1709results about How to "The recognition effect is accurate" patented technology

The invention belongs to the technical field of multi-agent collaboration, and provides an electric power cross-modal knowledge fusion multi-agent collaboration processing method and system, and the technical scheme is as follows: analyzing a task execution command instruction input by a user; key elements of the task are extracted, the task is decomposed into a plurality of sub-tasks according to the extracted key elements, and logic association and priorities among the sub-tasks are identified; according to the logical association and priority among the subtasks, allocating the subtasks to the corresponding agents; each agent executes the corresponding subtask according to a set execution path, the executed task result is shared to other agents to cooperatively complete the complex task, and finally a cooperative processing result is obtained through integration. Efficient execution of complex tasks such as document generation, review and management is realized, and smooth completion of the tasks is ensured.

The invention discloses a construction progress control method in project supervision, and the method comprises the steps: calculating the current punctual completion rate and process handover delay time of each subpackage provider according to a real-time progress data set and a pre-established project network diagram, recognizing the progress fluctuation modes of different project stages through a time sequence analysis method, and carrying out the construction progress control according to the progress fluctuation modes. Establishing a correlation model of the sub-packaging quotient and the engineering stage; according to the priority scores, taking minimization of overall construction period delay and resource idleness as objective functions, applying an optimization algorithm, dynamically adjusting a construction plan and a resource allocation scheme of each subpackage provider, and generating an optimized project progress plan which comprises specific work arrangement and resource allocation suggestions of each subpackage provider; a successful coordination strategy in a historical project is analyzed, coordination measures are recommended for project management personnel and a set of communication matrix is generated according to characteristics and progress conditions of a current project, and the coordination measures comprise resource allocation suggestions, process optimization schemes and communication strategy templates.

The invention relates to the technical field of machine vision, and discloses a lead screw module surface defect online detection method and device based on machine vision, and the method comprises the steps: carrying out the image collection and preprocessing of the surface of a lead screw module, and obtaining a preprocessed image; decomposing the structure layer and the texture layer to obtain an initial structure layer and an initial texture layer; generating an initial contour image based on the initial structure layer and a full convolution generator network, and performing edge detection and morphological processing to obtain a target contour image; performing texture optimization based on a mask weighted least square method and a filtering technology to obtain a target texture layer; carrying out feature extraction and fusion through an edge perception multi-stage interaction network to obtain a multi-scale feature map; based on the multi-scale feature map, defect detection and positioning are carried out through the full convolutional network, edge optimization is carried out in combination with the target contour image, a target defect detection result is obtained, and the method improves the accuracy of online detection of the surface defects of the lead screw module.

The invention provides a method and system for realizing electronic channel risk control processing based on a code insertion technology, and relates to the technical field of electronic channel risk control, and the method comprises the steps: encrypting user information and channel identification information through a dual Hash algorithm to generate a dynamic session identification code, and forming a target coding sequence through employing a self-adaptive segmentation algorithm and a business feature flag bit. And constructing a dynamic risk control rule model by using a graph neural network, extracting user multi-dimensional features, carrying out weighted fusion to generate a user risk score, comparing the user risk score with an adaptive risk threshold, analyzing abnormal behaviors based on a probability graph model, and generating an intelligent risk early warning report. And calling a reinforcement learning risk control strategy engine to determine an optimal disposal strategy, and issuing a multi-factor interaction verification instruction. And after verification, selectively activating a blocking mechanism according to a verification result, synchronizing interaction data and a result to a distributed risk control data center, and continuously optimizing the risk control model based on federal learning.

The invention relates to the technical field of environmental sound event detection, in particular to an environmental sound event detection method based on multi-modal data fusion, which utilizes a microphone array, a camera and a sensor to synchronously acquire audio, video and environmental data to form a multi-modal data source. And the collected multi-modal data is preprocessed, so that the data quality and the accuracy of subsequent analysis are improved. And key features are extracted from the processed data, and the features are fused to form multi-modal features. And a deep learning model is constructed, and multi-modal features are used for training, so that a model capable of identifying an environmental sound event can be obtained. And performing optimization such as pruning, quantification and knowledge distillation on the trained model, and deploying the model on edge equipment to realize real-time preliminary feature extraction and event detection. The problems that an environment sound event detection method in the prior art is poor in robustness, low in real-time performance and limited in generalization ability are solved.

