319 results about "Neighbor algorithm" patented technology

The invention discloses a typhoon wave forecasting method based on integrated machine learning. The method comprises the following steps: firstly, integrating historical typhoon wave data, meteorological data and marine environment data; preprocessing the data, including integration, cleaning, vacancy filling and standardization, and performing multi-source data completion by adopting a K-nearest neighbor algorithm and a spline interpolation method; secondly, screening key characteristic parameters through a Pearson's correlation coefficient, and reinforcing nonlinear correlation representation in combination with a mutual information method; then, constructing an integrated prediction model containing an LSTM (Long Short Term Memory), an XGBoost (X Goose Boost) and a Transform; and finally, dividing a training set and a verification set by adopting a dynamic time sequence division strategy, optimizing model hyper-parameters, and completing training and testing of the typhoon wave height prediction model. According to the method, the data sparsity problem is solved through multi-source data fusion and feature selection optimization, the generalization ability is improved through an integrated model architecture, and compared with a traditional single model, the training period is remarkably shortened, and the forecasting precision and timeliness are improved.

The invention relates to the technical field of elevator fault prediction, in particular to an elevator fault prediction method and system. The method comprises the following steps: collecting an operation vibration signal of an elevator brake through a vibration sensor, performing frequency domain conversion, performing vibration frequency disorder structure analysis, analyzing abnormal vibration frequency intensity, analyzing elevator braking force increment loss, and obtaining time sequence increment data; thirdly, incremental gradient nonlinear induction is carried out on the time sequence incremental data, and an elevator fault prediction model is constructed based on a K-nearest neighbor algorithm; the elevator fault prediction technology is optimized, so that the elevator fault prediction technology is more accurate.

The invention discloses a dynamic self-adaptive recommendation strategy optimization method for a business handling failure scene, and relates to the technical field of business handling recommendation and intelligent decision making, and the method comprises the steps: firstly collecting various types of data of a whole business handling process, and guaranteeing the integrity and real-time performance at a frequency of 100 milliseconds per time; a decision tree and Bayesian network fusion algorithm is used for attribution, and direct and indirect reasons are clarified; integrating data to construct a user portrait, and mining potential and subsequent demands; generating a recommendation scheme set based on attribution and portraits, and adjusting priorities and forms in combination with scene features; feedback data is introduced, a strategy weight is optimized by using a gradient descent algorithm, and the scheme is updated regularly; a multi-dimensional index weighted evaluation effect is set, and emergency optimization is carried out if the evaluation result does not reach the standard; and establishing a distributed strategy library, and reusing the optimal strategy of the similar scene by using a K-nearest neighbor algorithm. According to the method, failure reason accurate positioning and personalized recommendation are realized, the recommendation effect is continuously optimized along with data accumulation, and the method is adaptive to multiple service types and user groups.

The invention discloses a wire protection plastic sleeve production method based on an optimization algorithm. The method comprises the following steps: S1, collecting production process data through a sensor network; s2, preprocessing the collected production process data; s3, designing a nonlinear feedback control system and establishing a parameter adjustment mechanism; s4, performing staged feature learning by adopting an improved neighbor algorithm, and adjusting and optimizing process parameters in real time; s5, adjusting equipment and material parameters in real time by adopting a self-adaptive fuzzy control technology, and optimizing the production process through periodic evaluation and feedback; and S6, monitoring a production state in real time, optimizing a parameter adjustment strategy by using a weighted response function, and controlling an error range. According to the invention, through combination of a sensor network technology, an optimization algorithm, a nonlinear feedback control system and an adaptive fuzzy control technology, intelligent adjustment and optimization of a wire protection plastic sleeve production process are realized, production efficiency and product quality stability are improved, and energy consumption and operation errors are reduced.

The invention discloses a large model reasoning enhancement method based on knowledge graph sub-graph matching. The method comprises the following steps: firstly, establishing an index based on attribute information of nodes and edges in a multi-hop knowledge graph; and then, according to the established index, performing retrieval based on a nearest neighbor algorithm. And then, according to a retrieval result, constructing a minimum correlation subgraph, and performing reasoning enhancement. And finally, based on the minimum correlation subgraph, extracting and processing information, and generating a natural language answer understood by the user. According to the method, the multi-hop reasoning problem under a complex graph structure is effectively solved through the multi-stage optimization process, and the response quality of user query and the practicability of the system are improved.

