280 results about "Data registration" patented technology

The invention relates to the technical field of stamping part detection, and discloses a stamping part size and defect synchronous detection method and system. The method comprises the steps that the multi-sensor measurement module is used for collecting line laser scanning morphology data, infrared thermal image strain data and structured light projection contour data of a stamping part; multi-sensor data registration is achieved through a feature point matching algorithm, and a three-dimensional space coordinate mapping relation is generated; calculating the thermal expansion compensation amount of the material in combination with a thermal deformation correction model; filtering the structured light projection contour data, and extracting key contour feature points and defect region boundaries; inputting the related data into a multi-source data fusion model to obtain a dimensional deviation and defect fusion detection result; based on this, a measurement path is updated through a dynamic path planning algorithm, and a synchronous detection scheme is output. The device can synchronously detect the size and defect of the stamping part, improves the detection precision and efficiency, achieves the quality grade classification, and is high in adaptability.

A digital auxiliary repair method for an ancient building. The method comprises: point cloud data acquisition, point cloud model establishment, point cloud model optimization and model printing, which involve: scanning and photographing a damaged ancient building by using a three-dimensional scanner and an industrial camera, so as to obtain three-dimensional point cloud data of the ancient building; performing preprocessing, such as data registration, data denoising, data simplification and data segmentation, on the point cloud data to obtain data for establishing a three-dimensional model; optimizing the three-dimensional model; and by means of a 3D printer, obtaining a mold for a damage position of the ancient building, and on the basis of requirements, using the mold to perform molding to obtain a wooden or brick-mixed workpiece.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster detection method and monitoring system based on unmanned aerial vehicle scanning. Comprising the following steps: S1, configuring an unmanned aerial vehicle-mounted tilt camera, a multispectral laser radar and a high-precision positioning module, planning a route, carrying out multi-angle scanning on a target area, and obtaining earth surface three-dimensional point cloud data, a multispectral image and terrain elevation information; s2, preprocessing the collected original image data, including point cloud denoising, image distortion correction and multi-source data registration, and generating a high-resolution live-action model fused with three-dimensional geographic information data; s3, extracting various data based on the live-action three-dimensional model to construct a geological disaster hidden danger analysis model, calculating a risk index through multi-factor weighted fusion, training a transfer learning model in combination with historical disaster data, and outputting a hidden danger type and probability; according to the invention, the geological disaster type can be analyzed based on the geological condition and the targeted processing strategy can be specified.

The invention discloses a concrete crack depth detection method and system based on multi-modal data fusion. The method comprises the following steps: synchronously obtaining a visible light image sequence and a thermal imaging image sequence of a concrete crack; the thermal imaging image sequence is obtained based on adjustable thermal excitation; and through a preset multi-modal data registration algorithm, according to the visible light image, predicting a registration displacement vector field to generate a pseudo-infrared image corresponding to the enhanced visible light image, and migrating a temperature field of the thermal imaging image at the same moment and under the same picture to the pseudo-infrared image to generate a fusion modal image, obtaining a fusion modal image sequence; and through a preset heat conduction inversion model and a temperature attenuation characteristic curve generated based on the fusion modal image sequence, obtaining crack depth data and generating a three-dimensional crack map so as to visually present concrete crack depth detection data. According to the invention, the universality and accuracy of concrete crack depth detection can be improved.

The invention belongs to the technical field of medical image processing, and particularly relates to an automatic positioning and typing method for cervical posterior longitudinal ligament ossification, which is characterized in that ossification focus spatial distribution characteristics of CT images and spinal cord morphological parameters of MRI images are synchronously analyzed based on CT and MRI images, a vertebral body-ossification-spinal cord spatial relationship is explicitly modeled through anatomical structure topological constraint, and a cervical vertebra-ossification-spinal cord spatial relationship is obtained. Diffuse calcification artifacts, heterogeneity signal interference and multi-modal data registration deviation are overcome, accurate positioning and typing diagnosis of the ossification are achieved, multi-dimensional feature fusion analysis is achieved, the limitation of traditional single iconography index diagnosis is broken through, space continuity and clinical parameters are integrated through a hybrid classifier, and the accuracy of diagnosis is improved. The accuracy and robustness of typing judgment are improved, a structured diagnosis report is automatically generated in the whole process, and the clinical decision-making efficiency and the standardization level are remarkably improved; the principle is scientific and reliable, and the ossification continuity index, the spinal canal invasion rate and the spinal cord compression grading parameters are automatically calculated according to the typing standard.