The invention provides a visual project management method and system based on task tracking, and relates to the technical field of project management, and the method comprises the steps: collecting project risk information through employing a context awareness technology, generating a risk propagation network, and calculating a risk diffusion coefficient; performing hierarchical analysis on project requirements based on the risk diffusion coefficient to obtain a task decomposition scheme, constructing a task transmission chain based on the task decomposition scheme and generating a visual task relation graph, and generating a task state transition rule and an alternative task path based on an early warning state of a task node, meanwhile, constructing a task elastic scheduling mechanism and storing the task elastic scheduling mechanism into a rule database; and identifying a preorder task node and a post-order task node on the task transmission chain, sending the task reconstruction instruction to the preorder task node and the post-order task node to realize dynamic task adjustment, updating the risk propagation network according to an adjustment result, and optimizing the risk diffusion coefficient.

The invention discloses a PPY-YOLO-based steel surface defect detection method and a PPY-YOLO-based steel surface defect detection system. The PPY-YOLO-based steel surface defect detection method comprises the following steps: step 1, preprocessing a steel surface defect image; 2, a neck structure Faster-Neck, a deep learning module C3K2Faster and a spatial pyramid pooling module SPPFSim are introduced into the YOLOv11 network, and a PPY-YOLO target detection model is obtained; step 3, training a PPY-YOLO target detection model; and 4, acquiring a steel image, and inputting the steel image into the trained PPY-YOLO target detection model to obtain the category and positioning information of the steel surface defects. A new model is constructed, the backbone network and the neck network are improved, the backbone network is formed by connecting the input end of the C2PSA module with the output end of the SPPFSim module, and an up-sampling group and a down-sampling group are added to the neck network, so that the model has a relatively good detection effect.

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

The invention discloses a fatigue driving monitoring and early warning method based on multi-dimensional feature fusion, and relates to the field of intelligent traffic and safe driving, and the method comprises the steps: carrying out the feature extraction of a collected face image of a vehicle driver through a feature extraction model based on a convolutional neural network, and obtaining a face key point; then calculating the eye closing rate, the mouth opening rate, the head posture data and the facial muscle data to finally obtain multi-dimensional features, and fusing the multi-dimensional features by using a multi-layer perceptron to obtain fused facial features; and then, a fatigue state detection model obtained based on recurrent neural network training is utilized to detect the facial feature time sequence at a plurality of continuous sampling moments so as to detect whether a fatigue driving state occurs and trigger alarm feedback. According to the method, the fatigue state of the driver can be identified and judged more accurately through combination of multi-dimensional data fusion and a time sequence analysis algorithm, and the monitoring and early warning accuracy and reliability are improved. And the system can keep high robustness even in an illumination change or complex driving environment.

The invention relates to the technical field of fire monitoring and prediction, in particular to a multi-source fire data fusion and fire analysis and prediction system which comprises a data acquisition module, a data preprocessing module, a data fusion module, a fire analysis module and an early warning module. Wherein the data acquisition module is used for acquiring fire data from various sources; the data preprocessing module is used for preprocessing the fire data; the data fusion module is used for carrying out fusion processing on the multi-source data output by the data preprocessing module; the fire analysis module is used for identifying the current situation of the fire and the position of a fire source and predicting the spreading direction of the fire; and the early warning module is used for sending out an early warning signal in time. According to the invention, through the multi-source fire data fusion and intelligent analysis and prediction technology, the occurrence, spreading and risk level of the fire can be accurately monitored in real time, so that the timeliness and accuracy of fire emergency response are effectively improved.

The invention discloses a wind power plant equipment fault intelligent diagnosis method and system. The method comprises the following steps: acquiring multi-dimensional sensor data of wind power plant equipment; carrying out preliminary screening on the data by utilizing an edge analysis algorithm, and determining data needing to be further diagnosed; the screened data are input into a pre-constructed fault diagnosis model, the data are fused through a multi-modal deep learning model in the pre-constructed fault diagnosis model, and then a fault occurrence area is determined through transfer learning, domain adaptation and a multi-agent reinforcement learning framework; meanwhile, the causal inference engine and the knowledge graph enhanced neural symbol inference module are responsible for deep processing and fault diagnosis. And finally, inputting the determined fault area and the diagnosis result into a dynamic system health index calculation module to generate a final fault diagnosis result. According to the method, the diagnosis efficiency and accuracy of the equipment fault of the wind power plant are remarkably improved, and a powerful guarantee is provided for stable operation of the wind power plant.