The invention provides an information pushing method and system based on big data matching and GPS positioning, and the method comprises the steps: receiving position data, processing the position data through a Kalman filtering algorithm, predicting a movement trend through combining a timestamp and velocity vector information, and employing a trajectory prediction algorithm, and generating a potential region of interest list; using a spatial clustering analysis algorithm to analyze historical position data of the user, identifying a behavior mode and a preference location type of the user, and constructing a user interest model according to the behavior mode and the preference location type; utilizing the user interest model to configure a dynamic adaptive geo-fencing system; sequencing the service providing points by adopting a weighted K nearest neighbor algorithm, and combining geographic position information in the service recommendation list with other sensor data by utilizing a multi-modal fusion algorithm to generate a customized information packet; according to the technical scheme provided by the invention, the accuracy of the position information is improved, the prediction precision and timeliness are enhanced, and personalized service recommendation is enhanced.

The invention discloses a precision marketing recommendation system based on an AI large model, relates to the technical field of information recommendation, is used for improving the problem of product information recommendation efficiency reduction caused by tag recommendation delay, and comprises the steps of collecting user data, analyzing user information frequency modulation potentials by using a K nearest neighbor algorithm, performing classification, screening out users with high frequency modulation potentials, and performing recommendation on the users with high frequency modulation potentials. The method comprises the following steps: setting detection time to collect recommendation information data of a user, screening key tags from the recommendation information data, calculating a tag demand coefficient, detecting a user emptying behavior, carrying out secondary screening on the key tags to obtain evaluation tags, and judging whether demand down-regulation processing is carried out on the evaluation tags by utilizing a corrected geometric average method. And marking the evaluation labels requiring down-regulation processing, obtaining label attributes of the marked labels, calculating down-regulation coefficients of different marked labels according to the label attributes of the marked labels and the label demand coefficients, and setting skipping time for the marked labels according to the down-regulation coefficients.

The invention provides a geological disaster prediction method and device fusing space-time sequence analysis and causal reasoning, and belongs to the technical field of geological disaster monitoring and early warning. Aiming at the problems of non-uniform data space-time reference, lack of causal logic, poor real-time performance and weak scene adaptability of a model in the prior art, the method comprises the following steps: performing standardization processing on acquired multi-source data, processing missing values by adopting an improved K nearest neighbor algorithm in combination with stratum characteristics, and processing abnormal values through a 3 sigma criterion and geological verification; based on an information theory and an improved SURD algorithm, three types of causal entropies among variables are calculated, a time attenuation coefficient is introduced, a core causal chain is constructed, and a dynamic causal graph is constructed; a core causal variable is used as input, a multi-feature attention-multi-relation space-time diagram recursive network model is constructed, a hour-level predicted value is output through space-time diagram convolution, residual training and a geological physical constraint layer, and'causal-space-time 'fusion is realized through a causal weight adjustment model; the method can be widely applied to early warning of geological disasters such as landslide and debris flow.

The invention relates to a fault diagnosis method and device based on a graph attention convolution auto-encoder and a medium, and the method comprises the steps: S1, obtaining an original fault data set, and carrying out the standardization operation of the fault data set obtained after slicing; s2, calculating the distance between variable feature vectors in the standardized fault data set, and constructing an adjacency matrix by adopting a K adjacency algorithm; s3, inputting the adjacent matrix and the standardized fault data set into a graph attention convolution auto-encoder to extract spatial correlation features and fault information features; s4, inputting the fault information features into a double-layer full-connection layer, and outputting a classification prediction category; s5, calculating a loss function of the graph attention auto-encoder, and training the model through back propagation of the loss function; and S6, performing fault diagnosis on a fault data set to be diagnosed by using the trained graph attention convolution auto-encoder model, and outputting a classification prediction category. Compared with the prior art, the method has the advantages of high interpretability and high accuracy.