The invention relates to a BIM (Building Information Modeling)-based digital pre-assembly method and system for a glass curtain wall, and relates to the technical field of data processing, and the method comprises the following steps: determining geometric parameters and installation position parameters of each component; the method comprises the following steps: acquiring point cloud data of a construction site through three-dimensional laser scanning, performing multi-source data registration on the point cloud data, and calculating to obtain a point cloud registration error; determining a feature matching degree based on the point cloud registration error; obtaining a positioning optimization target according to a deviation analysis result and an engineering constraint condition; according to the positioning optimization target, an assembly precision evaluation value is obtained through calculation; based on the assembly precision evaluation value, the correction value of the glass curtain wall in the pre-assembly process is determined; and calculating a comprehensive quality score based on the assembly precision evaluation value, the point cloud registration error, the feature matching degree and the correction value, and determining to adopt a corresponding pre-assembly scheme when the comprehensive quality score is greater than a preset threshold value. The pre-assembly accuracy of the glass curtain wall can be improved.

The invention discloses a fixed dual-runway FOD detection method based on multi-source circumferential scanning, and belongs to the technical field of data fusion detection, and the method specifically comprises the steps: synchronously obtaining dual-runway multi-polarization echo data through a fixed circumferential scanning rada; performing time-varying clutter suppression by combining an adaptive clutter cancellation method matched with a dynamic background template and a runway material feature library, and generating a time-frequency domain three-dimensional feature matrix through time-frequency analysis; synchronously collecting and preprocessing image data, and marking a target area through a constant false alarm rate algorithm and image detection; a multi-source data geographic coordinate mapping model is established, data registration and cross validation are completed under a runway physical coordinate system, and real-time tracking of a real FOD target is realized based on radar and image fusion data through multi-dimensional confirmation of radar polarization / micro-motion features and image visual features; according to the method, the FOD detection accuracy and the position identification precision are improved through multi-source data fusion, the false alarm rate is effectively reduced, and a guarantee is provided for safe operation of an airport.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing and analysis method, device, equipment and medium, and the method comprises the steps: obtaining a data source set, and registering a source label and field mapping relation; executing extraction, cleaning, conversion and loading in the data processing assembly line based on the data source set, the source label and the field mapping relation to generate a processing table set; performing quality verification and exception handling on the processing table set to generate a verified processing table set; generating an index cloud based on the verified processing table set and the configuration strategy; the index cloud is pushed to the analysis tool set through the interface transmission channel, and the analysis tool set generates analysis output according to the index cloud. According to the method, through unified data registration, standardized processing and quality verification, the index cloud is generated in combination with the configuration strategy and pushed to the analysis tool set, the analysis result is generated and transmitted, the data consistency is ensured, and the real-time performance is improved.

The invention provides a report loading processing method, system and device in an industrial scene and a medium, and belongs to the technical field of report loading. Data source information associated with a service entity identifier is obtained; establishing a dynamic binding relationship between business entity attributes and data source fields, and generating an index metadata template; acquiring data source information associated with the service entity identifier through a data registration component; establishing a calculation mapping relation between the index field and the data source field; generating a standardized configuration file; persisting the generated standardized configuration file to a database; a three-party incidence relation index among an index instance identifier, a service entity identifier and a data source identifier is established, in actual industrial data processing, even if network faults and other problems occur, it can be guaranteed that configuration files are accurately updated, when service requirements change, calculation rules of corresponding levels can be rapidly positioned and adjusted, and the service requirements are met. And the flexibility and accuracy of data processing are improved.

The invention provides a data registration method and device for a space transcriptome and a space metabolome, electronic equipment and a storage medium. The method comprises the steps of obtaining a space transcriptome image and a space metabolome image to be registered; performing image registration on the space transcriptome image and the space metabolome image through an image registration model to obtain an optimal registration parameter; and performing space coordinate registration on the data points of the space transcriptome image and the space metabolome image after image registration by using the optimal registration parameter. According to the method, the tedious process of manual registration can be eliminated, and the unification of the space coordinates of the two groups of data points can be quickly and accurately completed. In addition, the data registration method provided by the invention is low in cost and extremely short in time consumption, does not depend on any closed source business software, reduces the use cost, provides space and freedom for subsequent algorithm upgrading and iteration, and can flexibly adapt to continuously changing research requirements.

