67 results about "Collaborative mapping" patented technology

The embodiment of the invention provides a multi-unmanned aerial vehicle cooperative surveying and mapping method and system for areas along a railway, and belongs to the technical field of railway surveying and mapping. The method comprises the steps that the unmanned aerial vehicle collects monitoring data in the flight process; the relative position and posture between the aircrafts are dynamically adjusted, and an observation configuration with variable baseline parameters is constructed; laser ranging modules carried by the unmanned aerial vehicles are controlled to execute synchronous ranging operation, and multiple sets of laser point cloud data covering the railway structure area are generated; performing coordinate registration and fusion on the collected laser point cloud data, and combining position information and attitude information of each unmanned aerial vehicle; and extracting geometric dimension parameters of the railway structure from the compensated three-dimensional model, and outputting the geometric dimension parameters as measurement results. According to the scheme, high-precision non-contact measurement of geometric dimension parameters such as the track gauge, the height difference and the roll angle of a railway track structure is achieved.

The invention relates to the technical field of forest fire management, in particular to an integrated forest fire intelligent analysis method and system. The system comprises a background service system, a central intelligent analysis system and a mobile intelligent analysis system. The background service system is deployed in a cloud computing server cluster and is used for uniformly storing personnel information, fire scene information and geographic information data and realizing multi-source data fusion and authority management through a standardized API (Application Program Interface); the central intelligent analysis system is deployed in a high-performance computer, integrates a forest fire danger auxiliary decision-making model, a forest fire spreading trend prediction model, a force distribution dynamic display module and a collaborative plotting module, and supports fire spreading prediction and real-time decision-making instruction issuing based on multi-dimensional data such as weather, terrain and vegetation; the mobile intelligent analysis system realizes fire spreading trend analysis, mobile plotting and instruction receiving in an offline environment, supports offline data caching and calling in an emergency scene, and effectively improves forest fire emergency response efficiency and resource scheduling accuracy.

The invention provides a multi-machine distributed collaborative mapping method and system based on a laser radar and an IMU (Inertial Measurement Unit), relates to the technical field of automatic driving, and aims to solve the problem that in the prior art, dynamic point cloud can influence the accuracy of measured data; the head carrier is high in information processing capacity and high in calculation difficulty; communication and mapping of other single machines can be influenced by communication disconnection or load collapse of the single machines; the sensor is high in cost and is interfered by a complex closed environment. According to the method, after laser point cloud data and IMU information are received, key frame point cloud data and pose data are generated through first-time loop-back matching of single machines, messages are forwarded to a back-end server through a communication module based on ROS + UDP, second-time loop-back matching between different single machines is carried out through a server side, and a global map is constructed and optimized. According to the method, the problems in the prior art are solved, and the accurate global pose estimation and the optimized global map are obtained.

The invention discloses a three-dimensional scene multi-dimensional instruction collaborative mapping method and system based on a multi-modal large model and a knowledge graph, and belongs to the technical field of artificial intelligence. The method comprises the following four steps: acquiring and preprocessing a multi-mode instruction; performing instruction analysis and element disassembly, calling a built-in pre-training large language model by the multi-modal large model, disassembling the instruction into appearance, scene and control interaction elements through customized Prompt, and processing logic and dependency relationships; multi-dimensional state driving is carried out, and appearance parameter mapping, action sequence generation and cross-modal conflict resolution and semantic-voice-expression linkage execution are achieved through appearance, action and language intelligent agents; and the central coordinator schedules agent parallel tasks based on the knowledge graph. The system correspondingly comprises a function module for executing four steps. According to the method, the multi-dimensional instruction cooperative control precision and response efficiency of the three-dimensional scene can be effectively improved, the labor cost is reduced, and the method is adaptive to diversified interaction scenes.

The invention relates to the technical field of artificial intelligence, and particularly discloses an interactive intelligent content generation method and system based on AIGC. The method comprises the steps that a user editing instruction is collected and analyzed to obtain a first semantic body, and meanwhile a target change vector is extracted; performing connectivity analysis on the high-response pixel points to determine a candidate linkage area; obtaining a candidate linkage area, judging whether a physical shielding or self-shielding effect exists or not, and screening an effective response area; performing attenuation correction on the target change vector, and deriving a collaborative mapping parameter; and constructing a light and shadow guide matrix based on the collaborative mapping parameters, and performing directional guide processing on the effective response area. The system comprises an intention analysis module, a communication analysis module, a region screening module, an attenuation correction module and a synchronous rendering module. According to the invention, synchronous rendering of semantic subject change and ambient light and shadow effects can be realized.

