79 results about "Mean-shift" patented technology

SOLUTION: Provided is a complex environment GNSS data quality-oriented high-precision data processing method which includes the steps of: preprocessing complex environment GNSS data (S1); searching for large errors in the complex environment GNSS data (S2); searching for the complex environment GNSS data (S21); identifying large errors in GNSS data (S22); processing large errors in the complex environment GNSS data (S3); removing the large errors in the complex environment GNSS data (S31); processing the GNSS data by an interpolation method (S32); analyzing and calculating ambiguities (S4); evaluating the quality of the complex environment GNSS data (S5); fusing multi-source data (S6); performing adaptive adjustment of parameters (S7); handling abnormal situations (S8); and performing real-time monitoring and correction of the complex environment GNSS data (S9).EFFECT: When processing GNSS observation values of complex environments such as a narrow-space environment, strong reflections, and multi-frequency multi-systems, the combination of the mean-shift and dispersion-broadening concepts represents a significant improvement in large-error search and data processing.SELECTED DRAWING: Figure 1

The invention discloses a defect edge extraction method and device based on a terahertz image, terminal equipment and a storage medium, and belongs to the technical field of defect detection, and the method comprises the steps: obtaining an original terahertz image; performing enhancement processing on the original terahertz image to obtain an enhanced terahertz image; segmenting the enhanced terahertz image into a plurality of target areas through a mean shift algorithm; for each target area, through a maximum between-class variance method, calculating segmentation threshold values of a foreground and a background in the target area; according to the segmentation threshold, segmenting a foreground region from the target region as a defect region; and inputting the defect area into an active contour model, so that the active contour model generates a defect edge curve according to the defect area. The problems that in the prior art, a defect area needs to be specified manually in advance, and the defect edge extraction error is large can be solved.

The invention provides a water chiller predictive maintenance method and system based on digital twinning, and the method comprises the steps: obtaining the evaporation pressure, suction temperature, refrigerant flow and outlet water temperature parameters during the operation of a main module of a water chiller, and carrying out the synchronous collection through multiple sensors, thereby forming a time series data set; according to the time sequence data set, a sliding window method is adopted to extract the mean shift amount, variance volatility and cross correlation coefficient of evaporation pressure and suction temperature, and a collaborative drift mode reflecting micro leakage is obtained; according to the corrected drift rate and cross correlation coefficient, generating a feature vector of a collaborative change direction among the evaporation pressure, the suction temperature and the refrigerant flow; and by calculating the Euclidean distance between the feature vector of the cooperative change direction and the health baseline, whether the micro leakage reaches the early warning triggering degree is judged, and the strength of an early warning signal is obtained.

The invention discloses a tracking optimization method for detecting a low-speed small unmanned aerial vehicle target by a single-photon laser radar, belongs to the technical field of optical detection and target tracking, and aims to solve the problem of unstable target imaging and tracking caused by speckle noise interference in the prior art. The invention provides a speckle noise suppression method based on a vibration emission optical fiber and a space-time dynamic kernel density estimation algorithm, and the method is combined with an improved mean shift-Kalman filtering algorithm to achieve the tracking optimization of a low-speed small unmanned aerial vehicle target, and obtains an echo signal through a single-photon laser radar. Constructing a three-dimensional data matrix and reconstructing distance and intensity images through space-time filtering and kernel density estimation; and then, in combination with gray level histogram probability estimation, similarity measurement and Mean Shift iteration, target area tracking is realized, and finally, a target position is predicted and output by using Kalman filtering. The method is suitable for the fields of long-distance unmanned aerial vehicle detection and monitoring, low-altitude security defense early warning, civil airspace management and control, military anti-unmanned aerial vehicle systems and the like.

The embodiment of the invention provides a dike abnormal target object detection method based on infrared and visible light image fusion, which is used for identifying a dike target object needing to be detected in a dike disaster scene, and comprises the following steps: obtaining an infrared image and a visible light image; registering the infrared image and the visible light image by adopting a feature matching algorithm; fusing the registered infrared image and visible light image by adopting an image fusion algorithm to obtain a fused image; and iteratively executing the following segmentation optimization operations until the calculated value of the target function of the mean shift algorithm is minimum to obtain the image segmentation result of the fused image: segmenting the fused image by adopting the mean shift algorithm to obtain the current segmentation result, determining the calculated value of the target function according to the current segmentation result, and obtaining the image segmentation result of the fused image. And optimizing hyper-parameters of the mean shift algorithm according to the calculated value of the target function. According to the scheme, the embankment target object with a small size can be accurately segmented, and the problem of excessive segmentation can be solved.