The invention discloses a fuzzy-control-based autonomous obstacle avoidance system for an underwater robot, and relates to the technical field of fuzzy control, which comprises the steps of realizing accurate perception and boundary correction of obstacles by analyzing sonar data, optical images and inertial navigation information, extracting a target position in combination with echo signal features, enhancing the accuracy of environmental data, and obtaining an autonomous obstacle avoidance result. A fuzzy K-nearest neighbor algorithm is adopted, target clustering, moving target classification and environmental impact factor calculation are carried out based on dynamic information of obstacles, so that obstacle avoidance path planning can adapt to different topographic conditions and obstacle distribution, the applicability of path calculation is improved, a Dikstra algorithm is adopted to demarcate a traffic area, and the obstacle avoidance path planning efficiency is improved. And combining path interference calculation and risk assessment, optimizing a path weight matrix, and ensuring that an obstacle avoidance path realizes an optimal decision at a balance point of safety and trafficability. The path optimization link is based on energy consumption calculation and propulsion power optimization, the navigation stability is improved, and the burden of a propulsion system is reduced.

The invention discloses a tunnel stratum recognition method based on shield tunneling machine tunneling parameter machine learning, and relates to the technical field of monitoring analysis, and the method comprises the steps: data collection: constructing a project data set, collecting rock core samples for drilling and coring from the project data set, and determining a learning sample set based on the rock core samples; data feature mining, which is used for obtaining tunneling parameters corresponding to various types of stratums from the learning sample set, mining a statistical feature set responding to stratum changes based on the tunneling parameters corresponding to various types of stratums, and forming a first feature parameter matrix; data feature screening, which is used for carrying out sensitivity analysis on the first feature parameter matrix so as to screen out a second feature parameter matrix; the model construction is used for constructing a stratum recognition model by adopting a K nearest neighbor algorithm based on the learning sample set and the second characteristic parameter matrix; and stratum identification, which is used for identifying the target stratum according to the stratum identification model. The method has the effect of improving the stratum recognition efficiency.

The invention discloses a point cloud classification / segmentation method based on local geometric contour and global topological structure maintenance, which comprises the following steps of: firstly, constructing a neighborhood relation graph according to a spatial distance through a neighborhood relation construction module, and accurately capturing complex geometric and semantic information in point cloud data through spatial distance calculation and a k-nearest neighbor algorithm; meanwhile, a neighborhood relation graph self-diffusion mechanism further enhances the relevance between local and global features of the point cloud, so that the model can better capture the geometric contour (shape) and global topological structure features of the point cloud, the geometric distribution characteristics in the point cloud data are fully considered, and the accuracy and robustness of feature extraction are improved. The sensitivity of the model to the local geometric details of the point cloud is enhanced, the efficiency of local feature aggregation is effectively enhanced, the influence of redundant information is reduced, feature redundancy caused by a method which only depends on a nearest neighbor method to extract features is avoided, the precision of point cloud classification and segmentation tasks is improved, and the accuracy of point cloud classification and segmentation is improved. And meanwhile, sharing of global information is enhanced, and the accuracy and robustness of point cloud classification and segmentation tasks are remarkably improved.

The invention discloses a newborn cry recognition system based on machine learning, which belongs to the technical field of intelligent newborn nursing and comprises a data acquisition module, a feature extraction module, a feature selection module, a newborn cry recognition module and a recognition result feedback module. According to the method, the improved badger optimization algorithm is adopted to perform feature selection on the cry audio feature set, key features beneficial to distinguishing different cry categories are automatically selected, manual intervention errors are avoided, feature selection can be better and flexibly adjusted according to individual differences, and it is ensured that the system has high recognition accuracy on different newborn cry; according to the method, the mixed kernel function support vector machine model combined with the K nearest neighbor algorithm is adopted to carry out newborn cry recognition, local similarity improvement between samples is captured, boundaries between different cry categories are accurately divided, and overlapping between the categories is avoided, so that different types of cry are recognized more accurately and efficiently, and the overall usability of cry recognition is improved.

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 present disclosure provides a cross-file question and answer knowledge extraction method and system, and an electronic device. The method includes: obtaining a user question; converting the user question into a user question embedding vector by using an embedding function; determining the user question embedding vector and a first similarity vector of a root node of a file embedding vector tree of each professional knowledge file; determining a plurality of similar vector trees by using a K-nearest neighbor algorithm based on first similarity vectors of all root nodes; determining a candidate node set by using the K-nearest neighbor algorithm based on all the similar vector trees; determining an optimally-matched node set based on the candidate node set; and determining, based on the optimally-matched node set, file knowledge content corresponding to the user question. The present disclosure improves accuracy of extracting the cross-file question and answer knowledge.