The invention relates to the field of image processing, in particular to a welding quality comprehensive evaluation method and system, and the method comprises the steps: obtaining an ultrasonic image of a welding joint, carrying out the contour detection, and extracting a defect region of the welding joint; gradient features and gray features of pixel points in the defect area are calculated, feature vectors of target pixel points in the defect area are constructed, and the feature vectors serve as the expression degree of the defect area; edge information of the defect area is obtained so as to determine defect types, the defect types comprise cracks and bubbles, the shape and size of a window are set according to the defect types, bilateral filtering processing is carried out, defect information is extracted, and comprehensive evaluation is carried out on the welding quality. According to the method, the appropriate window shape and size are selected according to the defect type, the detail information of the defect is effectively reserved, the definition of the ultrasonic image is remarkably enhanced through the self-adaptive filtering strategy, and therefore the welding quality is better evaluated.

The invention discloses a text keyword desensitization method, system and equipment based on a large model and a medium, belongs to the technical field of large model and natural language processing, and aims to solve the technical problem of how to automatically identify and desensitize sensitive information, improve the accuracy of sensitive information identification and further improve the text processing efficiency. According to the technical scheme, the method comprises the steps of data collection; preprocessing the data; marking sensitive information; segmenting the data; selecting a basic model; finely adjusting the model; training the fine-tuned large language model, and obtaining and storing the trained fine-tuned large language model; preprocessing the to-be-processed text data; detecting sensitive information, formulating a desensitization strategy and implementing desensitization processing, performing desensitization verification on the text after desensitization processing, and optimizing and improving the fine-tuned large language model according to a verification result; and packaging the optimized and improved fine-tuned large language model into an API (Application Program Interface) service for an application program to call.

The invention discloses an automatic ship berthing and leaving method based on video monitoring and image recognition, which comprises the following steps: S1, acquiring real-time video data, and carrying out image preprocessing; s2, extracting target feature information by using a target detection model based on an OfficientDet algorithm; s3, generating environment sensing information by using a multi-modal sensor fusion technology; s4, based on the environment perception information, constructing and updating a three-dimensional dynamic map of the surrounding environment of the ship in real time; s5, performing global path planning by adopting a fast extension random tree-star algorithm; s6, local dynamic obstacle avoidance is carried out in combination with a two-stage neural network algorithm; s7, based on the global path planning result and the local dynamic obstacle avoidance strategy, the adaptive control system accurately adjusts the speed, course and attitude of the ship according to the real-time environment change; and S8, performing big data analysis and historical data mining by using the cloud computing platform. According to the invention, image recognition and deep learning technologies are utilized to realize automatic berthing and unberthing of the ship, and the method has the advantages of high efficiency and accuracy.

The invention relates to the technical field of radar detection, in particular to an intelligent collision detection method and system, and the method comprises the following steps: setting an initial emission angle of a radar, monitoring a reflection signal during the movement of an object, recording the return time, calculating a reflection distance according to the light velocity and the time, adjusting the emission angle, optimizing the coverage range, and obtaining angle correction data. According to the method, the position of the object can be more accurately determined by recording the return time of the reflection signal in real time and calculating the reflection distance by using the light velocity and the time difference, and potential collision risks can be more accurately predicted and identified by iteratively optimizing the scanning path, enhancing the adaptability to the dynamic environment and updating the target trajectory information. Intelligent judgment of risk levels and adjustment of emergency response measures are carried out, the response efficiency and accuracy of an early warning system are improved, the function of a collision detection system is enhanced, traffic safety is remarkably improved, and potential accident risks are reduced.

