Hydroelectric generating set real-time simulation method and system based on integration of graph model and material

Abstract

A kind of water turbine unit real-time simulation method and system based on graph model integration belongs to the technical field of power system simulation.The method automatically identifies equipment elements and topological structure in governor engineering drawings through deep learning algorithm, generates parameterized governor model;Combined with pre-set follow-up system, water turbine, water diversion system, generator and excitation system and other water power special component model library, dynamically assembled to form complete water turbine unit simulation model;Fourth-order Runge-Kutta method is used to discretize and solve the model differential equation, and packaged as a configurable module, open key parameters for user modification;Finally, the model is automatically converted into C / C++, Python or Simulink S-Function, etc. Target language, adapt to real-time simulation device and realize hardware docking.The present application supports design verification, operation optimization and fault diagnosis and other whole life cycle applications, significantly reduces the modeling threshold, improves simulation efficiency and accuracy.

Description

Technical Field

[0001] This invention belongs to the field of data simulation and processing, and specifically relates to a real-time simulation method and system for hydropower units based on the integration of graphics, model, and physical objects. Background Technology

[0002] Current real-time simulations of hydropower units typically rely on engineers manually building mathematical models of subsystems such as governors, turbines, water intake systems, and generators on general-purpose platforms like MATLAB / Simulink, based on mechanistic equations. This modeling process requires a deep understanding of the water-mechanical-electric coupling characteristics of each component, and manual connection of interfaces and configuration of parameters—a time-consuming process demanding high levels of expertise. Furthermore, to adapt to different real-time simulation devices (such as RT-LAB and OPAL-RT), the same model often requires multiple format conversions, interface adjustments, and performance optimizations, resulting in high repetition and difficulties in cross-platform portability.

[0003] The main technical problem with the above methods is the lack of an automated construction mechanism from engineering drawings to executable simulation models. This results in low modeling efficiency, poor standardization, and difficulty in achieving efficient, zero-code simulation deployment integrating "drawings-model-hardware". Especially when facing multiple scenarios such as design verification, operational optimization, or fault diagnosis, traditional processes struggle to respond quickly, hindering the development of digital twins and intelligent operation and maintenance for hydropower units. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a real-time simulation method and system for hydropower units based on the integration of graphics, model and physical objects. The present invention supports the entire life cycle application such as design verification, operation optimization and fault diagnosis, significantly reduces the modeling threshold and improves simulation efficiency and accuracy.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: The real-time simulation method for hydropower units based on the integration of graphics, model, and physical components includes the following steps: S1. Automatically identify equipment components and topology in the engineering drawings of hydropower unit governors using deep learning algorithms, and generate a parametric model of the governor. S2. Call the preset follow-up system model, turbine model, water diversion system model, generator model and excitation system model, and combine them with the governor parameterized model to dynamically assemble a complete hydropower unit simulation model; S3. Based on the mathematical models of each component in the simulation model of the hydropower unit, the fourth-order Runge-Kutta method is used for numerical solution, and the simulation step size is determined to convert the continuous-time system into a discrete-time system. S4. Encapsulate the hydropower unit simulation model into a configurable module and open a key parameter input window for users to modify; S5. Automatically convert the hydropower unit simulation model into a programming language suitable for real-time simulation devices to achieve interface with hardware equipment.

[0006] Preferably, in step S1, an improved deep convolutional neural network is used to realize the intelligent parsing of engineering drawings. A multi-level feature pyramid structure is used to capture equipment components of different scales, and a spatial-channel dual attention mechanism is introduced to improve the extraction accuracy of parameter annotation and symbol identification.

[0007] Preferably, in step S1, a hybrid graph neural network architecture is adopted, which extracts the geometric and semantic features of the device components through a node feature encoder, and constructs a priori connection rule matrix based on domain knowledge to realize the reconstruction of topological relationships that conforms to engineering practice, and is equipped with a three-level verification mechanism to ensure the accuracy of the topological relationships.

[0008] Preferably, the parameterized model expression of the speed governor is: (1); In the formula, This is the output of the controller; For tracking errors of system variables; , and These are proportional, integral, and differential gains, respectively. is the time constant of the differential element. s is the Laplace operator. G: represents the transfer function, the standard representation for describing the input-output relationship in a control system; subscript c: represents the controller, specifically referring to the governor controller of a hydroelectric generator. Preferably, the pre-set follow-up system model, turbine model, water diversion system model, generator model, and excitation system model constitute a hydropower-specific component model library. This model library contains more than 20 types of hydropower-specific components. Each component model integrates core control equations, parameter constraint ranges, and input / output interface specifications, supporting automatic equation coupling based on component connection relationships.