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.

The invention provides an image splicing method based on improved ORB and MLESAC, and belongs to the technical field of image splicing. According to the method, after a to-be-spliced image is obtained, firstly, feature point detection is performed on the image by using a rapid feature detection method, a descriptor is generated by using a binary robust independent basic feature method, and preliminary matching points are obtained after violent matching; secondly, re-extracting feature descriptors from the screened feature points by using a scale invariant feature transformation method, finding two optimal matches for each descriptor through a K-nearest neighbor algorithm, and adding a ratio test to filter out weak matches; and performing weighting processing on the data points by using a maximum likelihood estimation random consistency algorithm, and performing multiple iterations to obtain an optimal model. And finally, generating a spliced image through a weighted average image fusion technology. According to the method, ORB and SIFT are used in a cascading mode in the rough matching stage, a more efficient MLESAC algorithm is provided to complete fine matching, and the registration precision is remarkably improved while the image splicing real-time performance is ensured.

The invention discloses a DTW and K nearest neighbor algorithm-based time sequence similarity evaluation method and device, and relates to the field of time sequence analysis, and the method comprises the steps: S1, collecting target time sequence data in real time, and carrying out the preprocessing of the data; s2, constructing a deep learning model comprising a DTW Function module, a DTW Layer and an optimizer; s3, inputting the preprocessed data and the collected historical data into a deep learning model to obtain the similarity of time sequences; and S4, based on the similarity of the time series, classifying the preprocessed data by using a K nearest neighbor algorithm, and completing time series similarity evaluation. According to the method, the nonlinear alignment capability of the DTW is combined with the learnable DTW kernel in the deep learning model, so that the calculation efficiency and classification precision of the large-scale time sequence data are obviously improved, and meanwhile, a transparent and interpretable classification result is provided.

An automated system attempts to characterize code as safe or unsafe. For intermediate code samples not placed with sufficient confidence in either category, human-readable analysis is automatically generated to assist a human reviewer in reaching a final disposition. For example, a random forest over human-interpretable features may be created and used to identify suspicious features in a manner that is understandable to, and actionable by, a human reviewer. Similarly, a k-nearest neighbor algorithm may be used to identify similar samples of known safe and unsafe code based on a model for, e.g., a file path, a URL, an executable, and so forth. Similar code may then be displayed (with other information) to a user for evaluation in a user interface. This comparative information can improve the speed and accuracy of human interventions by providing richer context for human review of potential threats.

The present invention discloses a method for completing missing power data, which includes obtaining original power data and dividing it into a complete data set and a missing data set; using the dynamic time warping algorithm to determine the dynamic time warping distance, and using the K-nearest neighbor algorithm to construct a nearest neighbor data matrix according to the dynamic time warping distance of the power sequence; optimizing the weight distribution of the nearest neighbor data matrix to obtain a first completion value; calculating the attribute correlation influence coefficient according to the nearest neighbor data matrix as the second completion value; determining the completion value of the power sequence in the missing data set according to the first completion value and the second completion value; moving the completed power sequence out of the missing data set and adding it to the complete data set. The present invention can accurately complete the missing data in the power data. The effective repair of the missing values through the completion method can truly reflect the real electricity consumption situation of users, providing complete and effective basic power data for the analysis of user-related electricity consumption behavior research.

The invention discloses a mining area exploration system based on artificial intelligence. The mining area exploration system comprises a multi-source data acquisition module, a data optimization module, a mining area candidate area potential identification module and a mining area intelligent exploration module. The invention relates to the technical field of artificial intelligence data analysis and computer vision, in particular to a mining area exploration system based on artificial intelligence, according to the scheme, a mining area candidate area potential identification module and a mining area intelligent exploration module are innovatively combined, and the accuracy of mining area identification and the pertinence of exploration are improved; a weighted graph structure is constructed by adopting a K nearest neighbor algorithm, and an improved graph convolutional neural network with a parallel updating mechanism is introduced, so that high-precision automatic potential classification of a large-range candidate mining area is realized; and a composite chaotic mapping initialization method and a sine index inertia weight improved particle swarm optimization algorithm are introduced, so that the accuracy and stability of yield prediction are improved, and high-precision yield prediction of each sub-region of the mining area is realized.

