97 results about "Neighbor algorithm" patented technology

The invention belongs to the technical field of three-dimensional point cloud processing, and particularly discloses an automatic tooth segmentation method and system for oral cavity scanning point clouds, and the method comprises the following steps: collecting and preprocessing dentition three-dimensional point clouds, extracting point cloud features containing coordinates and normal directions, and constructing a neighborhood structure; setting a first-stage multi-branch network, and performing tooth and gingiva coarse segmentation and instance initial clustering on the point cloud; setting a second-stage semantic refinement network, and carrying out local cutting on the original point cloud; setting a boundary detection and boundary offset network to enhance a tooth contact area, and refining a boundary instance by clustering to obtain a boundary enhanced instance tag; and performing label fusion, and propagating the fused label to the original high-resolution point cloud through a nearest neighbor algorithm to obtain a final semantic label and a final instance label. By adopting the technical scheme, high-precision instance-level segmentation of the complete dentition point cloud is realized through semantic segmentation, boundary detection and clustering cooperative work based on a biased field.

The invention discloses a big data measurement asset supply and demand matching and inventory optimization method, and relates to the technical field of big data processing and supply chain management. The method comprises the following steps: segmenting supply chain asset data through a hash function according to multi-dimensional attributes to generate a data fragment set, and constructing a global index tree according to the data fragment set; and monitoring the load state of the leaf nodes of the global index tree in real time, and migrating and verifying data during unbalance. And querying the global index tree, positioning fragment data associated with the query request from the uniformly distributed leaf nodes, generating a preliminary matching asset set, calculating the matching degree of each asset and the query request, and generating a resource allocation result. And updating the global index tree, and generating the latest data view representation. And adjusting the attribute weight coefficient of each dimension in the hash function, and generating optimized storage layout configuration. Irrelevant data are filtered through a neighbor algorithm, and a final matching result set is generated. Balanced storage, efficient query and accurate matching of asset data are realized, and the overall response speed and the resource utilization rate are improved.

Devices and techniques are generally described for natural language processing interfaces. In various examples, first natural language data may be received from an input device. First embedding data representing the first natural language data may be generated. A nearest neighbor algorithm may determine first data representing similarity between the first embedding data and second embedding data, the second embedding data representing second natural language data associated with a first skill. The nearest neighbor algorithm may determine second data representing similarity between the first embedding data and third embedding data, the third embedding data representing third natural language data associated with a second skill. First output data that indicates that the first skill and the second skill are candidates for processing the first natural language data may be generated.

The invention relates to the technical field of computers, and particularly discloses a security product self-evaluation report intelligent auditing method based on artificial intelligence assistance, and the method comprises the steps: analyzing a self-evaluation report to generate structured detection item data; carrying out multi-modal feature extraction and fusion on the text and image proof materials; semantic conflicts, configuration compliance, evidence credibility, image-text consistency and historical risk matching degree are analyzed in parallel through an artificial neural network, a support vector machine, a random forest, logistic regression and a weighted neighbor algorithm; according to a preset weight, dynamically fusing results of all dimensions to determine a compliance probability; similar historical cases are retrieved in combination with a security product compliance analysis knowledge graph, and auditing instructions and improvement suggestions with violation positioning bases are generated; and finally, outputting a structured auditing report and supporting continuous optimization of a manual reexamination feedback driving model. According to the method, high-precision, full-dimension and automatic security product self-evaluation report auditing can be realized, and the auditing efficiency, objectivity and large-scale processing capability are remarkably improved.

The invention discloses a multi-antenna RSSI human body indoor positioning method and system based on a CNN-LSTM model, and the method comprises the steps: arranging a plurality of groups of RFID antennas in an indoor positioning region, and constructing an RSSI fingerprint database; collecting real-time RSSI values respectively detected by four groups of RFID antennas at the position of the to-be-positioned human body; collecting an RSSI value of the to-be-positioned human body containing interference person mobile interference information at the moment; processing the collected real-time RSSI value of the to-be-positioned human body and the RSSI value containing the mobile interference information of the interference person through a deep learning model, eliminating dynamic interference, and outputting a corrected RSSI value; and through a weighted dynamic K-nearest neighbor algorithm based on a difference value, matching the corrected RSSI value with the RSSI average value, and calculating to obtain a three-dimensional coordinate of the to-be-positioned human body. In order to improve the accuracy of an indoor positioning algorithm and the adaptive capacity in a complex environment, the RFID technology and deep learning are fused, the multi-antenna RFID human body indoor positioning method is constructed, and the practicability is very high.

