A high-fidelity three-dimensional modeling method and system

Abstract

This invention discloses a high-fidelity 3D modeling method and system, belonging to the field of 3D modeling technology. The method specifically includes: real-time acquisition of relevant data from a rotating equipment; preprocessing the acquired surface data of the rotating equipment; constructing a 3D reference model of the rotating equipment; dividing the target wind field into grids; obtaining real-time wind speed and direction data for each grid point; generating a dynamic wind field map, which includes wind vector information for each coordinate point; mapping the wind field map to the 3D reference model of the rotating equipment; generating a 3D model of the rotating equipment with dynamic loads; locally optimizing and adjusting the blade angle and fan orientation parameters of the rotating equipment based on a preset optimization target; verifying the optimization effect through iterative calculation and outputting the optimal parameter scheme; by establishing a high-fidelity 3D model of the rotating equipment and locally optimizing parameters such as blade angle and fan orientation, maximizing wind energy utilization efficiency and improving the overall operating efficiency of the equipment.

Description

Technical Field

[0001] This invention belongs to the field of 3D modeling technology, specifically a high-fidelity 3D modeling method and system. Background Technology

[0002] In modern engineering, dynamic simulation and optimization design based on real-time data has become a trend. In the pursuit of higher energy efficiency and equipment safety, there is an increasing tendency to adopt iterative optimization and parameter adjustment techniques to make local improvements to key components (such as blade angle and fan orientation) to achieve a dual improvement in equipment performance and structural safety.

[0003] While traditional modeling and design methods have achieved certain successes in the past, they suffer from several shortcomings under current wind energy utilization and highly dynamic operating conditions: 1) Static models have limitations, neglecting the dynamic characteristics of wind field, load, and equipment stress state changes over time; 2) Data updates are not timely, relying on historical data or theoretical calculations and lacking the input of real-time monitoring data; 3) Parameter optimization is insufficient, as the determination of key parameters (such as blade angle and fan orientation) in traditional design processes mainly depends on experience or experimental data, lacking systematic local optimization and iterative calculations. Driven by the current demands of wind energy utilization and equipment operation, traditional static, simplified, and empirical 3D modeling techniques are no longer sufficient to meet the requirements of high-fidelity simulation and real-time optimization.

[0004] Chinese patent application CN117115357A discloses a 3D modeling method and apparatus. The method includes: performing target detection and recognition on a 2D drawing to obtain target detection and recognition results, the results including the position information and / or category information of the target bounding box; extracting the image region of the target bounding box from the 2D drawing based on the position information and / or category information; performing recognition processing on the image region of the target bounding box based on optical character recognition (OCR) technology and / or residual networks to obtain basic data information of the 2D drawing; obtaining 3D model data based on the basic data information, and generating a 3D model based on the 3D model data; standardizing and lightweighting the 3D model, the processed 3D model being compatible with at least two of the display frameworks of PC, mobile, and Web. This disclosure can improve the efficiency of model production, and the 3D model can be compatible with at least two of the display frameworks of PC, mobile, and Web.

[0005] For example, Chinese patent CN116664783B discloses a modeling method for complex 3D modeling in a web-based environment, including the following steps: Step 1: Constructing a reference cross-section; the reference cross-section is constructed according to a discretization strategy or a formulaic strategy; the discretization strategy includes drawing discrete points in the web page, constructing a closed curve based on the discrete points to obtain the reference cross-section; the formulaic strategy includes calling a surface function in the web page, setting a coordinate plane, setting equidistant points based on the coordinate axes of the coordinate plane; constructing a closed curve based on the equidistant points to obtain the reference cross-section; Step 2: Based on the reference cross-section, adjusting the position of the reference cross-section and performing a lofting operation in groups to generate a reference entity; Step 3: Based on the reference entity, assembling to generate a 3D model. This invention can complete complex 3D modeling on a web-based platform and has high timeliness, intuitiveness, and convenience.

[0006] The shortcomings of the existing technologies mentioned above are that they are based on static 3D models and lack dynamic features and real-time performance. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention proposes a high-fidelity 3D modeling method and system. Through real-time data acquisition, refined wind field mapping, dynamic load calculation, and iterative optimization of local parameters, it effectively compensates for the deficiencies of traditional technologies in dynamic response, data real-time performance, physical model accuracy, and parameter optimization. This enables accurate simulation of equipment operating status and performance improvement, providing solid technical support for the safety and economy of wind energy projects.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A high-fidelity 3D modeling method, comprising:

[0010] Real-time acquisition of relevant data from the rotating equipment; preprocessing of the acquired surface data of the rotating equipment; and construction of a three-dimensional reference model of the rotating equipment.