The invention discloses a brain multi-modal multi-sequence data registration method and device based on deep learning. The method comprises the steps of performing first iteration processing on a target image and a first moving image to obtain a first deformation field, wherein the first iteration processing comprises image alignment constraint processing of the target image, smooth constraint processing of the first deformation field, and area alignment constraint processing of a tumor area in the image; registering the first moving image to the target image based on the first deformation field to obtain a second moving image; performing second iteration processing on the target image and the second moving image to obtain a second deformation field; and registering the second moving image to the target image based on the second deformation field to obtain a registered moving image. In the process of performing unsupervised training on the deep learning model, after multiple times of iterative processing, model parameters are updated based on image alignment constraint loss with a target image, deformation field smooth constraint loss and region alignment constraint loss of a tumor region in the image.

The invention discloses an unmanned aerial vehicle low-altitude photography image enhancement method and system. The method comprises the steps of data preparation, motion joint deblurring optimization, multi-source data registration, scene adaptive enhancement and image comprehensive enhancement. The invention relates to the technical field of unmanned aerial vehicle image enhancement, and realizes high-precision deblurring, precise registration and adaptive visual enhancement of an unmanned aerial vehicle image by fusing inertial measurement unit guided blurring kernel prediction, cross-modal graph structure matching and elastic transformation optimization and an enhancement strategy dynamic adjustment mechanism based on scene analysis. The image definition, the structure consistency and the scene readability are remarkably improved, and the method is suitable for image processing tasks in a complex low-altitude flight environment.

The invention discloses a smart city planning three-dimensional scene reconstruction optimization system combined with semantic segmentation, and belongs to the technical field of three-dimensional scene reconstruction optimization. The system comprises a data sensing module which collects aerial images and laser point clouds, and complements a sheltered area to obtain city modeling data; the preprocessing module performs denoising, data registration and data fusion on the city modeling data to generate texture point cloud data; the semantic understanding module realizes multi-modal semantic segmentation of the texture point cloud data through a fine-tuned SAM network and an improved RandLA-Net, and semantic tag data is obtained through restoration and optimization; the reconstruction optimization module constructs an initial three-dimensional grid model based on the texture point cloud data, and optimizes a ground feature boundary and a missing region of the model in combination with a semantic tag; and the output application module converts the optimized model into a standard format and outputs the model. Through deep coupling of semantic segmentation and reconstruction optimization, the precision of the three-dimensional scene model is improved, and reliable support is provided for smart city planning.

The invention relates to the technical field of point cloud data registration, in particular to a point cloud registration optimization method based on spatial feature coding and geometric perception attention calculation, and effectively overcomes the problems of insufficient matching precision and low efficiency under low overlapping and complex spatial relationships. The method comprises the following steps: firstly, performing efficient feature extraction and encoding on point cloud data by using a spatial feature encoder to obtain feature representation containing rich spatial information; thirdly, designing a self-attention mechanism based on geometric perception, and weighting a key region in the point cloud through a self-adaptive attention weight, so that the model can focus on the most significant and highly recognizable features in the point cloud data; and then, through multi-stage feature fusion and local geometric modeling, the precision and robustness of point cloud registration are further improved. Experimental results show that in various complex environments, especially high noise, rotation, deformation and the like, the method shows registration performance superior to that of a traditional method.

The invention provides a Yangtze river export deep and far sea environment data association analysis method based on multi-modal fusion, belongs to the technical field of far sea environment analysis, and achieves accurate data registration by establishing a multi-source data space-time standardization model and adopting an adaptive space-time Kriging interpolation algorithm. A sparse coding ocean signal separation algorithm is utilized to construct an over-complete dictionary separation mixed signal to extract pure features, a feature extraction network is constructed based on an attention mechanism to automatically learn deep feature representation of physicochemical biological parameters, and a matrix rank loss detection algorithm is adopted to automatically supplement feature compensation vectors to ensure feature integrity. A cross-scale attention mechanism is constructed based on wavelet transform to fuse multi-scale features, a Shapley value interpretability evaluation system is established to quantify correlation feature importance, and the technical problem that correlation feature extraction is inaccurate in the multi-source heterogeneous marine environment data fusion process is solved.