[0009] Preferably, the mathematical models for each component in the hydropower unit simulation model include: Speed ​​controller parameterization model: adopts a parallel PID controller, which has two modes: speed control and power control; Turbine model: A linearized model under small fluctuation conditions is established based on the transmission coefficients of flow rate and torque to guide vane opening, rotational speed and head. Water diversion system model: Provide a rigid model or an approximate elastic model; The servo system model consists of an electro-hydraulic converter, a main pressure regulating valve, and a main servo motor, and is described using a first-order inertial element. Generator model: A second-order model is adopted; Excitation system model: A simplified model of a first-order excitation system is adopted.

[0010] Preferably, the dynamic characteristics of the turbine are approximated by its flow rate and torque characteristics in steady state, and the describing equation is shown in equation (1). The flow rate Q and torque M are closely related to the guide vane opening Y, the unit speed N, and the working head H, exhibiting a complex nonlinear functional relationship: (2); Under small fluctuation operating conditions, by performing a Taylor expansion on the flow and torque characteristics of the turbine at a specific operating point and neglecting second-order and higher-order infinitesimals, the constant coefficient algebraic equation of the turbine at that operating point is obtained: (3); , , , n and These are the relative deviations of flow rate, torque, guide vane opening, rotational speed, and head, respectively. , and These are the transfer coefficients of flow rate to guide vane opening, rotational speed, and head, respectively. , and These are the torque transmission coefficients to guide vane opening, rotational speed, and head, respectively.

[0011] Preferably, the water diversion system model provides a rigid model as shown in equation (4) and an approximately elastic model as shown in equation (5): (4); (5); in, T w Let be the inertial time constant of the water flow. T r Representing the mutual generation of water impact, s Represents the Laplace operator.

[0012] Preferably, the servo system model mainly consists of an electro-hydraulic converter, a main pressure regulating valve, and a main servo unit. The electro-hydraulic converter amplifies the speed governor control signal and converts it into a mechanical signal. The entire process is described by the following transfer function: (6); in T y1 This represents the reaction time constant of the electro-hydraulic converter; The main pressure regulating valve and main servo amplify the mechanical signal generated by the electro-hydraulic converter again, outputting the servo stroke to adjust the opening of the turbine guide vanes. Its mathematical expression is: (7); in T y The reaction time constant of the main relay is represented by the following mathematical expression for the servo system: (8).

[0013] Preferably, the generator model uses a second-order model, as shown in equation (9). (9); In the formula, The moment of inertia of the unit; The angular velocity of the unit, For the turbine torque, It is an electromagnetic torque. The damping coefficient is... δ This refers to the generator's power angle.

[0014] Preferably, the simplified model of the first-order excitation system is as follows: (10); This is the magnetizing electromotive force. This is the reference voltage for the excitation system. This refers to the generator terminal voltage. and These are the equivalent excitation system gain and time constant, respectively.

[0015] Preferably, based on the mathematical model of each component of the hydropower unit, the fourth-order Runge-Kutta method is used for numerical solution, and the specific formula is as follows: Suppose the differential equations of the mathematical model based on each component of the hydroelectric generator are as follows: (11); in, y For state variables, t For time; The iterative formula for the fourth-order Runge-Kutta method is: (12); (13); (14); (15); (16); in, h For simulating step size, and The first n Step and the first n +1 step state value.

[0016] Preferably, in step S5, the programming languages ​​supported by the target real-time simulation device include C / C++, Python, or Simulink S-Function, and the code conversion process of the hydropower unit simulation model includes syntax adaptation, interface alignment, and performance optimization, and generates a conversion instruction document.

[0017] Preferably, it also includes S6 for internal logic and verification functions: during the simulation process, the integrity and correctness of the internal logic of the hydropower unit simulation model are verified, and the simulation results are automatically compared and analyzed to generate a verification report.

[0018] Preferably, the method covers the entire life cycle of the hydropower unit and supports applications in design verification, operation optimization, and fault diagnosis scenarios.

