2805 results about "Spatial relationship" patented technology

The invention discloses an underwater detector cluster adaptive detection method and system based on distributed reinforcement learning, and the method comprises the steps: obtaining detector state data and environment parameter data, obtaining a standardized state matrix through adaptive standardization processing, building a group topological feature matrix based on spatial relation quantification, and obtaining a group topological feature matrix; generating a final feature representation; constructing an action prototype matrix and an action coupling tensor, optimizing an action space based on constraint conditions, and obtaining a feasible action set through multilayer evaluation and screening; a comprehensive reward function is constructed based on a multi-dimensional reward mechanism, strategy optimization and experience optimization are carried out, and a final optimization strategy is obtained; a multi-layer decision evaluation system and a real-time monitoring mechanism are established, and an experience knowledge base is constructed and continuously optimized. According to the invention, through multilevel feature extraction, dynamic action optimization, adaptive strategy learning and knowledge accumulation, the cooperative performance and environment adaptability of the underwater detector cluster are improved.

Systems, devices, and methods for presenting audio associated with events associated with spatialized audio effects or non-spatialized audio effects in three-dimensional environments are disclosed. The spatialized and / or non-spatialized audio effects correspond with displaying, via a display generation component, a three-dimensional environment from a viewpoint of a user. While displaying the three-dimensional environment from the viewpoint of the user, the computer system detects an event. When the computer system detects an event which corresponds to a spatialized sound effect, the computer system presents the sound effect as emanating from a location in the three-dimensional environment associated with the event. When the computer system detects an event which corresponds to a non-spatialized sound effect, the computer system presents the sound effect as emanating from a predetermined location for audio in the three-dimensional environment that has a predetermined spatial relationship relative to the viewpoint of the user

The invention discloses a PCBA circuit board welding spot detection method based on multi-modal data fusion, and relates to the technical field of electronic manufacturing quality detection.The PCBA circuit board welding spot detection method comprises the steps that a distributed sensing network is constructed, multi-modal data are collected, welding spot information is obtained in an omnibearing mode, and time-space alignment of the multi-modal data is carried out; performing feature extraction on the multi-modal data, dynamically weighting each modal feature through an attention mechanism, and highlighting key defect characterization; a welding spot spatial topological graph is constructed by using a graph neural network, and a spatial relationship between welding spots is modeled. By integrating optical, X-Ray, thermal, mechanics, electricity and other multi-dimensional data, the information limitation of single-mode detection is broken through, the complementation of different mode data is utilized, the attention mechanism is combined to dynamically weight each mode feature, the complex defect is accurately identified, the graph neural network is utilized to model the welding spot space topological relation, the associated defect is further captured, and the defect detection accuracy is improved. And the defect classification accuracy is improved.

The invention discloses a territorial space layout adjustment decision method based on planning conflict identification, and belongs to the technical field of territorial space planning and management. Comprising the following steps: constructing a space-time conflict feature vector library containing multi-dimensional attributes; generating a semantic knowledge graph containing a causal relationship, a sequential relationship, a spatial relationship and an affiliation relationship; accurate prediction of influence intensity and duration of the conflict state along with time lapse is realized; trend analysis and risk level evaluation of a cross-scale conflict propagation path are realized; automatically identifying dominant conflicts in the current layout and potential conflicts based on propagation prediction, and intelligently classifying composite conflicts by using fuzzy clustering; a staged differentiation conflict intervention strategy recommendation scheme is automatically generated; solving a territorial space layout adjustment scheme; judging whether the layout adjustment scheme meets a preset conflict relieving standard or not;

The invention discloses a full-period traffic flow prediction method based on spatial-temporal feature deep fusion, and aims to deeply integrate complex spatial-temporal dependence of traffic flow, form composite traffic situation perception and realize traffic multi-period comprehensive flow prediction. The method comprises the following steps: firstly, through spatio-temporal information comprehensive representation, extracting time sequence characteristics of multiple time modals and constructing a concrete real spatial relationship; in order to further construct a digital urban traffic flow prediction method fusing multi-scale and multi-granularity comprehensive elements, a comprehensive traffic flow prediction model deeply fusing short-term space-time dependence is provided for short-term traffic flow prediction, so that the sensitivity to instantaneous flow change is improved; the short-term dynamic change of the traffic flow in the multifunctional airspace node is fully modeled; in addition, a deep airspace deconstruction and time sequence feature fusion model is designed for medium and long term traffic prediction and deep extraction of complex traffic modes. Simulation results show that the method provided by the invention is superior to the existing traffic flow prediction technology.

