Hydropower station simulation system modular configuration method, system, equipment and medium based on five-dimensional model dynamic configuration

Abstract

The invention discloses a hydropower station simulation system modular configuration method, system, equipment and medium based on five-dimensional model dynamic configuration, and belongs to the technical field of hydropower station simulation. The method comprises the following steps: constructing a five-dimensional model component knowledge base, and dividing a hydropower station three-dimensional entity model into five-dimensional standardized components; the components are bound to the three-dimensional entity model of the hydropower station, a data flow topological relation graph between the components is generated, and a component relation dependency matrix is generated; based on the behavior tree logic arrangement system, disassembling the hydropower station operation rule into configurable nodes; parameter consistency verification between dimensions is triggered, and an optimization algorithm is called to dynamically correct component model parameters; and calculating a reusability index based on the component use condition, and adjusting a component recommendation strategy according to the reusability index. Through the five-dimensional model independent development framework, decoupling development of a multidisciplinary model is realized, the coupling defect of a traditional single-dimensional model is eliminated, the interoperability of a power system is improved, and the reuse rate of a standardized component is improved.

Description

Technical Field

[0001] This invention relates to the field of hydropower station simulation technology, specifically to a modular configuration method, system, equipment, and medium for a hydropower station simulation system based on dynamic configuration of a five-dimensional model. Background Technology

[0002] Traditional methods for building 3D models focus only on the device's geometry and basic attributes, lacking a systematic integration of control logic, physical characteristics, real-time data, and environmental factors. This results in excessive coupling between multidisciplinary models and low component reuse rates.

[0003] Traditional methods suffer from limited model dimensions, failing to accurately simulate the coupling effects of hydraulic, mechanical, and electrical systems, resulting in generally high error rates in fault reproduction. Configuration requires specialized programming to implement control logic, necessitating engineers to manually write simulation scripts, leading to lengthy configuration cycles for new power plant scenarios. Model validation coverage is also insufficient; traditional validation primarily focuses on two-dimensional structural-behavioral verification (such as geometric assembly checks and control logic sequence testing), neglecting dimensions like physical characteristics, real-time data flow, and environmental coupling. Testing resources are mostly used for routine operational state verification, with low coverage of extreme conditions (such as load shedding and emergency shutdowns).

[0004] The lack of standardized interface for existing system components makes it difficult to reuse models across projects and increases the cost of repetitive development of simulation systems. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is: how to solve the problem that traditional methods of 3D model construction only focus on the geometric structure and basic attributes of the equipment, lacking systematic integration of control logic, physical characteristics, real-time data and environmental factors, resulting in excessive coupling of multi-disciplinary models and low component reuse rate.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model, comprising: constructing a five-dimensional model component knowledge base, dividing the three-dimensional entity model of the hydropower station into standardized components of five dimensions; binding the components to the three-dimensional entity model of the hydropower station through a graphical interactive method, generating a data flow topology graph between components, and synchronously generating a component relationship dependency matrix; decomposing the hydropower station operation rules into configurable nodes based on a behavior tree logic orchestration system; injecting real-time sensor data into the simulation platform to drive model operation, triggering parameter consistency verification between dimensions, and calling optimization algorithms to dynamically correct component model parameters; calculating reusability indicators based on component usage, and adjusting component recommendation strategies according to the reusability indicators.

[0008] As a preferred embodiment of the modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model as described in this invention, the step of dividing the three-dimensional entity model of the hydropower station into standardized components of five dimensions includes: setting classification criteria for the five-dimensional model components; and constructing a component relationship graph in a five-dimensional model component knowledge base to support graph database queries and path reasoning.

[0009] As a preferred embodiment of the modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model as described in this invention, the step of binding components to a three-dimensional solid model of the hydropower station through graphical interaction and generating a data flow topology diagram between components, and synchronously generating a component relationship dependency matrix, includes: constructing a three-dimensional solid model of the hydropower station; distinguishing components of different dimensions through color coding; triggering collision detection and issuing warnings for components with spatial location conflicts; and generating component association configuration files.

[0010] As a preferred embodiment of the modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model as described in this invention, the step of binding components to the three-dimensional solid model of the hydropower station through graphical interaction includes: constructing a three-dimensional solid model of the hydropower station based on BIM and laser point cloud scanning; and marking GIS coordinates and equipment IDs in the three-dimensional solid model of the hydropower station.

[0011] As a preferred embodiment of the modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration as described in this invention, the hydropower station operation rules are decomposed into configurable nodes. This includes, through a behavior tree logic orchestration system, abstracting the hydropower station control logic into three types of nodes in an editor: control nodes, execution nodes, and monitoring nodes. The control nodes include sequential execution, parallel execution, and conditional judgment types; the conditional judgment nodes set a grid frequency deviation threshold as a trigger condition. The execution nodes include equipment start / stop, valve adjustment, and power setting types; the guide vane opening adjustment command embeds slope parameters and rate limiting rules. The monitoring nodes include voltage, current, vibration, temperature, and flow types; real-time capture of sensor data streams is compared with preset alarm thresholds. By dragging and dropping various types of nodes onto a logic canvas to build a logic framework, the system automatically generates the topological connection relationships between nodes, forming an uncompiled behavior tree structure. The nodes are configured in the logic canvas. The execution order and parallel relationships are defined in the load shedding logic chain. A frequency monitoring node is set up to detect the grid status. When the frequency exceeds the limit for a certain period of time, the parallel execution node is triggered to control the guide vanes to adjust their opening according to a piecewise function and start the emergency shutdown protection program. A rate limiter node is embedded in the logic chain to constrain the instantaneous change in mechanical action. The logic flow path is rendered in real time, and the normal execution path and fault handling branch are distinguished by color coding. The logic closure is automatically verified to ensure that all branches have a termination node. After the logic orchestration is completed, the behavior tree nodes are converted into blueprint scripts in the editor, the control nodes are converted into conditional statements and loop structures, the execution nodes are compiled into device driver interface functions, and the monitoring nodes generate data listening callback functions. The behavior tree logic containing multiple nodes is converted into a blueprint resource package. During the compilation process, an exception handling module and a thread lock mechanism are inserted. The script is injected into the running simulation kernel through hot loading technology, and it takes effect without restarting the system.