[0011] The target wind field is divided into grids, and real-time wind speed and direction data of each grid point are obtained to generate a dynamic wind field map. The wind field map contains wind vector information of each coordinate point.

[0012] The wind field diagram is mapped onto the three-dimensional reference model of the rotating equipment to generate a three-dimensional model of the rotating equipment with dynamic loads;

[0013] Based on the preset optimization objectives, the blade angle and fan orientation parameters of the rotating equipment are locally optimized and adjusted. The optimization effect is verified through iterative calculation and the optimal parameter scheme is output.

[0014] Specifically, the step of dividing the target wind field into grids, obtaining real-time wind speed and direction data for each grid point, and generating a dynamic wind field map includes:

[0015] Establish a coordinate system centered on the transfer equipment, calculate the radius of the influence area, set boundary conditions, and establish the target wind field;

[0016] The target wind field is divided into multiple regular grids according to a preset spatial resolution, with each grid representing a region;

[0017] For each grid point, wind speed and direction data are collected in real time;

[0018] Using real-time wind speed and direction data collected from each grid point, the corresponding wind force vector is calculated and displayed in the target wind field using visual symbols to generate a dynamic wind field map.

[0019] Specifically, mapping the wind field map to the three-dimensional reference model of the turbine equipment to generate a three-dimensional model of the turbine equipment with dynamic loads includes:

[0020] The wind force data in the dynamic wind field map generated in the previous stage are mapped onto the three-dimensional reference model of the rotating equipment according to the spatial coordinates.

[0021] After mapping the dynamic wind field map to the three-dimensional reference model of the rotating equipment, the dynamic loads acting on each part of the equipment are calculated based on real-time wind speed and direction data, and distributed to each node according to the shape and stress characteristics of the equipment, forming a dynamic load field in which the wind load changes over time.

[0022] The mapped wind load data is injected into the three-dimensional baseline model of the turbine equipment to generate a three-dimensional model of the turbine equipment with dynamic loads.

[0023] Specifically, mapping the wind vector data from the dynamic wind field map generated in the previous stage onto the three-dimensional reference model of the turnaround equipment according to spatial coordinates includes:

[0024] Align the coordinate systems of the dynamic wind field map and the three-dimensional reference model of the rotating equipment;

[0025] The wind speed and direction information of each grid point in the dynamic wind field map are mapped to the corresponding surface of the three-dimensional reference model of the rotating equipment based on its spatial coordinates.

[0026] After completing the initial data mapping, the mapping results are geometrically corrected.

[0027] Specifically, after the dynamic wind field map is mapped to the three-dimensional reference model of the rotating equipment, the dynamic loads acting on each part of the equipment are calculated based on real-time wind speed and direction data, and distributed to each node according to the shape and stress characteristics of the equipment, forming a dynamic load field in which the wind load changes over time, including:

[0028] Using wind speed data and wind direction information, real-time wind speed is converted into corresponding wind pressure, and wind pressure is decomposed into force components acting on the rotating equipment in different directions.

[0029] Based on the structural characteristics and geometry of the rotating equipment, a load distribution model is established, and the global wind load data is distributed to each node or surface area of ​​the three-dimensional reference model of the rotating equipment.

[0030] The loads applied to each node or region are dynamically calculated to form a dynamic load field in which the wind load changes over time.

[0031] Specifically, based on a preset optimization objective, the blade angle and fan orientation parameters of the rotating equipment are locally optimized and adjusted. The optimization effect is verified through iterative calculation, and the optimal parameter scheme is output. This includes:

[0032] Based on actual application needs and engineering goals, set optimization indicators and define constraints for parameter optimization;

[0033] Based on the design and historical data of the rotating equipment, the initial parameter values ​​of the blade angle and fan orientation are determined, and a local search range is defined. A parameter space model is constructed based on the initial parameter values ​​and the defined search range.

[0034] Local parameters such as blade angle and fan orientation are adjusted, and the parameters are continuously updated through iterative calculations to evaluate the performance indicators of each set of parameters in real time.