The invention discloses an image three-dimensional reconstruction method and system for orthopedic postoperative monitoring, and relates to the technical field related to image processing, and the method comprises the steps: positioning an operation region pixel mask of a user, and collecting reference three-dimensional image data; performing candidate base point analysis on the reference three-dimensional image data to obtain a candidate base point set; performing stability scoring on the candidate base point set, identifying an anchoring base point set greater than a preset stability scoring threshold based on a stability scoring result, and selecting at least one reconstructed anchoring base point; and obtaining at least one follow-up visit three-dimensional image data, and performing three-dimensional reconstruction on the at least one follow-up visit three-dimensional image data to obtain a three-dimensional image reconstruction result. The technical problems that in the prior art, image three-dimensional reconstruction dynamic change analysis is difficult, and feature point stability and data registration precision are insufficient are solved, and the technical effects that multi-temporal three-dimensional image high-precision registration is achieved, and the consistency of three-dimensional reconstruction results at different follow-up visit time points and three-dimensional reconstruction precision are improved are achieved.

Systems, methods, and computer readable media for routing inputs on a extended reality (XR) wearable device where the methods performed on an apparatus of a system include receiving, at an input framework service, an input data registration request from a client component, the input data registration request comprising an indication of a callback component and an indication of input data having a type, and verifying, at the input framework service, the client component is authorized to access the input data having the type. The method may further include registering, at the input framework service, the callback component with an input service associated with the input data having the type, receiving, at the input service, input data having the type, and invoking, at the input service, the callback component.

The invention discloses an identification-based industrial map data registration method and system. The method comprises the following steps: 1) an industrial map data registration system distributes a unique user id for each registered user; then generating a character string according to the feature value of the to-be-registered graph data filled by the user and the user id; the feature value comprises a picture name, a picture path, a storage server ip, a superior node id and a child node id; 2) encrypting the character string to generate a ciphertext as an identifier of the graph data; 3) receiving a data source access permission set by a user for the graph data; when duplicate checking judgment is carried out on the graph data, an identifier of the graph data is analyzed, and according to a picture storage path of the analyzed graph data, the storage server ip obtains an absolute path of a storage position of the graph data and accesses the data for comparison duplicate checking; and 4) updating the registered knowledge graph of the user by identifying the superior node id and the child node id of the analyzed graph data.

The invention discloses a focus intelligent detection and diagnosis system based on multi-modal medical image fusion, and relates to the technical field of medical image processing. A molybdenum target image, an ultrasonic image and a magnetic resonance image of the mammary gland of the patient are acquired through the data acquisition module; a data registration module is used for carrying out partition affine and local refinement registration on the multi-modal image by taking the molybdenum target image as a geometric anchor point and combining parameters such as compression force; extracting registered fusion feature data through a data fusion module; generating a candidate focus set by a focus detection module; finally, the risk assessment module calculates a comprehensive risk score through weighting, correction and consistency verification processes based on a plurality of interpretable feature indexes. And the output module generates a visual diagnosis result. According to the invention, intelligent detection and risk assessment of rechecking of breast lesions are realized, and the diagnosis accuracy and clinical reliability are effectively improved.

The invention discloses a vehicle diagnosis method, device and equipment based on data deposit and a medium, and relates to the technical field of vehicle diagnos.The method comprises the steps that a diagnosis request is generated based on a vehicle diagnosis instruction and sent to an electronic control unit, so that when the electronic control unit is busy, abnormity feedback is conducted on diagnosis equipment according to the diagnosis request; abnormal feedback returned by the electronic control unit is obtained, and whether the diagnosis request needs to be registered is judged according to the abnormal feedback; if the diagnosis request needs to be registered, a registration instruction is sent to a preset registration unit, so that the preset registration unit stores request data locally based on the registration instruction and feeds back storage information to the diagnosis equipment; and receiving storage information fed back by the preset register unit, and sending the storage information to the electronic control unit, so that the electronic control unit obtains request data from the preset register unit according to the storage information when the electronic control unit is idle, processes the diagnosis request through the request data, and feeds back a processing result to the diagnosis equipment.