The invention relates to the technical field of data processing, in particular to a data processing method for an AI data management platform. The method comprises the following steps: acquiring multiple pieces of historical data of each customer in a customer cluster, taking each customer as a historical data point, and clustering the multiple customers in the customer cluster by using mean shift clustering based on the multiple pieces of historical data corresponding to each historical data point to obtain multiple clustering clusters; and for any cluster, analyzing the direction consistency of historical data points in the neighborhood of the updated data points and the spatial distance between the historical data points and the center of the cluster, calculating the classification probability that the updated data points belong to the cluster, and classifying the updated data points according to the classification probability of the plurality of clusters corresponding to the updated data points. The sales data processing method and the sales data processing device have the effects of real-time performance and accuracy in sales data processing.

The invention provides a digestive tract tumor detection method and system, relates to the field of medical image detection, and solves the technical problem of low accuracy of tumor boundary detection and automatic positioning in a digestive tract tumor image acquisition process in the prior art. The method comprises the following steps: taking a pan-tumor region image as a training sample of a binary decision tree algorithm, constructing an objective function based on the training sample to obtain a first objective function value, and obtaining a depth threshold value and an optimal classification point of the training sample; optimizing the optimal classification point based on the candidate feature sound to obtain a second objective function value; an unknown digestive tract tumor image is accessed, image points of the digestive tract tumor image are marked through a trained binary decision tree algorithm and a regression forest algorithm, the position distribution probability of the image points is generated, a position distribution thermodynamic diagram is generated based on the position distribution probability, and a tumor core area is obtained through mean shift. And completing the separation of the tumor region and the non-tumor region. The application is used in the digestive tract tumor detection process.

The invention discloses an airborne multispectral point cloud individual tree segmentation method for a dense forest scene, and belongs to the technical field of multispectral point cloud data processing. In order to solve the problem that a point cloud single tree segmentation method used in a dense scene is large in calculation amount and limited in segmentation precision, the method aims at the spectral feature F of a multi-spectral point cloud, the spectral feature F is mapped to a normalized embedding space by using a neural network to obtain a normalized embedding space feature after mapping, and based on the feature, the point cloud single tree segmentation method is based on the normalized embedding space feature. Performing coarse segmentation on all points by using a mean shift algorithm, then performing segmentation optimization based on a point cloud classification prediction result, firstly judging whether a point cluster contains non-tree class points, and rejecting the non-tree class points; and then judging whether the point cluster contains a plurality of tree categories, and if the point cluster contains the plurality of tree categories, further segmenting the point cluster according to the tree categories. On this basis, a vertex offset vector is obtained based on the F; the vertex offset vector is applied to gather the points of each tree to the vicinity of the vertex of the tree.

The application provides a kind of spatio-temporal intelligent marine satellite internet of things sensing information screening method and device, belong to data processing technical field, this method is based on convolution bidirectional long short-term memory neural network realizes marine environment time series data repair, based on block time series transformer carries out marine environment data space-time prediction and based on mean shift density clustering algorithm carries out marine hotspot area mining, the marine environment data includes sea surface temperature, salinity, wave height and other sensing data, based on BP neural network carries out marine environment risk assessment and multi-agent reinforcement learning carries out information screening feedback control, meet the complex marine environment detection scene efficient information processing demand, realize low redundancy, high-precision data acquisition transmission adaptive feedback control effect.The present application can be directly applied to marine internet of things in marine observation detection facility networking system, can provide strong support in military application and civil application, has wide and important application prospect and value.

The invention relates to the technical field of lunar orbit image analysis, in particular to an annular mountain detection method based on orbit camera images, a training data set is constructed based on the lunar orbit camera images, and the training data set comprises an annular mountain edge thermodynamic diagram label, an offset direction label and a radius label based on multi-interval coding; training a deep learning network by using the data set, and setting a network output layer into three branches which are respectively used for predicting an edge thermodynamic diagram, an offset direction and a radius multi-interval; inputting a to-be-detected image into the network, obtaining a prediction result, screening an effective edge pixel set based on the thermodynamic diagram, and performing direction and radius decoding on the effective edge pixel set to generate an annular mountain center candidate point; the mean shift clustering algorithm is adopted to perform instance separation on the candidate points to obtain a ring-shaped mountain detection result, and edge prediction and multi-interval coding are combined to improve the detection rate and positioning precision of weak-edge, small-scale and adjacent ring-shaped mountains.