The application discloses a kind of dynamic self-adapting recommendation strategy optimization methods for business handling failure scene, it is related to business handling recommendation and intelligent decision-making technical field, first, it is collected to handle the whole process of business multi-class data, with 100 millisecond / second frequency guarantee complete real-time;With decision tree and bayesian network fusion algorithm attribution, direct and indirect reasons are clear;Integrate data to build user portrait, mine potential and subsequent demand;Based on attribution and portrait generation recommendation scheme set, priority and form are adjusted in combination with scene characteristics;Feedback data is introduced, and the strategy weight is optimized using gradient descent algorithm, and the scheme is updated regularly;Set multi-dimensional index weighted evaluation effect, not up to standard then emergency optimization;Establish distributed strategy library, and reuse similar scene optimal strategy using K nearest neighbor algorithm.The application realizes accurate positioning and personalized recommendation of failure causes, and the recommendation effect is continuously optimized with data accumulation, and is suitable for multiple business types and user groups.

The invention discloses a tennis ball collecting robot and a control method, system and equipment thereof, and the method comprises the steps: obtaining a coordinate data set of scattered tennis balls in a target area in response to a received ball collecting instruction; according to the coordinate data set, coordinates of a starting tennis ball are obtained, and the starting tennis ball is configured to be a tennis ball located at the edge of the scattered tennis ball; obtaining an initial path based on an improved nearest neighbor algorithm according to the coordinates of the initial tennis ball and the coordinate data set; and according to the initial path, eliminating path intersection by a local optimization algorithm to obtain an optimal path. According to the scheme, through an innovative mixed path planning algorithm, scattered tennis balls are collected within the shortest time, the collection efficiency is remarkably improved, and the energy consumption of the robot is reduced.

The application relates to a river closure date prediction method based on historical similar year performance driving, which comprises the following steps: using a plurality of feature selection algorithms and leave-one-out cross-validation to screen target predictor sets respectively matched with selected machine learning models and statistical models; based on parameter sensitivity analysis and a Bayesian optimization algorithm, sensitive hyperparameters of the machine learning models are optimized to obtain optimized machine learning models; the statistical models and the optimized machine learning models are configured as candidate river closure date prediction models; a K-neighbor algorithm is used to search a set of similar historical years in a historical observation data set according to current observation data, and a target river closure date prediction model is dynamically optimized according to the comprehensive prediction error of the candidate river closure date prediction models on the set of similar historical years, and a prediction result is output, so that the advantages of multiple models are effectively fused, the generalization limitation of a single model in a complex non-stationary environment is avoided, and the accuracy and robustness of the prediction result are significantly improved.

This invention discloses a crowdsourcing testing platform system and method based on intelligent matching. The method includes: receiving test task requests containing task description text; using a hierarchical attention network model to perform semantic understanding of the task description text, generating structured skill tags, and dynamically updating the tag confidence of testers using a Bayesian probabilistic framework; calculating the initial matching degree using a cosine similarity algorithm that incorporates a recent task completion quality correction factor, and then filtering and constructing a candidate pool; for each candidate, constructing a dynamic feature matrix, and using an improved K-nearest neighbor algorithm based on Mahalanobis distance for ranking and recommendation, where the K value is dynamically determined based on the number of candidates in the pool and the task dwell time. This invention significantly improves the efficiency and accuracy of task allocation through a capability assessment mechanism, achieving optimized allocation of testing resources.

This invention discloses a method for diverse image synthesis based on retrieval-enhanced diffusion, comprising: constructing a dynamic retrieval pool containing real images and generated images; extracting feature representations semantically aligned with intermediate states during the generation process using a pre-trained feature extractor; and, at a specific denoising time step, introducing Top-K reference samples retrieved through an approximate nearest neighbor algorithm into an anti-attention module to actively guide the generated features away from the existing data distribution, thereby continuously expanding the feature space coverage of the synthetic dataset. Using this invention, while maintaining the visual quality of the synthesized image, diversity data augmentation can be performed based on existing data, enhancing the diversity and practical value of the synthesized data.