[0012] This preferred solution can graphically decompose the hydropower station operation rules into a clearly structured behavior tree logic, avoiding structural chaos and logical omissions in the hard-coded configuration process, and improving the visualization and expression capabilities of the operation logic; through color coding and automatic closed-loop verification, it improves the efficiency of judging the integrity of the logical structure and reduces the risk of node configuration errors.

[0013] As a preferred embodiment of the modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model as described in this invention, the step of injecting real-time sensor data into the simulation platform to drive model operation includes: inputting real-time sensor data into the node model in the simulation platform; driving the operation of each component node through the injected data to generate a simulation response; extracting structural, behavioral, and physical parameters from various dimensions during the response process and performing explicit comparison; judging whether the model response meets the set threshold based on the comparison.

[0014] This preferred solution can effectively identify the difference between the component response and the actual observation, providing a data support basis for subsequent parameter optimization and model correction.

[0015] As a preferred embodiment of the modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model as described in this invention, the following steps are included: triggering inter-dimensional parameter consistency verification and calling optimization algorithms to dynamically correct component model parameters, including: establishing a spatiotemporal reference, ensuring strict consistency of all models in time, space, coordinate system, and physical units; single-model independent verification, ensuring that the internal logic and data of each model conform to design specifications and physical laws; cross-model interface verification, ensuring that the data interaction format, protocol, and real-time performance between models meet the requirements; dynamic coupling verification, verifying the consistency of dynamic behavior during multi-model joint simulation; and closed-loop calibration. Accuracy and iterative optimization: dynamically correct model parameters based on measured data to improve simulation prediction accuracy; comprehensive scenario verification: verify the collaborative performance of the five-dimensional model in typical scenarios throughout the entire lifecycle, generate verification reports and feed them back to the five-dimensional model component knowledge base; the calculation of reusability index based on component usage includes: calculating the component reuse index, performing Min-Max standardization on the component reuse index result, mapping it to the [0, 1] interval, if it is greater than the reuse index threshold, it is marked as a recommended component and added to the recommendation pool, if it is less than or equal to the reuse index threshold, it triggers an optimization reminder and pushes it to the development end for interface compatibility upgrade or logic rule reconstruction.

[0016] This preferred solution uses a six-stage consistency verification mechanism to ensure the structural closure and logical consistency of the five-dimensional model's collaborative operation. After standardization and normalization, the Reusability Index (CRI) can quantify the adaptability of components in different projects and scenarios, facilitating the system's automatic identification of high-value components and low-adaptability modules, and enhancing the targetedness and sustainability of knowledge base updates.

[0017] This invention provides a modular configuration system for a hydropower station simulation system based on dynamic configuration of a five-dimensional model.

[0018] To address the aforementioned technical problems, this invention provides the following technical solution: a modular configuration system for a hydropower station simulation system based on dynamic configuration of a five-dimensional model, comprising: a five-dimensional model component knowledge base construction module, a component binding module, a logic orchestration module, a verification module, and a reusability evaluation module; the five-dimensional model component knowledge base construction module is used to construct a five-dimensional model component knowledge base, dividing the three-dimensional entity model of the hydropower station into standardized components of five dimensions; the component binding module is used to bind components to the three-dimensional entity model of the hydropower station through a graphical interactive method, and generate a data flow topology diagram between components, and synchronously generate a component relationship dependency matrix; the logic orchestration module is used to decompose the hydropower station operation rules into configurable nodes based on a behavior tree logic orchestration system; the verification module is used to inject real-time sensor data into the simulation platform to drive model operation, trigger inter-dimensional parameter consistency verification, and call optimization algorithms to dynamically correct component model parameters; the reusability evaluation module is used to calculate reusability indicators based on component usage, and adjust component recommendation strategies according to the reusability indicators.

[0019] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration.

[0020] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration.

[0021] The beneficial effects of this invention are as follows: This invention achieves decoupled development of multidisciplinary models through a five-dimensional model development framework encompassing structure, behavior, physics, data, and environment, eliminating the coupling defects of traditional single-dimensional models. Each dimension of the model adopts the IEC61850 protocol standard interface, reducing cross-component communication latency to 50ms and improving power system interoperability. The CRI index-driven component recommendation mechanism, combined with graph database semantic retrieval, enhances the reuse rate of standardized components.

[0022] Based on the Unreal Engine platform, the visual drag-and-drop and behavior tree node compilation technology enables zero-code configuration of simulation logic, shortening the model building cycle and improving configuration efficiency. Hot-loading technology ensures that logic adjustments take effect immediately, and node logic can be automatically converted into C++ code blueprint scripts, reducing configuration time under extreme conditions and improving the efficiency of control strategy porting.

[0023] By using real-time data-driven closed-loop optimization and five-dimensional consistency collaborative verification, the model calibration accuracy and extreme condition coverage are improved. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 The present invention provides an overall flowchart of a modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model, which is an embodiment of the present invention.

[0026] Figure 2 The flowchart illustrates a modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model, as provided in one embodiment of the present invention.

[0027] Figure 3 This invention provides a flowchart of behavior tree logic orchestration and zero-code compilation for a modular configuration method of a hydropower station simulation system based on dynamic configuration of a five-dimensional model, as an embodiment of the present invention.

[0028] Figure 4 The main flowchart of the five-dimensional consistency collaborative verification of a modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model is provided in one embodiment of the present invention.

[0029] Figure 5 The flowchart below shows the sub-steps of a modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration, provided as an embodiment of the present invention, for five-dimensional consistency collaborative verification.

[0030] Figure 6 This is a schematic diagram of a modular configuration system for a hydropower station simulation system based on a five-dimensional model dynamic configuration, provided as an embodiment of the present invention. Detailed Implementation

[0031] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0032] Example 1, referring to Figure 1 and Figure 2 This is one embodiment of the present invention, which provides a modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration, including:

[0033] S1. Construct a five-dimensional model component knowledge base, dividing the three-dimensional solid model of the hydropower station into standardized components in five dimensions.

[0034] S2. Bind the components to the 3D solid model of the hydropower station through a graphical interactive method, generate a data flow topology diagram between the components, and simultaneously generate a component relationship dependency matrix.

[0035] S3. The behavior tree-based logic orchestration system decomposes the hydropower station operation rules into configurable nodes.

[0036] S4. Inject real-time sensor data into the simulation platform to drive the model operation, trigger cross-dimensional parameter consistency verification, and call the optimization algorithm to dynamically correct the component model parameters.