[0035] After each iteration, the performance of the current parameter scheme is verified through simulation or experiment. When the iteration process reaches the preset convergence criterion or achieves the optimal effect, the final optimal parameter scheme is output.

[0036] Specifically, based on the design and historical data of the rotating equipment, the initial parameter values ​​of the blade angle and fan orientation are determined, and a local search range is defined. A parameter space model is then constructed based on the initial parameter values ​​and the defined search range, including:

[0037] Collect design documents, technical parameters, and historical operating data of the transfer equipment; perform statistical and trend analysis on the collected data; and identify the key parameters that have the greatest impact on equipment performance.

[0038] Based on the design requirements of the rotating equipment and the data analysis results, the initial parameter values ​​for the blade angle and fan orientation are determined;

[0039] After determining the initial parameter values, a local search range is set based on the physical constraints, operating limits, and safety requirements of the equipment;

[0040] A parameter space model is constructed based on the initial parameter values ​​and the set search range.

[0041] A high-fidelity 3D modeling system for implementing the aforementioned high-fidelity 3D modeling method includes: a baseline model establishment module, a wind field module, a mapping module, and a parameter adjustment module;

[0042] The benchmark model building module is used to collect relevant data of the rotating equipment in real time, preprocess the collected surface data of the rotating equipment, and build a three-dimensional benchmark model of the rotating equipment.

[0043] The wind field module is used to divide the target wind field into grids, obtain real-time wind speed and wind direction data of each grid point, and generate a dynamic wind field map. The wind field map contains wind vector information of each coordinate point.

[0044] The mapping module is used to map the wind field map to the three-dimensional reference model of the rotating equipment, and generate a three-dimensional model of the rotating equipment with dynamic loads.

[0045] The parameter adjustment module is used to locally optimize and adjust the blade angle and fan orientation parameters of the rotating equipment based on a preset optimization target, verify the optimization effect through iterative calculation, and output the optimal parameter scheme.

[0046] Specifically, the mapping module includes: a data mapping unit, a dynamic load field unit, and a three-dimensional model update unit;

[0047] The data mapping unit is used to map the wind vector data in the dynamic wind field map generated in the previous stage to the three-dimensional reference model of the rotating equipment according to the spatial coordinates.

[0048] The dynamic load field unit is used to calculate the dynamic loads acting on each part of the equipment based on real-time wind speed and direction data after mapping the dynamic wind field map with the three-dimensional reference model of the rotating equipment, and to distribute them to each node according to the shape and stress characteristics of the equipment, forming a dynamic load field in which the wind load changes over time.

[0049] The three-dimensional model update unit is used to inject the mapped wind load data into the three-dimensional reference model of the turbine equipment to generate a three-dimensional model of the turbine equipment with dynamic load.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] 1. This invention proposes a high-fidelity 3D modeling method that utilizes real-time wind field data and precise mapping, dynamic load injection, and iterative calculation to accurately simulate key parameters such as structural vibration, fatigue, and local stress, thereby establishing a high-fidelity 3D model of the rotating equipment.

[0052] 2. This invention proposes a high-fidelity 3D modeling method that maximizes wind energy utilization efficiency and improves the overall operating efficiency of the equipment by locally optimizing parameters such as blade angle and fan orientation. The optimization scheme has been iteratively verified and can reduce energy loss and equipment wear caused by improper parameter settings, thereby extending the equipment life.

[0053] 3. This invention proposes a high-fidelity 3D modeling method. The high-fidelity model provides an intuitive data platform for design, monitoring and maintenance, supports multi-angle visualization and real-time monitoring, and reduces the risks caused by environmental changes. Attached Figure Description

[0054] Figure 1 This is a flowchart of a high-fidelity 3D modeling method according to the present invention;

[0055] Figure 2 The dynamic mapping flowchart provided by this invention;

[0056] Figure 3 This is a diagram illustrating the architecture of a high-fidelity 3D modeling system according to the present invention. Detailed Implementation

[0057] The present invention will be further described below with reference to specific embodiments.

