the equipment and the three-dimensional fluid dynamics equation describing the flow process of the airflow inside the equipment, a heat conduction model and an airflow model are constructed respectively; The heat conduction model and the airflow model are combined to construct a corresponding coupling model to describe the dynamic process of heat exchange and airflow distribution.

[0009] Optionally, the HVAC equipment is decomposed into several key components, and an independent dynamic model is established for each key component, including: Conduct a comprehensive analysis of HVAC equipment to identify multiple key components, including compressors, evaporators, condensers, fans, and heat exchangers; Analyze the physical characteristics of each key component to determine the dynamic behavior characteristics of the key components under different working conditions; Select a dynamic model that matches the characteristics of each key component, wherein for linear characteristic components, a transfer function is used to represent the dynamic model, and for nonlinear characteristic components, a state space model is used to represent the dynamic model; A system dynamic coupling relationship is established between the dynamic models of all key components to reflect the mutual influence between the components.

[0010] Optionally, the establishment of a dynamic behavior model describing the dynamic response characteristics of the HVAC equipment under different working conditions, and a nonlinear dynamic model describing the nonlinear characteristics exhibited by the HVAC equipment in actual operation, includes: Classify the key operating conditions of HVAC equipment; For each type of working condition, the dynamic response characteristics of the working condition are analyzed, and the input-output characteristics under each working condition are modeled using state space modeling technology to obtain a dynamic behavior model; According to the nonlinear characteristics appearing in the dynamic response, nonlinear modeling technology is used to build a nonlinear dynamic model.

[0011] Another embodiment of the present application provides a HVAC modeling and simulation system, the system comprising: The first building module is used to build a coupled model of heat conduction and air flow of HVAC equipment; The second building module is used to decompose the HVAC equipment into several key components, establish an independent dynamic model for each key component, wherein the dynamic model of each component is represented by a transfer function or a state space model, and establish a system dynamic coupling relationship between the components; The third building module is used to establish a dynamic behavior model that describes the dynamic response characteristics of the HVAC equipment under different working conditions, and a nonlinear dynamic model that describes the nonlinear characteristics exhibited by the HVAC equipment in actual operation; A generation module, used to combine the coupling model, the dynamic model, the dynamic behavior model and the nonlinear dynamic model to generate a unified HVAC equipment mechanism model; The simulation module is used to convert the mechanism model into a computable form, perform online simulation for different working conditions and visualize them through an interactive interface to cover all operating ranges of the HVAC equipment.

[0012] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0013] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0014] Compared with the prior art, the present invention provides a HVAC modeling and simulation method, which establishes a coupling model of heat conduction and air flow of HVAC equipment; decomposes the HVAC equipment into several key components, and establishes an independent dynamic model for each key component; establishes a dynamic behavior model that describes the dynamic response characteristics of the HVAC equipment under different working conditions, and a nonlinear dynamic model that describes the nonlinear characteristics exhibited by the HVAC equipment in actual operation; combines the coupling model, the dynamic model, the dynamic behavior model and the nonlinear dynamic model to generate a unified HVAC equipment mechanism model; converts the mechanism model into a computable form, performs online simulation for different working conditions, and visualizes it through an interactive interface, so that quantum processing can be combined with chaos theory to improve the security and confidentiality of transmitted data, improve the accuracy and reliability of HVAC system design and operation, and provide more effective support for building energy efficiency optimization and environmental quality improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A hardware structure block diagram of a computer terminal for a HVAC modeling and simulation method provided by an embodiment of the present invention; Figure 2 A schematic diagram of a flow chart of a HVAC modeling and simulation method provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a HVAC modeling and simulation system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0017] The embodiment of the present invention first provides a HVAC modeling and simulation method, which can be applied to electronic devices, such as computer terminals, specifically ordinary computers, etc.

[0018] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of a HVAC modeling simulation method provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0019] The memory 104 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the HVAC modeling simulation method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0020] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0021] See also Figure 2 The embodiment of the present invention provides a HVAC modeling and simulation method, which may include the following steps: S201, establish a coupled model of heat conduction and air flow of HVAC equipment; This step aims to build a unified model by combining heat conduction and airflow characteristics through physical equations to more accurately describe the heat exchange and airflow distribution within the equipment. This model lays the foundation for subsequent component analysis and model verification. The establishment of this coupled model is of great significance: 1. Improve accuracy: By combining heat conduction with air flow modeling, the heat exchange characteristics of HVAC equipment in actual operation can be more realistically reflected, improving the accuracy of the prediction. 2. Optimize design: During the design phase, the coupling model can be used to simulate and analyze different design schemes to help engineers determine the thermal performance and airflow distribution of each scheme, thereby optimizing the design. 3. Fault diagnosis: With the help of this model, we can better understand the operating status of the equipment under abnormal conditions, which is convenient for fault detection and maintenance. 4. Energy-saving management: Through the analysis and optimization of the model, it helps to improve the energy efficiency of the HVAC system and achieve energy-saving goals.