The invention provides a large model entity alignment prompting method and system. The method comprises the following steps of: obtaining semantic embedding representation by utilizing joint embedding and fine tuning of a knowledge graph and a text corpus; retrieving a text block set related to the semantics of the query entity in a text corpus through a k-nearest neighbor algorithm retrieval technology to obtain a retrieval result; searching k neighbor entity pairs of the query entity pair from an expert labeled sample pool based on the key information, and taking the k neighbor entity pairs as positive samples; searching k neighbor entity pairs of the query entity pair from a public data set based on key information as supplementary examples; constructing the positive samples and the supplementary samples into a demonstration sample set; converting the demonstration sample set into a natural language text to obtain a sample text representation; and inputting the retrieval result and the sample text representation into the large model as an entity alignment prompt. According to the large model entity alignment prompting method and system, the reasoning ability of the large model in the entity alignment task is remarkably improved.

The application discloses a steel structure welding method, and relates to the technical field of 3D visual welding, and comprises the following steps: step S1, scanning point cloud data of a steel structure; step S2, based on the scanned data in step S1, a random sample consensus method is used to find a plane; step S3, according to a visual coordinate, a normal line of the plane obtained in step S2 is unified; step S4, a boundary between planes is calculated according to the plane obtained in step S2; step S5, a nearest neighbor algorithm is used to search for the nearest neighbor points of each two groups of boundaries in step S4, and a common boundary is formed; step S6, straight line fitting is performed on the common boundary obtained in step S5, and end points are extracted as welds; and step S7, path planning is performed based on a distance method. The welding method provided by the application automatically solves the plate plate welding problem in a steel structure, uses a 3D visual system to identify welds, automatically plans a welding path, saves labor cost, improves welding precision stability, and shortens a welding cycle.

The invention provides an intelligent teaching method, system and device based on AI intelligence and a medium, and relates to the technical field of intelligent teaching, and the method comprises the steps: collecting learning track data, teaching feedback data and image data of students in a teaching process; constructing a data set based on the learning track data and the teaching feedback data, converting the image data into feature points, and encoding the feature points to obtain feature encoding data; performing collaborative analysis on the data set and the feature coding data by adopting a K-nearest neighbor algorithm to divide sample clusters in different learning states, and extracting typical features from the sample clusters; performing time sequence analysis on the typical characteristics by adopting a long-short-term memory network to obtain evaluation results of the teaching matching degree and the learning efficiency; and finally, a teaching resource pushing strategy of the platform is optimized based on an evaluation result. According to the method, accurate portraying and dynamic evaluation of the learning state of the student can be realized, so that adaptive optimization of a teaching resource pushing strategy is driven.

The application belongs to the technical field of genomic breeding, and provides a machine learning genomic selection system and method, which includes the whole process of data import, model selection, hyperparameter tuning, model training, evaluation and prediction. The platform integrates various machine learning algorithms, including kernel ridge regression, support vector regression, random forest, k-neighbor algorithm, and provides automatic hyperparameter optimization, model selection and result visualization functions. The platform also provides rich data visualization functions to help users better understand data and model results. The machine learning genomic selection platform of the application has the characteristics of high stability, simple operation and high integration, significantly reduces the technical threshold of genomic selection, enables breeding researchers without programming background to efficiently perform genomic data analysis, and promotes the wide application of machine learning technology in animal breeding.

The invention belongs to the technical field of man-machine cooperation and multi-modal perception, and particularly relates to a multi-modal human body action recognition method and system based on credible belief redistribution. The method specifically comprises the following steps: acquiring a multi-modal signal containing three-dimensional skeleton data, an electroencephalogram signal and an electrocardiosignal; preprocessing the signal to obtain a multi-source input sequence with consistent time sequence; respectively constructing an action recognition basic classifier based on each mode, and outputting basic trust distribution; calculating a discount factor by using a K-nearest neighbor algorithm; constructing a fuzzy matrix according to the confusion probability between action categories in the training set; carrying out credible redistribution on the belief value of each classifier according to the discount factor and the fuzzy matrix; and adopting a Dempster-Shafer evidence fusion rule to carry out fusion on the redistributed belief, and outputting a final action recognition result. The method can effectively model the uncertainty between modes, improves the accuracy and robustness of motion recognition in a complex environment, and is suitable for intelligent manufacturing, rehabilitation training and man-machine cooperation scenes.