[0019] A real-time simulation system for hydropower units based on integrated image-model-object modeling, employing the aforementioned real-time simulation method for hydropower units based on integrated image-model-object modeling, includes: The graphic intelligent recognition layer is used to perform intelligent drawing parsing and topology reconstruction to generate a parametric model of the speed controller. The model dynamic generation layer is used to call the preset follow-up system model, turbine model, water diversion system model, generator model and excitation system model, and combine them with the governor parameterized model to dynamically assemble a hydropower unit simulation model. The equipment adaptive layer is used to perform discretization, parameter configuration, code conversion, and hardware interface linkage on the simulation model of the hydropower unit. The graphic intelligent recognition layer, the model dynamic generation layer, and the device adaptive layer together constitute an integrated graphic-model-object architecture.

[0020] A computer device includes one or more processors, the processors storing one or more executable programs, which, when executed by the one or more processors, are used to implement the real-time simulation method for hydropower units based on the integration of graphics, models, and physical objects.

[0021] A storage medium storing one or more executable programs, which, when executed, are used to implement the real-time simulation method for hydropower units based on the integration of graphics, models, and physical objects.

[0022] The present invention can achieve the following beneficial effects: This invention utilizes a zero-code modeling technology based on intelligent drawing parsing. Through deep learning algorithms, it automatically identifies equipment components and topological structures in engineering drawings, enabling rapid construction and accurate simulation of hydropower unit models. This technology features dynamic parameter configuration and automated verification capabilities, meeting the application needs of various scenarios such as design verification, operational optimization, and fault diagnosis, significantly improving simulation efficiency and accuracy.

[0023] This invention significantly improves simulation efficiency through an integrated model-object real-time simulation method for hydropower units. Utilizing advanced intelligent analysis and zero-code modeling technologies, it drastically reduces the time required for traditional manual modeling, achieving highly efficient real-time simulation analysis. Real-time simulation monitoring allows for the timely and accurate identification and location of potential problems in the hydropower unit control system, effectively improving unit operating efficiency and stability. Furthermore, it provides high-precision simulation data support for power system planning, design, and operation. Simulation results, presented using 3D visualization technology, provide engineers with intuitive and reliable analytical support. This method covers the entire lifecycle of hydropower units, from initial planning and design to later operation and maintenance, achieving closed-loop management of design verification, fault diagnosis, and performance optimization through digital twin technology. Attached Figure Description

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a block diagram of the speed regulator control of the present invention; Figure 2 This is a flowchart of the real-time simulation method and system for hydropower units based on the integration of graphics, model, and physical objects, as described in this invention. Figure 3 This is a mathematical model diagram of a hydroelectric generator based on the integration of graphics and physical components, as presented in this invention. Detailed Implementation

[0025] Preferred solutions include Figures 1 to 3 As shown, a real-time simulation method and system for hydropower units based on integrated image-model-physical model is presented. This integrated image-model technology employs deep learning to achieve intelligent analysis of the entire hydropower station system. Based on physical constraint equations and a hybrid solver, it automatically generates a water-mechanical-electric coupling model. Through modeling the control system of the hydropower unit, a built-in, encapsulated configuration model is formed, capable of dynamic configuration to adapt to different simulation needs. The most distinctive feature of this model is its image recognition capabilities and the implementation of a low-code or no-code real-time simulation platform.

[0026] The key technologies of the integrated image and model technology specifically include: (1) Intelligently analyze speed controller drawings and automatically build speed controller models (frequency, opening degree, power mode) for different types of speed controllers. (2) Pre-set various models (follower, turbine, water intake, power generation, excitation) outside the speed governor. (3) Open the input window for important parameters, which can be modified by the user to design the numerical solver. (4) Convert the model into C language supported by the DSP device; (5) Verify the simulation results and generate a detailed verification report by comparing and analyzing the experimental data.

[0027] The real-time simulation method and system for hydropower units based on the integration of graphics, model, and physical model includes: integrated graphics and model technology, dynamic configuration of model and parameters, model parameter transformation, and internal logic and verification functions.

[0028] The integrated model and graphics system includes modeling common hydropower unit control systems to form a built-in, encapsulated configuration model that supports dynamic configuration of control system models and parameters as needed.

[0029] The following is a detailed introduction to the functions of each part of the integrated drawing, model, and object technology: (1) Zero-code modeling technology for intelligent drawing parsing This technology uses a deep learning-driven intelligent parsing engine to automate the recognition and modeling of governor drawings, and combined with a zero-code interactive interface, it significantly reduces the complexity of modeling.