[0022] Specifically, a heat conduction model and an airflow model can be constructed respectively according to a one-dimensional heat conduction equation describing the heat transfer process in the device and a three-dimensional fluid dynamics equation describing the flow process of the airflow inside the device; Set a one-dimensional heat conduction equation to ensure that the thermal conductivity and temperature changes of the materials in the equipment are taken into account to form a corresponding heat conduction model. Use a three-dimensional fluid dynamics equation to ensure that the velocity, pressure, density, etc. of the airflow are taken into account, and equations such as momentum and mass conservation are introduced to form an airflow model. By establishing heat conduction and airflow models separately, different physical processes can be analyzed separately, laying the foundation for subsequent coupling models. One implementation method may include: 1. Analysis of heat conduction equation: According to the material characteristics and structure of the equipment, the one-dimensional heat conduction equation is used to analyze the distribution of heat flow. Introduce material properties such as thermal conductivity, specific heat capacity, density, etc., establish appropriate boundary conditions, such as local temperature, heat source distribution, etc., use numerical methods (such as finite difference method) for spatial discretization, and solve the heat conduction equation to obtain the temperature distribution of each component.

[0023] Among them, the boundary conditions are set, including: constant temperature boundary conditions: for example, the surface temperature of a component is kept at a certain value; constant heat flow boundary conditions: for example, there is a heat source inside that generates a certain amount of heat; the equation is discretized using the finite difference method (FDM), and iterative calculations are performed to obtain the temperature distribution within each time step; the final output of the heat conduction model is the temperature distribution diagram of each component, which reflects the temperature changes inside the device at different time and space positions. That is, the heat conduction model outputs: the temperature distribution of each component, forming a temperature field diagram.

[0024] 2. Characterization of airflow model: The airflow channel of the HVAC equipment is divided in detail, and the airflow model is established using three-dimensional fluid dynamics equations such as the Navier-Stokes equations. Through computational fluid dynamics (CFD) software (such as ANSYS Fluent or OpenFOAM), fluid properties (such as density, viscosity, velocity) are set, and flow boundary conditions (such as flow inlet, pressure outlet, etc.) are introduced. Through meshing and solver calculation, the flow pattern and velocity distribution of the airflow inside the equipment are simulated.

[0025] Among them, the air flow channel is meshed according to the geometric shape of the equipment to generate a computational grid. This process is usually completed in CFD software to ensure that each area has sufficient resolution to capture changes in flow characteristics. Enter the physical properties of the fluid (such as density and viscosity) in the CFD software, and set boundary conditions according to actual conditions, including: inlet conditions: set flow rate or velocity as inflow conditions; outlet conditions: set to normal pressure or specific pressure; wall conditions: set to no-slip boundary conditions, or other appropriate boundary conditions. Run the CFD simulation solver to calculate the flow pattern and velocity distribution of the airflow inside the equipment. The software usually provides streamlines, velocity field distribution diagrams, and pressure distribution diagrams to show the dynamic characteristics of the airflow in the equipment. The final airflow model is a complete representation of the velocity and pressure distribution inside and outside each key component (such as fans, heat exchangers, etc.), as well as the flow field, reflecting how the airflow interacts with heat transfer. That is, the airflow model outputs: the velocity and pressure distribution inside each key component to form a flow field diagram.

[0026] 3. Design of coupling interface: Design coupling interface between heat conduction and airflow models to ensure that the two models can influence each other. Heat interaction is achieved by setting thermal convection boundary conditions, for example: connecting the velocity field in the airflow model with the temperature field in the heat conduction model so that the temperature change of the airflow can affect the heat conduction process, and conversely, the heat conduction results can also affect the density and flow velocity of the airflow. 4. Model verification and optimization: Use experimental data and field tests to verify the initially established heat conduction and airflow models. Optimize model parameters to improve accuracy by comparing simulation results with actual measurement data. Perform sensitivity analysis on the model based on actual working conditions, identify key parameters, and iteratively improve model performance based on optimization algorithms (such as genetic algorithms or particle swarm optimization). Through this digital modeling and numerical simulation process, a model was successfully constructed that can truly reflect the heat conduction and air flow characteristics within the HVAC equipment, laying a solid foundation for the subsequent coupling analysis and establishment of dynamic response characteristics.

[0027] The heat conduction model and the airflow model are combined to construct a corresponding coupling model to describe the dynamic process of heat exchange and airflow distribution.

[0028] By analyzing the interaction between the heat conduction model and the airflow model, matching the boundary conditions and state equations between the two, a coupling model is formed. This coupling model can describe the heat exchange process and the interaction of airflow under actual operating conditions, which helps to achieve an accurate evaluation of the overall performance of the equipment. One implementation method may include: 1. Model input preparation: Before starting to build a coupled model, make sure that both the heat conduction model and the air flow model have been completed and verified. The heat conduction model should contain the temperature distribution information of each component, while the air flow model should contain the flow velocity and pressure distribution information. 2. Define the coupling interface: Determine the coupling interface between the heat conduction model and the airflow model. The interface is mainly on the contact surface of each component, such as the surface of the heat exchanger, the inner wall of the pipe, etc. These interfaces are the key areas for heat and airflow exchange. 3. Establishment of heat transfer and momentum exchange relationship: - Heat transfer formula: On the coupling interface, Newton's law of cooling is applied to describe the heat exchange process and establish the relationship between heat flow Q and temperature difference Delta T:

[0029] Where h is the convective heat transfer coefficient, A is the area of ​​the coupling interface, is the surface temperature, is the fluid temperature. - Momentum exchange equation: Similarly, the continuity equation and momentum equation from fluid dynamics are applied to establish the interaction between the airflow over the heat exchanger surface and the solid surface. This can be done by correcting the velocity and pressure of the airflow to reflect the effect of heat conduction on the fluid. 4. Mathematical expression of the coupled model: The equations of the heat conduction model are combined with the equations of the airflow model to form a coupled set of partial differential equations. This set of equations will include the heat transfer equations and the fluid dynamics equations to form a comprehensive model:

[0030]

[0031]

[0032] in, represents the rate of change of temperature T with respect to time t. This describes how the temperature changes with time; is the thermal diffusivity of the material, which describes the ability of heat to diffuse in the medium; It is the second-order derivative of temperature with respect to the spatial position x, reflecting the conduction of heat in space; Heat transfer term: can include the influence of heat source or heat sink, such as ambient temperature or heat input inside the equipment, affecting the overall heat conduction effect.

[0033] is the velocity vector of the fluid, indicating the speed and direction of the airflow; is the divergence of the velocity field, which represents the volume conservation principle of airflow. If the divergence is zero, it means that the density of the fluid is constant and the airflow is incompressible; this equation ensures that the volume of the fluid does not increase or decrease during the flow process, and is applicable to most air flow situations.

[0034] is the density of the fluid, which expresses the mass per unit volume; Indicates fluid velocity The rate of change over time describes the flow state over time; It is the convection term, which represents the acceleration of the fluid due to its own velocity. It is a nonlinear term in fluid mechanics, which represents how objects affect each other during the flow process; It represents the effect of the pressure gradient of the fluid on the flow. The greater the pressure difference, the greater the air flow velocity. It is a viscosity term, which describes the resistance of the fluid viscosity to the flow, ensuring the smoothness and stability of the flow; Thermal convection term: It represents the flow influence related to the temperature field, which usually affects the heat transfer efficiency.

[0035] This coupled set of partial differential equations together describes the interaction of heat and airflow within the HVAC equipment: the heat transfer model describes how changes in temperature affect the properties of the airflow, while the flow model ensures the conservation of matter and energy during the flow.

[0036] 5. Selection of numerical solution method: According to the complexity of the coupled model, select an appropriate numerical solution method, such as finite element method (FEM) or finite volume method (FVM). This step needs to be implemented in numerical calculation software (such as MATLAB, ANSYS or COMSOL). 6. Iterative solution process: During the solution process, the outputs of the heat conduction model and the airflow model are calculated alternately. First, the heat conduction is calculated based on the current airflow temperature distribution, and then the flow characteristics in the airflow model are recalculated based on the updated temperature distribution. This process will continue to iterate until the convergence conditions of the model are met (such as the change in temperature and flow rate is less than a certain threshold). 7. Result analysis and optimization: After the model is solved, the output results are analyzed, including temperature distribution, velocity field, and pressure distribution. With the help of visualization tools, the heat exchange efficiency and airflow distribution of the coupled model are evaluated, and the design parameters (such as flow channel shape, heat exchanger layout, etc.) are adjusted through optimization algorithms to improve the overall system performance. 8. Sensitivity analysis and model validation: Finally, sensitivity analysis is used to identify the parameters that have the greatest impact on system performance, and experiments or field tests are conducted to verify the accuracy of the coupling model to ensure that the model can truly reflect the dynamic response characteristics of the equipment under different operating conditions. Through the above steps, a coupled model was successfully constructed to dynamically describe the heat exchange and air flow distribution in the HVAC equipment, which provided a theoretical basis and practical basis for the subsequent system performance optimization.

[0037] S202, decomposing the HVAC equipment into several key components, establishing an independent dynamic model for each key component, wherein the dynamic model of each component is represented by a transfer function or a state space model, and a system dynamic coupling relationship is established between the components; The purpose of this step is to systematize the entire HVAC equipment for easy analysis and modeling. By breaking down the equipment into key components, independent dynamic models can be built for each part, thereby achieving effective simulation and optimization of the overall system.

[0038] By decomposing the equipment into multiple key components (such as compressors, evaporators, condensers, etc.), each component can be modeled in more detail. This decomposition allows the characteristics and interactions of each component to be analyzed separately, simplifying the understanding of complex systems. The dynamic model of each key component is represented by a transfer function or state space model, which can effectively capture its dynamic behavior. This provides a quantitative tool for the overall performance analysis of the system and helps to achieve dynamic coupling between components. By determining the input-output relationship between each component, a dynamic coupling relationship is established to reflect the mutual influence between components. This step ensures the integrity of the model and can accurately simulate the behavior of the entire system. Role and significance: - Component-based design: By breaking down a device into multiple key components, the analysis process of complex systems can be simplified, making it easier to test and optimize each component independently. - Improved maintainability: The dynamic models of each component are independent and can be easily repaired and replaced during actual operation. - Accurate modeling: Each component is represented by a transfer function or state space model, which can more accurately describe its dynamic characteristics and behavior, laying the foundation for subsequent coupling analysis and system optimization. - Establishing coupling relationships: By establishing dynamic coupling relationships between components, the overall dynamic characteristics and interactions of the system can be reflected, thereby more realistically simulating the operation of the entire HVAC equipment.

