Wind power plant profile virtual-real fusion real-time reconstruction method and system

Abstract

The invention relates to the technical field of wind power plant monitoring and management, and particularly discloses a virtual-real fusion real-time reconstruction method and system for a wind power plant profile. Comprising five stages: firstly, realizing task adaptive allocation through a heterogeneous edge-cloud cooperative computing node cluster; secondly, performing semantic paragraph division and value marking on the wind power plant based on terrain, wake flow and power gradient features; thirdly, calculating a semantic fracture index to judge the profile stability, and generating a reconstruction instruction when needed; then, differential visual reconstruction is executed according to the value label, and dual-path robustness verification is carried out in parallel; and finally, performing closed-loop optimization on system parameters in combination with interactive feedback and a verification result. According to the method, the whole process from data acquisition, semantic understanding, intelligent reconstruction to continuous optimization is realized, and the real-time performance, the accuracy and the self-adaptive capability of visual analysis of the wind power plant are improved.

Description

A method and system for real-time reconstruction of wind farm profiles using virtual and real-world fusion Technical Field

[0001] This invention relates to the field of wind farm monitoring and management technology, specifically to a method and system for real-time reconstruction of wind farm profiles through virtual-real fusion. Background Technology

[0002] With the acceleration of the global energy transition, wind power is playing an increasingly important role in the electricity mix. However, the operational efficiency of wind farms remains limited by their complex operating environment and inadequate monitoring methods. Traditional wind farm monitoring systems often rely on two-dimensional static charts or simple 3D models with low refresh rates for cross-sectional display, making it difficult to reflect changes in transient physical fields such as turbulence, wake effects from complex terrain, and gusts in real time and intuitively. Studies indicate that wake effects can cause downstream turbine power losses of up to 20%, and traditional static display methods cannot effectively capture its dynamic evolution, leading to delayed operation and maintenance decisions and becoming one of the key bottlenecks restricting the overall power generation efficiency of wind farms.

[0003] At the computational and visualization levels, existing methods often employ a uniform rendering strategy, failing to differentiate the importance of different areas within the wind farm. For example, they allocate the same computational and rendering resources to areas with gentle terrain and weak wake effects as to high-value areas located at canyon entrances with severe wake superposition. An analysis of a typical 100-megawatt wind farm monitoring system shows that over 60% of graphics computing resources are consumed in low-value areas with low information entropy, resulting in insufficient visualization details of high-value information such as power surges and potential mechanical failure precursors, and an increased rate of missed alarms for critical alarm information.

[0004] Current technologies rely heavily on fixed rules or human experience to determine where key monitoring areas should be monitored, lacking adaptive and quantitative identification mechanisms. Although some studies have attempted to introduce machine learning for anomaly detection, these studies mostly focus on single units or single physical quantities, failing to integrate and collaboratively judge multiple semantic dimensions such as terrain, wake, and power gradient, resulting in limited accuracy in identifying high-value areas. Summary of the Invention

[0005] This invention aims to provide a method and system for real-time reconstruction of wind farm profiles through virtual-real fusion, addressing the problems in existing technologies such as static profile display lacking dynamic details, insufficient rendering of key information due to unreasonable allocation of edge and cloud computing resources, and inaccurate identification of high-value areas due to a lack of multi-dimensional quantitative judgment. By dynamically deploying and optimizing heterogeneous edge-cloud collaborative computing tasks in each reconstruction cycle, performing semantic segmentation and three-layer threshold judgment marking on the wind farm profile, and triggering differentiated reconstruction and dual-path verification based on a semantic breakage index, this invention achieves accurate identification of high-value areas in the wind farm profile, stable real-time reconstruction of the virtual-real fusion view, and intelligent optimization of the monitoring and maintenance closed loop.

[0006] The technical solution of this application specifically includes: According to one aspect of this application, a real-time reconstruction method for virtual-real fusion of wind farm profiles is provided, comprising: before the start of each reconstruction cycle, deploying a heterogeneous edge-cloud collaborative computing node cluster to collect real-time status and historical performance; automatically matching and allocating five types of sub-tasks—data acquisition, semantic extraction, deviation detection, structural rearrangement, and interactive feedback analysis—to the optimal node, and outputting a task allocation scheme; with the support of the task allocation scheme, dividing the wind farm along the prevailing wind direction into multiple semantic segments, each segment encapsulating three types of physical semantic features: terrain, wake, and power gradient; initiating a three-layer nested judgment for the semantic segments: setting thresholds for terrain complexity, wake significance, and power gradient steepness respectively, and comparing them; marking them as high-value areas, medium-value areas, and low-value areas based on the comparison results, and finally outputting a labeled semantic segment map. The semantic segment graph is calculated for each region. If the index is below a threshold, the current profile structure is considered stable, and a "no reconstruction needed" flag is output. If the index exceeds the threshold, a reconstruction instruction package is generated based on the semantic segment graph. The semantic segment graph is reconstructed according to the reconstruction instruction package, with parallel dual-path verification: one path performs adversarial robustness testing on the profile view through parameter perturbation, while the other path reverses the semantic segment division of the reconstruction instructions to verify whether the current reconstruction result is a necessary solution for the semantic segment. Finally, a verified profile view and verification result report are output. If the input is a "no reconstruction needed" flag, parameters are fine-tuned based on the current system state. If the input is a verified profile view and verification result report, the interaction trajectory between the operations personnel and the profile view is captured, and system parameters are adjusted based on the verification result report. After parameter adjustment, the system state is updated and the system waits for the next reconstruction cycle to trigger.

[0007] As a further option of the method of the present invention, the steps of deploying a heterogeneous edge-cloud collaborative computing node cluster to collect real-time status and historical performance, and automatically matching and allocating five types of sub-tasks to the optimal node include: the central management platform periodically sending status query instructions to each computing node in the cluster; after receiving the instructions, the node collects local CPU utilization, memory utilization, remaining network bandwidth percentage, average network latency between the node and the wind farm data source, and node type identifier, encapsulates them into a status vector, and reports them; the central management platform maintains a node historical performance database, records and statistically analyzes the historical average execution efficiency and result reliability score of each node executing different types of tasks; based on the reported real-time status vector and historical performance data, the central management platform calculates the real-time comprehensive performance evaluation value of each node; the real-time comprehensive performance evaluation value is obtained by weighted synthesis of real-time load factor, network condition factor, and historical performance factor; based on the real-time comprehensive performance evaluation value and the preset task-specific preference factor, the system constructs a task type to computing node fit matrix; using the fit matrix as the benefit matrix, an auction algorithm is used to solve the task allocation scheme that maximizes the total fit, and the scheme is distributed to each computing node.