[0037] S5. Calculate the reusability index based on component usage, and adjust the component recommendation strategy according to the reusability index.

[0038] It should be noted that the operation of hydropower stations involves a large number of interactions between model-driven processes, sensor responses, and logic configurations. Traditional simulation methods often suffer from problems such as difficulty in reusing model components, lag in logic updates, and insufficient data consistency verification.

[0039] Therefore, this invention establishes a closed-loop mechanism from modeling to deployment, and from simulation to feedback, by constructing a five-dimensional component knowledge base, configuring a logic structure based on behavior trees, implementing a real-time driving and consistency comparison mechanism, and establishing a component reuse index evaluation process. Through these steps, standardized management of multi-dimensional components in hydropower stations, visualized configuration of operational logic, real-time feedback verification of response behavior, and continuous accumulation and optimization of component knowledge can be achieved.

[0040] Example 2, refer to Figures 3-5 As an embodiment of the present invention, based on the previous embodiment, a modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration is provided, including:

[0041] Step S1 involves constructing a five-dimensional model component knowledge base, dividing the three-dimensional solid model of the hydropower station into standardized components in five dimensions, including the following steps A1-A2:

[0042] A1. Define the classification criteria for five-dimensional model components:

[0043] A five-dimensional model component knowledge base is constructed, which decomposes the hydropower station entity model into standardized components in five dimensions: structural model, behavioral model, physical model, data model, and environmental model. Each component adopts an independent development framework and encapsulates an interface that conforms to the IEC 61850 protocol to achieve cross-component communication and power system interoperability.

[0044] A2. Construct a component relationship graph in the five-dimensional model component knowledge base to support graph database query and path reasoning.

[0045] The three-dimensional solid model of the hydropower station is divided into five standardized components, and a component relationship graph is constructed in the five-dimensional model component knowledge base to support graph database query and path reasoning.

[0046] Construct a component relationship graph (node ​​= component, edge = call relationship) in the five-dimensional model component knowledge base, supporting graph database (Neo4j) query and path reasoning.

[0047] Specifically, the classification criteria for the five-dimensional model components in step A1 include the following steps B1-B5:

[0048] B1. Structural Model: Describes the geometric shape and assembly relationship of the physical components of the hydropower station; it is the static skeleton of the digital twin.

[0049] B2. Behavioral Model: Describes the dynamic operating logic and control strategy of the equipment, reflecting the response of the hydropower unit system to external inputs, such as the unit start-up and shutdown control logic, gate opening adjustment process, fault shutdown protection mechanism, etc.

[0050] B3. Physical Model: Quantify the physical laws of energy conversion, material deformation, fluid motion, etc., to support multi-disciplinary coupled simulation, such as the stress field of water turbine impeller and the fluid dynamics of pressure pipeline;

[0051] B4. Data Model: Connects to SCADA system and IoT sensor data streams, stores real-time monitoring data and historical records in a structured manner, and drives model calibration and predictive analysis;

[0052] B5. Environmental Model: Simulates the external natural conditions and ecological impacts of the hydropower station, and assesses the interaction between the hydropower unit system and the environment, such as the temperature and humidity field of the powerhouse and the water level fluctuation field of the reservoir.

[0053] In this application embodiment, the five-dimensional model component knowledge base decomposes the hydropower station entity model into standardized components in five dimensions: structural model, behavioral model, physical model, data model, and environmental model. Each dimension component adopts an independent development framework and encapsulates an interface that conforms to the IEC 61850 protocol to achieve cross-component communication and power system interoperability.

[0054] In one alternative implementation, the five-dimensional model component knowledge base can also support a multi-version management mechanism for components. Different versions of components can be stored in parallel in the knowledge base and quickly retrieved and switched through version tags to meet the iterative needs of simulation models.

[0055] In another alternative implementation, the five-dimensional model component knowledge base can also record the number of times each component is called and the applicable project tags, build a reusability evaluation system, and recommend components or trigger optimization and update operations accordingly.

[0056] This invention achieves dimensional organization and standardized encapsulation of modeling resources for complex hydropower station systems by constructing a five-dimensional model component knowledge base.

[0057] In this embodiment, the component relationship graph is constructed in the five-dimensional model component knowledge base (node ​​= component, edge = call relationship), which supports graph database (Neo4j) query and path reasoning.

[0058] In one alternative implementation, constructing a component relationship graph in the five-dimensional model component knowledge base can also support a multi-version component management mechanism, mapping different versions of components to independent nodes in the graph, distinguishing their source, update records and replacement relationships through version tags, and supporting version selection and switching during graph query.

[0059] In another alternative implementation, the component relationship graph constructed in the five-dimensional model component knowledge base can also be combined with the component call count and applicable project tags. The call frequency and project tags are recorded in the graph structure, and the reusability level of the components is evaluated in real time during the graph traversal process, which serves as the basis for subsequent recommendation mechanisms or optimization tags.

[0060] In this application embodiment, step S2 can be performed using a graphical interaction method, namely visual drag and drop. Through the plug-in component manager of the UE engine, the selected component is bound to the three-dimensional entity of the hydropower station in a visual drag and drop manner, and the data flow topology relationship diagram between components is automatically generated, and the component relationship dependency matrix is ​​generated synchronously.

[0061] In one alternative implementation, the graphical interaction method may also include triggering a spatial collision detection mechanism to detect spatial overlap and component layout conflicts that occur during dragging, and output a warning prompt when a conflict is detected to ensure that the physical structure remains reasonable during component binding.

[0062] In another alternative implementation, the graphical interaction method may also include automatically generating an XML-formatted component configuration file containing component identifiers, spatial location parameters, and component relationship mappings after component binding is completed. This configuration file can serve as the input structure for the behavior tree logic orchestration system, and can be used for subsequent simulation logic node mapping and data integration.

[0063] This invention enhances the intuitiveness, accuracy, and information structure integrity of the component configuration process, supporting efficient modeling and consistent integration of the simulation system.

[0064] Furthermore, in step S2, components are bound to the 3D solid model of the hydropower station through a graphical interactive method, and a data flow topology diagram between components is generated, and a component relationship dependency matrix is ​​generated simultaneously, including the following steps C1-C4:

[0065] C1. Construct a three-dimensional solid model of the hydropower station based on BIM and laser point cloud scanning, and bind the components to the three-dimensional solid model of the hydropower station.