[0039] Specifically, a comprehensive analysis of HVAC equipment can be performed to identify multiple key components, wherein the key components include: compressor, evaporator, condenser, fan and heat exchanger; The purpose of a comprehensive analysis of HVAC equipment is to understand the working principle and function of the equipment, so as to identify the key components that affect the overall performance. Identifying key components can help conduct in-depth research and modeling for each component to achieve comprehensive optimization of equipment performance.

[0040] First, you can understand the workflow, functions, and relationships of each component through literature research and actual equipment disassembly. Then, list all key components and define their roles in the entire system, such as the compressor is responsible for compressing the refrigerant, the evaporator is responsible for absorbing heat, and the heat exchanger is responsible for heat transfer.

[0041] Analyze the physical characteristics of each key component to determine the dynamic behavior characteristics of the key components under different working conditions; The physical characteristics of each key component are analyzed, including fluid dynamics, heat conduction, material properties, etc., to quantitatively and qualitatively understand its dynamic behavior. Understanding the behavioral characteristics of each component under different working conditions allows the model to more accurately reflect the dynamic changes in the actual operation process.

[0042] Experimental design is performed for each component, various data (such as temperature, pressure, flow rate, etc.) are collected through sensors, and data analysis is performed to form the dynamic behavior characteristics of each component. This process may be simulated and verified using software tools.

[0043] Select a dynamic model that matches the characteristics of each key component, wherein for linear characteristic components, a transfer function is used to represent the dynamic model, and for nonlinear characteristic components, a state space model is used to represent the dynamic model; According to the characteristics of each component, the most appropriate dynamic model type is selected to ensure accurate modeling. Components with different characteristics need to be described by appropriate models to ensure the accuracy and reliability of the simulation results.

[0044] For components with linear characteristics, such as fans, transfer functions can be used for modeling; for components with nonlinear characteristics, such as compressors, state space models are used for modeling. In specific implementations, existing modeling tools can be used to estimate parameters based on theoretical derivation and experimental data.

[0045] A system dynamic coupling relationship is established between the dynamic models of all key components to reflect the mutual influence between the components.

[0046] The coupling relationship between the component models is established to achieve a dynamic interactive description of the overall system. The establishment of the mutual influence relationship can truly reflect how the components work together under different working conditions, thereby optimizing the overall system performance.

[0047] The models of various components can be connected through mathematical equations (such as difference equations or partial differential equations) or through graphical modeling tools (such as Simulink) to form a complete dynamic system model. This coupling relationship may involve the interaction of multiple physical phenomena such as heat transfer and pressure changes.

[0048] Through the above steps, the dynamic model of HVAC equipment can be systematically established, providing a solid foundation for subsequent simulation and optimization.

[0049] S203, establishing a dynamic behavior model describing the dynamic response characteristics of the HVAC equipment under different working conditions, and a nonlinear dynamic model describing the nonlinear characteristics exhibited by the HVAC equipment in actual operation; Establishing dynamic behavior models for different working conditions can capture the performance changes of equipment under various environmental and load conditions, thereby helping to optimize control strategies and designs. Identifying and modeling the nonlinear characteristics of equipment enables the model to more realistically reflect actual operating conditions, especially behavior under extreme conditions.

[0050] Specifically, the key operating conditions of HVAC equipment can be classified; Systematically classify the operating conditions of HVAC equipment to determine the characteristics under different operating conditions. Key operating conditions usually include cooling mode, heating mode, dehumidification mode, etc. Classification can help engineers understand and analyze the performance of equipment under different working conditions, and facilitate targeted dynamic model establishment and performance optimization.

[0051] First, collect data from the actual operation of the equipment and record the operating status in different scenarios (such as different ambient temperatures, load changes, operating modes, etc.). Then, according to the working principle of the equipment and user needs, group these working conditions according to function and operation type. For example, the working conditions can be divided into low-load conditions, high-load conditions, transient conditions, and stable conditions, and define the corresponding parameter range and operating conditions for each type of condition.

[0052] For each type of working condition, the dynamic response characteristics of the working condition are analyzed, and the input-output characteristics under each working condition are modeled using state space modeling technology to obtain a dynamic behavior model; Perform dynamic response characteristic analysis for each type of working condition to determine its response behavior when input (such as temperature, pressure, flow, etc.) changes. This analysis can reveal the performance characteristics of the equipment under different working conditions, help to carry out more accurate dynamic modeling in the future, and improve the credibility and accuracy of the model.

[0053] For each operating condition, relevant data on input and output can be collected through experiments or computational fluid dynamics (CFD) simulations. Using state space modeling techniques, an input-output relationship model is established. The specific steps include: 1. Determine the input and output variables, for example, the input is the set temperature and the output is the actual temperature of the equipment. 2. Use experimental data or simulation results to establish state-space equations to describe the state changes of the system. 3. For complex dynamic behaviors, identification algorithms can be used for parameter estimation to ensure the accuracy of the model.

[0054] According to the nonlinear characteristics appearing in the dynamic response, nonlinear modeling technology is used to build a nonlinear dynamic model.

[0055] Considering the nonlinear behavior of many HVAC equipment under different working conditions, especially when the load changes greatly, nonlinear modeling technology is used to accurately describe these characteristics. By capturing nonlinear characteristics, the true dynamic response of the equipment can be better reflected, the accuracy and reliability of the system model can be improved, and better performance can be obtained in control and optimization.

