160 results about "Weighted distance" patented technology

The invention discloses an unmanned aerial vehicle airport site selection method suitable for large-scale electric power line inspection, and the method comprises the steps: S1, carrying out the primary selection of a geographic space, constructing a geographic constraint evaluation model, carrying out the processing of elements and historical disaster data through discrete coding, generating a safe and suitable region through the multi-factor weighted stacking space analysis, and carrying out the calculation of an evaluation result; and a K-Means clustering algorithm based on Euclidean distance is combined to screen a primary selection region. S2, risk assessment preselection: constructing an entropy weight-TOPSIS risk assessment model, determining a risk factor weight by using an entropy evaluation method, calculating a scheme closeness degree through a TOPSIS algorithm, and screening a low-risk preselection region; and S3, constructing a binary relationship between a demand center set and a pre-selected site set by combining an electric power inspection target and taking an electric power inspection task target as a demand node, measuring weighted traffic cost through Euclidean distance, designing a double-layer nested 0-1 decision variable, and realizing optimization of an airport deployment area by taking minimization of a total weighted distance as a target function.

The invention relates to the technical field of data processing, in particular to an abnormal mode data processing system driven by power marketing big data, which comprises a distributed collaborative acquisition module for constructing a space-time alignment three-dimensional data stream, a multi-modal feature reconstruction module for separating periodic noise and quantizing environmental interference, and a data processing module for processing abnormal mode data. The resistance feature decoupling module generates a purification feature vector set and a noise confidence index through orthogonal projection, and the dynamic algorithm adaptation module dynamically schedules an isolated forest algorithm, a weighted distance measurement algorithm and a sparse self-encoding clustering algorithm according to the noise confidence index. The behavior chain verification module establishes a combined physical rule verification mechanism of an environment temperature threshold value, a load deviation degree and an equipment state, and the closed-loop strategy engine module adaptively adjusts a feature decoupling loss function weight according to a decision boundary offset, so that the accuracy and the environmental adaptability of real electricity consumption abnormity identification in a complex noise environment are effectively improved.

The invention discloses a civil aviation passenger demand prediction method and system based on artificial intelligence. The method comprises the steps of data acquisition, abnormal demand elimination, civil aviation demand side sample supplementation, civil aviation passenger demand prediction model establishment and civil aviation passenger demand prediction. The invention belongs to the field of civil aviation demand prediction, and particularly relates to a civil aviation passenger demand prediction method and system based on artificial intelligence. According to the scheme, a time sequence weighted distance is introduced, and it is guaranteed that samples with off-season and low demand characteristics are not mistakenly removed; on the basis of route similarity weighting, trusted neighbors are generated, and rare low-demand samples are supplemented in a targeted manner; feature importance scores are introduced, feature sensitivity scores of civil aviation samples are calculated, and influences of feature-time points on demand prediction are evaluated from different angles; the adaptability to festival and holiday peaks and extreme weather scenes is guided through a punishment mechanism; based on the introduction of a holiday association weight enhancement scheduling strategy, the penalty weight growth rate is dynamically improved; and the reliability of civil aviation passenger demand prediction is improved.

The invention discloses a thermal load prediction method and system for regional heat supply, and belongs to the technical field of data processing, and the method comprises the steps of data collection, data preprocessing, regional heat supply data optimization, construction of a thermal load prediction model and intelligent prediction. According to the scheme, random forest splitting gain and mutual information are combined, global saliency of features is obtained, an initial centroid is selected, Euclidean distance and mutual information are fused, weighted distance is calculated, clustering is carried out, association purity is calculated based on mutual information and information entropy, and redundant features are screened and deleted according to redundancy conditions. The method comprises the following steps: generating a bimodal base adjacency matrix through a gating mechanism, generating a hidden feature matrix and a dynamic propagation adjacency matrix based on diffusion diagram convolution, fusing the two adjacency matrixes to generate a gating adaptive adjacency matrix, extracting spatial features based on diffusion diagram convolution, extracting time sequence features based on space-time embedding and a multi-head attention mechanism, and reducing prediction deviation. And the thermal load prediction speed is improved while the precision is ensured.