[0066] C2. Differentiate components of different dimensions by using color coding.

[0067] Components are bound to the 3D physical model of the hydropower station through a graphical interactive method, including differentiating components by color coding.

[0068] Different dimensional components are distinguished by color coding (e.g., structure-blue / behavior-green / physical-red).

[0069] C3. Trigger collision detection and issue a warning for components that are in spatial conflict.

[0070] The collision detection algorithm is automatically triggered to issue warnings for components that are in spatial conflict.

[0071] C4. Generate component association configuration files.

[0072] Generate an XML-formatted component association configuration file based on the drag-and-drop path.

[0073] In this embodiment, step C1, which involves constructing a 3D solid model of the hydropower station based on BIM and laser point cloud scanning, can be achieved by annotating the 3D solid model with GIS coordinates and equipment IDs. This annotation includes the GIS coordinates (WGS-84 coordinate system) and equipment IDs (e.g., Turbine_01@(x=102.3, y=34.5, z=1200)), and spatial positioning and attribute identification of key equipment in the model. The spatial positioning information uses the WGS-84 geographic coordinate system (World Geodetic System 1984), a globally unified geodetic reference frame widely used in GNSS positioning and geographic information systems. This system features a clearly defined ellipsoidal parameter with the Earth's center of mass as the origin, ensuring consistent positioning and map projection transformation of equipment coordinates globally. Based on this, each piece of equipment is assigned a unique equipment identifier and 3D spatial location information. For example, a hydro-generator unit can be represented as: Turbine_01@(x=102.3, y=34.5, z=1200). This annotation method can be used to achieve accurate mapping of equipment information between BIM models and GIS platforms, providing data foundation support for subsequent simulation analysis, operation and maintenance management, and risk assessment.

[0074] In one alternative implementation, the construction of a three-dimensional solid model of a hydropower station based on BIM and laser point cloud scanning can also be combined with a graphical interactive method to trigger a spatial collision detection mechanism. This mechanism can judge the spatial position overlap or layout conflict that exists during the component drag-and-bind process, and output prompt information or perform automatic position correction when a conflict occurs, so as to ensure the accuracy of component spatial configuration.

[0075] In another alternative implementation, the three-dimensional solid model of the hydropower station built based on BIM and laser point cloud scanning can also be connected with the operation and maintenance system at the field level. The equipment ID in the model can be associated with the equipment number, operating status and maintenance record fields in the operation and maintenance system, which can be used to drive the component behavior logic configuration and historical status playback.

[0076] Furthermore, in step S3, the hydropower station operation rules are decomposed into configurable nodes based on the behavior tree logic orchestration system, including the following steps D1-D2:

[0077] D1. Using the behavior tree logic orchestration system, the hydropower station control logic is abstracted into three types of nodes in the editor: control nodes, execution nodes, and monitoring nodes. Control nodes include sequential execution, parallel execution, and condition judgment types. Condition judgment nodes set the grid frequency deviation threshold as the trigger condition. Execution nodes include equipment start / stop, valve adjustment, and power setting types. The guide vane opening adjustment command embeds slope parameters and rate limit rules. Monitoring nodes include voltage, current, vibration, temperature, and flow types. Sensor data streams are captured in real time and compared with preset alarm thresholds.

[0078] By introducing a behavior tree logic orchestration system, various operating rules of a hydropower station can be broken down into configurable nodes, and configurable nodes can be connected to generate UE blueprint scripts, realizing zero-code compilation and hot-loading deployment of simulation logic.

[0079] The configurable nodes of the behavior tree logic orchestration system include control nodes, execution nodes, and monitoring nodes.

[0080] Among them, sequential execution, parallel execution, and conditional judgment can be used as control nodes to organize the logical structure; operation behaviors such as equipment start-up and shutdown, valve adjustment, and power setting can be set as execution nodes to drive simulation behavior; real-time physical quantity monitoring such as voltage, current, vibration, temperature, and flow rate are classified as monitoring nodes for decision logic to call.

[0081] D2. Visualize the logic orchestration by dragging and dropping various nodes onto the logic canvas to build a logic framework. The behavior tree logic orchestration system automatically generates the topological connections between nodes, forming an uncompiled behavior tree structure. Configure the execution order and parallel relationships of nodes in the logic canvas. In the load shedding logic chain, a frequency monitoring node is set to detect the grid status. When the frequency exceeds the limit for a certain period of time, the parallel execution node is triggered to control the guide vanes to adjust their opening according to a piecewise function and initiate the emergency shutdown protection program. A rate limiter node is embedded in the logic chain to constrain the instantaneous changes in mechanical actions. The logic flow path is rendered in real time. The system uses color coding to distinguish normal execution paths from fault handling branches and automatically verifies the logical closure to ensure that all branches have a termination node. After the logic orchestration is completed, behavior tree nodes are converted into blueprint scripts in the editor, control nodes are converted into conditional statements and loop structures, execution nodes are compiled into device driver interface functions, and monitoring nodes generate data listening callback functions. Behavior tree logic containing multiple nodes is converted into blueprint resource packages. During the compilation process, exception handling modules and thread lock mechanisms are inserted, and the scripts are injected into the running simulation kernel through hot loading technology, so that they take effect without restarting the behavior tree logic orchestration system.

[0082] Furthermore, the core process of visual logic orchestration in step D2 includes the following steps E1-E3:

[0083] E1. Define node types and construct the logical framework:

[0084] By using the behavior tree logic orchestration system built into the Unreal Engine editor, the control logic of the hydropower station can be abstracted into configurable node types.

[0085] Control nodes are responsible for managing logic branches, such as setting a grid frequency deviation threshold (±0.2Hz) as a trigger condition based on diamond-shaped conditions; executing nodes map specific equipment operation commands, such as embedding slope parameters (-7% / second) and rate limit rules (maximum ±3% / second) in guide vane opening adjustment commands; monitoring nodes are used to capture sensor data streams (vibration, temperature, flow) in real time and dynamically compare them with preset alarm thresholds. Engineers initially construct the logic framework by dragging and dropping nodes onto the canvas. The behavior tree logic orchestration system automatically generates the topological connection relationships between nodes, forming an uncompiled raw behavior tree structure.