[0056] Once the nonlinear behavior is identified, a variety of nonlinear modeling techniques can be used, such as: 1. Polynomial regression: Fit the relationship between input and output through a polynomial function. 2. Neural network: Use deep learning methods to build nonlinear models, which are suitable for processing complex nonlinear relationships. 3. Nonlinear state space model: Establish nonlinear state variable equations to describe the relationship between state and input, and estimate parameters through experimental data. 4. Piecewise linear modeling: Divide the nonlinear system into multiple linear intervals, and model each interval to form a piecewise linear model. Through the above steps, a comprehensive model of the dynamic response and nonlinear characteristics of HVAC equipment under different working conditions is constructed, which will provide strong support for subsequent system control, optimization design and fault diagnosis.

[0057] S204, combining the coupling model, the dynamic model, the dynamic behavior model and the nonlinear dynamic model to generate a unified HVAC equipment mechanism model; The core of this step is to effectively integrate multiple models in the network to form a unified equipment mechanism model. Through the combination of coupling models, dynamic models, dynamic behavior models and nonlinear dynamic models, the comprehensive performance of the entire HVAC equipment under different operating conditions can be simulated. This model can more accurately reflect the working status of the equipment, thus providing a basis for subsequent control, optimization and fault diagnosis.

[0058] Function and significance: - Integration: Integrate various models into a unified system, reduce contradictions and inconsistencies between models, and ensure the coordination and applicability of the models in all aspects. - Accurate simulation: By comprehensively considering heat conduction, air flow, dynamic response and nonlinear characteristics, a more accurate simulation of the performance of the equipment under various working conditions can be achieved. - Improved predictive capabilities: The unified model can improve the ability to predict future operating conditions, provide a more accurate basis for regulation, promote energy conservation and improve equipment efficiency. - Optimize resource allocation: It helps to dynamically adjust the operation strategy of equipment under various working conditions and optimize the use of resources. One implementation method may include: 1. Data preprocessing: Clean the data of each individual model (coupled model, dynamic model, dynamic behavior model and nonlinear model) to remove redundant data and noise to ensure the accuracy and validity of the input data. 2. Model parameter normalization: Normalize the outputs of different models (such as temperature, pressure, flow, etc.) and convert them into a unified dimension to facilitate subsequent fusion. 3. Build a fusion framework: Set up a structured framework, treat the output of each sub-model as an input node, and use the weighted sum form to perform preliminary model fusion. The weighting coefficient can be dynamically adjusted according to the accuracy and scope of application of the model. 4. Dynamic weight adjustment based on fuzzy logic: Fuzzy logic algorithm is used to evaluate the prediction ability of each model under specific working conditions in real time. By setting fuzzy rules (such as "if the system is under high load, the weight of the dynamic model increases"), the weight coefficient of each model is dynamically adjusted to achieve intelligent model fusion. 5. Iterative solution and optimization: Perform nonlinear optimization on the fused model, and use iterative algorithms (such as particle swarm optimization or genetic algorithm) to ensure that the final model can minimize the prediction error under the current working conditions. In each iteration, re-evaluate the model output, compare it with the actual operating data, and update the model parameters and weights. 6. Model verification and adjustment: Compare the fusion model with the actual device data to verify the effectiveness of the model and adjust the parameters in the fusion algorithm to improve accuracy. Through methods such as cross-validation, ensure the robustness and reliability of the fusion model. 7. Real-time monitoring and update mechanism: Establish a real-time monitoring mechanism to continuously track the operating status of the equipment and environmental changes, dynamically update model parameters, and ensure the effectiveness and accuracy of the model over a long period of time. Through the above implementation method, models with different characteristics can be effectively combined together to generate a high-precision HVAC equipment mechanism model, which enhances the adaptability and prediction ability of the entire system under complex working conditions.

[0059] S205, converting the mechanism model into a computable form, performing online simulation for different working conditions and visually displaying it through an interactive interface to cover all operating ranges of the HVAC equipment.

[0060] The core of this step is to convert the generated mechanism model into a computer-processable format for efficient online simulation. Through this conversion, the complexity of the model will become controllable and it will be possible to monitor and predict the performance of HVAC under actual operating conditions. Through the interactive interface, users can see the operating status of the equipment in real time and make corresponding adjustments to optimize its operation.

