High-fidelity three-dimensional modeling method and system

Abstract

The invention discloses a high-fidelity three-dimensional modeling method and system, and belongs to the technical field of three-dimensional modeling, and the method specifically comprises the steps: collecting related data of rotating machine equipment in real time, carrying out the preprocessing of the collected surface data of the rotating machine equipment, constructing a three-dimensional reference model of the rotating machine equipment, carrying out the grid division of a target wind field, and obtaining a three-dimensional reference model of the rotating machine equipment; real-time wind speed and wind direction data of all grid points are obtained, a dynamic wind power field graph is generated, the dynamic wind power field graph comprises wind power vector information of all coordinate points, the wind power field graph is mapped to a three-dimensional reference model of the rotating machine equipment, a rotating machine equipment three-dimensional model with a dynamic load is generated, and the rotating machine equipment three-dimensional model with the dynamic load is optimized based on a preset optimization target. Carrying out local optimization adjustment on the blade angle and fan azimuth parameters of the rotating machine equipment, verifying an optimization effect through iterative calculation, and outputting an optimal parameter scheme; by establishing a high-fidelity rotating machine equipment three-dimensional model, parameters such as the blade angle and the fan direction are locally optimized, the wind energy utilization efficiency is maximized, and the overall operation efficiency of the equipment is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of three-dimensional modeling, and specifically relates to a high-fidelity three-dimensional modeling method and system. Background Art

[0002] In modern engineering, dynamic simulation and optimization design based on real-time data have become a trend. Against the background of pursuing higher energy efficiency and equipment safety, there is an increasing tendency to adopt iterative optimization and parameter adjustment techniques to locally improve key components (such as blade angles, fan orientations) to achieve a dual improvement in equipment performance and structural safety.

[0003] Although traditional modeling and design methods have achieved certain success in the past, under the current wind energy utilization and high-dynamic working conditions, traditional technologies have the following deficiencies: 1) limitations of static models, ignoring the dynamic characteristics of the wind field, load, and equipment stress state that change over time; 2) untimely data update, relying on historical data or theoretical calculations and lacking the injection of real-time monitoring data; 3) insufficient parameter optimization. In the traditional design process, the determination of key parameters (such as blade angles, fan orientations) mainly relies on experience or experimental data, lacking systematic local optimization and iterative calculations, etc. Driven by the current requirements of wind energy utilization and equipment operation, traditional static, simplified, and empirical three-dimensional modeling technologies are difficult to meet the requirements of high-fidelity simulation and real-time optimization.

[0004] For example, the Chinese patent application with the publication number CN117115357A discloses a three-dimensional modeling method and its device. The method includes: performing target detection and recognition on a two-dimensional drawing to obtain a target detection and recognition result, where the target detection and recognition result includes the position information and / or category information of the target box; extracting the image region of the target box from the two-dimensional drawing according to the position information and / or category information of the target box; performing recognition processing on the image region of the target box based on optical character recognition OCR technology and / or a residual network to obtain the basic data information of the two-dimensional drawing; obtaining three-dimensional model data according to the basic data information, and generating a three-dimensional model according to the three-dimensional model data; performing standardization and lightweight processing on the three-dimensional model, and the processed three-dimensional model is compatible with the display frameworks of at least two of a personal computer (PC) terminal, a mobile terminal, and a World Wide Web (Web) terminal. This disclosure can improve the production efficiency of the model, and the three-dimensional model can be compatible with the display frameworks of at least two of the PC terminal, the mobile terminal, and the Web terminal.