The invention belongs to the technical field of safety monitoring of subway tunnels, and particularly relates to a subway tunnel water leakage monitoring method and system based on multi-modal data fusion, and the method comprises the steps: obtaining multi-source sensor time sequence data in a subway tunnel, carrying out the feature extraction, and extracting feature parameters related to water leakage; carrying out fusion analysis on the characteristic parameters through a data fusion algorithm, and evaluating the position and severity of water leakage, including extracting time-space correlation characteristics of data by using a dynamic time warping method; after feature extraction is performed on the data set and the space-time correlation features by using a convolutional neural network and a recurrent neural network, feature fusion is performed; and performing anomaly detection and classification on the fused high-level features by using a neural network method to obtain the position and severity of the leakage water. The advanced data fusion technology and the feature extraction algorithm are adopted, various collected data can be analyzed in real time, abnormal conditions can be found in time, and early warning is carried out.

The invention discloses a bad traffic monitoring method and system based on an LSTM and a clustering algorithm, belongs to the technical field of network security, and efficiently converges network traffic by using a BPF technology. The method comprises the following steps: accurately identifying fragmented traffic through an LSTM network, and capturing a complex dependency relationship by using a special memory unit structure of the LSTM network; performing session recombination by adopting an FCM clustering algorithm, restoring a complete network session, and quickly identifying malicious traffic through feature matching; and finally, timely treatment measures are taken for the confirmed malicious traffic. According to the method, rapid and accurate detection and identification of bad website traffic fragmentation behaviors are realized, and the technical problem that a traditional method is difficult to deal with is solved; through efficient caching and recombination mechanisms, the efficiency and accuracy of malicious traffic processing are improved; intelligent disposal measures are adopted, and it is ensured that malicious traffic is effectively restrained in time; the overall protection capability of network security is improved, and enterprise network resources and user data security are protected.

The invention discloses a ship obstacle avoidance and automatic berthing method based on a deep learning algorithm. The ship obstacle avoidance and automatic berthing method comprises the following steps: S1, collecting data and constructing a data set; s2, performing time synchronization and space calibration; s3, carrying out pretreatment; s4, performing feature extraction; s5, generating a unified feature space by using a self-attention mechanism based on Transform; s6, performing environment modeling, and generating a virtual marine environment and a complex scene by using the generative adversarial network; s7, training an obstacle avoidance and path planning decision model of the ship by adopting a method of combining a distributed depth deterministic strategy gradient with a game theory; s8, designing a hybrid power path planning system by adopting meta-reinforcement learning in combination with fuzzy logic control; s9, realizing accurate berthing based on a recurrent neural network control strategy; and S10, performing self-adjustment and optimization according to the new environment and condition by using an online learning technology. According to the invention, deep learning and multi-mode sensor technologies are comprehensively applied, and accurate obstacle avoidance and automatic berthing of the ship are realized.

The invention belongs to the technical field of intelligent document management and auditing in the electric power industry, and provides an electric power document autonomous review method and system fusing causal reasoning and compliance review. According to the technical scheme, obtained electric power document data is subjected to semantic analysis, and semantic representation of a document is obtained; based on the semantic representation of the document, performing event extraction on each sentence recognized in the document, and encoding each event to obtain the semantic representation of the event; constructing an initial event causal chain based on the semantic representation of the event; verifying the constructed initial event causal chain by adopting a causal reasoning algorithm, comparing each event in the verified causal chain and the incidence relation between the events with a power industry compliance rule, calculating a compliance score, and calculating the incidence relation between the events of which the compliance score is lower than a set score value or the incidence relation between the events of which the compliance score is lower than the set score value; and generating a correction suggestion, and generating a review report. And the efficiency and the accuracy of document review are obviously improved.

The invention discloses a robot grinding real-time defect detection and self-adaptive correction system based on deep learning. The problem that in the prior art, real-time detection, defect correction and accurate path optimization are difficult to achieve in the grinding process is solved. The image, the three-dimensional point cloud and the moment data are fused through a multi-modal sensing system, and data fusion processing is performed by adopting a time synchronization mechanism, so that high-precision defect detection is realized. And the self-adaptive correction module dynamically adjusts the pressure, the speed and the angle of the grinding tool according to real-time feedback by using a reinforcement learning algorithm, so that accurate grinding for different workpiece surfaces is ensured. The system is further combined with closed-loop feedback control, the grinding path is continuously optimized, and the consistency and stability of the grinding quality are improved. Compared with the prior art, efficient and automatic defect detection and self-adaptive correction can be achieved on the complex and irregular workpiece surface, and the production efficiency and the surface treatment quality are remarkably improved.