[0086] E2, Visualized dynamic orchestration of logical chains:

[0087] The logic canvas reveals the execution order and parallel relationships between nodes configuring the hydropower station's operation rules. For example, in the load shedding logic chain, a frequency monitoring node continuously monitors the grid status. When the frequency exceeds the limit for 5 seconds, it triggers the closure of the synchronous control guide vanes (adjusting the opening according to a piecewise function) and initiates the emergency shutdown protection program. A rate limiter node is embedded in the logic chain to constrain the instantaneous changes in mechanical actions and prevent hydraulic shocks. The visual path of the logic flow is rendered in real time, using color coding to distinguish between normal execution paths (green) and fault handling branches (red), and automatically verifying the closed-loop nature of the logic (e.g., ensuring that all branches have a termination node).

[0088] E3, zero-code compilation and hot-reload deployment:

[0089] After completing the logic orchestration, the behavior tree logic orchestration system maps behavior tree nodes to Unreal Engine Blueprint scripts. Control nodes are transformed into conditional statements (if-else) and loop structures (for / while), execution nodes are compiled into device driver interface functions (such as ServoMotor.SetPosition()), and monitoring nodes generate data listening callback functions. For example, a speed adjustment logic tree containing 89 nodes is transformed into a UE Blueprint resource package with 287 lines of C++ code. During code generation, exception handling modules (try-catch) and thread locking mechanisms are automatically inserted. Through hot-loading technology, newly compiled scripts are directly injected into the running simulation kernel, taking effect without a restart, allowing engineers to observe the simulation effects after logic adjustments in real time. The flowchart of behavior tree logic orchestration and zero-code compilation is as follows: Figure 3 As shown.

[0090] Furthermore, in step S4, real-time sensor data is injected into the simulation platform to drive the model operation, triggering cross-dimensional parameter consistency verification, and calling optimization algorithms to dynamically correct the component model parameters, including the following steps F1-F2:

[0091] F1. Input real-time sensor data into the node model in the simulation platform; drive the operation of each component node through the injected data to generate a simulation response; extract structural, behavioral, and physical parameters from various dimensions during the response process and perform explicit comparison; make a comparison judgment based on the set threshold to determine whether the model response meets the set threshold.

[0092] F2. Perform five-dimensional consistency and collaborative verification.

[0093] Specifically, five-dimensional consistency collaborative verification is performed. In the simulation platform, real-time sensor data (such as pressure and vibration frequency) is injected to drive the node model, triggering explicit comparison of five-dimensional parameters (preset thresholds: structural displacement deviation ≤ 0.1mm, behavioral command delay ≤ 10ms, physical stress error ≤ 5%). On-site experts, based on the physical equipment status, jointly verify extreme operating conditions (such as high head impact and unit load shedding) and fault models (such as bearing wear and communication interruption), dynamically correcting model errors through a parameter optimization engine driven by a genetic algorithm. Figure 4 The diagram shown is the main flowchart for five-dimensional consistency and collaborative verification. Figure 5 The diagram shows the sub-steps of the five-dimensional consistency collaborative verification.

[0094] Furthermore, the five-dimensional consistency co-verification in step F2 includes the following steps G1-G6:

[0095] G1. Establish a spatiotemporal benchmark to ensure that all models are strictly consistent in time, space, coordinate system, and physical units.

[0096] G2. Perform independent verification of each model to ensure that the internal logic and data of each model conform to design specifications and physical laws.

[0097] G3. Perform cross-model interface verification to ensure that the data interaction format, protocol and real-time performance between models meet the requirements.

[0098] G4. Perform dynamic coupling verification to verify the consistency of dynamic behavior during multi-model co-simulation.

[0099] G5. Perform closed-loop calibration and iterative optimization, dynamically correct model parameters based on measured data, and improve simulation prediction accuracy.

[0100] G6. Conduct comprehensive scenario verification, verify the collaborative effectiveness of the five-dimensional model in typical scenarios throughout the entire lifecycle, generate a verification report and feed it back to the five-dimensional model component knowledge base.

[0101] Furthermore, in step S5, a reusability index is calculated based on component usage, and the component recommendation strategy is adjusted according to the reusability index, including the following steps H1-H2:

[0102] H1. Calculate the component reuse index, and perform Min-Max standardization on the component reuse index result to map it to the [0, 1] interval.

[0103] The system calculates the Component Reusability Index (CRI) and makes automatic recommendations based on the results. Components with high CRI are prioritized for engineers, while components with low CRI will trigger optimization prompts.

[0104] The formula for calculating the component reuse index (CRI) is as follows:

[0105] CRI = α·Number of calls + β·Number of cross-projects

[0106] The CRI (Call Count) refers to the total number of times a component is called in different scenarios or projects, reflecting the horizontal reuse frequency of the component. The cross-project number refers to the total number of times a component is applied to different engineering projects, such as pumped storage power stations, dam-type hydropower stations, and diversion-type hydropower stations, reflecting the vertical versatility of the component. α and β are weighting coefficients (α+β=1), used to dynamically adjust the priority of the call count and cross-project number. For example, if a certain type of component needs to emphasize high-frequency reuse (such as a general valve model), α=0.7 and β=0.3 can be set; if cross-domain reuse needs to be emphasized (such as migrating a governor model to a pumped storage power station), α=0.4 and β=0.6 can be set. After the CRI calculation is completed, the calculation results are Min-Max standardized, mapped to the [0,1] interval, and normalized.

[0107] H2. If the reuse index is greater than the reuse index threshold, it is marked as a recommended component and added to the recommendation pool. If the reuse index is less than or equal to the reuse index threshold, an optimization reminder is triggered and pushed to the development side for interface compatibility upgrades or logic rule refactoring.

[0108] Components with a CRI > 0.8 are marked as recommended components and added to the knowledge base recommendation pool. Components with a CRI ≤ 0.6 trigger optimization reminders and are pushed to the development end for interface compatibility upgrades or logic rule refactoring, forming a closed-loop optimization mechanism of "use-evaluation-iteration".

[0109] Example 3 is an embodiment of the present invention, which provides a modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0110] The scenario is set as follows: a pumped storage power station needs to build a full-condition simulation system for the turbine on the UE platform to verify the pressure pulsation characteristics of the unit under load shedding conditions.

[0111] Step 1: Call up the five-dimensional model.