[0005] As disclosed in the Chinese patent with the authorization announcement number CN116664783B, a modeling method for complex 3D modeling in a web environment is provided, including the following steps: Step 1: Construct a reference cross-section. When constructing the reference cross-section, it is constructed according to a discretization strategy or a formulation strategy. The discretization strategy includes drawing discrete points in the web page, constructing a closed curve based on the discrete points, and obtaining the reference cross-section. The formulation strategy includes calling a surface function in the web page, setting a coordinate plane, setting equally divided points based on the coordinate axes of the coordinate plane, constructing a closed curve based on the equally divided points, and obtaining the reference cross-section. Step 2: Based on the reference cross-section, adjust the position of the reference cross-section and perform a lofting operation in groups to generate a reference entity. Step 3: Based on the reference entity, assemble to generate a 3D model. This invention can complete complex 3D modeling based on the web end and has high timeliness, intuitiveness, and convenience.

[0006] Defects of the above prior art: Analyzing based on a static 3D model lacks dynamic characteristics and real-time performance. Summary of the Invention

[0007] In view of the deficiencies of the prior art, the present invention proposes a high-fidelity 3D modeling method and system. Through real-time data acquisition, fine wind field mapping, dynamic load calculation, and local parameter iterative optimization, it effectively makes up for the deficiencies of traditional technologies in dynamic response, data real-time performance, physical model accuracy, and parameter optimization, thereby realizing accurate simulation and performance improvement of the equipment operation state, and providing a solid technical support for the safety and economy of wind energy projects.

[0008] To achieve the above object, the present invention provides the following technical solutions:

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

[0010] Real-time collect data related to the rotating equipment, preprocess the surface data of the collected rotating equipment, and construct a 3D reference model of the rotating equipment;

[0011] 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 force field map, where the wind force field map contains wind force vector information of each coordinate point;

[0012] Map the wind force field map to the 3D reference model of the rotating equipment to generate a 3D model of the rotating equipment with dynamic loads;

[0013] Based on a preset optimization goal, locally optimize and adjust the blade angle and fan azimuth parameters of the rotating equipment, verify the optimization effect through iterative calculation, and output the optimal parameter solution.

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

[0015] Establish a coordinate system with the rotating equipment as the center, calculate the influence area radius, set boundary conditions, and establish the target wind field;

[0016] Divide the target wind field into multiple regular grids according to the preset spatial resolution, and each grid represents an area;

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

[0018] Using the real-time wind speed and wind direction data collected at each grid point, calculate the corresponding wind force vector and display it as a visual symbol in the target wind field to generate a dynamic wind force field map.

[0019] Specifically, the process of mapping the wind force 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 includes:

[0020] Map the wind vector data in the dynamic wind force field map generated in the previous stage to the three-dimensional reference model of the rotating equipment according to the spatial coordinates;

[0021] After the dynamic wind force field map and the three-dimensional reference model of the rotating equipment are mapped, based on the real-time wind speed and wind direction data, calculate the dynamic loads acting on each part of the equipment and distribute them to each node according to the shape and force characteristics of the equipment to form a dynamic load field with wind loads changing over time;

[0022] Inject the mapped wind load data into the three-dimensional reference model of the rotating equipment to generate a three-dimensional model of the rotating equipment with dynamic loads.

[0023] Specifically, the process of mapping the wind vector data in the dynamic wind force field map generated in the previous stage to the three-dimensional reference model of the rotating equipment includes:

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

[0025] Map the wind speed and wind direction information of each grid point in the dynamic wind force field map to the corresponding surface of the three-dimensional reference model of the rotating equipment according to its spatial coordinates;

[0026] After the preliminary data mapping is completed, perform geometric correction on the mapping result.

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

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

[0029] According to the structural characteristics and geometric shape 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] Perform dynamic calculations on the loads applied to each node or area to form a dynamic load field in which the wind load changes with time.