[0008] As a further option of the method of the present invention, the step of marking regions as high-value, medium-value, and low-value regions based on the comparison results includes: setting corresponding high-value judgment thresholds for the three types of physical semantic features: terrain complexity, wake significance, and power gradient steepness; performing the following steps sequentially for each semantic segment: First-level judgment: determining whether the terrain complexity of the semantic segment is greater than or equal to the terrain complexity threshold; if yes, recording the first flag as 1, otherwise recording as 0; Second-level judgment: determining whether the wake significance of the semantic segment is greater than or equal to the wake significance threshold. If yes, record the second flag as 1; otherwise, record it as 0. The third layer of judgment: determine whether the power gradient steepness of the semantic paragraph is greater than or equal to the power gradient steepness threshold. If yes, record the third flag as 1; otherwise, record it as 0. Add the first, second, and third flags of the semantic paragraph to obtain the number of qualified layers of the semantic paragraph. If the number of qualified layers is equal to 3, the semantic paragraph is marked as a high-value region. If the number of qualified layers is equal to 2, the semantic paragraph is marked as a medium-value region. If the number of qualified layers is less than or equal to 1, the semantic paragraph is marked as a low-value region.

[0009] As a further option of the method of the present invention, the step of dividing the wind farm into multiple semantic segments along the prevailing wind direction, and encapsulating three types of physical semantic features of terrain, wake, and power gradient in each segment includes: acquiring digital elevation model data of the wind farm, wind turbine layout coordinates, and the current prevailing wind direction vector; starting from the wind farm inlet boundary, cutting the wind farm area into a series of continuous strips perpendicular to the prevailing wind direction vector along the direction of the prevailing wind direction vector, with each strip defined as a semantic segment; and for each semantic segment, executing data acquisition and feature extraction subtasks in parallel, with the subtasks scheduled to the optimal computing node according to the task allocation scheme, in order to extract and encapsulate the terrain complexity, wake significance, and power gradient steepness of the semantic segment.

[0010] As a further option of the method of the present invention, the formula for calculating the semantic breakage index is: ;in, and These are the weighting coefficients; For value label jump penalty items, , The penalty coefficient is... This represents the absolute value of the difference in tag ordinal numbers between the current semantic paragraph and the upstream semantic paragraph. This represents the absolute value of the difference in tag ordinal numbers between the current semantic paragraph and the downstream semantic paragraph. For the physical feature gradient magnitude term, , The feature vector difference between the current semantic paragraph and the upstream semantic paragraph. The feature vector difference between the current semantic paragraph and the downstream semantic paragraph. This represents the L2 norm of a vector.

[0011] As a further option of the method of the present invention, the step of generating a reconstruction instruction package based on the semantic breakage index includes: identifying all semantic segments in the semantic segment graph whose semantic breakage index exceeds a preset breakage threshold, forming a key breakage region set; for each semantic segment in the key breakage region set, specifying a reconstruction operation type based on the value tag already marked in the semantic segment graph, the reconstruction operation type includes: if it is a high-value region, specifying the operation type as enhanced rendering; if it is a medium-value region, specifying the operation type as standard rendering; if it is a low-value region, specifying the operation type as compression simplification; for semantic segments whose operation type is specified as enhanced rendering, introducing a spatial stretching factor generated based on the segment semantic breakage index mapping; and encapsulating the key breakage region set, the reconstruction operation type corresponding to each region, and the parameters into a structured reconstruction instruction package.

[0012] As a further option of the method of the present invention, the reconstruction of the semantic segment map according to the reconstruction instruction package includes: the rendering engine receiving the semantic segment map and the reconstruction instruction package; traversing each semantic segment in the semantic segment map, checking the value label of the semantic segment and the specified operation type in the reconstruction instruction package: if the semantic segment is marked as a high-value region and the reconstruction instruction package specifies enhanced rendering, then spatial stretching, calling a high-resolution model for detail enhancement, and superimposing dynamic particle tracing driven by computational fluid dynamics simulation data are performed on the segment; if the semantic segment is marked as a low-value region and the reconstruction instruction package specifies compression simplification, then wind turbine icon aggregation display and low-polygon terrain simplification rendering are performed on the segment; standard rendering with the system default precision is performed on other semantic segments.

[0013] As a further option of the method of the present invention, the parallel dual-path verification includes: executing the first path, namely the adversarial robustness test under parameter perturbation, including: applying random noise to the physical feature vectors in the semantic segment graph to generate a perturbation graph; reconstructing the perturbation graph based on the same reconstruction instruction package to generate a perturbation-reconstructed view; obtaining the difference between the perturbation-reconstructed view and the original reconstruction view, and comparing it with a first robustness threshold to determine whether the test passes; executing the second path, namely the logical necessity verification of the reverse backtracking of the reconstruction instruction, including: inferring the value label applied to each semantic segment from the visual style of the generated reconstructed profile view; re-obtaining the theoretical value label of each semantic segment using the original physical feature data and the same three-layer nested judgment rule; judging the overall consistency rate between the inferred label and the theoretical label, and comparing it with a second consistency threshold to determine whether the verification passes.

[0014] As a further option of the method of the present invention, the step of capturing the interaction trajectory between the maintenance personnel and the profile view, and adjusting the system parameters in conjunction with the verification result report, includes: the system displays the verified profile view through a human-computer interaction interface and captures the sequence of interaction events generated by the maintenance personnel; extracting interaction trajectory features from the interaction event sequence, including the dwell time in high-value marked areas, the frequency of grazing in low-value marked areas, and the set of coordinates of manually added points of interest; reading the conclusion and detailed data of the verification result report; if the verification result report concludes as successful, analyzing the overlap between the interaction trajectory features and the high-value areas marked by the system, and adjusting the terrain complexity, wake significance, or power gradient steepness thresholds involved in the semantic segment value marking based on the analysis results; if the verification result report concludes as failed, combining the reasons for failure in the report with the interaction trajectory features, locating the weak links in the system, and adjusting the feature extraction algorithm parameters, task allocation strategy, or judgment threshold.