[0030] (2) Integrated drawing and model technology The integrated model-graphics technology employs object-oriented techniques for system analysis and design, ensuring system stability and reliability. Furthermore, precise modeling and simulation technologies allow for real-time monitoring of the actual operation of the hydropower unit control system. This technology also utilizes computer languages ​​to establish mathematical models of power sources, loads, and network structures, enabling the calculation of power grid dynamics and exhibiting good robustness.

[0031] (3) Dynamic configuration model and parameter technology Dynamic model and parameter configuration technology supports on-demand dynamic configuration of control system models and parameters to meet the needs of different simulation scenarios. Furthermore, users can achieve low-code or zero-code adjustment of model parameters and scene switching through a simple operation interface.

[0032] (4) Model parameter conversion function The model parameter conversion function allows the system to automatically convert model parameters into the programming language of the simulation program, achieving seamless integration between the model and the simulation device. This technology ensures the real-time performance and accuracy of the simulation process, while improving simulation efficiency. It also enables the model and simulation device to work together with the hardware interface for more timely model adjustments.

[0033] (5) Internal logic and verification functions Internal logic and verification functions ensure the integrity and correctness of the internal logic during simulation, thereby guaranteeing the reliability of the simulation results. At the same time, the verification function can automatically verify and correct the simulation results, thereby improving the accuracy of the simulation.

[0034] Example 1: The detailed steps of this method are as follows: (1) Zero-code modeling technology for intelligent drawing parsing 1) Intelligent parsing system based on multimodal deep learning This system employs an improved deep convolutional neural network to intelligently parse engineering drawings. It accurately captures governor components of different scales through a multi-level feature pyramid structure and innovatively introduces a spatial-channel dual attention mechanism, significantly improving the extraction accuracy of parameter annotations and symbol identifiers. Addressing the dense distribution of components in the drawings, a non-maximum suppression algorithm based on dynamic thresholds is developed to effectively solve the recognition problem in complex scenarios such as component overlap and intersection. Simultaneously, the system adopts a hybrid graph neural network architecture, extracting the geometric and semantic features of components through a node feature encoder and constructing a prior connection rule matrix based on domain knowledge to achieve topological relationship reconstruction that conforms to engineering realities. The system also features a three-level verification mechanism (connection logic confidence, physical rule compliance, and engineering experience matching) to ensure the accuracy of topological relationships.

[0035] 2) Dynamic generation of the governor's parameterized model The system incorporates a parametric model library containing over 20 types of hydropower-specific components. Each component integrates core control equations (such as Navier-Stokes equations and electromagnetic field equations), parameter constraints (based on industry standards), and input / output interface specifications, supporting automatic equation coupling based on component connection relationships. Through an intelligent parameter mapping algorithm, the system can automatically convert governor drawing annotations into model parameters (numerical parameters are directly transferred, symbolic parameters are converted via knowledge graphs, and implicit parameters are derived based on empirical formulas). It also provides parameter sensitivity analysis, automatically identifying key parameters and recommending reasonable value ranges. The system further offers a visual interactive platform supporting drag-and-drop parameter configuration, real-time simulation result feedback, and multi-scheme comparative analysis. A built-in expert rule engine provides real-time guidance and suggestions during user operation.

[0036] 3) Mathematical model of the speed governor The speed controller model has two modes: speed control and power control, and uses a parallel PID controller. The logic block diagram is as follows: Figure 1 As shown in the figure, This is the permanent slip coefficient; (This refers to the droop rate). The transfer function of the parallel PID unit is shown below: (1) In the formula, The output of the controller; For tracking errors of system variables; , and These are proportional, integral, and differential gains, respectively. is the time constant of the differential element.

[0037] (2) Establishing a mathematical model for hydroelectric generator units Within the integrated simulation framework of graphics, model, and physical components, differentiated mathematical models need to be established for each key component of the hydropower unit (servo system, turbine, water intake system, generator, etc.). The model construction will follow these principles: Specifically: 1) Water turbine The dynamic characteristics of a water turbine can usually be approximated by its flow rate and torque characteristics in steady state. The describing equation is shown in equation (1). The flow rate Q and torque M are closely related to the guide vane opening Y, the unit speed N, and the working head H, exhibiting a complex nonlinear functional relationship.