[0031] Specifically, based on the preset optimization objectives, locally optimize and adjust the blade angle and fan azimuth parameters of the rotating equipment, verify the optimization effect through iterative calculations, and output the optimal parameter scheme, including:

[0032] According to the actual application requirements and engineering objectives, set optimization indicators and define the constraint conditions for parameter optimization;

[0033] According to the design of the rotating equipment and historical data, determine the initial parameter values of the blade angle and fan azimuth, delimit a local search range, and construct a parameter space model based on the initial parameter values and the delimited search range;

[0034] Locally adjust the blade angle and fan azimuth, continuously update the parameters through iterative calculations, and evaluate the performance indicators under each set of parameters in real time;

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

[0036] Specifically, according to the design of the rotating equipment and historical data, determine the initial parameter values of the blade angle and fan azimuth, delimit a local search range, and construct a parameter space model based on the initial parameter values and the delimited search range, including:

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

[0038] According to the design requirements of the rotating equipment and the data analysis results, determine the initial parameter values of the blade angle and fan azimuth;

[0039] After determining the initial parameter values, a local search range is set according to the physical constraints, operating limits, and safety requirements of the device.

[0040] Based on the initial parameter values and the set search range, a parameter space model is constructed.

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

[0042] The reference model establishment module is used to collect data related to the rotating equipment in real time, preprocess the surface data of the collected rotating equipment, and construct a 3D reference model of the rotating equipment.

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

[0044] The mapping module is used to map the wind field map to the 3D reference model of the rotating equipment to generate a 3D model of the rotating equipment with dynamic loads.

[0045] The parameter adjustment module is used to perform local optimization and adjustment on the blade angle and fan azimuth parameters of the rotating equipment based on a preset optimization goal, verify the optimization effect through iterative calculation, and output the optimal parameter solution.

[0046] Specifically, the mapping module includes: a data mapping unit, a dynamic load field unit, and a 3D model update unit;

[0047] The data mapping unit is used to map the wind force vector data in the dynamic wind field map generated in the previous stage to the 3D 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 the real-time wind speed and wind direction data after the dynamic wind field map is mapped to the 3D reference model of the rotating equipment, and distribute them to each node according to the shape and force characteristics of the equipment to form a dynamic load field where the wind load changes with time.

[0049] The 3D model update unit is used to inject the mapped wind load data into the 3D reference model of the rotating equipment to generate a 3D model of the rotating equipment with dynamic loads.

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

[0051] 1. The present invention proposes a high-fidelity 3D modeling method. By using real-time wind field data, precise mapping, dynamic load injection, and iterative calculations, key parameters such as structural vibration, fatigue, and local stress can be accurately simulated, enabling the establishment of a high-fidelity 3D model of the rotating machine equipment.

[0052] 2. The present invention proposes a high-fidelity 3D modeling method. By locally optimizing parameters such as blade angles and fan orientations, the wind energy utilization efficiency can be maximized, the overall operating efficiency of the equipment can be improved, and the optimization scheme is iteratively verified, which can reduce energy losses and equipment wear caused by improper parameter settings, thereby extending the equipment life.

[0053] 3. The present 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 display and real-time monitoring, and reduces risks brought about by environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0055] Figure 2 is a flowchart of the dynamic mapping provided by the present invention;

[0056] Figure 3 is an architecture diagram of a high-fidelity 3D modeling system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0057] The present invention will be further described below in conjunction with the specific embodiments.

[0058] Embodiment 1

[0059] Please refer to Figure 1 and Figure 2 , an embodiment provided by the present invention: a high-fidelity 3D modeling method, including the following specific steps:

[0060] Step S1: Real-time collect data related to the rotating machine equipment, preprocess the collected surface data of the rotating machine equipment, and construct a 3D reference model of the rotating machine equipment;

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

[0062] Data preprocessing includes: noise filtering and outlier removal, filtering the original collected data to remove noise and outliers caused by sensor errors or environmental interference; data registration, when the data comes from multiple angles or different sensors, aligning the data from each perspective through a registration algorithm (such as the ICP algorithm) to generate a complete point cloud dataset; scale correction and standardization, performing scale transformation and normalization on the data to ensure compatibility and unity between different datasets and provide a standardized data basis for subsequent modeling;