[0015] This application also provides a real-time reconstruction system for wind farm profiles, comprising: a task collaborative allocation module, used to deploy a heterogeneous edge-cloud collaborative computing node cluster before the start of each reconstruction cycle, collect real-time status data of computing nodes and obtain their historical performance data, automatically match and allocate five types of sub-tasks—data acquisition, semantic extraction, deviation detection, structural rearrangement, and interactive feedback analysis—to the optimal computing node based on the real-time status data and historical performance data, and output a task allocation scheme; a semantic value classification module, used to divide the wind farm area into multiple semantic segments along the prevailing wind direction under the support of the task allocation scheme, extract and encapsulate three types of physical semantic features of each semantic segment in parallel—terrain complexity, wake significance, and power gradient steepness—and perform three-level nested judgment on each semantic segment to mark it as a high-value region, medium-value region, or low-value region according to the comparison results of preset thresholds, and finally generate and output a semantic segment map with value labels; and an intelligent reconstruction decision module, used to calculate the semantic breakage index of each region in the semantic segment map, and classify the semantic value into three levels. The semantic fracture index is compared with a preset fracture threshold. If it is lower than the fracture threshold, the profile structure is determined to be stable and a "no reconstruction required" flag is output. If it exceeds the fracture threshold, a reconstruction instruction package containing a list of key fracture regions, differentiated reconstruction operation types, and related parameters is generated. The profile rendering and verification module is used to perform differentiated visual reconstruction of the semantic segment map based on the reconstruction instruction package to generate a preliminary reconstructed profile view, and to perform dual-path robustness verification in parallel: the first path performs adversarial robustness testing by applying perturbations to physical features, and the second path performs logical necessity verification by comparing backward inference with forward rules, and finally outputs the verified profile view and verification result report. The closed-loop optimization and update module is used to receive and determine the input type: if the input is a "no reconstruction required" flag, the parameters are fine-tuned based on the current system operating status; if the input is a verified profile view and verification result report, the interaction trajectory between the operation and maintenance personnel and the verified profile view is captured, and the system parameters are adaptively adjusted in combination with the verification result report; after the parameter adjustment is completed, the system status is updated, and the system waits for the next reconstruction cycle to be triggered.

[0016] The beneficial effects of this application are as follows: This method, by reconstructing the wind farm profile in real time and dynamically calculating the semantic fracture index, can promptly capture the dynamic evolution of the wake effect. This enables operation and maintenance personnel to quickly identify and respond to transient changes in the wind farm structure, thereby potentially mitigating the power loss of downstream units by up to 20% caused by the wake and improving the overall power generation efficiency of the wind farm.

[0017] This method, through multi-physical feature fusion and a three-layer nested judgment mechanism, enables the system to achieve semantic value classification of wind farm areas and perform differentiated rendering based on value tags. It completely reverses the inefficient situation in traditional monitoring systems where over 60% of graphics computing resources are consumed in low-value areas, precisely focusing core computing power and rendering resources on high-value information, significantly enhancing the visualization details and recognizability of critical alarms.

[0018] This method combines interactive feedback with dual-path verification results for self-optimization, continuously adjusting the value judgment threshold and task allocation strategy. This not only improves the accuracy of identifying high-value areas with complex terrain and significant wakes, but also endows the system with the ability to continuously evolve and adapt, thereby optimizing monitoring performance in the long term and effectively supporting intelligent operation and maintenance decisions for wind farms. Attached Figure Description

[0019] Figure 1 is an overall schematic diagram of the real-time reconstruction method of virtual and real fusion of wind farm profiles; Figure 2 is a flowchart of the real-time reconstruction method of virtual and real fusion of wind farm profiles S100; Figure 3 is a flowchart of the real-time reconstruction method of virtual and real fusion of wind farm profiles S200; Figure 4 is a flowchart of the real-time reconstruction method of virtual and real fusion of wind farm profiles S300; Figure 5 is a flowchart of the real-time reconstruction method of virtual and real fusion of wind farm profiles S400; Figure 6 is a flowchart of the real-time reconstruction method of virtual and real fusion of wind farm profiles S500. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The core theoretical foundation of this invention is built upon the theories of real-time processing and scheduling of high-dimensional data streams, multimodal semantic feature fusion and decision-making, and closed-loop verification and optimization driven by digital twins. By constructing a task adaptive allocation model in a heterogeneous computing environment, designing a regional value grading mechanism based on multi-semantic features and a three-layer nested criterion, and introducing a semantically fracture-driven intelligent reconstruction triggering and dual-path robust verification strategy, a complete link is ultimately formed from task collaboration, semantic understanding, intelligent reconstruction to interactive optimization.

[0022] The specific embodiments of the present invention will be described in detail below.

[0023] Example 1: Please refer to Figure 1, which shows the overall flowchart of a real-time reconstruction method for wind farm profiles using virtual-real fusion provided by an embodiment of the present invention. The method includes: S100: Deployment and adaptive task allocation of heterogeneous edge-cloud collaborative computing node clusters; S200: Wind farm semantic segmentation, physical feature encapsulation, and regional value marking; S300: Profile structure stability judgment and reconstruction instruction generation based on semantic fracture index; S400: Differentiated profile reconstruction execution and dual-path robustness verification; S500: Interactive feedback-driven system parameter fine-tuning and state update.

[0024] The following provides a detailed explanation of each stage.

[0025] In a real-time reconstruction method for virtual-real fusion of wind farm profiles, S100 dynamically deploys and evaluates heterogeneous computing clusters before the start of each reconstruction cycle, thereby achieving automatic matching and allocation of five key sub-tasks to the optimal computing nodes, laying an efficient and collaborative computing foundation for subsequent processing.

[0026] Please refer to Figure 2, which shows a flowchart of the S100 stage in an exemplary embodiment of this application.

[0027] S110: In this embodiment, the system maintains a heterogeneous computing resource pool that includes edge computing nodes and cloud virtual machines. Edge nodes are deployed inside wind farm booster stations or key wind turbine towers, responsible for low-latency data acquisition and preliminary processing; cloud nodes have powerful computing capabilities, responsible for complex semantic analysis and global optimization.

[0028] During the implementation of this step, the dynamic perception and state modeling of the heterogeneous edge-cloud collaborative computing node cluster includes: S111: Periodically, the central management platform sends data to each computing node in the cluster. Send a status query command. Node Upon receiving the instruction, collect local multidimensional state data and encapsulate it into a state vector. Report it to higher authorities.

[0029] In one possible implementation of this embodiment, the state vector Includes the following field: Node identifier Current CPU utilization Current memory utilization Current network bandwidth remaining percentage Average network latency between nodes and wind farm data sources and node type identifier ,in, Represents edge nodes. This represents a cloud node.