[0038] (2) Under small fluctuation operating conditions, by performing Taylor expansion on the flow and torque characteristics of the turbine at a specific operating point and reasonably neglecting second-order and higher-order infinitesimals, the constant coefficient algebraic equation of the turbine at that operating point can be obtained.

[0039] (3) , , , n and These are the relative deviations of flow rate, torque, guide vane opening, rotational speed, and head, respectively. The corresponding transmission coefficients, such as the transmission coefficients of flow rate to guide vane opening, rotational speed and head, and the transmission coefficients of torque to guide vane opening, rotational speed and head, play a key role in quantifying the degree of influence between various factors. , and These are the transfer coefficients of flow rate to guide vane opening, rotational speed, and head, respectively. , and These are the torque transmission coefficients to guide vane opening, rotational speed, and head, respectively.

[0040] 2) Water diversion system The water diversion system model provides a rigid model (as shown in Equation 3) and an approximately elastic model (as shown in Equation 4). (4) (5) in, T w Let be the inertial time constant of the water flow. T r Representing the mutual generation of water impact, s Represents the Laplace operator.

[0041] 3) Follow-up system The servo system model mainly consists of an electro-hydraulic converter, a main pressure regulating valve, and a main servo actuator. The electro-hydraulic converter amplifies the speed governor control signal and converts it into a mechanical signal. The entire process can be described by the following transfer function: (6) in T y1 This represents the reaction time constant of the electro-hydraulic converter.

[0042] The main pressure regulating valve and main servo valve amplify the mechanical signal generated by the electro-hydraulic converter again, outputting the servo valve stroke to adjust the opening of the turbine guide vanes. Its mathematical expression is: (7) in T y This represents the reaction time constant of the main relay. The complete mathematical expression for the servo system is: (8) 4) Generator The generator model uses a second-order model, as shown in equation (9). (9) In the formula, The moment of inertia of the unit; The angular velocity of the unit, For the turbine torque, It is an electromagnetic torque. The damping coefficient is... δ This refers to the generator's power angle.

[0043] 5) Excitation system The simplified model of the first-order excitation system is shown below: (10) The magnetizing electromotive force. This is the reference voltage for the excitation system. This refers to the generator terminal voltage. and These are the equivalent excitation system gain and time constant, respectively.

[0044] (3) Construct the solver, discretize it, and determine the simulation step size. Based on the mathematical models (such as differential equations or state-space equations) of each component of the hydropower unit, the fourth-order Runge-Kutta method (RK4) is used for numerical solution. The specific formula is as follows: Suppose the differential equation of the system is: (11) in, y For state variables, t For time.

[0045] The iterative formula for RK4 is: (12) (13) (14) (15) (16) in, h For simulating step size, and The first n Step and the first n +1 step state value.

[0046] The continuous-time system is converted into a discrete-time system, thus discretizing the simulation model. Furthermore, it is necessary to determine an appropriate simulation step size, i.e., the time interval between each calculation. The choice of step size affects simulation accuracy and computational efficiency, and a balance must be found between the two. For example, a smaller step size can improve accuracy but increase computational cost, while a larger step size has the opposite effect.

[0047] (4) Encapsulate the mathematical model and set key parameters. After the simulation, the established mathematical model needs to be encapsulated into easy-to-use modules or functions for management and reuse. During encapsulation, key parameters of the model, such as control gain, initial conditions, and boundary conditions, need to be set and configured. The settings of these parameters directly affect the accuracy of the simulation results. Encapsulation makes the model more modular, facilitating subsequent modifications and expansions. For first-time users, the individually encapsulated hydropower unit components make it easier to get started and use, and data can be modified to better suit on-site working requirements, thus integrating simulation results with actual field conditions.

[0048] The parameter list is shown in Table 1: Table 1 Key Parameter List

[0049] (5) Code conversion; converting to the language corresponding to the device. After encapsulation, the simulation model code is converted into languages ​​supported by various real-time simulation devices to ensure compatibility and flexibility. To achieve this, the programming languages ​​and interface standards supported by the target real-time simulation device must first be evaluated, such as C / C++, Python, and Simulink S-Functions. Then, using algorithms, the original model code is adjusted and optimized according to the requirements of the target platform, converting the source code into the syntax and logic of the target language. During this process, it is also ensured that the model can correctly exchange data with hardware devices and other software modules. Performance optimization is also performed to improve code efficiency when running on the corresponding hardware devices. Finally, corresponding conversion documentation is generated, detailing the code conversion process. This facilitates problem identification and solution development when issues arise, aiding in future maintenance and expansion.