[0063] Segmentation and feature extraction: By methods such as edge detection and region growing, segment the surface of the device and extract key features (such as edges, corners, and surface information) to provide accurate geometric features for 3D modeling;

[0064] Construct a 3D reference model: Use voxelization, triangular mesh construction, or surface-based reconstruction methods to convert the preprocessed point cloud data into a continuous 3D model, optimize the preliminary model using techniques such as smoothing and mesh simplification to remove redundant data while maintaining geometric accuracy, and calibrate the key coordinate system and reference points in the model so that the 3D model can be used as a unified reference for subsequent device status monitoring, blade angle, and fan azimuth adjustment.

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

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

[0067] Step S201: Establish a coordinate system with the rotating equipment as the center, determine the influence area radius, set boundary conditions, and establish the target wind field;

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

[0069] In practical applications, an appropriate grid size can be selected according to the region size, wind field change characteristics, and computing power; grid division can standardize the analysis of the entire wind field, and each region can be calculated and compared independently. By subdividing the large-scale wind field, the spatial changes in wind speed and wind direction can be captured more accurately. After grid division, the data of each grid point can be processed in parallel, which is convenient for data collection and subsequent numerical simulation, improving the overall computing efficiency.

[0070] Step S203: For each grid point, collect real-time wind speed and wind direction data;

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

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

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

[0074] Step S3: Map the dynamic wind force 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 force vector data in the dynamic wind force field map generated in the previous stage to 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 according to the actual position and geometric shape of the equipment in space. The specific steps include:

[0078] Step S3011: First, confirm the coordinate systems used by the wind force field map and the three-dimensional reference model respectively. Usually, the wind force field map may use geographical coordinates (such as longitude, latitude, and elevation), while the equipment model may use local or engineering coordinates. At this time, a coordinate transformation algorithm is needed 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 systems, there will be no errors caused by coordinate deviations between the 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 force 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 characteristics of the equipment, such as the normal direction of the equipment surface, the curved surface shape, and the positions of key structural nodes; the 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 can obtain the corresponding wind field information to capture the wind characteristics of each part of the capture device; use interpolation algorithms to process boundary and missing data, making the mapped data more continuous and smooth on the device model; conduct key mapping at key parts (such as the blades and local parts of the tower), which helps to more accurately reflect the local stress conditions and provides detailed data support for subsequent structural analysis.

[0082] Step S3013: After completing the preliminary data mapping, geometric correction needs to be performed on the mapping results. mainly aiming at the small deviations that may occur due to the geometric complexity of the model or during the coordinate conversion process. Use geometric correction algorithms to adjust the position and distribution of wind field data on the model surface, making the overall mapping effect more accurate and realistic.

[0083] Automatically correct the possible spatial errors during the mapping process to improve the overall mapping accuracy; the corrected data enables the wind field to better fit the device structure and reflects the real engineering application scenario.

[0084] Step S302: After mapping the dynamic wind force field diagram and the three-dimensional reference model of the rotating equipment, based on the real-time wind speed and wind direction data, calculate the dynamic loads acting on each part of the equipment, including converting the 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 with time;

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

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

[0087] According to the wind direction information, decompose the wind pressure into force components acting on different directions of the rotating equipment, such as horizontal components and vertical components, or more refined shear forces, normal pressures, etc.;

[0088] Through physical formula conversion, ensure that the load calculation has a solid theoretical basis, convert the indirect index of wind speed into specific wind pressure and wind force, which is convenient for subsequent mechanical calculations and structural analysis; be able to obtain a more detailed load distribution according to different stress directions and provide multi-angle data support for structural optimization;

[0089] Step S3022: According to the structural characteristics and geometric shape of the rotating equipment, establish a load distribution model, and use interpolation, piecewise functions or finite element methods to distribute the global wind load data to each node or surface area of the three-dimensional reference model of the rotating equipment, reflecting the actual load magnitude and direction received by different regions due to the action of wind force;

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

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

[0092] Dynamic update ensures that the load calculation can promptly capture wind field changes, adapt to sudden wind conditions and dynamic response requirements, helps to study the vibration, fatigue, and ultimate load states of rotating equipment under different wind conditions, and improves the early warning and wind-resistant design capabilities; numerical methods allow the simulation of complex nonlinear and time-varying systems, making the results more in line with real engineering conditions.