[0030] S112: The central management platform maintains a database of node historical performance. Whenever a computational task occurs on a node... Once the task is completed, the platform records the task type, input data volume, execution time, and final result accuracy. By statistically analyzing historical execution records of similar tasks on the same node, the node's... Based on task type Historical average execution efficiency and result reliability score .

[0031] S113: To quantify the real-time overall performance of nodes and provide a basis for task allocation, the system calculates the performance of each node. Real-time comprehensive performance evaluation value . The calculation combines real-time status and historical performance, and its formula is as follows: ;in, For adjustable weighting coefficients, satisfying This is used to balance the importance of real-time resources, network conditions, and historical performance. It is based on the task type The weight, . in the formula The item will translate high load into low efficiency contribution. This allows low-latency nodes to receive higher scores.

[0032] S120: In this embodiment, the five types of sub-tasks are defined as: data acquisition Semantic extraction Deviation detection Structural rearrangement and interactive feedback analysis .

[0033] During this step, the optimal node matching and allocation scheme for the five types of subtasks is generated, including: S121: System construction task type To compute node fitness matrix Elements in the matrix Indicates task type At the node The expected fit of the execution. The calculation is based not only on the overall performance of the nodes It also introduced task-specific preference factors. . The calculation formula is: ,in This is the global balance coefficient.

[0034] S122: The system constructs a bipartite graph matching problem by matching the five types of subtask instances that need to be executed within the current reconstruction cycle with the currently available set of computing nodes. The fitness matrix is ​​used as the basis for this matching. As the benefit matrix, an auction algorithm is used to solve for the task allocation scheme that maximizes the overall fitness. ,in Indicates the task instance Assigned to node implement.

[0035] S123: The central management platform will finalize the task allocation plan. The allocation plan is distributed to each computing node. Each node loads the corresponding task processing program and model according to the allocation plan, preparing to receive data and execute tasks. The platform synchronously outputs this allocation plan as the control basis for the subsequent data processing flow in the S200 phase.

[0036] In a real-time reconstruction method for virtual-real fusion of wind farm profiles, S200 performs semantic segmentation of the wind farm based on the task allocation scheme output by S100, extracts key physical features, and assigns value tags to each segment through a three-layer nested judgment rule to generate a semantic segment map with value tags.

[0037] Please refer to Figure 3, which shows a flowchart of the S200 stage in an exemplary embodiment of this application.

[0038] S210: In this embodiment, the system acquires the digital elevation model data of the wind farm, the wind turbine layout coordinates, and the current prevailing wind direction vector. Starting from the boundary of the wind farm inlet, along the prevailing wind direction vector... The direction divides the wind farm area into a series of continuous, perpendicular sections. The stripes. Each stripe is defined as a semantic segment. , .

[0039] S220: For each semantic paragraph The task of extracting three types of physical semantic features is executed in parallel, and the extraction task is assigned to the optimal node for execution according to the S100 scheme.

[0040] During this step, the physical characteristics of the semantic paragraphs include: S221: terrain complexity. Quantification The degree to which inland ground undulations interfere with wind flow.

[0041] S222: Wake Significance Quantification The severity of the wake effect of the internal fan.

[0042] S223: Power gradient steepness Quantification The degree of drastic change in the output power of the internal fan.

[0043] S230: In this embodiment, the terrain is complex. Wake salience and power gradient steepness Set high-value judgment thresholds respectively , , .

[0044] In this step, the three-layer nested judgment and value marking process includes: S231: The first layer judgment is the terrain complexity criterion assessment. Judgment conditions: If true, record the flag bit. ,otherwise .

[0045] S232: The second level of judgment is based on the wake significance meeting the standard. Judgment conditions: If true, record the flag bit. ,otherwise .

[0046] S233: The third layer judgment is based on whether the power gradient steepness meets the standard. Judgment conditions: If true, record the flag bit. ,otherwise .

[0047] S234: Calculating semantic paragraphs The number of qualified layers .

[0048] like Then Marked as a high-value area.

[0049] like Then Marked as a medium-value area.

[0050] like Then Marked as a low-value area.

[0051] S240: The system completes the semantic paragraphs with all tags. and their corresponding value tags and original physical characteristic values Including spatial location information, it integrates to generate structured data objects, namely labeled semantic segment maps. And output it to the subsequent stages.

[0052] In a real-time reconstruction method for virtual-real fusion of wind farm profiles, S300 performs structural stability assessment on semantic segment maps, determines whether profile reconstruction is needed by calculating semantic fracture index, and generates precise reconstruction instruction packages when necessary.

[0053] Please refer to Figure 4, which shows a flowchart of the S300 stage in an exemplary embodiment of this application.

[0054] S310: In this embodiment, the system reads the semantic segment graph. For semantic paragraph maps Each semantic paragraph Extracting value tags and physical feature vectors At the same time, define The upstream and downstream adjacent sections. Along the prevailing wind direction, Its upstream directly adjacent segment, It is the paragraph directly adjacent to its downstream segment.

[0055] S320: Semantic Breakage Index Used for quantification The degree to which the semantic continuity of the wind field profile is interrupted or abruptly changed.

[0056] In this step, the semantic fragmentation index calculation includes: S321: Defining the ordinal mapping of value tags, where low-value mapping is 1, medium-value mapping is 2, and high-value mapping is 3. Calculation Upstream absolute value of the difference in label ordinal numbers Similarly, computation and downstream The difference .

[0057] Value tag jump penalty item Defined as: ,in This is the penalty coefficient.

[0058] S322: Calculation Upstream eigenvector difference .

[0059] calculate Downstream eigenvector difference .

[0060] Physical feature gradient magnitude term Defined as the average of the magnitudes of two difference vectors: ,in This represents the L2 norm of a vector.

[0061] S323: Semantic Paragraph semantic break index The above two items are combined by weighting: ,in and This is a weighting coefficient used to balance the effects of label mutations and feature changes.

[0062] S330: System preset global semantic break threshold .

[0063] During this step, global stability assessment and branch handling include: S331: Computational graph Find the semantic breakage index of all semantic paragraphs and identify the maximum value. .

[0064] S332: Comparison and .like If the current wind farm profile structure is stable overall, then no large-scale reconstruction is required. The system generates a "no reconstruction required" flag. And then jump directly to the S500 stage.

[0065] like If the current profile structure is unstable, it is determined that there are semantic breakpoints that need to be reconstructed. The system then enters S340.

[0066] S340: When a refactoring is determined to be necessary, the system generates a detailed refactoring instruction package. .