The invention provides an intelligent graph recognition method and device based on collaborative drawing, equipment and a storage medium. The method is applied to the technical field of computers, and comprises the following steps: acquiring a canvas for collaborative drawing, performing pattern recognition processing on a pattern in the canvas for collaborative drawing, determining a boundary of the pattern, and performing segmentation processing on the pattern according to the boundary of the pattern to obtain at least one pattern and a pattern block corresponding to the pattern; the canvas for collaborative drawing is a drawing result obtained by collaborative drawing of a plurality of users; performing intelligent pattern recognition processing on the pattern block, and determining a visual component corresponding to a pattern in the pattern block; pixel point pickup processing is carried out on the graphic block, and each pixel point of the graphic block is traversed to obtain a pixel color value of the graphic block; and the data board is generated according to the visual component corresponding to the graph in the graph block and the pixel color value of the graph block, so that the identification accuracy is improved, and the development efficiency is improved.

This invention discloses a ground-air collaborative mapping method based on semantic features and ground plane multi-constraint fusion. The method includes: generating sub-map sets from local point cloud data collected by fire trucks and drones; and generating rotation-invariant semantic descriptors using a maximum height value rasterization strategy and rotation group enhancement. By calculating geometric and semantic distances, and combining isomorphic and heterogeneous pattern weighted fusion, candidate sub-map pairs with loop closures are selected and geometrically registered to generate loop closure constraints. Simultaneously, ground point clouds are extracted using the RANSAC algorithm, and the ground plane model is smoothly updated using a sliding window to construct height and pose alignment constraints. The loop closure constraints, ground plane constraints, and pose information are input into the Ceres optimization framework for joint optimization to generate a globally consistent overall map, effectively improving the accuracy and robustness of ground-air collaborative mapping, and is suitable for emergency scenarios such as complex fire rescue.

The invention provides a method for improving the production efficiency of a high-efficiency spring steel wire, and relates to the technical field of spring steel wire production.The method comprises the following steps that multi-source physical quantities in the spring steel wire winding process are collected in real time, and the multi-source physical quantities comprise steel wire tension distribution, the bending rod curvature radius, a roller temperature field and an equipment vibration frequency spectrum; based on the obtained multi-source physical quantity, a torque instruction, a rotating speed instruction and a bending radius compensation amount are generated through a dynamic collaborative mapping function; according to the generated instruction, a servo motor set is driven to adjust the wire feeding speed and the angular speed of a roller, and meanwhile a piezoelectric trimmer is controlled to correct the bending radius; and adopting a reinforcement learning strategy to optimize control parameters on line, and dynamically updating a mapping function according to winding quality feedback. According to the method, the problem that the remodeling debugging time is long can be effectively solved, the wire breakage rate can be remarkably reduced, and the size consistency can be effectively improved.

The invention discloses a ground-air collaborative mapping method based on semantic feature and ground plane multi-constraint fusion, which comprises the following steps: generating a sub-map set through local point cloud data collected by a fire-fighting unmanned vehicle and an unmanned aerial vehicle, and generating a rotation-invariant semantic descriptor by utilizing a maximum height value rasterization strategy and rotation group enhancement; by calculating a geometric distance and a semantic distance and combining isomorphic and heterogeneous mode weighted fusion, loopback candidate sub-map pairs are screened, geometric registration is carried out, and loopback constraints are generated. Meanwhile, ground point cloud is extracted through an RANSAC algorithm, a sliding window smoothly updates a ground plane model, and height constraint and attitude alignment constraint are constructed. And the loopback constraint, the ground plane constraint and the pose information are input into a Ceres optimization framework for joint optimization, a globally consistent total map is generated, the precision and robustness of ground-air collaborative mapping are effectively improved, and the method is suitable for emergency scenes such as complex fire rescue and the like.