The invention belongs to the technical field of power information, and particularly relates to a power load clustering method based on multi-dimensional feature fusion and dynamic optimization, which comprises the following steps: acquiring load data, and preprocessing the load data; based on the preprocessed load data, time domain features and frequency domain features of different time scales are extracted, and multi-scale time-frequency domain fusion features are constructed; improving an unsupervised importance evaluation model of the random forest, generating a pseudo tag by using a Kmeans algorithm, training the improved unsupervised importance evaluation model of the random forest, and updating a feature importance sequence; based on the updated feature importance ranking, obtaining a load feature matrix, obtaining a new load feature matrix through dimension reduction, and calculating weights of different time scale features; and based on weights of different time scale features, defining a weighted DTW distance, using a GEP algorithm to initialize a clustering center, carrying out load clustering based on a Kmeans algorithm, and outputting a final load mode label.

The invention discloses a network security threat detection system based on artificial intelligence. The system comprises a network security data acquisition module, a network security data optimization module, a network security threat prediction model establishment module, a model optimization module and a network security threat intelligent detection module. The invention relates to the technical field of network security data processing, in particular to a network security threat detection system based on artificial intelligence, which innovatively introduces weighted distance measurement driven by field correlation, adaptive neighbor number selection and a distance deviation threshold control strategy. A K-nearest neighbor method is improved to realize data completion, and the accuracy of a network security threat detection result is improved; a double-layer long-short-term memory network and self-adaptive threat threshold calculation are provided, and high-precision threat prediction of network security is realized; a sine smooth attenuation inertia weight strategy and a sine modulation contraction factor strategy are introduced to improve a particle swarm optimization algorithm, and the prediction accuracy and stability of the model are improved.

The invention provides a photovoltaic module surface defect positioning method based on image processing, and the method comprises the steps: carrying out the preprocessing of an original image, obtaining a gray image, obtaining a second abnormal seed point through a multi-level abnormal seed point screening strategy, including global statistical judgment, gray deviation analysis and neighborhood aggregation optimization, and obtaining a second abnormal seed point; and taking the second abnormal seed point as a starting point, realizing accurate extraction of a real defect area of the photovoltaic module through a self-adaptive multi-threshold iterative expansion algorithm in combination with weighted distance measurement of gray, gradient and texture, and finally performing characteristic parameter analysis on the defect area to realize identification and classified output of real defects. The method has the advantages of being high in positioning precision, high in robustness and suitable for various defects, and is suitable for automatic detection of the surface defects of the photovoltaic module.

The invention relates to a side cloud collaborative adaptive workflow scheduling method and system, and belongs to the technical field of distributed computing and artificial intelligence. The method comprises the following steps of: firstly, in a macroscopic candidate screening stage, reducing problem granularity through task clustering, and obtaining balance between utilization and exploration based on a weighted distance probabilistic preferential strategy; then, in a collaborative scheduling decision-making stage, a global network state diagram is constructed through a graph neural network, deep spatial features of nodes and neighborhoods of the nodes are extracted, context-aware state representation is formed, and a reinforcement learning agent makes an optimal collaborative decision in multiple options such as local execution, edge migration or cloud unloading according to the state representation; and finally, in a local adaptive optimization stage, performing fine-grained optimization after the task is issued, dynamically adjusting a scheduling frequency and a multi-target weight through an online learning mechanism, realizing balance between a task deadline and a resource utilization rate, and ensuring efficient and robust execution of a node level.

The application discloses a kind of big data-based optimization analysis method and system of territorial space planning, it is related to big data and optimization analysis technical field of territorial space planning, including real-time data acquisition and dynamic preprocessing, dynamic regional division, multi-model integrated space-time prediction, multi-objective dynamic optimization planning, real-time feedback and adaptive adjustment.The big data-based optimization analysis method of territorial space planning provided in the application adopts space-time weighted fuzzy clustering method, divides the territorial space into several sub-regions according to the spatial coordinates and time attributes of data, constructs the weighted distance regular objective function between data points and regional center, and updates the membership and the position of regional center iteratively, realizes the self-adaptation and space-time smoothing of regional segmentation, effectively captures the dynamic change characteristics in region.

The invention provides an automatic joint registration method for a brain MRI image and an MEG sensor. The automatic joint registration method comprises the following steps: acquiring three-dimensional face point cloud data; calculating the curvature of each point; performing comprehensive calculation based on the distance and the curvature to obtain a curvature weighted distance based on curvature weighting; sequentially selecting points from the three-dimensional face point cloud data based on the curvature weighted distance to form a sampling point set; extracting feature points based on the sampling point set; and performing registration of the three coordinate systems based on the feature points. According to the configuration, the curvature is introduced when the sampling point set is selected, so that the face salient region can be better focused, the selected sampling points are more representative, and the subsequent registration effect is improved. According to the method, automatic registration of the magnetoencephalogram equipment coordinate system, the magnetoencephalogram head coordinate system and the magnetic resonance imaging coordinate system can be achieved, and the magnetoencephalogram technology can better play the effect in diagnosis and treatment of diseases such as epilepsy.