[0058] Example 1

[0059] Please see Figure 1 and Figure 2 The present invention provides an embodiment of a high-fidelity 3D modeling method, comprising the following specific steps:

[0060] Step S1: Collect relevant data of the rotating equipment in real time, preprocess the collected surface data of the rotating equipment, and construct a three-dimensional reference model of the rotating equipment;

[0061] Multiple data source acquisition: Obtain the original CAD model from the equipment manufacturer, such as .stp / .igs format; extract geometric parameters from the factory inspection report, such as blade curvature radius and tower wall thickness; collect material property data, such as elastic modulus and Poisson's ratio; import assembly process documents to obtain component connection relationships;

[0062] Data preprocessing includes: noise filtering and outlier removal, which filters the raw acquired data to remove noise and outliers caused by sensor errors or environmental interference; data registration, which aligns data from multiple angles or different sensors using registration algorithms (such as the ICP algorithm) to generate a complete point cloud dataset when the data comes from multiple angles or different sensors; and scale correction and standardization, which scales and normalizes the data to ensure compatibility and consistency between different datasets, providing a standardized data foundation for subsequent modeling.

[0063] Segmentation and Feature Extraction: The device surface is segmented using methods such as edge detection and region growing to extract key features (such as edges, corners, and surface information), providing accurate geometric features for 3D modeling.

[0064] Constructing a 3D baseline model: Using voxelization, triangular mesh construction, or surface-based reconstruction methods, the preprocessed point cloud data is converted into a continuous 3D model. The preliminary model is optimized using techniques such as smoothing and mesh simplification to remove redundant data while maintaining geometric accuracy. Key coordinate systems and reference points are calibrated in the model so that the 3D model can serve as a unified reference for subsequent equipment status monitoring, blade angle, and fan orientation adjustment.

[0065] Step S2: Divide the target wind field into grids, obtain real-time wind speed and wind direction data for each grid point, and generate a dynamic wind field map, which includes wind vector information for each coordinate point;

[0066] The specific steps of step S2 are as follows:

[0067] Step S201: Establish a coordinate system centered on the transfer equipment, determine the radius of the affected area, set boundary conditions, and establish the target wind field;

[0068] Step S202: Divide the target wind field into multiple regular grids according to a preset spatial resolution, with each grid representing a region;

[0069] In practical applications, the appropriate grid size can be selected based on the size of the region, the characteristics of wind field changes, and computing power. Gridding can standardize the analysis of the entire wind field, and each region can be calculated and compared independently. By subdividing a large-scale wind field, the spatial changes of wind speed and direction can be captured more accurately. After gridding, the data of each grid point can be processed in parallel, which facilitates data acquisition and subsequent numerical simulation and improves the overall computing efficiency.

[0070] Step S203: Collect wind speed and wind direction data in real time for each grid point;

[0071] When necessary, data preprocessing, filtering, and imputation operations are also required to ensure the accuracy and continuity of the data.

[0072] Step S204: Calculate the corresponding wind force vector using the real-time wind speed and direction data collected from each grid point, and display it in the target wind field using visual symbols to generate a dynamic wind field map.

[0073] The visualization symbols include arrows, color markers, etc. This dynamic wind field map can be continuously updated according to time series data to form a real-time wind field display. Through the graphical wind vector display, users can intuitively see the wind direction, speed and wind field distribution. The dynamically updated image can reflect the changing trend of the wind field state, which is convenient for early warning, decision-making and subsequent dynamic simulation.

[0074] Step S3: Map the dynamic wind field map to the three-dimensional reference model of the rotating equipment to generate a three-dimensional model of the rotating equipment with dynamic loads;

[0075] The specific steps of step S3 are as follows:

[0076] Step S301: Map the wind vector data in the dynamic wind field map generated in the previous stage onto the three-dimensional reference model of the rotating equipment according to the spatial coordinates;

[0077] This process requires accurately matching the wind speed and direction information at each grid point to the corresponding surface or area of ​​the equipment, based on the actual location and geometry of the equipment in space. Specific steps include:

[0078] Step S3011: First, confirm the coordinate systems used by the wind field map and the 3D reference model. Usually, the wind field map may use geographic coordinates (such as latitude and longitude, elevation), while the equipment model may use local or engineering coordinates. At this time, it is necessary to use a coordinate transformation algorithm to align the two coordinate systems to ensure that the wind field data and the equipment model can be correctly matched under the same spatial reference.