The application belongs to the technical field of three-dimensional reconstruction and intelligent agriculture, and particularly relates to a three-dimensional Gaussian multi-machine collaborative mapping and optimization method and system for complex agricultural and forestry scenes. In view of the problem of low reconstruction accuracy caused by inconsistent exposure, dynamic disturbance of vegetation and sparse multi-machine view angles in complex agricultural and forestry scenes, the application sequentially carries out multi-machine data preprocessing, spatiotemporal outlier elimination, intermediate frame generation based on FILM and DepthAnything, and 3DGS joint optimization. Dynamic masks are generated through spatiotemporal residual double modal judgment to block dynamic interference at the level of loss function; sparse view angles are completed by using virtual intermediate frames to expand the observation data set. The application can effectively eliminate dynamic artifacts and cavities, realize high-precision three-dimensional reconstruction of complex agricultural and forestry scenes, and be directly applied to digital operation scenes such as vegetation monitoring and automatic path planning of forest land and orchards.

The application relates to the technical field of operation and maintenance management, and discloses an operation and maintenance management method and system for process industrial equipment, the method comprising the following steps: coupling multi-modal features of time sequence operation parameters and field state information of the process industrial equipment to obtain a fusion feature set of the process industrial equipment; quantifying fluctuation trends of the fusion feature set to obtain a degradation feature index of the process industrial equipment; performing evolution evaluation on the degradation feature index to obtain a health quantitative value of the process industrial equipment; constructing operation and maintenance decision boundary conditions of the process industrial equipment; performing collaborative mapping on the fusion feature set and the health quantitative value to obtain an optimized operation and maintenance strategy of the process industrial equipment; and associating and integrating the operation and maintenance decision boundary conditions and the optimized operation and maintenance strategy to obtain an operation and maintenance management report of the process industrial equipment; and the application can improve the operation and maintenance management efficiency of the process industrial equipment.

The application discloses a kind of based on fuzzy adaptive PID's roll rolling force thickness collaborative control method, belongs to industrial control technical field field.It includes: deployment instrument is at mill press-down hydraulic cylinder, process data are collected and preprocessed;Online calculation rolling force-thickness coupling degree factor, coupling degree factor is used as input with thickness error, error change rate, constructs three-dimensional fuzzy reasoning engine and carries out fuzzy reasoning;Using the collaborative coefficient matrix based on coupling degree factor and adaptive domain scaling mechanism, parameter adjustment amount is decoupled distribution and dynamic mapping, generates the PID parameter adjustment value of rolling force and thickness double loop respectively coordinated;Drive hydraulic servo valve to carry out roll gap and rolling force collaborative control.The application aims to realize the collaborative control of rolling force and thickness by introducing coupling degree factor, constructing three-dimensional fuzzy reasoning engine and double loop collaborative mapping.

The invention relates to the technical field of positioning and mapping related to intelligent transportation and automatic driving / vehicle infrastructure cooperation, in particular to a city-level crowd-source cooperation mapping and real-time positioning method and system, and the method and system achieve the stable fusion of city-level crowd-source data through standardized multi-source observation data, space-time reference construction and cross-source registration. And loop constraint and graph optimization are optimized through dynamic regulation and control parameters, so that the stability and the expandability under the conditions of heterogeneous sensors and data quality fluctuation are improved. By adopting multi-dimensional credibility characterization and a dynamic gating mechanism, loopback starting conditions are effectively controlled, the situation that a global map is polluted by wrong loopback caused by semantic anchor drift is avoided, the risk that structured errors are solidified into global consistency is reduced, and map safety is improved. Through a layered map and dynamic regulation and control, parameters can be optimized according to positioning residual errors and regional risk statistics during real-time positioning, and a closed-loop updating mechanism is formed.

The present application relates to the technical field of data processing, and more particularly to a twin model construction method, system, device and medium for tank decommissioning, the construction method comprising the following steps: acquiring point cloud data, reconstructing a point cloud network, extracting size information, and constructing a twin model; real-time monitoring of sensor health status through time series feature analysis, capturing device degradation rules using a gated recurrent network, generating a hidden vector reflecting the degree of performance degradation, constructing a Poisson equation to describe the implicit surface, and using the finite element method to solve to obtain a continuous mesh model; and collaborative mapping of three-dimensional mesh and two-dimensional projection data, which not only preserves the overall topological form of the tank, but also extracts key size parameters, providing dual precision protection for path planning, and solving the problem of data drift in the model construction method of the prior art.