The invention provides an adaptive multi-modal feature fusion well logging interpretation method based on multi-task joint learning, and the model comprehensively employs a graph convolution network, a multi-head attention mechanism, a gating mechanism and an adaptive loss weight adjustment algorithm, and effectively integrates multi-well group well logging data and geological structure information. And the complex spatial relationship and geological similarity between the wells are fully captured. The model can process multi-source data in a unified manner, extracts high-level feature representation containing spatio-temporal correlation, avoids repeated feature learning, reduces calculation complexity, and ensures the consistency and complementarity of prediction task features at the same time. The self-adaptive loss weight adjustment mechanism dynamically optimizes weight distribution according to the loss change of each task, realizes balance optimization among different tasks, and comprehensively improves the prediction precision of each task. The physical constraint regularization module ensures that the prediction result not only has statistical accuracy, but also conforms to the actual geological physical law, so that the reasonability and reliability of the prediction result are improved.

Methods and systems are described which allow a classification of first and second objects in an X-ray projection image. A respective representation and localization of both objects are determined by applying the models to match the objects in the X-ray image and a spatial relation of the classified objects is obtained. Such methods and systems take advantage of artificial intelligence.

The invention discloses an artificial board surface defect intelligent detection method and system based on machine vision, and particularly relates to the technical field of artificial board surface defect detection. An artificial board surface image is collected, image preprocessing, feature extraction, defect segmentation and classification recognition are carried out through a deep learning algorithm, a multi-scale convolutional neural network is adopted to carry out feature extraction on the image, a shallow convolutional layer captures small defect details, a deep convolutional layer recognizes global features of large defects, and a multi-scale convolutional neural network is adopted to carry out feature extraction on the image. Accurate defect segmentation is carried out through a Mask R-CNN model, a redundant frame is removed in combination with a non-maximum suppression algorithm, the classification problem of adjacent defects is corrected by using an error correction algorithm in combination with the spatial relationship and morphological characteristics of the defects, and defect information is fed back to a production line control system in real time; and defective products are automatically removed or production process parameters are automatically adjusted, so that the automation level of a production line is effectively improved, the product quality is optimized, and human intervention and production cost are reduced.

The invention relates to the technical field of model review, in particular to a BIM model review method based on text classification and named entity recognition, and provides the following scheme: building a building specification knowledge graph, obtaining BIM review data through a review data extraction strategy, obtaining a matching rule set by utilizing knowledge graph matching, and adjusting the priority for review. Outputting the report; the knowledge graph construction adopts an ontology model and a knowledge extraction network, component attributes, spatial relationships and time dimensions are considered in the examination data extraction, the matching process is combined with multi-dimensional semantic matching and reasoning, and the examination accuracy and efficiency are ensured.

The invention discloses an ophthalmologic operation real-time navigation system and method based on dynamic visual field tracking, and relates to the technical field of medical systems. Comprising the following steps: matching operation data closest to a current operation from a database, and generating standard operation navigation of the current operation; the real-time image of the operation process is compared with the image of the standard operation navigation in real time, the step of the current operation is determined, and meanwhile subsequent image navigation of the operation of the patient is displayed; generating a spatial mapping matrix between an eyeball movement track and a target region by combining target region boundary data of preoperative OCT image segmentation and an intra-operative navigation coordinate system; the virtual navigation path of the surgical instrument is dynamically corrected according to the real-time eyeball movement track, so that the surgical instrument is aligned with the target area, the spatial relation between the instrument and the target area can be visually mastered through the overlapping display of the three-dimensional virtual path and the real-time image, unconscious displacement or physiological tremor of a patient in an operation is actively adapted, and the operation accuracy is improved. And the continuous accuracy of the navigation path is ensured.