[0079] After unifying the coordinate system, there will be no errors caused by coordinate deviations between different data sources, ensuring the accuracy of subsequent mapping work; ensuring that each wind field data point can be accurately located in the three-dimensional model, providing a reliable basis for subsequent wind load calculations;

[0080] Step S3012: Map the wind speed and direction information of each grid point in the dynamic wind field map to the corresponding surface of the three-dimensional reference model of the rotating equipment according to its spatial coordinates; this process involves understanding the geometric features of the equipment, such as the surface normal, curved shape and the position of key structural nodes; a smooth transition of wind field data on the model surface can be achieved by establishing data correspondence or using interpolation methods.

[0081] Ensure that each local area receives corresponding wind field information and capture the wind characteristics of each part of the equipment; use interpolation algorithms to process boundary and missing data, so that the mapped data is more continuously and smoothly distributed on the equipment model; perform key mapping on critical parts (such as blades and towers) to help more accurately reflect the local stress situation and provide detailed data support for subsequent structural analysis.

[0082] Step S3013: After completing the initial data mapping, the mapping results need to be geometrically corrected. This mainly addresses small deviations that may occur due to the geometric complexity of the model or during coordinate transformation. The geometric correction algorithm is used to adjust the position and distribution of the wind field data on the model surface, making the overall mapping effect more accurate and realistic.

[0083] Automatic correction of spatial errors that may occur during the mapping process improves the overall mapping accuracy; the corrected data allows the wind field to better fit the equipment structure, reflecting the real engineering application scenario.

[0084] Step S302: After mapping the dynamic wind field map to the three-dimensional reference model of the rotating equipment, the dynamic loads acting on each part of the equipment are calculated based on real-time wind speed and wind direction data. This includes converting wind speed into pressure, shear force, etc., and distributing them to each node according to the shape and stress characteristics of the equipment, forming a dynamic load field where the wind load changes over time.

[0085] The specific steps of step S302 are as follows:

[0086] Step S3021: Using wind speed data, convert the real-time wind speed into the corresponding wind pressure using physical formulas;

[0087] Based on wind direction information, wind pressure is decomposed into force components acting on the rotating equipment in different directions, such as horizontal and vertical components, or more refined shear force, normal force, etc.

[0088] By converting physical formulas, the load calculation is ensured to have a solid theoretical foundation. The indirect indicator of wind speed is converted into specific wind pressure and wind force, which facilitates subsequent mechanical calculations and structural analysis. It can obtain more detailed load distribution according to different force directions, providing multi-angle data support for structural optimization.

[0089] Step S3022: Based on the structural characteristics and geometry of the rotating equipment, establish a load distribution model. Using interpolation, piecewise functions, or finite element methods, distribute the global wind load data to each node or surface area of ​​the three-dimensional reference model of the rotating equipment to reflect the actual load magnitude and direction of different areas due to wind force.

[0090] Refining the overall wind pressure data to each local area of ​​the model helps to identify key stress points; the distributed model can improve the accuracy of load distribution through numerical methods, ensuring more accurate stress calculation of the overall model; for equipment with complex shapes or uneven stress, the distributed model can better simulate actual working conditions and provide a basis for structural safety assessment.

[0091] Step S3023: Combining physical formulas and numerical methods, dynamically calculate the loads applied to each node or region to form a load field that varies with time. Update the model using real-time data to ensure that the load calculation reflects the latest wind field conditions within each time step. Use appropriate numerical solution methods (such as explicit / implicit integration methods, finite element methods, etc.) to simulate the time-varying response of the load and the dynamic behavior of the structure.

[0092] Dynamic updates ensure that load calculations can capture wind field changes in a timely manner, adapt to sudden wind conditions and dynamic response requirements, and help to study the vibration, fatigue and ultimate load states of rotating equipment under different wind conditions, thereby improving early warning and wind-resistant design capabilities. Numerical methods allow for the simulation of complex nonlinear and time-varying systems, making the results more consistent with real engineering conditions.

[0093] Step S303: Inject the mapped wind load data into the three-dimensional reference model of the turbine equipment to generate a three-dimensional model of the turbine equipment with dynamic loads.

[0094] In this embodiment, the wind load data obtained in the previous steps, after mapping and correction, is injected into the three-dimensional reference model of the turbine equipment. This step automatically updates the model through a data interface or script, ensuring that each part of the equipment model includes the load information calculated in real time.