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

[0094] In this embodiment, the wind load data obtained, mapped, and corrected in the previous steps is injected into the three-dimensional reference model of the rotating equipment. This step automatically updates the model through a data interface or script, enabling each part of the equipment model to carry the load information calculated in real time;

[0095] The advantages of doing this are: real-time performance, which can dynamically update the model with the latest wind load data and reflect the real working conditions; seamless data docking, ensuring the accurate correspondence between the wind load data and the equipment structure, laying a good foundation for subsequent analysis; reducing human intervention, improving data processing efficiency and update frequency, and facilitating long-term continuous monitoring.

[0096] Step S4: Based on preset optimization objectives, locally optimize and adjust the blade angle and fan azimuth parameters of the rotating equipment, verify the optimization effect through iterative calculations, and output the optimal parameter solution.

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

[0098] Step S401: According to the actual application requirements and engineering objectives, clarify the optimization indicators, such as maximizing the wind energy capture efficiency, minimizing vibration, or reducing equipment wear, etc. At the same time, define the constraint conditions for parameter optimization (such as equipment physical limits, environmental factors, and safety standards) to provide the basis for the target and scope of subsequent parameter adjustment;

[0099] Setting clear optimization goals helps to focus on key performance indicators, ensuring that the optimization work has a clear direction. By determining the constraints, it is possible to avoid unrealistic or safety-standard-violating parameter schemes 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 the fan orientation, and delimit a reasonable local search range. This process usually combines previous engineering experience and data analysis to initially define the parameter interval where better performance may be obtained;

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

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

[0103] The so-called key parameters here refer to clarifying which parameters have a greater impact on the overall performance through data analysis to guide subsequent parameter setting; Deeply understanding the working characteristics of the equipment helps to establish a parameter space model that is more in line with the actual working conditions;

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

[0105] The initial parameter values serve as the starting point for optimization, ensuring that subsequent iterations are carried out within a reasonable parameter interval with an engineering background; Taking reasonable initial parameters as the benchmark helps to converge quickly and reduce the high computational cost brought by large-scale search;

[0106] Step S4023: After determining the initial parameter values, set a local search range based on the physical constraints, operation limits, and safety requirements of the equipment. This range should be wide enough to cover the possible optimization space while avoiding 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 during the subsequent iterative optimization process. Usually, mathematical functions, discrete grids, or other numerical description methods can be used to establish the model.

[0108] Step S403: Using a mathematical model and numerical optimization algorithms (such as gradient descent, genetic algorithm, or particle swarm optimization, etc.), locally adjust the blade angle and fan orientation. Continuously update the parameters through iterative calculations, evaluate the performance indicators under each set of parameters in real-time, and adjust the parameter direction based on this until the preset optimization goal is met or converges to the optimal solution;

[0109] Iterative calculations can flexibly adapt to changes in the wind field and equipment operating conditions, and improve the parameter settings in real-time; Optimization algorithms can quickly screen out the parameter combinations that are most beneficial to performance improvement and shorten the optimization cycle;

[0110] Step S404: After each iteration, verify the performance of the current parameter scheme through simulation or experiment, and evaluate indicators such as wind energy utilization efficiency, structural response, and equipment safety in actual operation. When the iterative process reaches the preset convergence standard or achieves the optimal effect, output the final optimal parameter scheme and conduct a detailed result analysis.

[0111] Step S4 gradually realizes the fine control of the blade angle and fan orientation of the rotary equipment by clarifying the optimization goal, initial parameter setting, local parameter optimization iteration, and effect verification.