[0067] During this step, the refactoring instruction package generation includes: S341: Identify all that satisfy... semantic paragraphs Semantic paragraph The key fracture areas are marked as a set. .

[0068] S342: For each critical fracture region The type of reconstruction operation is specified based on the value tag obtained in S200.

[0069] like For high-value areas, specify the operation type as enhanced rendering.

[0070] like If the value region is specified, then the operation type is set to standard rendering.

[0071] like If the region is of low value, then the operation type is specified as compression and simplification.

[0072] For non-critical fracture areas, the default rendering operation is also specified based on their value tags.

[0073] S343: For enhanced rendering areas, a spatial stretching factor is introduced. . based on The magnitude is obtained by linear or nonlinear mapping. Other parameters, such as the intensity of detail enhancement and the ratio of compression simplification, are also introduced parametrically based on the value tag and fracture index.

[0074] S344: List of key fracture areas The reconstruction operation type for each region, the reconstruction parameters introduced, and the entire graph. The metadata is collectively encapsulated into a structured refactoring instruction package. Output to the S400 stage.

[0075] In a real-time reconstruction method for virtual-real fusion of wind farm profiles, the S400 performs differentiated visual reconstruction of semantic segment maps based on reconstruction instruction packages, and performs dual-path verification in parallel to ensure the robustness and logical necessity of the reconstruction results.

[0076] Please refer to Figure 5, which shows a flowchart of the S400 stage in an exemplary embodiment of this application.

[0077] S410: In this embodiment, the rendering engine receives the semantic segment graph. and refactoring instruction package The engine traverses each semantic segment in the graph. .

[0078] During the implementation of this step, the value-label-based differentiated profile reconstruction includes: S411: For For high value and being Paragraphs designated for enhanced rendering include: Performing spatial stretching: In the visualization view, the display width of the paragraph is increased along a direction perpendicular to the prevailing wind direction. times.

[0079] Enhanced execution details: High-resolution terrain textures, refined wake vortex visualization models, and gradient coloring algorithms for power curves are invoked to improve visual information density.

[0080] Perform dynamic tracing: Overlay a set of time-rolling particle streamlines onto the region, with particle velocity vectors driven by instantaneous flow field data from computational fluid dynamics simulations.

[0081] S412: For For low value and being The paragraphs specified for compression and simplification include: performing visual compression: aggregating all fan icons in the paragraph into a group icon, displaying only the average power and number of fans in the group.

[0082] Perform terrain simplification: Use a low-poly model instead of a high-precision digital elevation model to render the area terrain.

[0083] S413: For For medium value or not Specific paragraphs are rendered using standard rendering, which means they are drawn using the system's default rendering precision and visual style.

[0084] S414: After completing the differential rendering of all semantic paragraphs, the engine generates a complete, preliminary reconstructed profile view. .

[0085] S420: To ensure the reliability of the reconstruction results, the system starts two verification paths in parallel.

[0086] During the implementation of this step, two verification paths are included: S421: Path 1: Adversarial robustness test under parameter perturbation.

[0087] S421a: For the original semantic segment map Physical feature vectors in Apply small random noise Generate perturbation map .

[0088] S421b: Uses the same refactoring instruction package However, based on Execute the differential rendering logic in S410 to generate a perturbed and reconstructed view. .

[0089] S421c: Calculation and Difference at the pixel level or feature level In one possible implementation, the degree of difference It is calculated using a structural similarity index or feature matching error.

[0090] S421d: Set robustness threshold .like Then it is considered that the view is reconstructed. The adversarial robustness test has been passed; otherwise, path one verification would fail.

[0091] S422: Path 2: Verification of the logical necessity of reconstructing instructions by backtracking backward.

[0092] S422a: Through analysis The visual styles of each region are used to infer the semantic paragraphs. The value tags applied in this restructuring .

[0093] S422b: Raw physical characteristic data saved using the S200 stage With the same three-level nested judgment rule, recalculate each Theoretical value label .

[0094] S422c: Value Labels Based on Reverse Inference Value labels of positive reasoning Calculate the label consistency rate: ;in, This is an indicator function; it returns 1 if the two labels are the same, and 0 otherwise. This represents the total number of paragraphs.

[0095] S422d: Set consistency threshold ,like If the result is correct, then the current reconstruction result is considered a necessary solution for the semantic features; otherwise, the path two verification fails.

[0096] S430: The system waits for the two verification paths to complete before integrating and outputting the verification results.

[0097] During this step, the integration and output of verification results include: S431: The report content includes: the test results and differences for Path 1. Specific numerical values; verification results and consistency rate of Path 2. Specific numerical values; comprehensive verification conclusions.

[0098] S432: The system will verify the cross-sectional view. Verification Result Report The common output serves as the input for the S500 stage.

[0099] In a real-time reconstruction method for virtual-real fusion of wind farm profiles, the S500 performs fine-tuning of system parameters according to different inputs, updates the system status based on interactive feedback from maintenance personnel, completes the closed loop of the reconstruction cycle, and waits for the next cycle to be triggered.

[0100] Please refer to Figure 6, which shows a flowchart of the S500 stage in an exemplary embodiment of this application.

[0101] S510: There are two possible inputs for the S500 stage: one is the no-reconfiguration flag from S330. Second, verified cross-sectional views from S430. Verification Result Report .

[0102] During the implementation of this step, the input type judgment and branch processing include: S511: If the input is If the input is..., then jump to S520. If so, it will jump to S530.

[0103] S520: When the cross-sectional structure is stable and does not require reconstruction, the system enters the normal optimization mode and fine-tunes the parameters based on the stable state.

[0104] During this step, parameter fine-tuning based on the steady state includes: S521: The system collects routine performance indicators within the current period, including: average node efficiency of task allocation. Average time spent on semantic feature extraction Frame rate of view rendering .

[0105] S522: Compare the above performance indicators with the preset target range.

[0106] like If the value is lower than the target value, the weighting coefficients in S100 are fine-tuned. .

[0107] like If the target value is exceeded, the balance parameters between the computational accuracy and speed of the feature extraction algorithm in S200 are fine-tuned.

[0108] like If the value is lower than the target value, the global parameters of the rendering engine at the detail level in S400 are fine-tuned.

[0109] The fine-tuning process employs lightweight optimization algorithms based on gradient descent or heuristic rules.

[0110] S523: Record the parameters modified in this fine-tuning, the reasons for the modification, and the expected improvement effect. Then jump to S540.

[0111] S530: After the profile is reconstructed and verified, the system enters an interactive enhancement optimization mode to adjust system parameters through interactive feedback and verification reports.