[0061] After being converted into a computable form, the model can be simulated in real time in the software, and dynamic simulation can be performed for different input conditions to better meet the needs of actual applications. The combination of online simulation and visualization can not only monitor the status of the equipment in real time, but also provide users with a friendly operation interface, enhance user experience, and promote equipment optimization and maintenance. - Real-time monitoring: It realizes dynamic monitoring of HVAC system, allowing users to obtain the equipment operation status in time to ensure its normal and efficient operation. - Convenient operation: Through the visual interface, complex model data will be displayed in an intuitive way, allowing operators to quickly understand the equipment status and make decisions. - Optimization control: During the simulation process, operating parameters can be adjusted according to the simulation results to optimize energy consumption and improve system efficiency. - Risk assessment and prediction: By combining historical data with real-time data, it is possible to warn of potential failures or abnormal situations and provide corresponding solutions. One implementation method may include: 1. Model digitization: First, use mathematical modeling tools (such as MATLAB / Simulink or SciPy in Python) to digitize the equations of the mechanism model to ensure that they can be solved in the computer. This includes converting physical parameters (such as heat transfer coefficient, flow rate, etc.) into numerical formats that can be recognized by the computer. 2. Dynamic data interface construction: Design and implement a set of data interfaces that can obtain sensor data from the device in real time. This data interface will collect device status (such as temperature, humidity, airflow, etc.) and convert it into a unified format for subsequent simulation. 3. Online dynamic simulation engine development: Develop an online dynamic simulation engine that can continuously update the input parameters of the mechanism model based on real-time data. This engine will be responsible for monitoring the dynamic characteristics of each component and calculating the response behavior of the system. 4. Adaptive algorithm implementation: Introduce adaptive algorithms (such as adaptive filters or Bayesian optimization strategies) to automatically adjust model parameters based on real-time operating conditions. For example, when the equipment load changes, the adaptive algorithm will optimize the model to match the new input conditions. 5. Visualization interface design: Develop a user-friendly visualization interface and use data visualization tools (such as D3.js or WebGL) to generate dynamic dashboards. Display equipment parameter changes (for example, heat flow, pressure changes, energy consumption, etc.) in a graphical way so that operators can operate and view them conveniently. 6. Implementation of user interaction functions: Add user interaction functions to the visual interface, such as sliders, buttons, chart selections, etc., allowing users to choose to view different operating parameters and their changing trends according to their needs, and even adjust some control parameters (such as wind speed and temperature setting values) in real time. 7. Simulation result analysis and report generation: Finally, the system will generate an operation report after the simulation is completed, analyzing the equipment operation efficiency, energy consumption and possible risks, and based on this, provide optimization suggestions. Through the implementation of the above-mentioned adaptive online simulation and visualization framework, the mechanism model can be effectively converted into a computable form, realizing comprehensive monitoring and optimal control of HVAC equipment. This is a highly creative and practical technical solution.

[0062] Furthermore, in practical applications, design simulation, load simulation, online playback of historical data, or one-click import of large-scale data for simulation can also be performed based on the mechanism model.

[0063] The core of this step is to use the established mechanism model to conduct various types of simulations to comprehensively evaluate the performance of HVAC equipment. Through design simulation, the impact of different design schemes on system performance can be tested; load simulation is used to analyze the response and efficiency of the system under changing loads; online playback of historical data verifies the accuracy of the model by comparing it with actual operating data; and one-click import of large-scale data into simulation makes data analysis under different working conditions more efficient and convenient for rapid evaluation. - Optimize Design: Design simulation allows engineers to test different design parameters and configurations to determine the best system design to improve energy efficiency and performance. - Dynamic adaptability: Load simulation helps predict how the system will perform under different seasons or usage conditions, identifying potential problems in advance and resolving them. - Model verification: Through online playback of historical data, the mechanism model can be verified in a timely manner to improve the credibility of the model and provide a basis for subsequent decision-making. - Improve efficiency: One-click simulation of large-scale data import can effectively reduce the time cost of manual input, improve work efficiency, and quickly respond to changing needs. One implementation method may include: 1. Model interface design: Design the model interface so that the mechanism model can receive input parameters from various simulation tasks (such as design simulation, load simulation). This interface should support multiple data types to facilitate subsequent flexible operations.

[0064] 2. Construction of simulation task scheduling system: Develop a task scheduling system to manage the scheduling and execution of different simulation tasks. The system can not only allocate resources according to priority, but also dynamically adjust simulation parameters to adapt to real-time requirements.

[0065] 3. Design simulation module development: Create a dedicated design simulation module that allows users to input different design variables (such as equipment capacity, air volume, temperature setting, etc.), and use the mechanism model for rapid simulation, and output design optimization suggestions and expected operating results.

[0066] 4. Load simulation engine implementation: Implement a load simulation engine that combines real-time weather data and equipment usage records to simulate the impact of actual load changes on system performance. This engine will generate detailed load response curves and analyze the efficiency of the system under different operating conditions.

[0067] 5. Historical data playback mechanism: Build a historical data playback function to extract historical operation data from the system database in real time and compare it with the current operation model. This function can automatically generate reports to identify model deviations and operation efficiency.

[0068] 6. Large-scale data import and integration: Develop a one-click import function to support users to upload large quantities of simulation data in data formats (such as CSV, Excel). This process requires data preprocessing and format conversion to ensure that the data can be smoothly integrated into the simulation environment.

[0069] 7. Real-time feedback and visual result generation: After the simulation is completed, a real-time feedback mechanism is created to generate visual reports based on the simulation results, including dynamic charts and analysis views, to help users quickly understand the simulation results and make decisions accordingly.

[0070] 8. Cloud-based sharing and collaboration platform: Finally, a cloud-based sharing platform is established to allow multiple users to collaborate and share data in the same environment. The platform can store all simulation results, and users can perform version management and retrospective analysis.

[0071] Through the implementation steps of the above-mentioned adaptive simulation and data integration framework, various simulation tasks can be carried out efficiently and accurately, greatly improving the design, optimization and operation management level of HVAC equipment. This is an innovative technical solution.