The invention discloses a unit commitment rolling solving method based on a weighted K-nearest neighbor algorithm, and the method comprises the steps: carrying out the sampling of historical load data through defining a rolling optimization period and a load data sampling proportion; inputting power system grid data, unit operation parameters and unit power generation cost related information to define a unit combination model; a sample is formed through unit combination calculation; for a target load curve, considering the similarity with a historical data sample, selecting a nearest neighbor from the sample, and carrying out normalized weighted voting by taking the reciprocal of the distance as a weight; a unit start-stop state prediction value is determined in combination with a neighbor consensus threshold value, so that prediction is performed by using historical data, the start-stop state prediction value is corrected according to the initial operation state of the unit and the minimum start-stop time constraint, and finally, a prediction result is used as a solving initial point of a unit commitment model, and unit commitment calculation is completed in a rolling manner section by section; therefore, the solving time is shortened, and the solving speed can be improved while the quality of the solution is ensured.

The invention is applicable to the technical field of electric leakage detection, and particularly relates to an electric leakage detection method and system for electrical equipment, and the method comprises the steps: determining the electrical equipment and cables which need to be subjected to electric leakage detection, drawing an electrical plan, connecting all the electrical equipment and cables through employing a nearest neighbor algorithm, and generating a detection channel; a separation point is selected between two adjacent electrical devices in the detection channel, the detection channel is divided into a plurality of segments, each segment at least comprises one electrical device, attribute data of the electrical devices are collected, the risk level of each segment is configured, and the risk levels comprise high, middle and low. And creating wiring rules in one-to-one correspondence with the risk levels. According to the invention, by arranging the adjustable resistor, the segment where the electric leakage signal is located can be further refined and positioned, the detection sensitivity is enhanced, the accuracy of electric leakage position detection is ensured, and the electric leakage detection efficiency of the electrical equipment is greatly improved.

The invention discloses a structural reliability optimization design method based on a probability and probability box hybrid model, and the method comprises the steps: building a solving model of a reliability index through employing a quadratic four-moment method based on a maximum entropy principle, so as to improve the calculation efficiency of reliability analysis; the classification capability of a K-nearest neighbor algorithm model is adopted to replace a reliability evaluation process in constraint conditions, and design variables are classified so as to reduce the calculation cost of the constraint conditions in the optimization design process; according to the method, a complex nested optimization design problem can be converted into a single-layer deterministic optimization design problem, the optimization calculation efficiency can be improved essentially, and the method has wide engineering application value in the field of engineering mechanical structure design.

This invention belongs to the field of data processing technology and relates to a synthetic minority class oversampling method for classifying multi-label imbalanced tabular data. The method can handle tabular data with mixed data types, and the designed two-stage weighted nearest neighbor algorithm is suitable for tabular data containing both discrete and continuous features. By introducing boundary samples and isolated samples, and employing a random selection followed by judgment approach, the requirements for computational cost and synthesis quality are balanced. This invention considers the impact of feature importance on the distance between calculated samples; the obtained nearest neighbor samples are close in distance to key features, which helps improve the selection quality of nearest neighbor samples, thereby improving the synthesis quality of new samples.

The application discloses a LoRa device identification system and method based on clock fingerprint characteristics, and belongs to the technical field of device identification. A time synchronization module utilizes frequency hopping spread spectrum in the LoRa device to change the time stamp when the channel interruption record sends the preamble and header of a data packet, writes the time stamp into a radio frequency FIFO to be sent together with the message, and records the time stamp when the receiving party receives the completed message, calculates the compensation time through the transmission parameters and the data packet size, and obtains a time stamp pair reflecting the clock crystal relationship of the device; a feature extraction module fits the clock relationship between the devices through the least square method, obtains the clock offset reflecting the clock crystal characteristics of the device as the device fingerprint; a fingerprint identification module maintains a fingerprint library of the legal devices, performs fingerprint matching of the devices through the nearest neighbor algorithm and a set threshold, and realizes identification of the devices. The application has the advantages of being not easy to be attacked, high safety, and low cost.