The invention discloses a multi-period time difference weighted ranging method based on obstacle perception, which relates to the technical field of intelligent perception and sensor fusion, and specifically comprises the steps of multi-period time difference measurement, obstacle attenuation coefficient modeling, weighted fusion calculation, dynamic error compensation and final distance output. According to the multi-period time difference weighted distance measurement method based on obstacle perception, through multi-period time difference measurement and weighted fusion, random errors are reduced, and the distance measurement precision is effectively improved; by introducing the number of obstacles as a key variable, the weight of a distance measurement formula is dynamically adjusted to adapt to a complex scene, and dynamic environment adaptation is realized; and through combination with an obstacle attenuation model, the influence of multi-path interference on a distance measurement result is effectively suppressed, and the anti-interference capability is effectively enhanced.

The invention discloses a hydroelectric equipment anomaly detection method based on physical mechanism guidance and sequential topological entropy fluctuation characteristics, and belongs to the technical field of hydroelectric equipment monitoring and fault diagnosis. The method comprises the steps that multi-source sensor data of hydroelectric equipment is collected and preprocessed; constructing a physical weighted distance function in combination with an equipment physical mechanism, and embedding time sequence data into a high-dimensional point cloud space; extracting persistent homology features through a sliding window, generating a persistent graph sequence and calculating topological feature indexes; a persistence graph entropy fluctuation index is provided, and anomaly detection is realized by quantifying time sequence fluctuation of topological entropy; and finally, visual output and an alarm mechanism are combined to assist diagnosis. According to the method, equipment physical characteristics and topological data analysis are fused, the problems that a traditional method is insufficient in nonlinear system modeling, insensitive to dynamic evolution and the like are solved, the early warning capacity and detection precision of early faults are improved, and the method is suitable for anomaly detection application of core equipment such as a water turbine and a generator.

The invention provides a traffic tunnel preventive monitoring and risk early warning system based on an unmanned aerial vehicle group, and relates to the technical field of traffic tunnel safety monitoring, and the method comprises the steps: laying reference mark points on the inner surface of a tunnel as monitoring nodes, collecting the crack width, surface temperature and deformation of each node through the unmanned aerial vehicle group, and calculating the crack width, surface temperature and deformation of each node; and constructing a multi-dimensional feature vector and calculating a space-time weighted distance in combination with the space coordinates and the timestamps. And adjusting a neighborhood search radius according to the tunnel curvature, identifying a risk region through a density clustering algorithm, and marking unclustered abnormal nodes as isolated risk points. And dividing risk levels based on comparison of the structure safety degree index and a dynamic threshold value, and registering a result with the BIM model to realize three-dimensional visualization. Trend early warning is triggered for an area with monotonically decreasing monitoring safety index for three continuous times, full-process automatic monitoring from data acquisition, intelligent analysis to risk early warning is realized, and the accuracy and timeliness of tunnel structure health monitoring are remarkably improved.

The invention discloses a data optimization storage method and system for smart agriculture, and relates to the technical field of smart agriculture, and the method comprises the steps: collecting agricultural field data, remote sensing data and management data, and carrying out the preprocessing of the data, and constructing a multi-source heterogeneous data matrix; the method comprises the steps of calculating fuzzy similarity of a multi-source heterogeneous data matrix, constructing a time sequence distance matrix, calculating weight vectors of different data types, constructing a weighted distance matrix according to distribution weights of different data types, calculating a weighted fuzzy similarity matrix, constructing a Boolean similarity matrix, distinguishing redundant nodes and representative nodes, and determining difference vectors. According to the method, redundant data are accurately identified by using the weighted fuzzy similarity matrix, only the difference vector is stored, the storage space occupation is greatly reduced, the consumption of cloud or chain end storage resources is effectively reduced, the actually transmitted data volume is obviously reduced through storage compression and differential storage, and the data transmission efficiency is improved. And the data transmission burden of the edge device or the cloud node is reduced.