The invention relates to the technical field of visual rendering, and provides a three-dimensional data visual rendering method and a visual rendering engine system.The method comprises the steps that space segmentation processing is conducted on three-dimensional model data to be rendered through the LOD technology to generate block slice data with different geometric detail levels, organizing the block slice data through a tree structure with a spatial relationship, and outputting the tree structure as an index file; the method comprises the following steps: adaptively screening block and slice data of different levels of detail based on a user vision field range, storing the block and slice data into a rendering request queue, querying block and slice data to be rendered from the rendering request queue according to index codes of the block and slice data, and updating a layer browser database; according to the method, to-be-rendered block slice data are downloaded and analyzed in parallel in batches in a separated collaborative operation mode, and a rendering task is executed according to a data analysis result, so that the rendering scheduling frame rate is remarkably improved, and rapid visualization of urban mass three-dimensional slice data at a webpage end is realized.

The invention discloses a runoff prediction method and terminal based on a space-time heterogeneous graph neural network, and the method comprises the steps: obtaining hydrological time series data and space station information data collected by each station, and carrying out the preprocessing of the hydrological time series data and the space station information data; performing weighted fusion according to the features of the preprocessed hydrological time series data and the space station information data, generating a feature embedding vector representing a global variable time-space relationship through a graph attention network, and inputting the feature embedding vector into a full connection layer to obtain an output multi-station runoff prediction sequence. Constructing a space-time heterogeneous graph flood forecasting model; performing model parameter optimization on the space-time heterogeneous graph flood forecasting model; and carrying out runoff prediction on the hydrological time sequence data and the space station information data which are received in real time according to the optimized space-time heterogeneous graph flood forecasting model. In this way, hydrological space-time correlation characteristics can be effectively captured in forecasting, so that the accuracy and reliability of forecasting are improved.

The invention discloses a near-shore wind wave prediction method and system based on data driving, and the method comprises the steps: obtaining multi-source data of long-time series data of each meteorological data variable of a research region, carrying out the preprocessing, selecting a target variable and a feature variable, and carrying out the region grid point division of the research region; taking the characteristic variables of the open sea lattice points as a reference sequence, taking the target variables of the inner sea or near-shore lattice points as a target sequence, and analyzing a time delay effect and a spatial propagation mode between the reference sequence and the target sequence; introducing the analysis result as priori knowledge into a deep learning model, and training the deep learning model to obtain a typhoon prediction model; and acquiring real-time data of each meteorological data variable in the research area, preprocessing the real-time data, and inputting the characteristic variable in the real-time data into the typhoon prediction model to obtain a prediction result of the target variable. According to the method, the time lag effect and the spatial relation of all variables of the open sea on the offshore area information are comprehensively considered, fusion and integration of multi-source data are achieved, and efficient and accurate real-time prediction is carried out.

The invention provides a building equipment operation data intelligent analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: extracting historical positioning data in a space calibration interval, and calculating a position offset vector set between a predicted trajectory point and an actual trajectory point according to the trajectory point on a predicted motion trajectory; taking the weighted sum of squares of minimizing the position offset vector as an optimization target to obtain a three-dimensional space transformation parameter optimization model; according to the optimization model, a quasi-Newton optimization algorithm is adopted to solve the optimization model, and a final calibration parameter set is generated; and based on the final calibration parameter set, correcting the predicted motion trajectory, performing spatial relationship analysis on the corrected trajectory and a spatial risk field, calculating a collision risk probability and an accident severity level, generating a comprehensive risk score, and triggering a response action according to a preset threshold value. The construction safety level and the management efficiency are improved.

The invention discloses a human body tumble identification method based on millimeter wave radar perception. The method comprises the following steps: acquiring echo data of various human body actions by using a millimeter wave radar; processing the echo data to respectively obtain point cloud data of human body actions; a DBSCAN clustering method is used for clustering point cloud data, point clouds are divided into different clusters based on a central point, then the obtained point cloud data are input into a deep neural network, space structure and dependency relationship information of the point cloud data is captured, the category of human body actions is recognized according to the space structure and dependency relationship information, and a human body tumble recognition result is output. According to the invention, a deep neural network model fusing a PointNet network and a Transform module is designed, and the model can enhance the description of the spatial relationship of point clouds, thereby improving the judgment of human body tumble recognition, and reducing the false alarm and false alarm rate. According to the invention, human body postures and actions are sensed through millimeter wave radar signals, human body tumble recognition is realized, and the system has the advantages of good anti-interference performance, high recognition accuracy and the like.