The application provides a remote sensing target detection sample sampling method and device based on active learning. The method comprises the following steps: performing difference judgment on a plurality of first multi-level features and a plurality of second multi-level features based on a feature difference mechanism, and obtaining difference distance information; obtaining stability results based on a sample consistency mechanism and a second data set; performing weighted summation on the difference distance information and the stability results to obtain uncertainty results of the second data set; obtaining a target category and a target category quantity of a first data set, and obtaining a category sampling weight according to the target category and the target category quantity; generating a sampling score of each second sample in the second data set according to the category sampling weight and the uncertainty results; performing clustering processing on the second samples based on a Mean Shift algorithm to obtain clustering clusters; and taking the second samples with sampling scores meeting requirements in each clustering cluster as target remote sensing sampling samples. The time cost of target detection sample acquisition is reduced.

The present application relates to a kind of cluster collaborative control method based on continuous mean shift theory, belong to involve multi-agent distributed collaborative control field, including three parts: desired configuration figure input and contour fitting, target shape physical parameter determination, distributed configuration control algorithm design based on continuous mean shift theory.The present application adopts Bezier curve to express desired shape, has strong representation ability to complex non-convex shape, target shape data storage is less, reduces memory requirement, and continuous control law can avoid the oscillation possibly caused by grid-based discrete control law.The present application uses distributed computing mode, and multi-agent system can enter desired shape area according to local perception information and actively explore unknown shape area, effectively reduce the requirement to agent communication ability, increase engineering feasibility, have strong robustness and scale expandability.The present application has strong adaptive capacity, and does not need global label and initial position configuration.

The present invention discloses a method, system, device and storage medium for autonomous collaborative exploration of an unknown environment by multiple intelligent agents, which belongs to the field of intelligent unmanned technology. The method comprises the following steps: obtaining a first boundary point set obtained by exploration based on a fast random tree strategy and sending the first boundary point set to a mean shift clustering module, calculating the center point of the cluster, and using the set of center points as a second boundary point set; filtering the second boundary point set by using an information gain function and an obstacle boundary point filter; calculating the benefit of each intelligent agent after reaching the remaining boundary points, processing the benefit by using a discount strategy, and then adding a hysteresis gain to obtain the total benefit of each intelligent agent after reaching each boundary point, subtracting the cost from the total benefit with the Euclidean distance from the intelligent agent to the boundary point as the cost, obtaining the total benefit of each intelligent agent reaching each boundary point, allocating the boundary point corresponding to the maximum value of the total benefit of the intelligent agent to the intelligent agent, and performing autonomous collaborative exploration of the unknown environment after the allocation is completed. The present invention reduces the amount of calculation and improves the operation efficiency and robustness.

The invention relates to the technical field of physiological data processing, in particular to a patient physiological data monitoring method and system based on the Internet of Things. The method comprises the steps that multiple pieces of historical physiological data and physiological data of a target patient are acquired, all the historical physiological data are clustered through mean shift clustering, and multiple clusters are obtained; and calculating the classification possibility of the physiological data belonging to each cluster, and selecting the cluster corresponding to the maximum value in the normalized values of the classification possibility as the category of the physiological data so as to complete the classification of the physiological data. According to the scheme, classified monitoring of the physiological data, collected in real time, of the target patient can be timely and accurately carried out.

The invention discloses a model dynamic compression method and system based on a Mean Shift clustering and SVD decomposition algorithm, electronic equipment and a storage medium. The method comprises the steps that S1, a convolutional neural network model subjected to sparse training is acquired; s2, performing clustering and pruning on the convolutional neural network model after the sparse training by using a Mean Shift clustering algorithm; s3, carrying out Finning training on the convolutional neural network model after clustering and pruning; s4, pruning operation is carried out on the convolutional neural network model after finishing the Finishing training based on an SVD decomposition algorithm; s5, after pruning operation is completed, whether the parameters meet the threshold value requirement or not is judged till a final model is output. After the dynamic clipping scheme is added, the three problems which are originally left can be effectively solved: 1, the size of the model is effectively reduced, so that the model can be deployed in equipment with a relatively small memory; 2, the model parameters can be effectively cut according to the requirements of a hardware deployment platform; and 3, for the pruned model, the performance similar to that of the original model can be effectively ensured.