The invention discloses a territorial space planning optimization analysis method and system based on big data, and relates to the technical field of big data and territorial space planning optimization analysis, and the method comprises the steps: real-time data collection and dynamic preprocessing, dynamic region division, multi-model integration space-time prediction, multi-target dynamic optimization planning, and real-time feedback and adaptive adjustment. According to the homeland space planning optimization analysis method based on the big data, a space-time weighted fuzzy clustering method is adopted, homeland space is divided into a plurality of sub-regions according to space coordinates and time attributes of the data, and a weighted distance regular objective function between a data point and a region center is constructed, so that the homeland space planning optimization analysis is realized. And the membership degree and the position of the center of the region are updated through iteration, so that self-adaption and space-time smoothing of region segmentation are realized, and dynamic change characteristics in the region are effectively captured.

Quantum computers pose significant threat to modern day cryptography. To mitigate this risk, migration of enterprise applications to a quantum safe state is needed. Crypto inventory, an automatic process to discover all crypto application programming interfaces usage in an enterprise application, is a first problem to be addressed in the migration journey. Embodiments of the present disclosure provide a method and system for a dynamic generation of rules. An input program statement includes an unseen crypto application programming interface (API) is received which represents a method invocation. The invoked method is mapped by the input program statement with a rules database to infer and generate new rules for an unseen crypto application programming interface (API) belonging to (a) a same library by a weighted distance approach, (b) a library implementing known contracts by a graph traversal approach, and (c) a library implementing unknown contracts by a pattern matching approach respectively.

The present invention discloses a heat load prediction method and system for district heating, belonging to the field of data processing technology. The method includes: data collection, data preprocessing, district heating data optimization, construction of a heat load prediction model, and intelligent prediction. This solution combines random forest split gain and mutual information to obtain the global significance of features and select the initial centroid, fuse Euclidean distance and mutual information, calculate weighted distance and perform clustering, calculate association purity based on mutual information and information entropy, and filter and delete redundant features based on redundancy conditions; generate a bimodal base adjacency matrix through a gating mechanism, generate a hidden feature matrix and a dynamic propagation adjacency matrix based on diffusion graph convolution, fuse the two adjacency matrices to generate a gated adaptive adjacency matrix, extract spatial features based on diffusion graph convolution, and extract temporal features based on spatiotemporal embedding and multi-head attention mechanism, thereby reducing prediction bias and improving the heat load prediction speed while ensuring accuracy.

The present invention relates to a multi-source data fusion method, system, electronic device and storage medium. The fusion method comprises: aligning the timestamps of measurement data to be fused with existing valid fusion targets; performing weighted summation of the Mahalanobis distance of the position and the Euclidean distance of the velocity between the measurement data and the aligned valid fusion targets to obtain a weighted distance, and calculating an association threshold range and an association probability between the measurement data and the aligned valid fusion targets based on the weighted distance; judging whether the measurement data is associated and matched to any valid fusion target to generate a matching pair based on the association threshold range and the association probability; for each matching pair, updating a filtering algorithm in the valid fusion target using the measurement data; performing weighted calculation of the Mahalanobis distance of the position vector and the Euclidean distance of the velocity vector between the fusion target and the sensor measurement, and calculating the joint association probability between the fusion target and the sensor measurement based on the weighted distance, thereby improving the association matching accuracy.

The invention discloses a film and television content intelligent recommendation method and system based on artificial intelligence. The method comprises the steps of multi-source data collection, data optimization, group film and television content recommendation, personalized recommendation optimization sorting and film and television content intelligent recommendation. The invention relates to the technical field of data processing, in particular to an intelligent film and television content recommendation method and system based on artificial intelligence. According to the scheme, group recommendation and personalized optimization are innovatively combined, and dynamic balance between group representativeness and individual difference is achieved; a density correction mechanism, weighted distance calculation, a representative point merging mechanism and a fuzzy weighted distribution strategy are innovatively proposed to improve the clustering algorithm, so that the clustering accuracy is improved, and the group recommendation precision and the user satisfaction are improved; and an adaptive reference individual selection strategy and an iteration-based global exploration weight improvement optimization algorithm are adopted, so that the accuracy of a model output result is remarkably improved, and the stability of personalized film and television content recommendation optimization sorting is realized.