[0072] By forming a flexible and configurable equipment mechanism model and system simulation technology, combined with different types of projects, a fully user-friendly interface interaction is realized, so that non-professionals can also easily get started, solving the problem that people with different knowledge structures cannot operate and maintain; it supports online simulation functions such as design simulation, load simulation, online playback of historical data, and one-click import of large-scale data for simulation.

[0073] It can be seen that a coupling model of heat conduction and air flow of HVAC equipment is established; the HVAC equipment is decomposed into several key components, and an independent dynamic model is established for each key component; a dynamic behavior model that describes the dynamic response characteristics of the HVAC equipment under different working conditions, and a nonlinear dynamic model that describes the nonlinear characteristics exhibited by the HVAC equipment in actual operation are established; the coupling model, dynamic model, dynamic behavior model and the nonlinear dynamic model are combined to generate a unified HVAC equipment mechanism model; the mechanism model is converted into a computable form, and online simulation is performed for different working conditions and visualized through an interactive interface, so that quantum processing can be combined with chaos theory to improve the security and confidentiality of transmitted data, improve the accuracy and reliability of HVAC system design and operation, and provide more effective support for building energy efficiency optimization and environmental quality improvement.

[0074] Another embodiment of the present invention provides a HVAC modeling and simulation system. Figure 3 , the system may include: The first building module 301 is used to build a coupling model of heat conduction and air flow of HVAC equipment; The second building module 302 is used to decompose the HVAC equipment into several key components, establish an independent dynamic model for each key component, wherein the dynamic model of each component is represented by a transfer function or a state space model, and establish a system dynamic coupling relationship between the components; The third building module 303 is used to build a dynamic behavior model that describes the dynamic response characteristics of the HVAC equipment under different working conditions, and a nonlinear dynamic model that describes the nonlinear characteristics exhibited by the HVAC equipment in actual operation; A generation module 304, for combining the coupling model, the dynamic model, the dynamic behavior model and the nonlinear dynamic model to generate a unified HVAC equipment mechanism model; The simulation module 305 is used to convert the mechanism model into a computable form, perform online simulation for different working conditions and visualize the simulation through an interactive interface to cover all operating ranges of the HVAC equipment.

[0075] It can be seen that a coupling model of heat conduction and air flow of HVAC equipment is established; the HVAC equipment is decomposed into several key components, and an independent dynamic model is established for each key component; a dynamic behavior model that describes the dynamic response characteristics of the HVAC equipment under different working conditions, and a nonlinear dynamic model that describes the nonlinear characteristics exhibited by the HVAC equipment in actual operation are established; the coupling model, dynamic model, dynamic behavior model and the nonlinear dynamic model are combined to generate a unified HVAC equipment mechanism model; the mechanism model is converted into a computable form, and online simulation is performed for different working conditions and visualized through an interactive interface, so that quantum processing can be combined with chaos theory to improve the security and confidentiality of transmitted data, improve the accuracy and reliability of HVAC system design and operation, and provide more effective support for building energy efficiency optimization and environmental quality improvement.

[0076] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0077] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, establish a coupled model of heat conduction and air flow of HVAC equipment; S202, decomposing the HVAC equipment into several key components, establishing an independent dynamic model for each key component, wherein the dynamic model of each component is represented by a transfer function or a state space model, and a system dynamic coupling relationship is established between the components; S203, establishing a dynamic behavior model describing the dynamic response characteristics of the HVAC equipment under different working conditions, and a nonlinear dynamic model describing the nonlinear characteristics exhibited by the HVAC equipment in actual operation; S204, combining the coupling model, the dynamic model, the dynamic behavior model and the nonlinear dynamic model to generate a unified HVAC equipment mechanism model; S205, converting the mechanism model into a computable form, performing online simulation for different working conditions and visually displaying it through an interactive interface to cover all operating ranges of the HVAC equipment.

[0078] It can be seen that a coupling model of heat conduction and air flow of HVAC equipment is established; the HVAC equipment is decomposed into several key components, and an independent dynamic model is established for each key component; a dynamic behavior model that describes the dynamic response characteristics of the HVAC equipment under different working conditions, and a nonlinear dynamic model that describes the nonlinear characteristics exhibited by the HVAC equipment in actual operation are established; the coupling model, dynamic model, dynamic behavior model and the nonlinear dynamic model are combined to generate a unified HVAC equipment mechanism model; the mechanism model is converted into a computable form, and online simulation is performed for different working conditions and visualized through an interactive interface, so that quantum processing can be combined with chaos theory to improve the security and confidentiality of transmitted data, improve the accuracy and reliability of HVAC system design and operation, and provide more effective support for building energy efficiency optimization and environmental quality improvement.

[0079] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0080] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0081] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, establish a coupled model of heat conduction and air flow of HVAC equipment; S202, decomposing the HVAC equipment into several key components, establishing an independent dynamic model for each key component, wherein the dynamic model of each component is represented by a transfer function or a state space model, and a system dynamic coupling relationship is established between the components; S203, establishing a dynamic behavior model describing the dynamic response characteristics of the HVAC equipment under different working conditions, and a nonlinear dynamic model describing the nonlinear characteristics exhibited by the HVAC equipment in actual operation; S204, combining the coupling model, the dynamic model, the dynamic behavior model and the nonlinear dynamic model to generate a unified HVAC equipment mechanism model; S205, converting the mechanism model into a computable form, performing online simulation for different working conditions and visually displaying it through an interactive interface to cover all operating ranges of the HVAC equipment.