The invention discloses a multi-extended target joint tracking and classification method based on star convex RHM and LMB filters, and belongs to the field of radar target tracking. According to the method, an LMB parameter set is initialized by using target prior information, and then a sensor measurement set is divided through a mean shift algorithm; then, state prediction is achieved by combining survival target parameter updating and new target parameter sampling, and then measurement updating is completed through LMB-to-GLMB, GLMB updating and GLMB-to-LMB; and then trimming and fusing the LMB parameter set, estimating the number of targets, extracting state information, and circularly executing until observation is finished. According to the method, a star convex RHM modeling expansion state is adopted to reduce dimensions, the low detection probability / high clutter scene performance is improved based on an LMB framework, the tracking classification effect and the real-time performance are both considered, and the engineering application value is high.

The invention discloses a doubly-fed wind power plant equivalence method based on a mean shift algorithm, and the method is specifically implemented according to the following steps: building a doubly-fed wind power generation grid-connected system model, obtaining the power data during the normal operation and fault period of a unit, and determining a grouping index; the grouping index is preprocessed, then the grouping bandwidth is calculated, then data of each wind turbine generator set serves as an initial grouping center, and initial grouping of the wind turbine generator sets is completed through a mean shift algorithm; introducing a Devensenberg index (DBI) as an evaluation index, and optimizing the preliminary grouping result to obtain the number of equivalent units; and calculating equivalent unit parameters by adopting a capacity weighting method according to an optimized grouping result to obtain a wind power plant equivalent model. According to the method, grouping fits the real characteristics of the unit, and the data deviation of the grouped unit is reduced.

The invention relates to the technical field of data processing, in particular to a network risk assessment method for an industrial internet, which comprises the following steps: acquiring a network flow value and a network bandwidth value of the industrial internet in real time; determining a potential abnormal factor at the current moment; determining an algorithm bandwidth demand degree used for carrying out adaptive correction on a fixed bandwidth in a mean shift clustering algorithm at the current moment; determining the self-adaptive bandwidth at the current moment; and carrying out clustering by using a mean shift clustering algorithm so as to realize network risk assessment of the industrial internet. According to the invention, by dynamically adjusting the bandwidth, two extreme problems caused by a fixed bandwidth are avoided, so that the abnormal traffic is identified more accurately; according to the real-time network flow value, historical data and potential abnormal factors, the optimal bandwidth suitable for the current condition is calculated, it is ensured that clustering parameters are adjusted along with changes of the network state, and more flexible and agile risk detection is achieved.

The application relates to the technical field of image processing, in particular to an ultrasound-guided nerve blockage image processing method based on artificial intelligence, which comprises the following steps: obtaining the weight of other pixel points in the neighborhood of each pixel point according to the gradient amplitude of each pixel point, the distance between other pixel points in the neighborhood and the center point of the neighborhood, and the neighborhood radius; obtaining the weighted weight of each pixel point according to the weight of other pixel points in the neighborhood of each pixel point; obtaining the search window size according to the weighted weight of each pixel point and a window adjustment parameter, and obtaining a search window with each pixel point as the center according to the search window size; obtaining the iterative convergence condition according to the pixel point density difference in the search window and the distance of the clustering center; performing mean shift clustering according to the convergence condition and an initial clustering center to obtain a plurality of clustering clusters; and performing segmentation and visualization of nerve tissue regions according to the obtained clustering clusters.

The invention discloses a multipath clustering and tracking method for a dynamic wireless channel, which belongs to the technical field of wireless communication, and comprises the following steps: acquiring time delay, angle and power parameters of multipath components in each time snapshot; running an adaptive neighborhood robust mean shift clustering algorithm, and combining a neighborhood structure quality index to realize static division of a non-uniform density cluster; constructing a directional bounding box of the cluster in a three-dimensional feature space by utilizing principal component analysis, and accurately representing a geometric boundary of the cluster; executing cluster-level tracking based on a directional bounding box and Kalman filtering, and correcting a prediction state; a cluster-level joint similarity is defined based on the shape and density of the bounding box to determine the birth and death of the cluster, and the remaining multipath is re-clustered to identify a new cluster. According to the method, the problems of over-segmentation or under-segmentation and association fuzziness in the prior art are solved. According to the method, the variable density structure of the multipath cluster and the spatio-temporal evolution trajectory of the variable density structure can be adaptively represented, and the precision and robustness of channel modeling in a high-dynamic non-stationary scene are remarkably improved.