[0112] Standardized components related to the mixed-flow turbine are called from the five-dimensional model component knowledge base. The structural model uses three-dimensional assemblies such as the runner and main shaft based on BIM modeling, with assembly tolerances controlled within ±0.05 mm; the behavioral model loads a dedicated control logic package for load shedding conditions, with a built-in piecewise function for the guide vane closing rate, closing linearly to 30% opening for the first 10 seconds, and then gradually closing using an exponential curve; the physical model calls a pre-calculated transient flow field database with a time step accurate to 0.01 seconds and a pressure pulsation data sampling frequency as high as 10 kHz; the data model interfaces with the SCADA system via the Modbus TCP protocol, synchronizing real unit vibration data every 500 milliseconds; the environmental model integrates a reservoir water level dynamic change module, simulating water level fluctuations at a rate of 0.5 m / s, and incorporates the calculation of the impact of water temperature stratification on density.

[0113] Step 2: 3D scene construction.

[0114] Based on laser point cloud scanning data, a high-precision 3D scene was constructed using the Nanite virtual geometry system within the Unreal Engine platform, achieving a model accuracy level of LOD3. The runner component (ID: Runner_PSH-2024) was positioned to 3D coordinates (X = 102.34°, Y = 34.56°, Z = 1200 meters) via drag-and-drop. The system automatically detected an assembly gap of 0.08 mm between the spindle and the runner, triggering a yellow warning indicating that the assembly error was approaching the threshold. During scene construction, component color coding rules automatically took effect: the structural model was displayed with a blue outline, the behavioral model had physical equipment covered with a semi-transparent green layer, and the physical model visualized the flow field pressure gradient using red particle effects.

[0115] Step 3: Behavioral logic arrangement.

[0116] The load shedding control strategy is configured visually in the behavior tree editor. First, a monitoring node is set up to continuously detect whether the grid frequency exceeds 52.5 Hz for 5 seconds. Then, a parallel node is configured to synchronously execute the logic of linearly reducing the guide vane opening from 100% to 30% (slope -7% / second) and the emergency shutdown logic for excessive top cover pressure. Finally, a data recording node is connected to save the peak-to-peak pressure pulsation and spectral characteristic data in real time. The system automatically converts the logic nodes into a UE blueprint script containing 287 nodes, which is directly deployed to the simulation kernel using hot-loading technology, achieving zero-code compilation and immediate effect.

[0117] Step 4: Multi-dimensional coupling verification.

[0118] The simulation system was injected with real-time SCADA data streams to drive model operation, triggering a five-dimensional consistency and collaborative verification process. In the structural dimension verification, the simulated axial displacement of the runner was 3.2 mm, with an error rate of 8.6% compared to the measured value of 3.5 mm by the laser vibrometer. The physical dimension comparison showed that the simulated peak-to-peak pressure pulsation was 0.78 MPa, with an error controlled within 4.9% compared to the measured value of 0.82 MPa by the pressure sensor. In the environmental coupling verification stage, the system dynamically corrected the water density parameters based on the measured water temperature of 18 degrees Celsius and recalculated the thrust bearing load to 1123 kN. During the verification process, the extreme condition simulation module simultaneously loaded a high-head impact model to simulate the force distribution on the runner blades under an 80-meter head, and cross-verified the results with the finite element analysis.

[0119] Step 5: Parameter optimization and iteration.

[0120] A genetic algorithm was used to optimize the guide vane closure strategy across multiple objectives, with a population size of 200 and 50 iterations. The optimization variables focused on the inflection point position and rate adjustment of the closure curve, with constraints requiring the pressure pulsation peak to not exceed 0.8 MPa and the rotational speed increase rate to be less than 130%. After 30 iterations, the algorithm output the Pareto front optimal solution set, recommending a three-stage closure strategy: rapid closure at a slope of -9% / second for the first 5 seconds, a smooth transition at -5% / second for the middle 8 seconds, and a slow closure at -2% / second for the last 7 seconds. The optimized model was verified through field testing, reducing the simulation error rate under load shedding conditions from 15.7% to 5.3% using traditional methods, improving the vibration prediction accuracy of the pressure steel pipe to 93.4%, and significantly reducing the configuration cycle from 42 man-days to 6 man-days.

[0121] Example 4 is an embodiment of the present invention, which provides a modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0122] The scenario is set as follows: a unified arc gate control model is needed for a group of hydropower stations across river basins to enable the reuse of components across projects.

[0123] Step 1: Component retrieval and matching.

[0124] Engineers input the characteristic parameters of the arc gate into the five-dimensional model component knowledge base. These parameters include key indicators such as orifice size of 10m × 8m, opening and closing force ≥ 800 kN, and applicable head of 50-100m. Based on the semantic search function of the graph database, three candidate components with high reuse indices (CRI = 0.87-0.92) were quickly matched, with the Gate_HYQ-2023 component meeting all parameter requirements. The Gate_HYQ-2023 component includes a NURBS surface-modeled structural model (chord height error < 0.1 mm), a PID control algorithm behavior model conforming to IEC61131-3 standard, and a pre-calculated fluid-structure interaction physical database (covering hydraulic moment matrices under 12 opening degrees). An automatic component specification comparison report is generated, the optimal component is recommended, and loaded into the current project environment.

[0125] Step 2: Component adaptive adjustment.

[0126] After loading the selected components, the environmental parameters are automatically adjusted based on the target power station's GIS geographic coordinates (X = 104.12°, Y = 30.67°), and the calculation model for the influence of Coriolis force on the gate opening and closing torque is corrected. Simultaneously, combined with local measured water sediment concentration data of 1.8 kg / m³, the cavitation number prediction algorithm for the gate bottom edge is optimized, embedding sediment abrasion factors into the fluid dynamics equations. The component interface adapter automatically converts the data format, uniformly converting the millimeter units from the original project to the SI system of the current project, and calibrates the pressure sensor's range (0-10 MPa) to ensure cross-project data compatibility.

[0127] Step 3: Logical rule transfer and conversion.

[0128] Engineers reused the flood control scheduling strategy from the original project, transforming the logical condition "upstream water level > 1820 meters warning level and 24-hour rainfall forecast > 50 mm" into behavior tree nodes. They automatically parsed the tiered activation strategy in the original code (25% activation in the first hour, 50% in the second hour, increasing progressively) and converted it into a UE blueprint script containing conditional judgments, parallel execution, and data feedback. Through protocol conversion middleware, the original C++-based control logic was seamlessly migrated to the IEC61850 communication framework, generating cross-platform executable code and injecting a real-time water level monitoring data interface (sampling period of 1 second).