The invention discloses a method and system for detecting defects of a packaging box by using image processing, and relates to the technical field of automatic production, and the method comprises the steps: obtaining a high-resolution image of a key area on the surface of the packaging box, and carrying out the preprocessing; matching feature points in the feature descriptor matrix Euclidean with feature points of a standard template image through a K-nearest neighbor algorithm, and aligning the matched feature points by adopting a deformation tolerant image alignment algorithm; feature point density analysis is carried out on the aligned packaging box image, and a grid area with potential flaws on feature points is identified; based on the identified potential flaw area, using a gray-level co-occurrence matrix to carry out texture feature analysis, and further judging whether the change of the surface texture of the packaging box accords with the flaw features or not; through feature point density analysis, an area with possible flaws on the surface can be quickly identified, and texture feature analysis is performed based on GLCM, so that the detection precision of tiny flaws is further improved.

The invention relates to the technical field of mechanical fault diagnosis, and provides a fault diagnosis and classification method for a bearing of an aluminum alloy impeller die-casting ladling machine, which comprises the following steps: acquiring a vibration signal of the bearing of the ladling machine, carrying out continuous wavelet transform on the vibration signal, generating a time-frequency diagram, and carrying out adaptive grid segmentation on the time-frequency diagram. Grid granularity is adjusted according to the local change rate of the time-frequency graph, multi-scale nodes are generated, edge connection is generated for the multi-scale nodes based on a K-nearest neighbor algorithm, and a multi-scale graph structure is constructed; performing unsupervised feature extraction on the multi-scale image structure, including: performing data enhancement on the multi-scale image structure through edge deletion and feature mask to generate an enhanced view; a graph attention network encoder is used for encoding the enhanced view, graph-level embedding is generated, and graph-level embedding is optimized by comparing a loss function; and based on the optimized graph-level embedding, performing fault classification by using a classifier constructed by a graph attention network and a multi-layer perceptron, and outputting a fault category.

An automated system attempts to characterize code as safe or unsafe. For intermediate code samples not placed with sufficient confidence in either category, human-readable analysis is automatically generated to assist a human reviewer in reaching a final disposition. For example, a random forest over human-interpretable features may be created and used to identify suspicious features in a manner that is understandable to, and actionable by, a human reviewer. Similarly, a k-nearest neighbor algorithm may be used to identify similar samples of known safe and unsafe code based on a model for, e.g., a file path, a URL, an executable, and so forth. Similar code may then be displayed (with other information) to a user for evaluation in a user interface. This comparative information can improve the speed and accuracy of human interventions by providing richer context for human review of potential threats.

The invention discloses a method and system of an Internet of Things management platform for big data, and relates to the technical field of Internet of Things, comprising the steps of establishing a signal difference model through historical signal data to generate radio frequency fingerprint features of physical layer signal data, and obtaining acceleration features of application layer flow data through calculation; the method comprises the following steps: acquiring two features, fusing the two features to obtain a cross-protocol stack fusion feature vector, calculating a distance between physical devices by using a K nearest neighbor algorithm to obtain a device contact table, sorting the cross-protocol stack fusion feature vector into a three-dimensional time sequence table, and adjusting the device contact table through a space-time diagram convolutional network to obtain a space-time feature table. According to the invention, deep integration of physical layer identity characteristics and application layer behavior trends is realized. And comprehensive feature representation with higher expressive power is provided for an Internet of Things management platform. The limitation of traditional single-layer feature extraction is broken through, and the accuracy of anomaly detection is remarkably improved.

The invention provides a mechanical equipment structure key pressure-bearing component real-time simulation method based on digital twinning, which takes a cubic press hinge beam as an example, and combines finite element analysis, Latin hypercube sampling, a K nearest neighbor algorithm, Gaussian interpolation and an RBF (Radial Basis Function) proxy model to realize stress-strain rapid prediction and three-dimensional visualization. According to the method, an efficient prediction model is established through structure database construction, dimension reduction processing, neighbor search and interpolation calculation, a simulation result is presented in real time by utilizing Python and Unity interaction, the design efficiency and accuracy are improved, and the method is suitable for structure optimization analysis under complex working conditions.