This invention relates to the field of image processing technology and discloses an adaptive smoothing and enhancement method for animation capture trajectories based on temporal feature fusion. The method includes the following steps: S1, acquisition and preprocessing of raw trajectory data; S2, extraction of multi-dimensional temporal features; S3, noise and signal identification based on gated feature fusion; S4, generation of adaptive smoothing kernel and trajectory repair; S5, trajectory enhancement and output: post-processing based on physical constraints is performed on the smoothed trajectory to enhance the dynamic rationality of the action, and a smoothed and feature-enhanced animation capture trajectory is output. This invention intelligently distinguishes between real high-frequency motion and noise by fusing kinematic features, spatial correlation features, temporal semantic features, and attention weights, avoiding the over-smoothing distortion phenomenon of traditional methods. Through a multi-scale feature fusion method optimized by mean drift, key motion regions are automatically identified and assigned higher detail preservation weights, achieving differentiated smoothing enhancement.

The application discloses a power distribution box electricity monitoring method based on big data analysis and relates to the technical field of big data analysis, which comprises the following steps: collecting loop-level electricity and operation data, and obtaining time series data sets through preprocessing; calculating an incremental energy density curve and a distribution entropy curve, and generating a joint sequence sample set; constructing an improved DLinear model to obtain a prediction sequence; performing singular spectrum decomposition and phase space re-embedding to form an abnormal recognition feature vector set; obtaining clustering attribution information based on a mean shift clustering algorithm; performing target risk integration to output a graded alarm result; and generating loop-level electricity monitoring to form an electricity monitoring result. The application realizes reliable electricity trend prediction and abnormal grading positioning monitoring of the power distribution box by combining the improved DLinear model prediction and the risk grading alarm of the mean shift clustering algorithm.

The present application relates to the technical field of data processing, more particularly, the present application relates to a network risk assessment method for industrial internet, comprising: acquiring network traffic value and network bandwidth value of industrial internet in real time; determining potential abnormal factors at the current time; determining algorithm bandwidth demand degree for self-adaptive correction of fixed bandwidth in mean shift clustering algorithm at the current time; determining adaptive bandwidth at the current time; clustering by using mean shift clustering algorithm to realize network risk assessment of industrial internet. The present application avoids two extreme problems caused by fixed bandwidth by dynamically adjusting bandwidth, so as to more accurately identify abnormal traffic; according to real-time network traffic value, historical data and potential abnormal factors, the optimal bandwidth suitable for the current situation is calculated, the clustering parameters are adjusted according to the network state, and more flexible and agile risk detection is realized.

The application provides a seismic facies classification method and device for seismic data, and the method comprises the following steps: determining the similarity between each data and other data in a seismic data set in a target work area according to the kernel function of each data, so as to determine the similarity between all data in the seismic data set; dividing the seismic data set in the target work area into K cluster data according to the similarity between all data, so as to generate a division result; wherein K is equal to the number of seismic facies types in the target work area; and calibrating the division result according to the petrophysical parameters and / or sedimentary facies of the target work area, so as to determine the seismic facies classification result of the seismic data set. According to the transverse variability of the seismic signal in a certain target layer, the mean shift algorithm is used to classify the seismic trace shape, the classification result forms discrete'seismic facies', and the petrophysical parameters or the sedimentary facies plane distribution rule is described by using the seismic data.

The invention provides a depth estimation model training method and device, a terminal and a storage medium. The training method of the depth estimation model comprises the following steps: acquiring selective depth data; training a depth estimation model by using the selective depth data to obtain a trained depth estimation model; wherein the step of acquiring the selective depth data comprises: acquiring a depth data set; performing quality evaluation on the depth data set to obtain first depth data; performing mean shift on the first depth data to obtain second depth data; performing fine tuning on the pre-trained depth model by using the second depth data to obtain an absolute depth model; and performing necessity evaluation on the first depth data by using an absolute depth model to obtain selective depth data. According to the invention, the depth estimation model is trained by using the selective depth data, so that the generalization ability of the model in various subdivision scenes is significantly improved.