[0095] The advantages of doing this are: real-time performance, enabling the model to be dynamically updated with the latest wind load data, reflecting the actual working conditions; seamless data integration, ensuring accurate correspondence between wind load data and equipment structure, laying a good foundation for subsequent analysis; reduced human intervention, improved data processing efficiency and update frequency, and facilitate long-term continuous monitoring.

[0096] Step S4: Based on the preset optimization target, locally optimize and adjust the blade angle and fan orientation parameters of the rotating equipment, verify the optimization effect through iterative calculation, and output the optimal parameter scheme.

[0097] The specific steps of step S4 are as follows:

[0098] Step S401: Based on actual application needs and engineering goals, clarify the optimization indicators, such as maximizing wind energy capture efficiency, minimizing vibration, or reducing equipment wear. At the same time, define the constraints for parameter optimization (such as equipment physical limits, environmental factors, and safety standards) to provide a basis for targets and ranges for subsequent parameter adjustments.

[0099] Setting clear optimization goals helps to focus on key performance indicators and ensures that optimization work has a clear direction. By defining constraints, unrealistic or safety-incompatible parameter schemes can be avoided during the optimization process.

[0100] Step S402: Based on the design and historical data of the rotating equipment, determine the initial parameter values ​​of the blade angle and fan orientation, and define a reasonable local search range. This process usually combines previous engineering experience and data analysis to initially define the parameter range of the better performance that can be obtained.

[0101] The specific steps of step S402 are as follows:

[0102] Step S4021: Collect the design documents, technical parameters and historical operating data of the rotating equipment, including blade angle, fan orientation, operating efficiency, load conditions, etc. These data come from engineering design documents, on-site monitoring records or past test results. Perform statistical and trend analysis on the collected data to identify the key parameters that have the greatest impact on equipment performance, such as the optimal angle of attack of the blades and the optimal orientation of the fan.

[0103] The most critical parameter here refers to identifying which parameters have a significant impact on overall performance through data analysis, thus guiding subsequent parameter settings; a deeper understanding of the equipment's operating characteristics helps in establishing a parameter space model that better reflects actual working conditions.

[0104] Step S4022: Based on the design requirements of the rotating equipment and the data analysis results, determine the initial parameter values ​​of the blade angle and fan orientation. These initial parameter values ​​usually represent the best-performing schemes in history or the theoretically optimal reference points.

[0105] Initial parameter values ​​serve as the starting point for optimization, ensuring that subsequent iterations are conducted within a reasonable parameter range with engineering implications. Using reasonable initial parameters as a benchmark helps to achieve rapid convergence and reduces the high computational cost associated with large-scale searches.

[0106] Step S4023: After determining the initial parameter values, a local search range is set according to the physical constraints, operating limits and safety requirements of the equipment. This range should be wide enough to cover the possible optimization space, and should avoid exceeding the actual allowable parameter limits of the equipment.

[0107] Step S4024: Construct a parameter space model based on the initial parameter values ​​and the set search range. This model is used to evaluate and compare different parameter combinations in the subsequent iterative optimization process. It can usually be built using mathematical functions, discrete grids or other numerical description methods.

[0108] Step S403: Using mathematical models and numerical optimization algorithms (such as gradient descent, genetic algorithm or particle swarm optimization, etc.), local parameters of blade angle and fan orientation are adjusted. The parameters are continuously updated through iterative calculations, and the performance indicators under each set of parameters are evaluated in real time. Based on this, the parameter direction is adjusted until the preset optimization target is met or the optimal solution is converged.

[0109] Iterative calculations can flexibly adapt to changes in wind field and equipment operating conditions, and improve parameter settings in real time; optimization algorithms can quickly select the parameter combination that is most beneficial to performance improvement, shortening the optimization cycle.

[0110] Step S404: After each iteration, the performance of the current parameter scheme is verified through simulation or experimentation, evaluating its wind energy utilization efficiency, structural response, and equipment safety in actual operation. When the iteration process reaches the preset convergence criterion or achieves the optimal effect, the final optimal parameter scheme is output, and a detailed result analysis is performed.

[0111] Step S4 gradually achieves fine control over the blade angle and fan orientation of the rotating equipment by clarifying the optimization goal, setting initial parameters, iterating local parameter optimization, and verifying the effect.

[0112] Example 2

[0113] Please see Figure 3 Another embodiment of the present invention provides: a high-fidelity 3D modeling system, comprising: a baseline model establishment module, a wind field module, a mapping module, and a parameter adjustment module;

[0114] The benchmark model building module is used to collect relevant data of the rotating equipment in real time, preprocess the collected surface data of the rotating equipment, and build a three-dimensional benchmark model of the rotating equipment.