[0112] During the implementation of this step, the system parameter adjustments for interactive feedback and verification reports include: S531: The system provides a human-computer interaction interface display. Operations and maintenance personnel may perform interactive operations such as zooming in and out of specific areas, clicking to query details, dragging and dropping views, and marking points of interest. The system captures and records all interaction event sequences within a time window, extracting interaction trajectory features, such as the duration of time spent in high-value areas. Frequency of rapid traversal in low-value areas The set of manually marked points of interest wait.

[0113] S532: Read the verification result report If the report concludes that the verification was successful, it indicates that the reconstruction result is reliable. Analysis of interaction patterns: If the areas frequently interacted by operations and maintenance personnel highly overlap with the high-value areas marked by the system, the effectiveness of the S200 value marking rules is verified; if operations and maintenance personnel show abnormally high attention to a certain medium-value area, it may indicate that the value judgment threshold for that area needs to be adjusted.

[0114] If the report concludes that the verification failed, it indicates that there is a problem with the reconstruction results. By combining the reasons for the failure in the report and analyzing the interaction trajectory, we can jointly pinpoint the weak points in the system.

[0115] S533: Based on the in-depth analysis results of S531 and S532, the system parameters are adjusted in a more targeted manner.

[0116] In one possible implementation, the adjustment includes: if the interaction trajectory shows high interest in a certain type of feature but the system does not adequately mark it as high value, then adjust step S230. , , Equal threshold.

[0117] If path 1 verification fails frequently, adjust the random noise in S421. The range of magnitude, or adjustment of the task allocation strategy in S100.

[0118] If path 2 verification fails, check the consistency of the feature extraction algorithm in S200, or calibrate the threshold of the three-layer judgment.

[0119] S534: Record the process of this interactive analysis, the conclusions of the verification report on which it was based, the specific parameters adjusted, and the decision-making logic for the adjustment. Then jump to S540.

[0120] S540: Regardless of whether the path is S520 or S530, this step should be performed after the parameter adjustment is completed.

[0121] The implementation of this step includes the following steps: S541: Archive all new data generated in this round of reconstruction to the historical database.

[0122] Update the versions of all relevant models and configuration files in the system according to the latest parameter settings.

[0123] S542: The system enters sleep or low-power monitoring mode, continuously monitoring the wind farm data stream. The next reconstruction cycle can be triggered by a fixed time interval or dynamically by an external event. When the triggering conditions are met, the system restarts from S100, initiating a new virtual-real fusion real-time reconstruction cycle.

[0124] In summary, this method achieves adaptive and efficient allocation of wind farm data processing tasks by constructing a heterogeneous edge-cloud collaborative computing framework; it completes semantic value classification of wind farm profiles by extracting multiple physical features such as terrain, wake, and power gradient and designing three-layer nested judgment rules; it realizes intelligent judgment of profile structural stability and accurate generation of reconstruction instructions by defining and calculating a semantic fracture index; it ensures the quality and reliability of the reconstructed view by performing value-label-based differentiated rendering and parallel dual-path robustness verification; and finally, it achieves closed-loop self-optimization of system parameters by integrating stable state monitoring, interactive behavior analysis, and verification result feedback. This method significantly improves the real-time performance, accuracy, interpretability, and adaptability of wind farm profile visualization, providing powerful virtual-real fusion decision support for intelligent operation and maintenance of wind farms.

[0125] Example 2: This invention was fully deployed and verified in a large coastal wind farm, and compared with traditional static monitoring methods and basic dynamic rendering methods. The implementation environment and key parameter configurations are shown in the table below: Table 1: System Deployment Environment and Configuration Parameters During the three-month testing period, the system underwent various typical wind conditions and fault scenarios. To quantitatively evaluate the effectiveness of this invention, multiple sets of comparative experiments were designed. The comparison results of key performance indicators are as follows: Table 2: Comparison Experiment Results of Key Performance Indicators Table 3: Performance Analysis of Typical Test Scenarios In particular, the system of this invention played a crucial role in a real-world gearbox temperature anomaly warning event. The temperature of a certain wind turbine gearbox slowly increased, causing a slight change in its output power characteristics, thus altering its power gradient with that of adjacent downstream wind turbines. The system was slightly enlarged. Although this did not trigger a separate system refactoring, the operations personnel zoomed in on the area where the wind turbine was located to view details during the S500 interaction analysis. The system captured this interaction and subsequently slightly reduced the value of that area through the S533 logic in a later cycle. Judgment threshold The system demonstrated its sensitivity to the problem. A few days later, when the wind turbine's power fluctuations intensified, the system marked the affected section as a medium-value area and provided visual cues two refactoring cycles in advance. This ultimately helped maintenance personnel identify early potential faults and prevent unplanned downtime.

[0126] Example 3: A real-time reconstruction system for virtual-real fusion of wind farm profiles, comprising: a task collaborative allocation module, used to deploy a heterogeneous edge-cloud collaborative computing node cluster before the start of each reconstruction cycle, collect real-time status data of computing nodes and obtain their historical performance data, automatically match and allocate five types of sub-tasks—data acquisition, semantic extraction, deviation detection, structural rearrangement, and interactive feedback analysis—to the optimal computing node based on the real-time status data and historical performance data, and output a task allocation scheme; a semantic value classification module, used to divide the wind farm area into multiple semantic segments along the prevailing wind direction with the support of the task allocation scheme, extract and encapsulate three types of physical semantic features of each semantic segment in parallel—terrain complexity, wake significance, and power gradient steepness, and perform three-level nested judgment on each semantic segment to mark it as a high-value area, medium-value area, or low-value area according to the comparison results of preset thresholds, and finally generate and output a semantic segment map with value labels; and an intelligent reconstruction decision module, used to calculate the semantic breakage index of each region in the semantic segment map, and classify semantic breaks... The index is compared with a preset fracture threshold. If it is lower than the fracture threshold, the profile structure is determined to be stable and a "no reconstruction required" flag is output. If it exceeds the fracture threshold, a reconstruction instruction package containing a list of key fracture areas, differentiated reconstruction operation types, and related parameters is generated. The profile rendering and verification module is used to perform differentiated visual reconstruction of the semantic segment map according to the reconstruction instruction package to generate a preliminary reconstructed profile view, and to perform dual-path robustness verification in parallel: the first path performs adversarial robustness testing by applying perturbations to physical features, and the second path performs logical necessity verification by comparing backward inference with forward rules, and finally outputs the verified profile view and verification result report. The closed-loop optimization and update module is used to receive and determine the input type: if the input is a "no reconstruction required" flag, the parameters are fine-tuned based on the current system running state; if the input is a verified profile view and verification result report, the interaction trajectory between the operation and maintenance personnel and the verified profile view is captured, and the system parameters are adaptively adjusted in combination with the verification result report; after the parameter adjustment is completed, the system state is updated, and the system waits for the next reconstruction cycle to be triggered.