[0112] Embodiment 2

[0113] Please refer to Figure 3 , Another embodiment provided by the present invention: A high-fidelity 3D modeling system, including: a reference model establishment module, a wind field module, a mapping module, and a parameter adjustment module;

[0114] The reference model establishment module is used to collect relevant data of the rotary equipment in real-time, preprocess the surface data of the collected rotary equipment, and construct a 3D reference model of the rotary equipment;

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

[0116] The mapping module is used to map the wind field map to the 3D reference model of the rotary equipment to generate a 3D model of the rotary 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 rotary equipment based on a preset optimization goal, verify the optimization effect through iterative calculations, 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 the real-time wind speed and wind direction data after the dynamic wind field map is mapped to the three-dimensional reference model of the rotating equipment, and distribute them to each node according to the shape and force characteristics of the equipment, forming a dynamic load field in which the wind load changes with time;

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

[0122] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the present invention and the claims. These all fall within the protection scope of the present invention.

[0123] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A high-fidelity three-dimensional modeling method, which is applied to a turning machine equipment adjustment system. The turning machine equipment adjustment system includes turning machine equipment to be confirmed for adjustment and a processing device. The processing device is used to execute the high-fidelity three-dimensional modeling method, and is characterized in that, Including: Collecting relevant data of the rotating equipment in real time, preprocessing the surface data of the collected rotating equipment, and constructing a three-dimensional reference model of the rotating equipment; Dividing the target wind farm into grids, obtaining real-time wind speed and wind direction data of each grid point, and generating a dynamic wind force field map, where the wind force field map contains wind force vector information of each coordinate point; Mapping the wind force 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; Based on a preset optimization goal, locally optimizing and adjusting the blade angle and fan azimuth parameters of the rotating equipment, verifying the optimization effect through iterative calculation, and outputting the optimal parameter solution.

2. The high-fidelity three-dimensional modeling method according to claim 1, characterized in that The dividing the target wind farm into grids, obtaining real-time wind speed and wind direction data of each grid point, and generating a dynamic wind force field map includes: Establishing a coordinate system with the rotating equipment as the center, calculating the influence area radius, setting boundary conditions, and establishing the target wind farm; Dividing the target wind farm into multiple regular grids according to a preset spatial resolution, and each grid represents a region; For each grid point, collecting real-time wind speed and wind direction data; Using the real-time wind speed and wind direction data collected from each grid point, calculating the corresponding wind force vector, and visualizing it with symbols in the target wind farm to generate a dynamic wind force field map.

3. The high-fidelity three-dimensional modeling method according to claim 1, characterized in that, The mapping the wind force 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 includes: Mapping the wind force vector data in the dynamic wind force field map generated in the previous stage to the three-dimensional reference model of the rotating equipment according to the spatial coordinates; After the dynamic wind force field map is mapped to the three-dimensional reference model of the rotating equipment, based on the real-time wind speed and wind direction data, calculating the dynamic loads acting on each part of the equipment, and distributing them to each node according to the shape and force characteristics of the equipment, forming a dynamic load field with wind loads changing over time; Injecting the mapped wind load data into the three-dimensional reference model of the rotating equipment to generate a three-dimensional model of the rotating equipment with dynamic loads.

4. A high-fidelity three-dimensional modeling method according to claim 3, characterized in that The mapping the wind force vector data in the dynamic wind force field map generated in the previous stage to the three-dimensional reference model of the rotating equipment according to the spatial coordinates includes: Aligning the coordinate systems of the dynamic wind force field map and the three-dimensional reference model of the rotating equipment; Mapping the wind speed and wind direction information of each grid point in the dynamic wind force field map to the corresponding surface of the three-dimensional reference model of the rotating equipment according to its spatial coordinates; After completing the preliminary data mapping, performing geometric correction on the mapping result.