A human body posture estimation method based on a dynamic graph convolutional network belongs to the field of human body posture estimation, and comprises the following steps: step 1, simplifying a human body structure in a video, and constructing a human body skeleton key point structure chart; 2, defining a graph structure to represent a spatial relationship between human joints; thirdly, a graph structure is defined to represent the motion relevance between the joints of the human body; 4, constructing a dynamic graph convolutional network by using dynamic space-time graph convolution; 5, 3D human body posture behavior data enhancement is carried out; and 6, constructing human body behavior data in the virtual scene by using the 3D human body posture data. According to the method, the modeling capability of the complex relation between the joints is improved, the robustness of the model is improved, and complex behaviors are recognized more accurately.

The invention relates to the technical field of anomaly detection, in particular to an intelligent gas hidden danger detection system which comprises a leakage monitoring module, a time difference extraction module, a direction positioning module, a color temperature analysis module and a risk aggregation module. According to the method, the time and space relation of gas concentration response nodes is extracted, a leakage diffusion path is constructed, and a dominant propagation direction is identified, so that a leakage trend region has a clear positioning basis, regional brightness and hue changes in an image frame are combined, the time sequence stability proportion is counted, and a cross-modal trend verification channel is established; the defect of a single sensing dimension is effectively made up, in the image and trend direction matching process, abnormal area numbers are recognized through stable proportion difference value changes, intersection confirmation is carried out on trend areas, the accuracy and spatial directivity of hidden danger marking are remarkably improved, the risk recognition chain is more complete through cooperative calculation of multi-source data, and the accuracy of hidden danger marking is improved. And the method has higher linkage response capability and dynamic judgment depth.

The invention discloses an electric power optical communication network alarm analysis method and system based on a time-space diagram neural network. The method comprises the following steps: collecting data of an electric power optical communication network, preprocessing the collected data, and modeling the preprocessed data into a graph structure; extracting and learning the features of the nodes in the graph structure by using a space-time graph neural network, and generating space-time embedded representation of the nodes in combination with space feature extraction and time feature extraction; classifying the nodes based on the spatio-temporal embedding representation of the nodes to obtain prediction labels of devices corresponding to the nodes; and according to the prediction label of the equipment corresponding to the node, carrying out analysis and influence evaluation on the alarm condition in the power optical communication network. According to the method, the electric power optical communication network is represented as the graph structure containing the space-time information, the space-time dependence of the electric power optical communication network can be effectively captured based on the space-time graph neural network, the space relationship in the graph structure and the dynamic change in the time sequence are processed at the same time, and the accuracy and timeliness of alarm analysis are improved.

The invention relates to a gated multi-graph convolution perception modeling method for traffic flow prediction. The method integrates multi-graph structure construction, gating graph convolution and time feature extraction, and aims to solve the problems of strong time fluctuation and heterogeneous spatial relationship in traffic data. The method comprises the following steps of: firstly, respectively constructing a geographic map and a semantic map according to the maximum mutual information measurement between the spatial distribution information of a sensor and historical traffic data; and then, designing a dual-adaptive gating graph convolution module, and dynamically adjusting an information propagation path of a multi-graph structure by introducing an attention mechanism and a gating factor, thereby improving the modeling performance of the model on spatial isomerism dependence. On the time dimension, a time sequence interactive sensing module is constructed in combination with multi-scale causal convolution and an attention mechanism, time dependence characteristics of a short period and a long period are captured, and fusion and expression of time characteristics are completed. According to the method, the modeling precision and stability of the traffic prediction model in a complex traffic scene can be effectively enhanced, and the method has relatively high practical application value.

While a first view of a three-dimensional environment is visible, a computer system detects a first input meeting selection criteria. If, when the first input was detected, a user was directing attention to a first portion of the first view that has a spatial relationship to a viewport through which the three-dimensional environment is visible, the computer system displays a user interface object including affordances for accessing functions of the computer system; otherwise, the computer system forgoes displaying the user interface object. While a different view of the three-dimensional environment is visible, the computer system detects a second input meeting the selection criteria. If, when the second input was detected, the user was directing attention to a second portion of the different view that has the same spatial relationship to the viewport, the computer system displays the user interface object; otherwise, the computer system forgoes displaying the user interface object.