The invention belongs to the technical field of crossing of artificial intelligence and intelligent robots, and particularly relates to a ground-air robot storage environment intelligent sensing and anomaly detection system based on AI vision. The invention discloses an AI vision-based intelligent sensing and anomaly detection system for a storage environment of a ground-air robot, and aims to solve the problem of incomplete anomaly detection caused by isolated sensing and shallow data fusion of a ground-air platform. The system comprises a multi-modal acquisition unit, a ground-air collaborative mapping unit, a unified semantic cognition unit, a multi-scale anomaly detection unit and a dynamic task planning unit. Through fusion of air wide-area perception and ground fine observation data, a high-precision three-dimensional semantic map is constructed, comprehensive detection of macroscopic layout, microscopic state and time sequence logic abnormity is realized, and the ground-air robot is driven to cooperatively respond, so that the perception integrity and intelligent decision-making capability of a storage environment are improved.

This paper discloses a facial acne grading method based on collaborative mapping multi-scale self-distillation. This method, applied to the computer field, addresses the low accuracy of existing acne severity grading techniques. The method uses a self-distillation architecture to train a convolutional neural network model. Taking into account the characteristics of acne itself, a novel distillation module optimizes the training process, enabling the model to account for the uniqueness of acne images and improving the accuracy of facial acne severity grading. The method can automatically identify acne features and grade their severity, assisting doctors in diagnosis and treatment and accelerating the recovery period of acne.

The application discloses a kind of publishing field knowledge service construction methods based on knowledge graph, it is related to intelligent digital publishing technical field, including, according to publishing content structure node set, constructs publishing content structure diagram, and in combination with publishing original resource set executes publishing field semantic information extraction and semantic correlation analysis, obtains publishing semantic diagram;From the structural distribution characteristics and content anchoring characteristics of semantic node in publishing resource in publishing semantic diagram are extracted, and the anchor point edge between publishing semantic diagram and publishing content structure diagram is established, and the multi-granularity anchor point double diagram is formed;Based on graph service execution plan, execute double diagram collaborative retrieval and service path generation, obtain knowledge service candidate set, and carry out service ordering and arrangement, generate publishing field knowledge service content.The application realizes the collaborative mapping of semantic relationship and content bearing position, enhances knowledge organization precision and service direction.

The invention relates to the technical field of data mining, in particular to a patent-driven digital economic evaluation method and system, and the method comprises the following steps: based on a digital economic patent database, distributing innovation participation proportions, summarizing main body distribution characteristics, combing the complete-period changes of legal states and operation behaviors, and analyzing industrial classification codes and obligee distribution. And evaluating the innovation level in a zoning manner, and outputting a digital economic grading index. According to the invention, through hierarchical induction of a patent subject sorting structure and a contribution proportion, dynamic performance of innovation participation dominance is enhanced, and interactive tracking of operation behaviors of obligees and a legal state cycle is combined, so that structured integration of multiple types of events in a patent life cycle is realized; according to the method, content association in a patent cluster is identified by utilizing technical content fusion characteristics, a multi-level collaborative mapping unit is constructed in combination with obligee behaviors and innovation participation data under industrial coding, and innovation characteristics are displayed in a refined manner in graded archiving of innovation ability expression.

The invention relates to the technical field of data processing, in particular to a twin model construction method, system and device for storage tank decommissioning and a medium, and the construction method comprises the following steps: obtaining point cloud data, reconstructing a point cloud network, extracting size information, and constructing a twin model: monitoring the health state of a sensor in real time through time sequence feature analysis; the method comprises the following steps: capturing an equipment degradation law by utilizing a gated loop network, generating a hidden vector reflecting the performance degradation degree, constructing a Poisson equation to describe an implicit curved surface, solving by adopting a finite element method to obtain a continuous grid model, a three-dimensional grid and collaborative mapping of two-dimensional projection data, retaining the overall form topology of a storage tank, extracting key size parameters, and obtaining a three-dimensional grid model; double precision guarantee is provided for path planning, and the problem of data drift existing in a model construction method in the prior art is solved.

The invention relates to the technical field of image data processing, and discloses an image data adaptive enhancement method in a high-speed rail platform end complex environment, which comprises the following steps: acquiring an environment image and extracting a brightness component, and executing spatial sampling to construct a brightness distribution matrix; calculating a brightness gradient vector of a sampling node to determine a spatial weighting coefficient; performing spatial filtering on the preliminary mapping gain by using a spatial weighting coefficient, generating a weight component, and realizing gain correction; calculating a brightness change rate to determine a time sequence smooth step length, executing time sequence constraint adjustment on a mapping function control parameter, and determining a collaborative mapping function; according to the method, the slicing feeling caused by a strong-directivity light source is eliminated and video flicker is suppressed through spatial weight correlation and time sequence smooth constraint, and the track edge topological features are locked while noise is filtered out.