5. The high-fidelity three-dimensional modeling method according to claim 3, wherein, After the dynamic wind force field map is mapped to the three-dimensional reference model of the rotating equipment, based on the real-time wind speed and wind direction data, calculating the dynamic loads acting on each part of the equipment, and distributing them to each node according to the shape and force characteristics of the equipment, forming a dynamic load field with wind loads changing over time includes: Using the wind speed data and wind direction information, converting the real-time wind speed into the corresponding wind pressure, and decomposing the wind pressure into force components acting on different directions of the rotating equipment; According to the structural characteristics and geometric shape of the rotating equipment, establishing a load distribution model, and distributing the global wind load data to each node or surface area of the three-dimensional reference model of the rotating equipment; Dynamically calculate the loads applied to each node or region to form a dynamic load field where the wind load varies with time.

6. The high-fidelity three-dimensional modeling method according to claim 1, wherein, Based on the preset optimization objectives, locally optimize and adjust the blade angles and fan azimuth parameters of the rotating equipment, verify the optimization effect through iterative calculations, and output the optimal parameter solution, including: Set optimization indicators and define the constraint conditions for parameter optimization according to the actual application requirements and engineering objectives; Determine the initial parameter values of the blade angles and fan azimuth based on the design and historical data of the rotating equipment, delimit a local search range, and construct a parameter space model based on the initial parameter values and the delimited search range; Locally adjust the blade angles and fan azimuth, continuously update the parameters through iterative calculations, and evaluate the performance indicators under each set of parameters in real time; After each iteration, verify the performance of the current parameter solution through simulation or experiment. When the iterative process reaches the preset convergence criterion or the optimal effect is achieved, output the final optimal parameter solution.

7. The high-fidelity three-dimensional modeling method according to claim 6, wherein, The step of determining the initial parameter values of the blade angles and fan azimuth based on the design and historical data of the rotating equipment, delimit a local search range, and construct a parameter space model based on the initial parameter values and the delimited search range includes: Collect the design documents, technical parameters, and historical operation data of the rotating equipment, perform statistical and trend analysis on the collected data, and identify the key parameters that have the greatest impact on the equipment performance; Determine the initial parameter values of the blade angles and fan azimuth according to the design requirements of the rotating equipment and the data analysis results; After determining the initial parameter values, set a local search range based on the physical constraints, operating limits, and safety requirements of the equipment; Construct a parameter space model based on the initial parameter values and the set search range.

8. A high-fidelity 3D modeling system for implementing a high-fidelity 3D modeling method according to any one of claims 1-7, characterized in that, Including: A benchmark model establishment module, a wind force field module, a mapping module, and a parameter adjustment module; The benchmark model establishment module is used to collect relevant data of the rotating equipment in real time, preprocess the surface data of the collected rotating equipment, and construct a three-dimensional benchmark model of the rotating equipment; The wind force field module is used to divide the target wind field into grids, obtain the real-time wind speed and wind direction data of each grid point, and generate a dynamic wind force field map, which contains the wind force vector information of each coordinate point; The mapping module is used to map the wind force field map to the three-dimensional benchmark model of the rotating equipment to 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 angles and fan azimuth parameters of the rotating equipment based on the preset optimization objectives, verify the optimization effect through iterative calculations, and output the optimal parameter solution.

9. A high-fidelity three-dimensional modeling system according to claim 8, 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 force vector data in the dynamic wind force field map generated in the previous stage to the three-dimensional benchmark model of the rotating equipment according to the spatial coordinates; The dynamic load field unit is used to calculate the dynamic loads acting on various parts of the equipment based on real-time wind speed and direction data after mapping the dynamic wind field map to the three-dimensional reference model of the rotating equipment, and distribute them to each node according to the shape and force characteristics of the equipment, forming a dynamic load field where the wind load changes with time; The three-dimensional model updating unit is used to inject the mapped wind load data into the three-dimensional reference model of the rotating equipment to generate a three-dimensional model of the rotating equipment with dynamic loads.

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