[0115] The wind field module is used to divide the target wind field into grids, obtain real-time wind speed and wind direction data of each grid point, and generate a dynamic wind field map. The wind field map contains wind vector information of each coordinate point.

[0116] The mapping module is used to map the wind field map to the three-dimensional reference model of the rotating equipment, and generate a three-dimensional model of the rotating equipment with dynamic loads.

[0117] The parameter adjustment module is used to locally optimize and adjust the blade angle and fan orientation parameters of the rotating equipment based on a preset optimization target, verify the optimization effect through iterative calculation, and output the optimal parameter scheme.

[0118] The mapping module includes: a data mapping unit, a dynamic load field unit, and a 3D model update unit;

[0119] The data mapping unit is used to map the wind vector data in the dynamic wind field map generated in the previous stage to the three-dimensional reference model of the rotating equipment according to the spatial coordinates.

[0120] The dynamic load field unit is used to calculate the dynamic loads acting on each part of the equipment based on real-time wind speed and direction data after mapping the dynamic wind field map with the three-dimensional reference model of the rotating equipment, and to distribute them to each node according to the shape and stress characteristics of the equipment, forming a dynamic load field in which the wind load changes over time.

[0121] The three-dimensional model update unit is used to inject the mapped wind load data into the three-dimensional reference model of the turbine equipment to generate a three-dimensional model of the turbine equipment with dynamic load.

[0122] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

[0123] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-fidelity 3D modeling method, wherein the high-fidelity 3D modeling method is applied to a rotating equipment adjustment system, the rotating equipment adjustment system comprising a rotating equipment to be adjusted and a processing device, the processing device being used to execute the high-fidelity 3D modeling method, characterized in that, include: Real-time acquisition of relevant data from the rotating equipment; preprocessing of the acquired surface data of the rotating equipment; and construction of a three-dimensional reference model of the rotating equipment. The target wind field is divided into grids, and real-time wind speed and direction data of each grid point are obtained to generate a dynamic wind field map. The wind field map contains wind vector information of each coordinate point. The wind field diagram is mapped onto the three-dimensional reference model of the rotating equipment to generate a three-dimensional model of the rotating equipment with dynamic loads; Based on the preset optimization objectives, the blade angle and fan orientation parameters of the rotating equipment are locally optimized and adjusted. The optimization effect is verified by iterative calculation and the optimal parameter scheme is output. The step of mapping the wind field map to the three-dimensional reference model of the turbine equipment to generate a three-dimensional model of the turbine equipment with dynamic loads includes: The wind vector data in the dynamic wind field map generated in the previous stage are mapped onto the three-dimensional reference model of the rotating equipment according to spatial coordinates. After mapping the dynamic wind field map to the three-dimensional reference model of the rotating equipment, the dynamic loads acting on each part of the equipment are calculated based on real-time wind speed and direction data, and distributed to each node according to the shape and stress characteristics of the equipment, forming a dynamic load field in which the wind load changes over time. The mapped wind load data is injected into the three-dimensional baseline model of the turbine equipment to generate a three-dimensional model of the turbine equipment with dynamic loads. After the dynamic wind field map is mapped to the three-dimensional reference model of the rotating equipment, the dynamic loads acting on each part of the equipment are calculated based on real-time wind speed and direction data, and distributed to each node according to the shape and stress characteristics of the equipment, forming a dynamic load field in which the wind load changes over time, including: Using wind speed data and wind direction information, real-time wind speed is converted into corresponding wind pressure, and wind pressure is decomposed into force components acting on the rotating equipment in different directions. Based on the structural characteristics and geometry of the rotating equipment, a load distribution model is established, and the global wind load data is distributed to each node or surface area of ​​the three-dimensional reference model of the rotating equipment. The loads applied to each node or region are dynamically calculated to form a dynamic load field in which the wind load changes over time.

2. The high-fidelity 3D modeling method as described in claim 1, characterized in that, The step of dividing the target wind field into grids, obtaining real-time wind speed and direction data for each grid point, and generating a dynamic wind field map includes: Establish a coordinate system centered on the transfer equipment, calculate the radius of the influence area, set boundary conditions, and establish the target wind field; The target wind field is divided into multiple regular grids according to a preset spatial resolution, with each grid representing a small region; For each grid point, wind speed and direction data are collected in real time; Using real-time wind speed and direction data collected from each grid point, the corresponding wind force vector is calculated and displayed in the target wind field using visual symbols to generate a dynamic wind field map.