[0127] Those skilled in the art will understand that the embodiments of this application are provided as methods, systems, or computer program products. Therefore, this application takes the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application takes the form of a computer program product implemented on one or more computer storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer program code. The solutions in the embodiments of this application are implemented using various computer languages, exemplified by the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0128] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, are implemented by computer program instructions. These computer program instructions are provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams.

[0129] These computer program instructions are also stored in a computer read-memory that can direct a computer or other programmed data processing device to operate in a particular manner, such that the instructions stored in the computer read-memory produce an article of manufacture including instruction means that implement the functions specified in the flowchart or multiple flowcharts and / or block diagram blocks or multiple block diagrams.

[0130] These computer program instructions are also loaded onto a computer or other programming data processing device to cause a series of operational steps to be performed on the computer or other programming device to produce a computer-implemented process, such that the instructions, which execute on the computer or other programming device, provide steps for implementing the functions specified in the flowchart flow or multiple flows and / or the block diagram blocks or multiple blocks.

[0131] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0132] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for real-time reconstruction of wind farm profiles using virtual-real fusion, characterized in that, include: Before each reconstruction cycle begins, a heterogeneous edge-cloud collaborative computing node cluster is deployed to collect real-time status and historical performance data. Five sub-tasks—data acquisition, semantic extraction, deviation detection, structural rearrangement, and interactive feedback analysis—are automatically matched and assigned to the optimal nodes, generating a task allocation scheme. With the support of this scheme, the wind farm is divided into multiple semantic segments along the prevailing wind direction. Each segment encapsulates three types of physical semantic features: terrain, wake, and power gradient. A three-layer nested judgment is initiated for each semantic segment: thresholds are set for terrain complexity, wake significance, and power gradient steepness, and comparisons are performed. Based on the comparison results, areas are labeled as high-value, medium-value, and low-value regions. The final output is a labeled semantic segment map. A semantic breakage index is calculated for each region of the semantic segment map. If the index is below a threshold, the current profile structure is considered stable, and a "no reconstruction needed" flag is output. If the index exceeds the threshold, a reconstruction instruction package is generated based on the semantic breakage index. The semantic segment map is reconstructed according to the reconstruction instruction package, with parallel dual-path verification: one path performs adversarial robustness testing on the profile view through parameter perturbation, while the other path reverses the semantic segment division of the reconstruction instructions to verify whether the current reconstruction result is a necessary solution for the semantic segment. The final output is a verified profile view and a verification result report. If the input is a "no reconstruction needed" flag, parameter fine-tuning is performed based on the current system state. If the input is a verified profile view and a verification result report, the interaction trajectory between the operations and maintenance personnel and the profile view is captured, and the system parameters are adjusted in conjunction with the verification result report; after the parameter adjustment is completed, the system status is updated and the system waits for the next reconstruction cycle to be triggered.

2. The method for real-time reconstruction of wind farm profiles by virtual-real fusion according to claim 1, characterized in that, The steps for deploying a heterogeneous edge-cloud collaborative computing node cluster to collect real-time status and historical performance data, and automatically match and allocate five types of sub-tasks to the optimal node include: the central management platform periodically sends status query commands to each computing node in the cluster; upon receiving the command, the node collects local CPU utilization, memory utilization, remaining network bandwidth percentage, average network latency between the node and the wind farm data source, and node type identifier, encapsulates this data into a status vector, and reports it; the central management platform maintains a historical performance database for the nodes, records and statistically analyzes the historical average execution efficiency and result reliability scores of each node for different types of tasks; based on the reported real-time status vectors and historical performance data, the central management platform calculates the real-time comprehensive performance evaluation value for each node; the real-time comprehensive performance evaluation value is obtained by weighted synthesis of real-time load factor, network condition factor, and historical performance factor; based on the real-time comprehensive performance evaluation value and preset task-specific preference factors, the system constructs a task type-to-computing node fit matrix; using the fit matrix as the benefit matrix, an auction algorithm is used to solve for the task allocation scheme that maximizes the total fit, and the scheme is distributed to each computing node.

3. The real-time reconstruction method for wind farm profile fusion according to claim 1, characterized in that, The steps for marking regions as high-value, medium-value, and low-value based on the comparison results include: setting corresponding high-value judgment thresholds for the three types of physical semantic features: terrain complexity, wake significance, and power gradient steepness; performing the following judgments sequentially for each semantic segment: First-level judgment: determining whether the terrain complexity of the semantic segment is greater than or equal to the terrain complexity threshold; if yes, recording the first flag as 1, otherwise recording as 0; Second-level judgment: determining whether the wake significance of the semantic segment is greater than or equal to the wake significance threshold; if yes, recording the second flag as 1, otherwise recording as 0; Third-level judgment: determining whether the power gradient steepness of the semantic segment is greater than or equal to the power gradient steepness threshold; if yes, recording the third flag as 1, otherwise recording as 0; adding the first, second, and third flags of the semantic segment to obtain the qualifying level; if the qualifying level is equal to 3, the semantic segment is marked as a high-value region; if the qualifying level is equal to 2, the semantic segment is marked as a medium-value region; if the qualifying level is less than or equal to 1, the semantic segment is marked as a low-value region.

4. The real-time reconstruction method for wind farm profile fusion according to claim 3, characterized in that, The steps of dividing the wind farm into multiple semantic segments along the prevailing wind direction, and encapsulating three types of physical semantic features in each segment, including terrain, wake, and power gradient, are as follows: acquiring digital elevation model data of the wind farm, wind turbine layout coordinates, and the current prevailing wind direction vector; starting from the wind farm inlet boundary, cutting the wind farm area into a series of continuous strips perpendicular to the prevailing wind direction vector, with each strip defined as a semantic segment; and for each semantic segment, executing data acquisition and feature extraction subtasks in parallel, with the subtasks scheduled to the optimal computing node according to the task allocation scheme, in order to extract and encapsulate the terrain complexity, wake significance, and power gradient steepness of the semantic segment.