The invention discloses a customer voice analysis system based on a large model, and the system comprises a voice data collection and preprocessing module which is used for collecting and preprocessing customer voice data; the voice recognition module is used for recognizing the preprocessed customer voice data; the feature fusion module is used for extracting semantic features of the text data and fusing customer voice data features to generate voice text representation; the large model fine tuning module is used for performing fine tuning on the pre-trained starry fire cognition large model; the semantic analysis and emotion recognition module is used for processing the voice text representation, extracting semantic information, recognizing an emotion state and generating an analysis result; and the dialogue context management module is used for associating customer historical voice data, establishing dialogue context information and updating a voice analysis result. According to the method, the stellate fire cognitive large model and the improved quantitative low-rank adaptation are utilized, and the method has the advantages of high precision, low calculation cost and high intelligent adaptability.

The invention provides a voiceprint processing method and system and a storage medium, and relates to the technical field of voice processing, and the voiceprint processing method comprises the steps: obtaining audio sample data; performing noise separation on the audio sample data to obtain a voice signal; performing voiceprint extraction on the voice signal; inputting the extracted target voiceprint features into a selected clustering algorithm; comparing the voiceprint feature points of the voiceprint cluster with voiceprint features in a pre-constructed voiceprint library, and calculating the similarity; and selecting the voiceprint cluster of the corresponding speaker to carry out gain processing. According to the method, voiceprints with similar characteristics are clustered through the clustering algorithm, the subsequent comparison process is simplified, the recognition accuracy is improved, the identity of the speaker is determined based on the voiceprint clustering cluster, gain processing is carried out on the voiceprint clustering cluster of a specific speaker, the system can more accurately extract and recognize the voiceprint characteristics of the speaker, and the recognition accuracy is improved. And high identification accuracy can be maintained even in a noisy environment.

The invention discloses a conflict detection processing method and system based on a BIM model, a medium and a product, and relates to the technical field of constructional engineering, and the method comprises the steps: obtaining a construction project design scheme; based on a preset model framework, determining a BIM model according to the construction project design scheme; predicting potential conflict points in the BIM model based on a preset conflict mode library; matching the potential conflict points with the building structure, the electromechanical pipeline and the equipment layout by using a space mapping algorithm, and determining possible conflict areas; on the basis of a natural language processing technology, operable solution suggestions are generated according to the possible conflict areas; after the operable solution suggestions are sent to the user, the solution suggestions determined by the user are received; and adjusting the BIM model based on the solution suggestion. By implementing the method, the accuracy and intelligence of conflict detection can be improved.

The invention discloses a target detection tracking method and system based on automatic detection, and relates to the technical field of target detection tracking, and the method comprises the following steps: carrying out the target position and category labeling of an image through a labeling tool based on an image data set of a typical target, and obtaining a training data set with a label, establishing a target detection model according to a YOLO architecture, and introducing a dynamic weighted loss function; according to the training data set, a training batch is generated by using a hierarchical sampling strategy, an optimized training sample is obtained through an online difficult case mining mechanism, and iterative training is performed on the target detection model based on the generated training batch and the optimized sample; and performing target detection on the real-time video stream by using the trained target detection model to obtain the tracking position and category information of each frame. According to the method, the problem of class imbalance can be effectively solved, the detection capability of rare targets is improved, and meanwhile, the overall performance of the model is improved through difficult case mining.

The invention provides a model fine tuning training method and device, an answer output method and device and electronic equipment, and aims to solve the problem of low safety of a large language model. The method comprises the following steps: acquiring a plurality of question texts to be processed; for each question text in the plurality of question texts, acquiring a principle keyword corresponding to the question text, and generating a prompt text corresponding to the question text according to the question text and the principle keyword corresponding to the question text; inputting a prompt text corresponding to the question text into a large language model to enable the large language model to output a plurality of answer texts; determining the answer text with the highest security from the plurality of answer texts; and taking the prompt text corresponding to each question text as training data, taking the answer text with the highest security as a training label, and performing fine tuning training on the large language model to obtain the large language model after fine tuning training.