The invention discloses an intelligent food pesticide residue color reaction image analysis system, relates to the technical field of computer vision and image recognition, and realizes physical decoupling and accurate qualification of mixed color signals through collaborative mapping of blind source separation and a standard fingerprint database. The system models a mixed Lab color signal collected by a reaction hole into a linear mixture of a plurality of unknown source signals, and inversely solves an inverse matrix of a mixed matrix by taking maximization of non-Gaussian property as a criterion through a blind source separation analysis engine, so that the mixed signal is decomposed into a plurality of statistically independent source signal components, and a plurality of unknown source signals are obtained. According to the method, the characteristics of the separated pure components are subjected to similarity calculation with standard developing characteristic data of various single pesticides pre-stored in a standard pesticide developing fingerprint database, so that direct cross interference of mixed signals is avoided, and each component in a mixture can be accurately judged even under the condition that various pesticides coexist.

The embodiment of the invention provides a multi-sensing-node collaborative mapping method and system, electronic equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: constructing a local map taking a local node as a reference according to multi-frame point cloud data locally acquired by the node, discretizing the local map to obtain a plurality of subspaces, and performing geometric feature coding and intensity feature coding on each subspace to correspondingly obtain a geometric structure code and an intensity structure code; and feature description is carried out on the local map according to the strength structure codes and the geometric structure codes, a bimodal feature descriptor of the local map is obtained, the bimodal feature descriptor and node poses of the local map are interacted by multiple nodes, and a global map is constructed based on interaction information. According to the invention, the efficiency and accuracy of multi-node collaborative mapping can be improved.

The application relates to the technical field of artificial intelligence, and particularly discloses an interactive intelligent content generation method and system based on AIGC. The method comprises the following steps: collecting user editing instructions and analyzing the first semantic subject to obtain a target change vector; performing connectivity analysis on high-response pixel points to determine a candidate linkage area; obtaining the candidate linkage area and judging whether there is physical shielding or self-shielding effect, and screening an effective response area; performing attenuation correction on the target change vector, and deducing a collaborative mapping parameter; constructing a light and shadow guide matrix based on the collaborative mapping parameter, and performing directional guide processing on the effective response area. The system comprises an intention analysis module, a connectivity analysis module, an area screening module, an attenuation correction module and a synchronous rendering module. The application is beneficial to realizing synchronous rendering of semantic subject change and environmental light and shadow effect.

The application relates to a multi-robot cooperative mapping system in the field of simultaneous localization and mapping, which comprises a plurality of mobile platforms and a central control center; each mobile platform is loaded with a laser radar, a laser odometer and a computing host; the laser radar is used for acquiring point cloud data of a surrounding environment; the laser odometer is used for acquiring pose data of the mobile platform; the computing host processes the point cloud data and the pose data to obtain key frame point cloud and a key pose graph, and sends the key frame point cloud and the key pose graph to the central control center through a wireless network card; the central control center performs loop detection based on the key frame point cloud and the key pose graph to find a loop candidate pair, performs pose graph aggregation and optimization based on a loop detection result, the key frame point cloud and the key frame point cloud to obtain a global point cloud map of a region to be mapped. The application solves the problems of low mapping efficiency and long mapping time consumption in the prior art.

The utility model relates to the technical field of unmanned aerial vehicles, in particular to a collaborative surveying and mapping unmanned aerial vehicle based on pyroelectric human body infrared detection, which comprises a main control module, a power supply module, a communication module and an infrared sensor module, the power supply module provides electric energy for the main control module, the communication module and the infrared sensor module, and the communication module is used for wireless communication, data transmission and instruction receiving between the unmanned aerial vehicle and a ground station or other unmanned aerial vehicles. The infrared sensor module is used for detecting obstacles, measuring distance or navigating, converting detected infrared signals into digital signals and then processing the digital signals by the main control module, so that the unmanned aerial vehicle can stably fly in various complex environments and complete surveying and mapping tasks, and the problem that an existing surveying and mapping unmanned aerial vehicle cannot fly stably due to shooting dead angles is solved. The surveying and mapping unmanned aerial vehicle cannot avoid obstacles at dead angles, and collision occurs.