[0050] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A real-time simulation method for hydropower units based on integrated image-model-object model, characterized in that, Includes the following steps: S1. Automatically identify equipment components and topology in the engineering drawings of hydropower unit governors using deep learning algorithms, and generate a parametric model of the governor. S2. Call the preset follow-up system model, turbine model, water diversion system model, generator model and excitation system model, and combine them with the governor parameterized model to dynamically assemble a complete hydropower unit simulation model; S3. Based on the mathematical models of each component in the simulation model of the hydropower unit, the fourth-order Runge-Kutta method is used for numerical solution, and the simulation step size is determined to convert the continuous-time system into a discrete-time system. S4. Encapsulate the hydropower unit simulation model into a configurable module and open a key parameter input window for users to modify; S5. Automatically convert the hydropower unit simulation model into a programming language suitable for real-time simulation devices to achieve interface with hardware equipment.

2. The real-time simulation method for hydropower units based on integrated image-model-object model as described in claim 1, characterized in that, In step S1, an improved deep convolutional neural network is used to realize intelligent parsing of engineering drawings. A multi-level feature pyramid structure is used to capture equipment components of different scales, and a spatial-channel dual attention mechanism is introduced to improve the extraction accuracy of parameter annotation and symbol identification.

3. The real-time simulation method for hydropower units based on integrated image-model-object model as described in claim 1, characterized in that, In step S1, a hybrid graph neural network architecture is adopted. The geometric and semantic features of the device components are extracted through a node feature encoder. A prior connection rule matrix is ​​constructed based on domain knowledge to realize the reconstruction of topological relationships that conforms to engineering practice. A three-level verification mechanism is also provided to ensure the accuracy of the topological relationships.

4. The real-time simulation method for hydropower units based on integrated image-model-object model as described in claim 1, characterized in that, The parameterized model expression for the speed governor is: (1)； In the formula, The output of the controller; For tracking errors of system variables; , and These are proportional, integral, and differential gains, respectively. The time constant of the differential element; s represents the Laplace operator; G represents the transfer function, which is the standard representation of the relationship between input and output in a control system; the subscript c refers to the governor controller of the hydroelectric generator.

5. The real-time simulation method for hydropower units based on integrated image-model-object model as described in claim 1, characterized in that, The pre-set follow-up system model, turbine model, water diversion system model, generator model, and excitation system model constitute a hydropower-specific component model library. This model library contains more than 20 types of hydropower-specific components. Each component model integrates core control equations, parameter constraint ranges, and input / output interface specifications, and supports automatic equation coupling based on component connection relationships.

6. The real-time simulation method for hydropower units based on integrated image-model-object model as described in claim 5, characterized in that, The mathematical models for each component in the hydropower unit simulation model include: Speed ​​controller parameterization model: adopts a parallel PID controller, which has two modes: speed control and power control; Turbine model: A linearized model under small fluctuation conditions is established based on the transmission coefficients of flow rate and torque to guide vane opening, rotational speed and head. Water diversion system model: Provide a rigid model or an approximate elastic model; The servo system model consists of an electro-hydraulic converter, a main pressure regulating valve, and a main servo motor, and is described using a first-order inertial element. Generator model: A second-order model is adopted; Excitation system model: A simplified model of the first-order excitation system is adopted.

7. The real-time simulation method for hydropower units based on integrated image-model-object model as described in claim 6, characterized in that, The dynamic characteristics of a water turbine are approximated by its flow rate and torque characteristics in steady state, as shown in equation (1). The flow rate Q and torque M are closely related to the guide vane opening Y, the unit speed N, and the working head H, exhibiting a complex nonlinear functional relationship: (2)； Under small fluctuation operating conditions, by performing a Taylor expansion on the flow and torque characteristics of the turbine at a specific operating point and neglecting second-order and higher-order infinitesimals, the constant coefficient algebraic equation of the turbine at that operating point is obtained: (3)； , , , n and These are the relative deviations of flow rate, torque, guide vane opening, rotational speed, and head, respectively. , and These are the transfer coefficients of flow rate to guide vane opening, rotational speed, and head, respectively. , and These are the torque transmission coefficients to guide vane opening, rotational speed, and head, respectively.