The invention relates to the technical field of power detection, and discloses a vegetation risk hidden danger detection method and system based on sparse point cloud segmentation, and the method comprises the steps: collecting image data of a monitoring region at different angles, generating sparse point cloud data, and carrying out the preprocessing of the sparse point cloud data; and performing point cloud segmentation on the sparse point cloud data through a neural network, dividing the monitoring region according to region types, analyzing a spatial relationship between vegetation and power equipment in a target region, performing risk assessment, and generating an early warning signal in combination with multi-modal data. According to the method, the sparse point cloud data is precisely segmented, the three-dimensional space characteristics of the tree are extracted, and the potential risk of the tree and the power transmission facility can be dynamically monitored in real time by precisely calculating the space relation between the tree and the power transmission facility; according to the multi-modal fusion data, multi-level early warning information is generated, so that the efficiency and precision of power transmission line inspection are greatly improved, and the pre-judgment and timely treatment of hidden dangers are realized.

The invention relates to the technical field of vision, in particular to an object monitoring method based on a multi-camera joint calibration technology and a monitoring camera thereof. The method comprises the following steps: driving all cameras to synchronously shoot a calibration reference object with known geometric characteristics, and solving internal and external parameters of each camera and an accurate space pose relationship between the internal and external parameters by utilizing shot images to form a joint space relationship chain; controlling the camera array to synchronously shoot a target object, and correspondingly converting the two-dimensional image points acquired by the cameras into spatial point coordinates in a unified three-dimensional coordinate system by using the joint spatial relation chain to generate three-dimensional point cloud data of the target object; and processing the generated three-dimensional point cloud data, extracting key geometric features of the target object in real time, performing comparative analysis on the extracted features and a preset standard or a historical state, and outputting a state monitoring result of the target object. According to the invention, the influence of temperature drift and mechanical vibration on the measurement precision is effectively suppressed, and the stability of long-term monitoring of an industrial field is guaranteed.

The invention discloses a road slope modeling method based on an unmanned aerial vehicle inspection route, and relates to the technical field of unmanned aerial vehicle inspection. The method comprises the following steps: measuring the road slope length, the landform and the geological complexity, and selecting an adaptive unmanned aerial vehicle type and sensor combination according to the road slope length, the landform and the geological complexity; planning unmanned aerial vehicle surface inspection and shallow geological exploration routes, ensuring space matching, and synchronously obtaining high-resolution images and ground penetrating radar data; and processing image data through a photogrammetry technology, generating a digital elevation model and an orthophoto map, and combining to construct a three-dimensional model of the earth surface. Geophysical inversion is carried out on the ground penetrating radar data, reflection features are extracted and projected to the ground surface three-dimensional model, and space correlation between the ground surface and a shallow layer structure is established; the reflection features are digitalized and geometrically reconstructed into three-dimensional surface elements, the three-dimensional surface elements are embedded into an earth surface model to form a three-dimensional geologic body model, time dimensions are introduced, and a dynamic evolution model is constructed. According to the dynamic evolution model, the dynamic analysis of the slope stability is realized.

The invention belongs to the technical field of knowledge graph construction, and discloses an urban rainstorm flood disaster chain affair graph construction method and system, and the method comprises the steps: firstly obtaining a research document, a first historical event and a secondary event, constructing a disaster chain directed network graph, and constructing an ontology model according to the disaster chain directed network graph; acquiring a second historical event and a secondary event, preprocessing, and inputting into the ontology model to obtain an event corpus; extracting event trigger words from a corpus to build a database by using a multi-modal dynamic fusion model MDF-Net; performing event extraction in a corpus according to the trigger word to obtain an event type and an argument; extracting a time sequence, a spatial relationship and a semantic relationship of a trigger word by using an improved ELCTRA-BiLSTM model; and finally, constructing a disaster chain affair map by taking the event type, the argument, the time sequence and the spatial relationship as nodes and taking the semantic relationship as an edge. According to the method, multi-source data and complex disaster information can be effectively integrated, and the integrity and accuracy of the urban rainstorm flood disaster chain affair map are improved.

The invention provides a thinking tree guidance-based large model target spatial relationship determination method and apparatus, relates to the technical field of artificial intelligence, and aims to solve the technical problems of low interpretability and low data processing efficiency when an existing large model processes a complex spatial reasoning task. The method comprises the following steps: acquiring a spatial reasoning task to be processed; according to the spatial reasoning task, task description features corresponding to the spatial reasoning task are analyzed, and the task description features comprise story features and problem features representing spatial relations of all objects; the task description features are input into a pre-trained large language model, and the following operations are executed: the problem features are split into a thinking tree structure with a plurality of nodes, and each node represents a thinking step; according to each thinking step, identifying potential spatial relationship features between the source object and the target object in the story features; and determining the spatial relationship between the source object and the target object according to the potential spatial relationship characteristics.