The invention relates to the technical field of information matching and positioning, and discloses a multi-agent positioning and graph optimization method and system based on multi-feature matching, a terminal and a storage medium, and the method comprises the steps: obtaining a plurality of groups of environment images, and constructing a corresponding text key frame; calculating text similarity of a plurality of positions to screen candidate matching pairs; further calculating MAC address similarity and signal strength similarity to screen a plurality of true matching position pairs; therefore, updating of the pose map of the intelligent agent is realized. According to the method, simulation data and real data are matched for multiple rounds, and a shared area is identified in a large, complex and repeated indoor environment, so that reliable map fusion and global consistent collaborative mapping are realized.

The invention discloses an intelligent lighting integrated circuit cooperative control method based on edge computing, and the method comprises the following steps: deploying an edge computing unit, and collecting environment data in real time; utilizing an improved artificial fish swarm algorithm to generate a local control result; a lighting control instruction is generated according to the local optimal control result, and working state feedback is obtained; establishing a structured state space model which comprises an edge layer, a cloud layer and a data storage and management layer; a self-adaptive collaborative mapping function is introduced, and a global optimization control strategy and an increment parameter packet are generated; issuing the incremental parameter packet to an edge node, and dynamically balancing the parameter according to the adaptive fusion coefficient; and adjusting parameters and interlayer weights of the improved artificial fish swarm algorithm to obtain globally collaborative intelligent illumination. According to the invention, global cooperative control and adaptive optimization adjustment of intelligent illumination are realized through the improved artificial fish swarm algorithm and the adaptive cooperative mapping function.

The invention provides a medical health management system and method based on virtual reality, and the method comprises the steps: predicting the training adaptability of a target patient to a current medical rehabilitation training item through the difficulty level of the current medical rehabilitation training item and the posture deviation in a medical rehabilitation training process, and obtaining a fitness prediction value; determining behavior association degrees between the target patient and other patients in the medical virtual reality interaction process, and further performing collaborative mapping on the adaptability of the current medical rehabilitation training item according to all the behavior association degrees to obtain collaborative characteristics of the target patient and other patients in the current medical rehabilitation training item; and carrying out cooperative constraint on the fitness predicted value through the cooperative features, and when the constrained fitness predicted value is lower than a preset fitness threshold value, adjusting the guidance speed of rehabilitation guidance of the current medical rehabilitation training item in the virtual reality equipment. By adopting the scheme of the invention, personalized medical health management based on the patient group effect can be realized.

The invention relates to the technical field of storage, in particular to a solid state disk cache management system and method based on HMB technology and host memory collaboration, and the system comprises a data collection module which is used for establishing an HMB collaborative mapping relation between a solid state disk cache and a host memory, synchronously collecting the running state of the solid state disk cache and the available resource state of the host memory, obtaining collaborative mapping-state fusion data; and the data division module is used for performing hierarchical division on the solid state disk cache based on the collaborative mapping-state fusion data to obtain multiple levels of cache regions, and extracting basic association features of each level of cache region to obtain a hierarchical cache-association feature data set. By monitoring the running states and resource availability of the solid state disk and the host memory in real time, the system can dynamically adjust cache layering and resource allocation according to the current state, so that high-frequency access data is always kept in a storage area with faster response, the delay is reduced, and the data transmission efficiency is improved.

The invention discloses a fan wireless monitoring data loss restoration method and system based on space-time correlation, and the method comprises the steps: designing a self-adaptive phase weighted interpolation algorithm (APWI) based on the rotation period and wind speed fluctuation characteristics of a fan blade, and precisely filling periodic lost data through dynamic matching of historical in-phase data and wind speed gradient correction; a collaborative mapping model (MSCM) is constructed in combination with multi-sensor spatial relevance, sudden loss is repaired by using strong association node data and LSTM time sequence prediction in a combined manner, and collaborative speculation of cross-sensor data is realized. The stress distribution reasonability of repair data is verified through finite element simulation, and a closed-loop optimization mechanism is introduced to dynamically update model parameters to adapt to complex environment changes. According to the invention, through high-precision data restoration and transmission reliability improvement, dependence on traditional wired transmission is substantially reduced, and application of a wireless communication technology in wind field data transmission is effectively promoted.