[0129] Step 4: Full-scenario verification and effect evaluation.

[0130] Twenty-seven historical flood peak datasets were injected into the digital twin platform, and Monte Carlo simulations (5000 random samplings) were performed, covering the entire range of gate opening from 0% to 100%. The extreme condition verification module simulated 12 types of failure modes, including power outages in the hoist and hydraulic system leaks. Combined with structural resonance frequency analysis under a magnitude 7 earthquake (frequency range 0.5-15 Hz), the robustness of the components in complex environments was verified. Final tests showed that the gate opening control accuracy reached ±0.3 degrees, the failure coverage increased from 55% using traditional methods to 92%, and the component's reuse across four projects reduced development costs by 68%. Verification reports were automatically generated and archived in a knowledge base for subsequent projects.

[0131] Example 5 is an embodiment of the present invention, which provides a modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0132] Step 1: Visual configuration of logical nodes.

[0133] Engineers constructed speed control logic in the Unreal Engine editor by dragging and dropping behavior tree nodes. First, a frequency monitoring node was placed, setting a threshold to trigger regulation when the grid frequency deviation exceeded ±0.2 Hz. Next, a node connecting to the proportional-integral regulator was configured with a proportional coefficient Kp = 1.2 and an integral time Ti = 8 seconds. In the regulation direction determination branch, guide vane opening increase / decrease nodes were attached and bound to the servo motor control interface. Finally, a rate limiter node was connected, setting the maximum regulation rate to ±3% / second to prevent mechanical shock. The graphical logic was automatically converted into finite state machine code containing 89 state nodes, generating an executable UE blueprint resource package, achieving zero-code logic compilation and immediate deployment.

[0134] Step 2: Real-time data-driven simulation synchronization.

[0135] The simulation model accesses real-time data streams from the actual governor, including relay stroke and guide vane opening, via the OPC UA protocol, with a sampling period of 100 milliseconds. Upon receiving the data, the simulation model dynamically adjusts the virtual machine group's state, including controlling the error rate between the simulated relay stroke (435 mm) and the actual value (428 mm) to within 1.6%. The simulated dynamic response time during adjustment is 2.8 seconds, with an error of 9.7% between the simulated result and the measured value of 3.1 seconds. The model simultaneously displays the governor oil pressure curve (0-6.3 MPa range) and the unit speed fluctuation diagram (±2% of rated speed band), and uses a data comparison window to mark key parameter deviations in real time, supporting engineers in making dynamic parameter fine-tuning.

[0136] Step 3: Five-dimensional consistency and collaborative verification.

[0137] A five-dimensional consistency and collaborative verification process was initiated to spatially align the virtual model with the physical speed control system (positioning error <2 mm). In the structural dimension verification, comparison of laser scanning data with the BIM model showed a guide vane connecting rod assembly clearance error of 0.12 mm, triggering an automatic calibration module to correct the three-dimensional coordinates. In the behavioral dimension, a preset fault sequence (such as a power grid frequency change of ±1 Hz) was injected to verify that the emergency stop node response delay in the speed control logic tree was 8.3 milliseconds, meeting the 10 millisecond threshold required by the IEC61850 protocol. In the physical dimension, the error curve between the simulated pressure pulsation value of 0.85 MPa and the sensor's measured value of 0.83 MPa was synchronously displayed, and the water density parameter in the fluid-structure interaction equation was adjusted using a genetic algorithm (correction coefficient β = 1.032). In the data dimension, an OPC UA channel was established to achieve bidirectional data synchronization 20 times per second, with the guide vane opening command transmission delay controlled within 18 milliseconds. In the environmental dimension, the impact of the plant temperature gradient (15℃-35℃) on the hydraulic system was simulated, verifying that the oil pressure characteristic curve matched the temperature change model with a 94.7% accuracy rate. Finally, a five-dimensional coupling verification report was generated, which marked the deviations of 12 cross-dimensional parameters and recommended optimization schemes, improving the simulation accuracy of the speed control system under all operating conditions to 97.2%.

[0138] Based on the measured average values ​​of five hydropower station projects, the technical effectiveness verification data is shown in Table 1.

[0139] Table 1 Technical Effect Verification Data Table

[0140] index Traditional methods Method of the present invention Increase Model configuration efficiency 35 person-days / system 6 people per day / system 83％ Multidimensional coupling error 12.4％ 4.7％ 62％ Extreme operating condition coverage 58％ 91％ 57％ Component reuse rate 22％ 76％ 245％ Real-time data synchronization accuracy 300ms 50ms 83％

[0141] Example 6, refer to Figure 6 This embodiment of the present invention provides a modular configuration system for a hydropower station simulation system based on dynamic configuration of a five-dimensional model, including a five-dimensional model component knowledge base construction module, a component binding module, a logic orchestration module, a verification module, and a reusability evaluation module.

[0142] The Five-Dimensional Model Component Knowledge Base Building Module is used to build a five-dimensional model component knowledge base, dividing the three-dimensional solid model of a hydropower station into standardized components in five dimensions.

[0143] The component binding module is used to bind components to the 3D solid model of the hydropower station through a graphical interactive method, and generate a data flow topology diagram between components, and simultaneously generate a component relationship dependency matrix.

[0144] The logic orchestration module is used to decompose the operation rules of hydropower stations into configurable nodes in a behavior tree-based logic orchestration system.

[0145] The verification module is used to inject real-time sensor data into the simulation platform to drive the model operation, trigger the cross-dimensional parameter consistency verification, and call the optimization algorithm to dynamically correct the component model parameters.

[0146] The reusability assessment module is used to calculate reusability metrics based on component usage and adjust component recommendation strategies according to the reusability metrics.

[0147] This embodiment also provides an electronic device applicable to a modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration proposed in the above embodiment.

[0148] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model as proposed in the above embodiment.