The invention provides an automatic test method and system for an intelligent power distribution automation terminal, and relates to the technical field of automatic test, and the method comprises the steps: obtaining the model information and environment information of a to-be-tested terminal; generating test data of the to-be-tested terminal in combination with the model information, the environment information and the Lyapunov index; inputting the test data into the to-be-tested terminal, and outputting operation data; analyzing the operation data by using a dynamic threshold value based on the potential energy field, and outputting abnormal data; fusing and classifying the abnormal data based on a pre-training model and the coupling weighting distance, and determining a fault type; and performing early warning according to the fault type. According to the analysis method based on the Lyapunov index and the dynamic threshold value, the test can comprehensively cover various functions and limit states of the equipment, so that potential anomalies and faults can be effectively found.

The invention discloses a disparity map repairing method and system based on iterative filling, and the method comprises the steps: obtaining disparity map data of a target scene and possible missing values in a disparity map through initializing the disparity map; scanning each pixel point in the disparity map, and filling a hole region of the disparity map by adopting a weighted average value; further filling and repairing a cavity region in the filled disparity map by using an edge perception filling method; and performing multiple iterations on the results subjected to the weighted distance filling and the edge perception filling, further correcting a missing value region in the disparity map by using the weighted distance filling and the edge perception filling after each iteration, and finally repairing the disparity map. According to the method, the processing precision of the disparity map is improved, the holes can be accurately filled in a complex scene, and the method has a wide application prospect.

The invention discloses a line sequence automatic detection algorithm based on machine vision and an implementation method thereof, and belongs to the technical field of machine vision and image processing. Aiming at the problems of low efficiency and poor accuracy of traditional manual detection of a multi-core cable, the invention provides the following technical scheme: preprocessing a collected cable image, and enhancing the image by adopting adaptive histogram equalization of an LAB color space; determining a scanning band at the center of the image and converting the scanning band to an HSV color space; the comprehensive value of the HSV color gradient is calculated, the peak value is detected by adopting an adaptive threshold value, and the boundary of each wire core is accurately positioned; establishing an HSV (hue, saturation and value) feature library containing 10 standard colors, and realizing color matching through weighted distance measurement; and an automatic detection mode and a manual assistance mode are provided. According to the method, the detection accuracy reaches 95% or above, the processing time of a single image is shorter than 1 second, the technical problems of automatic color recognition and sorting detection of the multi-core cable are effectively solved, and the method can be widely applied to cable production quality control.

The invention discloses a low-power-consumption mobile sensor adaptive communication method based on dynamic grouping. The method comprises the following steps: acquiring position data, residual electric quantity data and link quality data of each node; a weighted distance algorithm is adopted to process the position data, a weighted value is determined according to a product of link quality data and residual electric quantity data, and a relative position matrix between nodes is generated; establishing a dynamic network topology structure according to the relative position matrix, and marking the node as a low-energy node when detecting that the residual electric quantity of the node is lower than a preset threshold value; an improved K-means algorithm is adopted to process a dynamic network topology structure, the selection weight of a clustering center is determined by a linear combination of node position coordinates and residual electric quantity, low-energy nodes are preferentially distributed to a group with the minimum load, and communication group configuration is generated. According to the invention, the communication efficiency, the reliability and the energy utilization rate of the wireless sensor network are improved.

An electric vehicle charging station clustering and grouping method based on operation and maintenance consideration comprises the steps that S1, analysis processing is carried out based on historical charging station operation information, and an extracted reference feature sequence is obtained by adopting a time sequence feature extraction method of correlation analysis; s2, performing statistical analysis on the reference feature sequence, and extracting a statistical feature sequence; s3, calculating the weight of the operation information of each dimension by adopting a Sierphi method, and respectively calculating the weights of different statistical characteristic values of each dimension by adopting an entropy weight method to obtain a weight distribution set; s4, calculating a multi-dimensional weighted distance from each charging station to a clustering center based on the weight distribution set and the statistical feature sequence, and obtaining a clustering result of all the charging stations based on a K-means multi-dimensional time sequence clustering algorithm; and S5, according to the clustering result of the charging station, designing an operation and maintenance plan for each type in the clustering result. The design dynamically reflects the interaction relationship between the charging stations, is more flexible and has robustness.

The invention discloses an intelligent power quality control system for distributed photovoltaic. The intelligent power quality control system comprises a data acquisition module, a self-adaptive clustering parameter acquisition module, a clustering detection module and an intelligent regulation and control module. The invention belongs to the field of electric power energy control, and particularly relates to an intelligent power quality control system for distributed photovoltaic. According to the scheme, time sequence smoothing processing is introduced, and instantaneous sudden change interference is restrained; the key electric energy quality state is highlighted through a weighted distance function, and the grouping judgment precision is improved; determining a maximum scale parameter by analyzing a spatial aggregation effect; by introducing a sample distribution stability constraint, analyzing neighbor distance distribution and selecting a minimum sample number parameter; a fuzzy logic mechanism and local density evaluation are introduced, for data in a critical state, the interference of local noise on overall clustering judgment is relieved by giving weights, the overall misjudgment risk caused by the local noise is relieved, and then the electric energy quality intelligent control effect is improved.