8. The real-time simulation method for hydropower units based on integrated image-model-object model as described in claim 6, characterized in that, The water diversion system model provides a rigid model as shown in equation (4) and an approximately elastic model as shown in equation (5): (4)； （5）； in, T w Let be the inertial time constant of the water flow. T r Representing the mutual generation of water impact, s Represents the Laplace operator.

9. The real-time simulation method for hydropower units based on integrated image-model-object model according to claim 6, characterized in that, The servo system model mainly consists of an electro-hydraulic converter, a main pressure regulating valve, and a main servo motor. The electro-hydraulic converter amplifies the speed governor control signal and converts it into a mechanical signal. The entire process is described by the following transfer function: (6)； in T y1 This represents the reaction time constant of the electro-hydraulic converter; The main pressure regulating valve and main servo amplify the mechanical signal generated by the electro-hydraulic converter again, outputting the servo stroke to adjust the opening of the turbine guide vanes. Its mathematical expression is: (7)； in T y The reaction time constant of the main relay is represented by the following mathematical expression for the servo system: (8)。 10. The real-time simulation method for hydropower units based on integrated image-model-object model according to claim 6, characterized in that, The generator model uses a second-order model, as shown in equation (9). （9）； In the formula, The moment of inertia of the unit; The angular velocity of the unit, For the turbine torque, It is an electromagnetic torque. The damping coefficient is... δ This refers to the generator's power angle.

11. The real-time simulation method for hydropower units based on integrated image-model-object model as described in claim 6, characterized in that, The simplified model of the first-order excitation system is shown below: (10)； This is the magnetizing electromotive force. This is the reference voltage for the excitation system. This refers to the generator terminal voltage. and These are the equivalent excitation system gain and time constant, respectively.

12. The real-time simulation method for hydropower units based on integrated image-model-object model according to claim 1, characterized in that, Based on the mathematical model of each component of the hydropower unit, the fourth-order Runge-Kutta method is used for numerical solution, and the specific formula is as follows: Suppose the differential equations of the mathematical model based on each component of the hydroelectric generator are as follows: (11)； in, y For state variables, t For time; The iterative formula for the fourth-order Runge-Kutta method is: (12)； (13)； (14)； (15)； (16)； in, h For simulating step size, and The first n Step and the first n +1 step state value.

13. The real-time simulation method for hydropower units based on integrated image-model-object model according to claim 1, characterized in that, In step S5, the programming languages ​​supported by the target real-time simulation device include C / C++, Python, or Simulink S-Function. The code conversion process of the hydropower unit simulation model includes syntax adaptation, interface alignment, and performance optimization, and generates a conversion documentation.

14. The real-time simulation method for hydropower units based on integrated image-model-object model according to claim 1, characterized in that, It also includes S6 for internal logic and verification functions: during the simulation process, it verifies the integrity and correctness of the internal logic of the hydropower unit simulation model, and automatically compares and analyzes the simulation results to generate a verification report.

15. The real-time simulation method for hydropower units based on integrated image-model-object model according to claim 1, characterized in that, The method is applicable throughout the entire lifecycle of hydropower units and supports applications in design verification, operation optimization, and fault diagnosis scenarios.

16. A real-time simulation system for hydropower units based on integrated image-model-object model, characterized in that, The real-time simulation method for hydropower units based on the integration of graphics, model, and physical objects, as described in any one of claims 1-15, comprises: The graphic intelligent recognition layer is used to perform intelligent drawing parsing and topology reconstruction to generate a parametric model of the speed controller. The model dynamic generation layer is used to call the preset follow-up system model, turbine model, water diversion system model, generator model and excitation system model, and combine them with the governor parameterized model to dynamically assemble a hydro-generator simulation model. The equipment adaptive layer is used to perform discretization, parameter configuration, code conversion, and hardware interface linkage on the simulation model of the hydropower unit. The graphic intelligent recognition layer, the model dynamic generation layer, and the device adaptive layer together constitute an integrated graphic-model-object architecture.

17. A computer device, characterized in that, It includes one or more processors, each processor storing one or more executable programs, which, when executed by the one or more processors, are used to implement the real-time simulation method for hydropower units based on the integration of graphics, model, and physical objects according to any one of claims 1-15.

18. A storage medium, characterized in that, It stores one or more executable programs, which, when executed, are used to implement the real-time simulation method for hydropower units based on the integration of graphics, models, and objects as described in any one of claims 1-15.

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