The invention relates to a forensic physical evidence multi-person identity authentication method. The method comprises the following steps: S1, optimizing a short tandem repeat map mathematical model; s2, preprocessing the short tandem repeat map mathematical model to obtain sites containing information; s3, using a mean shift algorithm to process peak points in the atlas, converging to a density local maximum point, and outputting the number of modes as the preliminary estimation of the number of contributors of the locus; s4, judging the mixing ratio of each contributor by using a gradient descent algorithm; and S5, generating an initial vector depending on the mixing ratio according to the mixing ratio, performing sliding matching by keeping the contribution amount of the contributor unchanged, finding an allele size value suitable for genome information contributed by each contributor, and generating a genotype by searching an id corresponding to the nearest allele size in an original map. By means of the design, DNA information of multiple persons can be processed, and contributor composition and proportion of all components in mixed data are analyzed.

The invention discloses a carpet printing multi-level accurate positioning method based on image processing. The method comprises the following steps: S1, collecting and preprocessing a carpet image; s2, extracting pattern features by using a convolutional neural network, and performing classification through a random forest algorithm to obtain pattern feature tags; s3, performing mean shift clustering on the pattern feature tag, identifying a central point and a region boundary, and constructing a CNN-RF model to complete a pre-training process; s4, calculating a deviation according to a clustering result, correcting the error through a CNN-RF model, and adjusting positioning to realize pattern alignment; s5, designing and applying a positioning avoidance scheme, and optimizing a pattern alignment relationship; s6, the alignment deviation in the printing process is monitored, and the positioning strategy is adjusted in real time; and S7, adjusting clustering parameters and feature classification rules, and realizing accurate positioning of dynamically optimized pattern levels. The carpet printing multi-layer pattern alignment precision is improved, manual intervention is reduced, and the production efficiency and the product quality are improved.

This invention discloses a method and system for sample augmentation and lightweight deep learning model construction in power distribution line defect identification. The method includes: performing adaptive contrast stretching preprocessing on the input image and stitching normalized coordinate grids to enhance input information; constructing a lightweight detection model with two branches, one based on segmentation and the other on row classification, where the two branches share a partial feature extraction network and are fused through a feature interaction module to simultaneously output pixel-level segmentation results and row classification prediction results; employing a feature-response distillation method based on channel attention to transfer knowledge to a more streamlined student model; using a binary segmentation map to filter instance embedded features and performing mean-shift clustering to obtain independent instance segmentation results, which are then fused with row classification predictions for verification and supplementation; finally, deploying the compressed model to an embedded device to achieve real-time defect identification and localization. This invention effectively improves the detection accuracy and efficiency of the model in embedded environments.

The present application relates to the technical field of communication network session flow splitting, and particularly relates to a session flow splitting method and system based on mean shift clustering. Firstly, the elbow rule is used to determine the sliding window radius of each cluster of message packets. Then the selected cluster center is constantly shifted to a new cluster center until the number of packets within the sliding window radius range no longer increases, and a candidate cluster center is obtained. Next, the Hamming distance between the candidate cluster center and the cluster centers in the existing cluster center set is calculated. If the Hamming distance is greater than a set threshold, the candidate cluster center is added to the existing cluster center set, otherwise it is merged into the nearest existing cluster center. The above steps are repeated to obtain a cluster center set. Finally, the Hamming distance between each received packet and the cluster centers in the cluster center set is calculated, and the estimation of the quintuple is completed according to the minimum Hamming distance criterion, so as to realize the separation of session flow. The present application can fully utilize all data packets, and the flow splitting performance is greatly improved.

The application discloses a kind of based on spatial data mining and geological constraint seismic activity fault model construction method, system, equipment and medium.The method is first arranged and analyzed to seismic data, extracts minimum complete subdirectory;Using improved mean shift algorithm for spatial clustering, identify each active fault corresponding small earthquake cluster;Subsequently, each fault cluster is three-dimensional slice, and least square method fitting is generated fault interpretation line;With interpretation line as foundation, initial seismic activity fault three-dimensional model is constructed, and is optimized by multi-source geological constraint, and active fault three-dimensional fine model is obtained;Finally, the model is integrated with digital earth spatial registration.This application can realize the whole process automation and quantization from seismic data analysis to active fault three-dimensional fine model construction and application, improve the objectivity, efficiency, precision and reliability of model construction, provide important technical support for seismic disaster risk assessment, active fault detection.