The invention discloses an unmanned vehicle multi-target data association method and system. According to the method, observation data and prediction data are subjected to intuitionistic fuzzification processing through an improved intuitionistic fuzzy C-means clustering algorithm, the weighted distance between intuitionistic fuzzy sets is calculated to obtain the membership degree of observation and a track, and association of observation and the track is achieved by searching the maximum membership degree. According to the method, the uncertainty and fuzziness in the data can be effectively processed, the accuracy of target tracking is improved, and meanwhile, the high efficiency and the real-time performance of the algorithm are kept. The method is especially suitable for multi-target tracking in a complex traffic environment, such as multi-target, strong interference, dense clutter, cross track and the like, and the driving safety and reliability of the unmanned vehicle in the complex environment are improved.

The application is a kind of double robust causal dose-response curve estimation method based on high-dimensional independent variables, comprising the following steps: 1) constructing a new target function based on the modified adaptive LASSO method to realize dimension reduction; 2) constructing a double weighted distance correlation coefficient DWDC to select the optimal lambda n ; 3) using DR estimator to estimate the dose-response curve. The method mainly aims at the characteristics of the potential confounding variable set contained in the health medical big data, such as high dimension, nonlinear relationship between health outcome and (or) exposure factor, etc. In the framework of GOAL method, a double robust estimation method of causal dose-response curve is provided, which is called GOALDeR method. A large number of statistical simulations show that the estimation accuracy and precision of GOALDeR method are better than those of existing methods, and the method has double robustness and is less affected by the correlation structure between covariates and the n / p (sample size / covariate dimension) ratio.

The embodiment of the invention discloses a weld defect intelligent detection method and device based on multi-modal feature fusion. The method comprises the steps that an excitation unit is controlled to apply standardized mechanical knocking excitation to a target weld joint, and response signals generated by the weld joint are collected; transforming the time domain signals corresponding to the acoustic signal and the vibration signal based on Lie group transformation to obtain an invariant feature vector so as to construct a multi-modal feature vector; the weight of each feature in the multi-modal feature vector is determined based on the physical parameters of the target welding seam, and the weighted distance between the multi-modal feature vector and each clustering center in a standard database is calculated based on the weight, so that a target clustering center is determined; and in response to the fact that the target weighted distance corresponding to the target clustering center is larger than a distance threshold value, determining the defect type of the target weld joint based on the similarity between the multi-modal feature vector and each defect feature vector. In the embodiment of the invention, higher-precision and more reliable weld defect identification can be realized, and the whole process can be automatically carried out without manual operation.

The present invention provides a method and device for detecting abnormal points of indoor positioning Bluetooth beacons, which relates to the field of indoor positioning. In this method, a first neighborhood and a second neighborhood of a first beacon point are obtained from a set of beacon points; a first average value of the coordinates of the beacon points in the first neighborhood is calculated, and a second average value of the coordinates of the beacon points in the second neighborhood is calculated, and an offset score of the first beacon point is calculated according to the first average value and the second average value; an average offset score of all the beacon points in the set of beacon points is calculated, and the offset score is compared with the average offset score; a weighted distance is calculated according to the comparison result, a subspace offset factor is calculated according to the weighted distance, and the beacon points with the subspace offset factor greater than a threshold are marked as abnormal points. Implementing the technical solution provided by this application can effectively identify the abnormal points of Bluetooth beacons indoors and improve the stability and accuracy of the indoor positioning system.

The invention relates to an exploration data-driven three-dimensional space mineral migration path generation method, which comprises the following steps of: based on exploration data of a three-dimensional geological space, calculating distance and direction weighting of space points in a neighborhood of each voxel to obtain an optimal direction of each voxel; calculating a pairing point closest to each voxel in the optimal direction, and connecting the pairing points to form a migration path; according to the connection condition, the migration attribute of the space point is judged, and the migration attribute is a starting point, an end point or an intermediate point. The invention innovatively provides a migration path generation method oriented to deep three-dimensional space formation of minerals from a quantitative angle.