3. The high-fidelity 3D modeling method as described in claim 2, characterized in that, The step of mapping the wind vector data from the dynamic wind field map generated in the previous stage onto the three-dimensional reference model of the turnaround equipment according to spatial coordinates includes: Align the coordinate systems of the dynamic wind field map and the three-dimensional reference model of the rotating equipment; The wind speed and direction information of each grid point in the dynamic wind field map are mapped to the corresponding surface of the three-dimensional reference model of the rotating equipment based on its spatial coordinates. After completing the initial data mapping, the mapping results are geometrically corrected.

4. The high-fidelity 3D modeling method as described in claim 1, characterized in that, The process involves locally optimizing the blade angle and fan orientation parameters of the rotating equipment based on a preset optimization objective, verifying the optimization effect through iterative calculations, and outputting the optimal parameter scheme. This includes: Based on actual application needs and engineering goals, set optimization indicators and define constraints for parameter optimization; Based on the design and historical data of the rotating equipment, the initial parameter values ​​of the blade angle and fan orientation are determined, and a local search range is defined. A parameter space model is constructed based on the initial parameter values ​​and the defined search range. Local parameters such as blade angle and fan orientation are adjusted, and the parameters are continuously updated through iterative calculations to evaluate the performance indicators of each set of parameters in real time. After each iteration, the performance of the current parameter scheme is verified through simulation or experiment. When the iteration process reaches the preset convergence criterion or achieves the optimal effect, the final optimal parameter scheme is output.

5. The high-fidelity 3D modeling method as described in claim 4, characterized in that, The process involves determining initial parameter values ​​for blade angle and fan orientation based on the design and historical data of the rotating equipment, defining a local search range, and constructing a parameter space model based on the initial parameter values ​​and the defined search range, including: Collect design documents, technical parameters, and historical operating data of the transfer equipment; perform statistical and trend analysis on the collected data; and identify the key parameters that have the greatest impact on equipment performance. Based on the design requirements of the rotating equipment and the data analysis results, the initial parameter values ​​for the blade angle and fan orientation are determined; After determining the initial parameter values, a local search range is set based on the physical constraints, operating limits, and safety requirements of the equipment; A parameter space model is constructed based on the initial parameter values ​​and the set search range.

6. A high-fidelity 3D modeling system, used to implement the high-fidelity 3D modeling method according to any one of claims 1-5, characterized in that, include: The module includes a baseline model establishment module, a wind field module, a mapping module, and a parameter adjustment module. The benchmark model building module is used to collect relevant data of the rotating equipment in real time, preprocess the collected surface data of the rotating equipment, and build a three-dimensional benchmark model of the rotating equipment. The wind field module is used to divide the target wind field into grids, obtain real-time wind speed and wind direction data of each grid point, and generate a dynamic wind field map. The wind field map contains wind vector information of each coordinate point. The mapping module is used to map the wind field map to the three-dimensional reference model of the rotating equipment, and generate a three-dimensional model of the rotating equipment with dynamic loads. The parameter adjustment module is used to locally optimize and adjust the blade angle and fan orientation parameters of the rotating equipment based on a preset optimization target, verify the optimization effect through iterative calculation, and output the optimal parameter scheme.

7. A high-fidelity 3D modeling system as described in claim 6, characterized in that, The mapping module includes: a data mapping unit, a dynamic load field unit, and a three-dimensional model update unit; The data mapping unit is used to map the wind vector data in the dynamic wind field map generated in the previous stage to the three-dimensional reference model of the rotating equipment according to the spatial coordinates. The dynamic load field unit is used to calculate the dynamic loads acting on each part of the equipment based on real-time wind speed and direction data after mapping the dynamic wind field map with the three-dimensional reference model of the rotating equipment, and to distribute them to each node according to the shape and stress characteristics of the equipment, forming a dynamic load field in which the wind load changes over time. The three-dimensional model update unit is used to inject the mapped wind load data into the three-dimensional reference model of the turbine equipment to generate a three-dimensional model of the turbine equipment with dynamic load.

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