The application discloses an annual cloud coverage prediction method and device based on a graph neural network and monthly features, and belongs to the technical field of remote sensing information intelligent processing, atmospheric science and deep learning. The method comprises the following steps: acquiring historical cloud coverage data of a geographical sampling point, and constructing a 12-dimensional monthly feature vector as an input feature for each sampling point; constructing an adjacency matrix representing the topological relationship of a space according to the geographical coordinates of the sampling point and based on a spherical distance and a K nearest neighbor algorithm; inputting the input feature and the adjacency matrix into a pre-trained spatio-temporal graph neural network model, and outputting a daily cloud coverage grade prediction result of a target year; and the model is composed of a cascaded residual graph convolution network module, a bidirectional long short-term memory network module, a multi-head attention module and a plurality of independent monthly classifiers. The application realizes annual scale cloud coverage prediction only by relying on historical data, and has significant precision advantages and generalization capabilities in complex terrain areas and scenes with insufficient historical data.

The invention relates to the technical field of equipment management, in particular to a food processing equipment management system based on cloud edge collaboration, which comprises a production disturbance quantification module, a micro-shutdown time window identification module, a maintenance task dynamic matching module and a maintenance state closed-loop feedback module. According to the method, disturbance of maintenance on production takt is quantified by constructing a discrete event simulation model, complex maintenance is decomposed into an atomization action sequence, non-fault shutdown time windows such as production switching are actively recognized, the duration time is accurately predicted through a K-nearest neighbor algorithm, atomization maintenance actions are dynamically matched and combined, and the production takt time is accurately predicted. The method comprises the steps of generating a micro task package which can just fill the downtime and pushing and executing the micro task package, then forming closed-loop feedback according to a completion signal, updating the global task progress in real time, changing passive planned maintenance into active opportunity maintenance, and completing maintenance without interrupting the production process in a seaming manner, so that the comprehensive efficiency and the production continuity of equipment are remarkably improved.

This invention provides a dynamic networking anti-interference diagnostic method for GIS sensors based on the K-nearest neighbor algorithm, belonging to the field of power Internet of Things (IoT) communication technology. The method includes: collecting raw data of multi-dimensional communication link characteristics between GIS sensor nodes within a time sliding window of a set size and sliding step, and performing Z-score standardization; calling a dynamic anti-interference training sample library with online incremental learning and forgetting mechanisms; using an improved K-nearest neighbor classification model that incorporates a time decay factor, determining the current communication link status category in real time by calculating weighted Euclidean distance, employing an adaptive K-value selection strategy based on sample density, and using Gaussian kernel function weighted voting; and finally triggering targeted adaptive frequency selection networking strategies or hierarchical fault warning operations based on the determined category. This invention can effectively distinguish between environmental interference and hardware faults in strong electromagnetic environments of GIS, significantly improving the communication reliability and self-healing capability of wireless sensor networks.

The application provides an employment post recommendation method and system based on big data analysis, a device and a storage medium. Firstly, talent demand data, basic characteristics of job seekers and trajectory data of job seekers in the talent market in a target administrative region are obtained. Then, multi-dimensional features of talent demand are extracted to construct a demand feature set, and combined with the basic characteristics and the demand feature set, industry inclination data of the job seekers is obtained by using a support vector machine model. Then, the inclination data is adapted and screened based on the trajectory data to obtain the target industry corresponding to the job seekers. The K-nearest neighbor algorithm is used to calculate the similarity between the job seeker characteristics and the post characteristics corresponding to the target industry, and all target posts with a similarity value greater than a preset threshold are sorted to generate a post recommendation list for the job seekers. The method can effectively solve the problem of insufficient post matching accuracy in the regional talent market, and further realize more accurate and adaptive regional employment recommendation.

The invention discloses a bearing fault identification method based on multi-scale space-time synchronization attention and space-time alignment. The method comprises the following steps: carrying out sliding window segmentation and normalization preprocessing on an original vibration signal; constructing an adjacent matrix by using a K-nearest neighbor algorithm, and extracting local spatial features by using a graph convolutional neural network; bidirectional sequence features are extracted through a bidirectional gating loop unit network, and weighted fusion is carried out in combination with a global attention mechanism; and after space and time sequence features are spliced, fault prediction is realized through adaptive average pooling and a full-connection classifier. According to the method, graph modeling, GCN, BiGRU and an attention mechanism are fused, cooperative extraction of space-time double-path features is realized, the problems of weak space modeling and lack of time sequence dependence in a traditional method are effectively solved, and the bearing fault recognition precision and generalization ability in a multi-working-condition and strong-noise environment are remarkably improved.