The invention relates to the technical field of image processing, in particular to an intelligent detection method and system for shaft forgings. The method comprises the following steps: acquiring gray values, positions and gradient directions of all pixel points in a surface image of the shaft forging; clustering all pixel points in the surface image by using a condensed hierarchical clustering algorithm to obtain a plurality of regions; calculating the suspicious degree of each region; determining a scratch burr area in the surface image of the shaft forging to realize intelligent detection of the shaft forging; in each round of iteration process of the condensed hierarchical clustering algorithm, for two to-be-combined clusters in each round of iteration process, calculating the separation degree of the two clusters; calculating the feature similarity of the two clustering clusters; determining the number of clustering clusters of the current iteration; a plurality of regions of the surface image of the shaft forging are determined. By dynamically adjusting the number of clusters, the problem of wrong merging or premature iteration stopping of a traditional condensed hierarchical clustering algorithm under the fixed number of clusters is avoided.

The invention belongs to the technical field of fault prediction, and discloses a fault prediction method and system for an intelligent accessory monitoring platform. The method comprises the following steps: an intelligent accessory monitoring platform constructs a knowledge graph, a fault prediction model and a fault maintenance strategy generation model; performing knowledge mapping on the real-time monitoring log by using a knowledge graph to obtain a semantic enhanced real-time monitoring log; performing fault prediction by using a fault prediction model according to the real-time operation monitoring data and the semantic enhanced real-time monitoring log to obtain a real-time fault prediction result; generating a fault maintenance strategy by using a fault maintenance strategy generation model according to the real-time fault prediction result to obtain a real-time fault maintenance strategy; and visually displaying the real-time fault prediction result and the real-time fault maintenance strategy of the attachment. According to the invention, the problems of insufficient fault prediction accuracy, insufficient data processing capability and low intelligent degree in the prior art are solved.

The invention discloses a road surface detection method based on artificial intelligence and a road inspection robot, and the method employs a self-designed RCDIT network model, proposes an implicit expression method and a multi-head cross attention mechanism in the network model, guarantees the expression integrity and continuity of image blocks, and improves the detection precision. Complex interaction of image blocks and position information can be effectively processed, and the integration capability and information relevance of the model to multi-modal data are enhanced. In addition, the introduced position feedforward network further improves the abstract ability of feature representation, and is helpful for more effectively processing complex features and improving the expressive force of the model. And the RCDIT network model is combined with the road detection robot, so that the crack defects of the road surface and the road can be accurately and quickly identified and segmented, and the reliability and the accuracy of detection are greatly improved. The road detection robot can work in a 24 / 7 all-weather mode and is not limited by time and environment, manpower resources are effectively saved, and continuous monitoring on the road surface can be achieved.

The invention relates to a supply relation management method based on artificial intelligence. The method comprises the following steps: performing data analysis on public text data and communication records of suppliers by adopting an artificial intelligence technology based on deep learning, respectively extracting semantic feature representations of the public text data and the communication records of the suppliers, and performing semantic feature adaptive fusion processing on the public text data and the communication records of the suppliers; and the potential risk level of the supplier is judged based on cross validation analysis of public text data and communication records. In this way, an enterprise can be helped to identify and manage potential risks in a supply chain more accurately, and therefore the stability and safety of production and operation of the enterprise are improved.

The invention discloses an intelligent reconstruction and forecasting system and method for a three-dimensional wave field in a real marine environment, and belongs to the technical field of marine engineering. Comprising an unmanned aerial vehicle image capture module, an image preprocessing module, a feature recognition and parameter extraction module, a low-contrast and high-noise image processing module, a wave crest and wave trough recognition module, a wave feature analysis module, a wave feature analysis and prediction module, an unmanned aerial vehicle position adjustment module and a wave simulation and evolution module. By integrating an advanced image processing technology and an artificial intelligence algorithm, the spatial wave surface resolution of free waves and high-order bound waves is improved, and an accurate three-dimensional wave field is synthesized through synchronous superposition. Multiple high-altitude unmanned aerial vehicles can be used for collecting wave surface images under various sea conditions in real time, wave feature recognition and parameter extraction are carried out, and three-dimensional reconstruction of near-field, middle-field and far-field wave surfaces in the ocean is effectively achieved based on a wave evolution physical control equation.