The invention belongs to the technical field of material lumpiness statistics, and particularly discloses a material lumpiness statistics method based on multi-view three-dimensional laser point clouds, which comprises the following steps of: obtaining point cloud data of materials through a three-dimensional laser scanner, preprocessing the obtained point cloud data of the materials, and storing the preprocessed point cloud data in a tree structure; determining point cloud groups of all surfaces of the material blocks by using a point cloud region growth algorithm, dividing the relationship between adjacent point cloud groups by using spatial feature normal vectors of the point cloud groups, and obtaining all point cloud groups of each material block in combination with a nearest neighbor algorithm so as to judge the spatial relationship of adjacent materials; according to the method, the point cloud data are preprocessed to reduce illumination variation, shielding objects, noise interference and the like, and the material identification precision and accuracy are improved, so that the problems of low material identification precision and poor accuracy caused by factors such as illumination variation, shielding objects, noise interference and the like in a complex environment during statistics of different forms of materials are solved.

The present invention proposes a lightweight industrial environment defect detection method based on a feature memory bank. First, the top view / left view or right view of qualified products on the production line is photographed, preprocessed after being sorted into a data set. Secondly, the data set is input into a pre-trained standard teacher model and an initialized lightweight student model to perform feature knowledge distillation training on the student model. After that, the defect-free product data set is input into the lightweight student model trained by feature knowledge distillation. After extracting the corresponding feature set of the product image and performing multi-scale feature set fusion, maximum core set distance sampling is carried out to establish a feature memory bank of qualified products on the current production line. Finally, the production line images to be detected are input into the lightweight student model and multi-scale feature fusion is performed, and then compared with the feature memory bank by the nearest neighbor algorithm to detect whether the current product contains defects. The present invention can improve the efficiency of anomaly detection without sacrificing accuracy as much as possible.

The invention discloses a talent evaluation and post matching system and method, and relates to the technical field of information, and the system comprises a personality evaluation module, a post personality demand modeling module, a matching calculation module, a feedback optimization module and a data storage module. The personality evaluation module is used for acquiring a nine-dimensional personality feature vector of an employee based on a nine-type personality theory; the post personality demand modeling module is used for constructing and storing a nine-type personality weight vector model of a post; the matching calculation module calculates matching degree scores between the employees and the posts and generates a recommendation sequence based on a rule algorithm and a K-nearest neighbor algorithm; a feedback optimization module collects performance feedback data after employees enter the job, and automatically updates a matching model to improve prediction accuracy; and the data storage module is used for centrally managing evaluation data, post models and feedback records. According to the method, personalized matching and dynamic optimization between the employees and the posts are realized, and the organization configuration efficiency and the man-post adaptation quality can be improved.

The invention discloses an efficient routing algorithm for offshore buoy networking. The problem that existing offshore buoy networking information transmission is poor in real-time performance and stability is mainly solved. According to the scheme, the method comprises the following steps: inputting the position of an offshore buoy network, constructing a communication link connectivity model, and abstracting a network link topology by means of a graph theory representation mode; comprehensively considering network efficiencies such as time delay, packet loss rate and network load balancing, and determining link cost between two buoy nodes; aiming at the defect that the ant colony algorithm is easy to fall into local optimum, two-point improvement is carried out on the basic ant colony algorithm. The method comprises the following steps: firstly, during construction of initial pheromones, in a first iteration process of an algorithm, 20% of ants in a population are selected, a path with the minimum cost is selected based on a neighbor algorithm, and the remaining ants adopt random selection paths, so that the convergence of the algorithm is improved, and meanwhile, the diversity of the population is ensured; and a second point, aiming at the problem that a large number of ants are easy to walk to a repeated path when the ant colony is converged to the local optimum in the later stage of iteration, adding repeated check operation in an algorithm updating process. According to the specific operation, when 20% of ants select repeated paths, the ants of the repeated paths select new paths in a random mode, the path search space is expanded, and the algorithm performance is improved. According to the method, the efficient route of buoy networking communication can be dynamically planned, and the real-time performance and the stability of the network are met.