The application discloses a multi-view clustering-based consumer classification method and related device, the method comprising: performing feature extraction on a multi-view consumer data set of a consumer to obtain a multi-view data matrix; analyzing the reverse neighbor relationship between users based on a K reverse neighbor algorithm according to the multi-view data matrix to obtain a K reverse neighbor data set; calculating the Gaussian kernel similarity between any two user consumption operations in the K reverse neighbor data set to construct a metric matrix view; performing similarity fusion analysis of the users based on a preset diagonal line Mahalanobis matrix according to the metric matrix view to obtain a fused similarity matrix graph; and performing user clustering analysis on the fused similarity matrix graph through Laplacian rank constraint to obtain a consumer clustering result. Therefore, the application can solve the technical problems that the prior art cannot exclude noise interference, the analysis of consumer behavior lacks pertinence, and the separation of graph analysis and clustering operation leads to a lack of accuracy of the result.

Groups of patrons may be discovered by measuring website and mobile site patron clickstream data in a mathematical and unsupervised way over a predetermined time and by graphically clustering the patron clickstream data using non-linear dimensionality reduction in the form of a Uniform Manifold Approximation and Projection algorithm (UMAP). The data from the UMAP may then be feed into a Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) in order to identify a center of each cluster. Next, using the data from the UMAP and the center of each cluster from the DBSCAN, a K-Nearest Neighbor algorithm (KNN) may be applied to identify data points closest to the center of each cluster and to shade each of the data points to graphically identify each cluster of the plurality of clusters. Next, illustrate a graph on the display representative of the data points shaded following application of the KNN.

This invention relates to a method and system for rapid prediction of 3D flow fields of aircraft based on graph encoder networks, belonging to the interdisciplinary field of computational fluid dynamics and deep learning. This invention extracts unstructured meshes into point cloud data, defines the features and weights of graph nodes and edges, and constructs a graph structure using the K-nearest neighbor algorithm. It designs a fusion network containing a graph encoder and decoder, introducing normalization, pooling aggregators, and DropEdge techniques to deconstruct flow field information and avoid overfitting. Through redundant channel identification, model pruning, and hierarchical point-by-point decoder design, it achieves network lightweighting, ensuring prediction accuracy while improving computational efficiency, adapting to practical engineering needs. This invention also provides a system for implementing the above method, including electronic devices and computer-readable storage media, which can be deployed on conventional computing platforms to achieve rapid prediction of 3D flow fields of aircraft.

The application discloses a core oil displacement simulation method and device based on a K nearest neighbor algorithm, equipment and a medium, and relates to the field of oil and gas reservoir development. The method comprises the following steps: constructing an oil displacement numerical model based on a core physical region, dividing coarse and fine grids, and obtaining a corresponding grid set; carrying out dynamic numerical simulation of oil displacement based on the coarse grid, solving a multiphase flow control equation set, and obtaining a coarse grid cell oil displacement dynamic parameter set at each time step; constructing a coarse and fine grid cell characteristic vector, taking a fine grid target cell as a query object, calculating the characteristic similarity between the fine grid target cell and the coarse grid cell, selecting K nearest neighbor coarse grid cells, mapping the coarse grid parameters to the fine grid through distance inverse ratio weighting, obtaining an initial prediction result, and applying physical constraint correction to the initial prediction result to obtain fine grid oil displacement parameters conforming to physical laws, so as to simulate the dynamic change of core oil displacement under fine scales. The application can reproduce the dynamic change of the oil displacement process close to the simulation precision of the fine grid simulation under the premise of ensuring the calculation efficiency.

This application discloses a path planning method and related apparatus for in-flight measurement of aerospace parts, relating to the field of path planning technology. The method includes: after acquiring the measurement point set of each measurement feature of the aerospace part's 3D model, using a nearest-neighbor algorithm to determine the starting and ending measurement points of each feature; for each feature, using an ant colony algorithm to perform path planning to obtain a collision-free local path; simultaneously, using the RRT* algorithm to perform path planning to obtain a collision-free connecting path; and connecting all local paths and all connecting paths according to the connection order to obtain a complete measurement path for in-flight measurement of the aerospace part's 3D model. This application combines three algorithms, optimizing the path planning process without manual intervention, thereby efficiently and automatically completing path planning for in-flight measurement of aerospace parts and improving measurement efficiency.