5. The real-time reconstruction method for virtual-real fusion of wind farm profiles according to claim 1, characterized in that, The formula for calculating the semantic breakage index is as follows: ;in, and These are the weighting coefficients; For value label jump penalty items, ， The penalty coefficient is... This represents the absolute value of the difference in tag ordinal numbers between the current semantic paragraph and the upstream semantic paragraph. This represents the absolute value of the difference in tag ordinal numbers between the current semantic paragraph and the downstream semantic paragraph. For the physical feature gradient magnitude term, ， The feature vector difference between the current semantic paragraph and the upstream semantic paragraph. The feature vector difference between the current semantic paragraph and the downstream semantic paragraph. This represents the L2 norm of a vector.

6. The real-time reconstruction method for virtual-real fusion of wind farm profiles according to claim 5, characterized in that, The steps for generating a reconstruction instruction package based on the semantic breakage index include: identifying all semantic segments in the semantic segment graph whose semantic breakage index exceeds a preset breakage threshold, forming a key breakage region set; for each semantic segment in the key breakage region set, specifying a reconstruction operation type based on the value tag already marked in the semantic segment graph, the reconstruction operation type includes: if it is a high-value region, specifying the operation type as enhanced rendering; if it is a medium-value region, specifying the operation type as standard rendering; if it is a low-value region, specifying the operation type as compression simplification; for semantic segments whose operation type is specified as enhanced rendering, introducing a spatial stretching factor generated based on the segment semantic breakage index mapping; and encapsulating the key breakage region set, the reconstruction operation type corresponding to each region, and the parameters into a structured reconstruction instruction package.

7. The real-time reconstruction method for wind farm profile fusion according to claim 1, characterized in that, The reconstruction of the semantic segment graph based on the reconstruction instruction package includes: the rendering engine receiving the semantic segment graph and the reconstruction instruction package; traversing each semantic segment in the semantic segment graph, checking the value label of the semantic segment and the specified operation type in the reconstruction instruction package: if the semantic segment is marked as a high-value region and the reconstruction instruction package specifies enhanced rendering, then spatial stretching is performed on the segment, high-resolution models are called for detail enhancement, and dynamic particle tracing driven by computational fluid dynamics simulation data is superimposed; if the semantic segment is marked as a low-value region and the reconstruction instruction package specifies compression simplification, then wind turbine icon aggregation display and low-polygon terrain simplification rendering are performed on the segment; standard rendering with the system default precision is performed on other semantic segments.

8. The method for real-time reconstruction of wind farm profiles by virtual-real fusion according to claim 1, characterized in that, The parallel dual-path verification includes: executing the first path, namely the adversarial robustness test under parameter perturbation, including: applying random noise to the physical feature vectors in the semantic segment graph to generate a perturbation graph; reconstructing the perturbation graph based on the same reconstruction instruction package to generate a perturbation-reconstructed view; obtaining the difference between the perturbation-reconstructed view and the original reconstruction view, and comparing it with a first robustness threshold to determine whether the test passes; executing the second path, namely the logical necessity verification of the reverse backtracking of reconstruction instructions, including: inferring the value label applied to each semantic segment from the visual style of the generated reconstructed profile view; re-obtaining the theoretical value label of each semantic segment using the original physical feature data and the same three-layer nested judgment rule; judging the overall consistency rate between the inferred label and the theoretical label, and comparing it with a second consistency threshold to determine whether the verification passes.

9. The real-time reconstruction method for wind farm profile fusion according to claim 1, characterized in that, The steps for capturing the interaction trajectory between maintenance personnel and the profile view, and adjusting system parameters based on the verification result report, include: the system displays the verified profile view through a human-computer interaction interface and captures the sequence of interaction events generated by maintenance personnel; extracting interaction trajectory features from the interaction event sequence, including dwell time in high-value marked areas, grazing frequency in low-value marked areas, and the set of coordinates of manually added points of interest; reading the conclusions and detailed data of the verification result report; if the verification result report concludes as successful, analyzing the overlap between the interaction trajectory features and the high-value areas marked by the system, and adjusting the terrain complexity, wake significance, or power gradient steepness thresholds involved in the semantic segment value marking based on the analysis results; if the verification result report concludes as a failure, combining the reasons for failure in the report with the interaction trajectory features, locating the weak links in the system, and adjusting the feature extraction algorithm parameters, task allocation strategy, or judgment threshold.

10. A real-time reconstruction system for virtual-real fusion of wind farm profiles according to any one of claims 1-9, characterized in that, The system includes: a task collaborative allocation module, used to deploy a heterogeneous edge-cloud collaborative computing node cluster before the start of each reconstruction cycle, collect real-time status data of computing nodes and obtain their historical performance data, automatically match and allocate five types of sub-tasks—data acquisition, semantic extraction, deviation detection, structural rearrangement, and interactive feedback analysis—to the optimal computing node based on the real-time status data and historical performance data, and output a task allocation scheme; a semantic value classification module, used to divide the wind farm area into multiple semantic segments along the prevailing wind direction with the support of the task allocation scheme, extract and encapsulate three types of physical semantic features of each semantic segment in parallel—terrain complexity, wake significance, and power gradient steepness—and perform three-level nested judgment on each semantic segment to mark it as a high-value region, medium-value region, or low-value region according to the comparison results of preset thresholds, and finally generate and output a semantic segment map with value labels; and an intelligent reconstruction decision module, used to calculate the semantic breakage index of each region in the semantic segment map, and compare the semantic breakage index with a preset breakage threshold. The system performs a comparison. If the value is below the fracture threshold, the profile structure is determined to be stable and a "no reconstruction needed" flag is output. If the value exceeds the fracture threshold, a reconstruction instruction package containing a list of key fracture areas, differentiated reconstruction operation types, and related parameters is generated. The profile rendering and verification module performs differentiated visual reconstruction of the semantic segment graph based on the reconstruction instruction package to generate a preliminary reconstructed profile view. It performs a dual-path robustness verification in parallel: the first path performs adversarial robustness testing by applying perturbations to physical features, and the second path performs logical necessity verification by comparing backward inference with forward rules. Finally, it outputs a verified profile view and a verification result report. The closed-loop optimization and update module receives and determines the input type: if the input is a "no reconstruction needed" flag, it fine-tunes the parameters based on the current system operating state; if the input is a verified profile view and a verification result report, it captures the interaction trajectory between the maintenance personnel and the verified profile view, and adaptively adjusts the system parameters based on the verification result report; after the parameter adjustment is completed, it updates the system state and waits for the next reconstruction cycle to be triggered.

Images