[0082] It can be seen that a coupling model of heat conduction and air flow of HVAC equipment is established; the HVAC equipment is decomposed into several key components, and an independent dynamic model is established for each key component; a dynamic behavior model that describes the dynamic response characteristics of the HVAC equipment under different working conditions, and a nonlinear dynamic model that describes the nonlinear characteristics exhibited by the HVAC equipment in actual operation are established; the coupling model, dynamic model, dynamic behavior model and the nonlinear dynamic model are combined to generate a unified HVAC equipment mechanism model; the mechanism model is converted into a computable form, and online simulation is performed for different working conditions and visualized through an interactive interface, so that quantum processing can be combined with chaos theory to improve the security and confidentiality of transmitted data, improve the accuracy and reliability of HVAC system design and operation, and provide more effective support for building energy efficiency optimization and environmental quality improvement.

[0083] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A HVAC modeling and simulation method, characterized in that: The method comprises: Establish a coupled model of heat conduction and air flow in HVAC equipment; Decompose the HVAC equipment into several key components, establish an independent dynamic model for each key component, where the dynamic model of each component is represented by a transfer function or a state space model, and establish a system dynamic coupling relationship between the components; Establish a dynamic behavior model to describe the dynamic response characteristics of HVAC equipment under different working conditions, and a nonlinear dynamic model to describe the nonlinear characteristics of HVAC equipment in actual operation; Combining the coupling model, the dynamic model, the dynamic behavior model and the nonlinear dynamic model to generate a unified HVAC equipment mechanism model; The mechanism model is converted into a computable form, and online simulation is performed for different working conditions and visualized through an interactive interface to cover all operating ranges of HVAC equipment.

2. The method according to claim 1, characterized in that The method further comprises: Based on the mechanism model, design simulation, load simulation, online playback of historical data or one-click import of large-scale data for simulation are performed.

3. The method according to claim 2, characterized in that The establishment of a coupled model of heat conduction and air flow of HVAC equipment includes: According to the one-dimensional heat conduction equation describing the heat transfer process in the equipment and the three-dimensional fluid dynamics equation describing the flow process of the airflow inside the equipment, a heat conduction model and an airflow model are constructed respectively; The heat conduction model and the airflow model are combined to construct a corresponding coupling model to describe the dynamic process of heat exchange and airflow distribution.

4. The method according to claim 3, characterized in that The HVAC equipment is decomposed into several key components, and an independent dynamic model is established for each key component, including: Conduct a comprehensive analysis of HVAC equipment to identify multiple key components, including compressors, evaporators, condensers, fans, and heat exchangers; Analyze the physical characteristics of each key component to determine the dynamic behavior characteristics of the key components under different working conditions; Select a dynamic model that matches the characteristics of each key component, wherein for linear characteristic components, a transfer function is used to represent the dynamic model, and for nonlinear characteristic components, a state space model is used to represent the dynamic model; A system dynamic coupling relationship is established between the dynamic models of all key components to reflect the mutual influence between the components.

5. The method according to claim 4, characterized in that The establishment of a dynamic behavior model describing the dynamic response characteristics of the HVAC equipment under different working conditions and a nonlinear dynamic model describing the nonlinear characteristics exhibited by the HVAC equipment in actual operation includes: Classify the key operating conditions of HVAC equipment; For each type of working condition, the dynamic response characteristics of the working condition are analyzed, and the input-output characteristics under each working condition are modeled using state space modeling technology to obtain a dynamic behavior model; According to the nonlinear characteristics appearing in the dynamic response, nonlinear modeling technology is used to build a nonlinear dynamic model.

6. A HVAC modeling and simulation system, characterized in that: The system comprises: The first building module is used to build a coupled model of heat conduction and air flow of HVAC equipment; The second building module is used to decompose the HVAC equipment into several key components, establish an independent dynamic model for each key component, wherein the dynamic model of each component is represented by a transfer function or a state space model, and establish a system dynamic coupling relationship between the components; The third building module is used to establish a dynamic behavior model that describes the dynamic response characteristics of the HVAC equipment under different working conditions, and a nonlinear dynamic model that describes the nonlinear characteristics exhibited by the HVAC equipment in actual operation; A generation module, used to combine the coupling model, the dynamic model, the dynamic behavior model and the nonlinear dynamic model to generate a unified HVAC equipment mechanism model; The simulation module is used to convert the mechanism model into a computable form, perform online simulation for different working conditions and visualize them through an interactive interface to cover all operating ranges of the HVAC equipment.

7. The system according to claim 6, characterized in that The system further comprises: The implementation module is used to perform design simulation, load simulation, online playback of historical data, or one-click import of large-scale data for simulation based on the mechanism model.

8. The system according to claim 7, characterized in that The first establishing module is specifically used for: According to the one-dimensional heat conduction equation describing the heat transfer process in the equipment and the three-dimensional fluid dynamics equation describing the flow process of the airflow inside the equipment, a heat conduction model and an airflow model are constructed respectively; The heat conduction model and the airflow model are combined to construct a corresponding coupling model to describe the dynamic process of heat exchange and airflow distribution.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 5 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 5.

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