The invention discloses an edge protection early warning method and device based on LSTM and a medium, and relates to the technical field of edge protection. The method comprises the following steps: acquiring a video stream of a target person through a camera array, and acquiring three-dimensional point cloud data of the target person through a laser radar; performing background modeling on the video stream, and fusing the three-dimensional point cloud data to generate a motion track with three-dimensional coordinates; receiving a motion track, and predicting a motion path and a motion speed in a preset time period through a stacked bidirectional LSTM network; and an audible and visual alarm, an intelligent terminal pushing device and a mechanical braking device are integrated, and graded alarm is triggered according to the predicted path, the movement speed and the spatial relationship with a preset dangerous area. According to the method, the motion path and the motion speed in the preset time period are predicted through the stacked bidirectional LSTM network, and comprehensive perception, intelligent prediction and graded alarm are performed on target personnel on a construction site.

Systems, video cameras, and methods for predictive adjustment of multi-camera surveillance video data capture are described. A plurality of networked video camera is deployed with known spatial relationships and non-overlapping fields of view among cameras. When a video event is detected from video data for one of the video cameras, a video capture update message is selectively sent to other video cameras to modify their video capture operations. For example, an object detected by one camera may trigger adjustment of video capture operating parameters for cameras in a direction of travel of the object before it has entered the field of view of those other cameras.

A unified platform for multi-modal data compression and decompression that enables efficient processing of correlated data streams while preserving relationships between different modalities. The platform employs a virtual management layer to analyze and route input streams, implementing correlation analysis to identify temporal and spatial relationships between streams. Multiple compression methods, including neural network-based approaches, are utilized to compress data sets while maintaining cross-modal dependencies. A neural upsampling system leverages learned correlations between streams to enhance reconstruction quality. The platform includes a synchronization manager that maintains temporal alignment and relationship preservation throughout processing. By integrating correlation-aware compression with neural upsampling techniques, the platform provides comprehensive multi-modal compression capabilities while preserving critical relationships between different data types. The system is particularly suited for applications involving synchronized audio-visual data, sensor streams, and other multi-modal content.

The invention relates to the technical field of pneumoconiosis image detection, and discloses a pneumoconiosis-oriented chest X-ray image processing method based on an AI small shadow detection rate. According to the method, a basic mask is generated through an initial segmentation network, and then an accurate lung contour is obtained through correction of a hierarchical optimization unit. The multi-scale feature extraction module analyzes gray features in the contour and constructs an initial small shadow probability distribution map. A morphological structure analyzer identifies discrete shadow regions and marks suspicious shadow clusters, and a dynamic confidence feedback mechanism evaluates its credibility to generate an optimized feature map. A shadow cluster topological incidence matrix is established by the spatial relation modeling network and matched with a pneumoconiosis pathological feature library to screen a target shadow area. The three-dimensional reconstruction engine generates a small shadow volume density thermodynamic diagram, the hierarchical fusion module integrates the thermodynamic diagram and original image space coordinates, an enhanced distribution map is output, and finally structured diagnosis report data is generated.

The invention relates to the technical field of navigation planning, and discloses an unmanned aerial vehicle automatic inspection nest path planning method and system, and the method comprises the steps: carrying out the multi-source data fusion of a target inspection region, and obtaining space measurement environment data; analyzing an airspace limiting condition in the spatial measurement environment data, and determining a navigation path measurement constraint condition in combination with an inspection task demand of the target inspection area; performing gridding measurement on the target inspection area to identify candidate waypoints; according to the spatial position information, analyzing a spatial relationship of the candidate waypoints to construct an initial navigation network; performing multi-constraint evaluation on the initial navigation network to obtain constraint weights corresponding to the candidate waypoints, and integrating the candidate waypoints with the constraint weights higher than a preset weight threshold into a global optimal waypoint set; carrying out passable connection on adjacent waypoints in the global optimal waypoint set to obtain a navigation path; according to the invention, the efficiency of automatic inspection nest path planning of the unmanned aerial vehicle can be improved.