[0149] The storage medium proposed in this embodiment and the modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0150] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model, characterized in that: include, A five-dimensional model component knowledge base is constructed, dividing the three-dimensional solid model of the hydropower station into standardized components in five dimensions; The components are bound to the 3D solid model of the hydropower station through a graphical interactive method, and a data flow topology diagram between the components is generated, and a component relationship dependency matrix is ​​generated simultaneously. The behavior tree-based logic orchestration system decomposes the hydropower station operation rules into configurable nodes. Real-time sensor data is injected into the simulation platform to drive the model operation, trigger cross-dimensional parameter consistency verification, and call optimization algorithms to dynamically correct the component model parameters. The reusability index is calculated based on component usage, and the component recommendation strategy is adjusted according to the reusability index.

2. The modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration as described in claim 1, characterized in that: The standardized components that divide the three-dimensional solid model of the hydropower station into five dimensions include: Define the classification criteria for the five-dimensional model components; Construct a component relationship graph in the five-dimensional model component knowledge base to support graph database queries and path reasoning.

3. The modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration as described in claim 2, characterized in that: The process involves binding components to the 3D solid model of the hydropower station through a graphical interactive method, generating a data flow topology diagram between components, and simultaneously generating a component relationship dependency matrix, including... Construct a three-dimensional solid model of the hydropower station; Different dimensional components are distinguished by color coding; Trigger collision detection to issue warnings for components with conflicting spatial positions; Generate component association configuration files.

4. The modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration as described in claim 3, characterized in that: The method of binding components to the 3D solid model of the hydropower station through graphical interaction includes: Constructing a 3D solid model of a hydropower station based on BIM and laser point cloud scanning; The GIS coordinates and equipment IDs are then marked on the 3D solid model of the hydropower station.

5. The modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration as described in claim 4, characterized in that: The process of breaking down the hydropower station's operating rules into configurable nodes includes, Using the behavior tree logic orchestration system, the control logic of the hydropower station is abstracted into three types of nodes in the editor: control nodes, execution nodes, and monitoring nodes. The control node includes sequential execution, parallel execution, and condition judgment types. The condition judgment node sets the power grid frequency deviation threshold as the trigger condition. The execution nodes include equipment start / stop, valve adjustment, and power setting types. The guide vane opening adjustment command embeds slope parameters and rate limiting rules. The monitoring nodes include voltage, current, vibration, temperature, and flow type, and capture sensor data streams in real time and compare them with preset alarm thresholds; By dragging and dropping various nodes onto the logic canvas to build a logical framework, the system automatically generates the topological connection relationships between nodes, forming an uncompiled behavior tree structure. Configure the execution order and parallel relationship of nodes in the logic canvas. In the load shedding logic chain, set the frequency monitoring node to detect the grid status. When the frequency exceeds the limit for a certain period of time, trigger the parallel execution node to control the guide vane to adjust the opening degree according to the piecewise function and start the emergency shutdown protection program. A rate limiter node is embedded in the logic chain to constrain the instantaneous change in mechanical motion. The logic flow path is rendered in real time, and the normal execution path and fault handling branch are distinguished by color coding. The logic closure is automatically verified to ensure that all branches have a termination node. After completing the logic orchestration, the behavior tree nodes are converted into blueprint scripts in the editor, the control nodes are converted into conditional statements and loop structures, the execution nodes are compiled into device driver interface functions, and the monitoring nodes generate data listening callback functions. The behavior tree logic containing multiple nodes is transformed into a blueprint resource package. An exception handling module and a thread lock mechanism are inserted during the compilation process. The script is injected into the running simulation kernel through hot loading technology, and it takes effect without restarting the system.

6. The modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration as described in claim 5, characterized in that: The process of injecting real-time sensor data into the simulation platform to drive model operation includes, Real-time sensor data is input into the node model in the simulation platform; The injected data drives the operation of each component node, generating a simulation response. During the response process, structural, behavioral, and physical parameters are extracted from various dimensions, and explicit comparisons are performed. The comparison is based on a set threshold to determine whether the model response meets the set threshold.

7. The modular configuration method for a hydropower station simulation system based on a five-dimensional model dynamic configuration as described in claim 6, characterized in that: The system triggers cross-dimensional parameter consistency verification and calls an optimization algorithm to dynamically correct the component model parameters. include, A spatiotemporal benchmark is established, and all models are strictly consistent in time, space, coordinate system, and physical units; Each model is independently validated to ensure that the internal logic and data of each model conform to design specifications and physical laws. Cross-model interface verification ensures that the data interaction format, protocol, and real-time performance between models meet the requirements. Dynamic coupling verification verifies the consistency of dynamic behavior during multi-model co-simulation; Closed-loop calibration and iterative optimization dynamically correct model parameters based on measured data to improve simulation prediction accuracy; Comprehensive scenario verification: Verify the collaborative performance of the five-dimensional model in typical scenarios throughout the entire lifecycle, generate verification reports, and feed them back to the five-dimensional model component knowledge base; The calculation of reusability metrics based on component usage includes, Calculate the component reuse index, perform Min-Max standardization on the component reuse index result, and map it to the [0, 1] interval. If it is greater than the reuse index threshold, it is marked as a recommended component and added to the recommendation pool. If it is less than or equal to the reuse index threshold, an optimization reminder is triggered and pushed to the development end for interface compatibility upgrade or logic rule reconstruction.

8. A modular configuration system for a hydropower station simulation system based on dynamic configuration of a five-dimensional model, employing the modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model as described in any one of claims 1 to 7, characterized in that, include: The five-dimensional model component knowledge base construction module, component binding module, logic orchestration module, verification module, and reusability evaluation module; The five-dimensional model component knowledge base construction module is used to build a five-dimensional model component knowledge base, dividing the three-dimensional entity model of the hydropower station into standardized components in five dimensions. The component binding module is used to bind components to the three-dimensional solid model of the hydropower station through a graphical interactive method, and generate a data flow topology diagram between components, and simultaneously generate a component relationship dependency matrix; The logic orchestration module is used to decompose the hydropower station operation rules into configurable nodes based on the behavior tree logic orchestration system; The verification module is used to inject real-time sensor data into the simulation platform to drive the model to run, trigger the cross-dimensional parameter consistency verification, and call the optimization algorithm to dynamically correct the component model parameters. The reusability assessment module is used to calculate reusability metrics based on component usage and adjust component recommendation strategies according to the reusability metrics.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model, as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the modular configuration method for a hydropower station simulation system based on dynamic configuration of a five-dimensional model, as described in any one of claims 1 to 7.

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