The invention discloses a power distribution network inspection robot system and method, and relates to the technical field of intelligent inspection, and the system comprises a data collection module, a data registration module, an anomaly detection module, a space verification module, a time sequence analysis module and a comprehensive judgment module. The data acquisition module controls the robot to acquire multi-modal data and record space-time positioning information; the data registration module registers the multi-modal data to an equipment coordinate system; the anomaly detection module extracts anomaly features and calculates confidence; the space verification module carries out space consistency verification and physical logic consistency verification according to the physical rule base and evaluates the abnormal level; the time sequence analysis module evaluates a defect evolution state; and the comprehensive judgment module performs defect judgment and generates a processing strategy. According to the method, single-mode false detection and environmental interference are effectively eliminated through a dual verification mechanism, comprehensive evaluation of the current state and the development trend of the defect is realized in combination with time sequence evolution analysis, and the accuracy and the reliability of defect detection are remarkably improved.

The invention relates to the technical field of electric data processing, and provides a fault diagnosis method and device for an AGV battery management system, and the method comprises the steps: collecting the monomer voltage sequence data of a battery monomer group, and carrying out the data registration, and obtaining a drift curve parameter and a stability evaluation index; performing difference fitting calculation on the drift curve parameters to obtain inflection point characteristic data, performing two-dimensional space superposition on stability evaluation indexes by using the inflection point characteristic data to obtain a temperature correlation group and average efficiency data of each charging and discharging type, and performing dynamic thermal attenuation mapping processing on all the average efficiency data by using the temperature correlation group to obtain a stability evaluation result. And obtaining fault evaluation result data. The attenuation law of the battery in a temperature change environment is effectively revealed by utilizing dynamic thermal attenuation mapping, and the problems that when early faults occur in the battery, the faults are difficult to capture, and potential faults or performance degradation of the battery cannot be predicted in time are solved.

The invention relates to the technical field of point cloud registration, in particular to a multi-view point cloud data registration method and system. The method comprises the following steps: acquiring single-view point cloud data to perform adjacent field configuration construction to obtain local structure chart data; performing resonance mode analysis on the local structure diagram data to obtain diagram embedded data; performing geometric potential energy mapping according to the graph embedded data to obtain morphological stability distribution graph data; performing spectral domain supporting point family extraction according to the morphological stability distribution diagram data to obtain a skeleton topology node set; constructing cross-view structure chart data according to the skeleton topology node set corresponding to the multiple pieces of single-view point cloud data; performing semantic perception fingerprint generation on the cross-vision-field structure chart data to obtain structure recognition cluster data; obtaining different-source structure coupling graph data according to the structure identification cluster data; and performing low-degree-of-freedom rigid body calculation according to the heterogeneous structure coupling graph data to obtain point cloud registration data. According to the invention, the stability and precision of multi-view point cloud registration are improved.

The invention relates to the technical field of agricultural intelligent equipment, in particular to intelligent monitoring and weeding equipment for gramineous weeds in a winter wheat field, and aims to solve the technical problem of high missing detection rate and false detection rate of a traditional visual identification method caused by similar forms of winter wheat and gramineous weeds in a seedling stage. The equipment comprises an autonomous mobile platform, a multi-mode sensing assembly, a data processing and decision-making unit and a targeted clearing execution mechanism. The multi-mode sensing assembly integrates a hyperspectral imaging module, a three-dimensional shape measurement module and a polarization characteristic analysis module, and pixel-level data registration is achieved in combination with the cooperative illumination system. The data processing unit performs high-confidence classification decision on the multi-source features through a hierarchical Bayesian fusion model; and the targeted clearing mechanism adopts a laser galvanometer system to carry out non-contact precise clearing on the identified weed growing points. Intelligent identification and efficient physical weeding of weeds in the winter wheat field are achieved, and the method has the advantages of being free of chemical residues and high in operation continuity.

The invention relates to the technical field of earthquake disaster emergency monitoring, in particular to an intelligent recognition system and method for an earthquake ground surface broken zone, and the system comprises a heterogeneous data registration module, a feature extraction and fusion module, a broken zone segmentation module, an active learning module, a morphological optimization module and the like, and achieves the precise processing of remote sensing data and the intelligent recognition of the broken zone. The system adopts multi-scale feature point matching and self-adaptive weight registration, so that the data registration precision is improved by more than 30%, and the dependence of a traditional method on artificial experience is overcome; through feature fusion of multi-modal feature decoupling and attention guidance, and in combination with multi-scale spatial pyramid pooling and deformation sensitive segmentation, the system effectively identifies a fractured zone and evaluates and predicts uncertainty; the active learning module optimizes training data through Bayesian estimation and a hybrid sampling strategy, and the form optimization module finally outputs accurate broken zone distribution and attribute information by utilizing geological constraint and fault topology reasoning, so that the intelligent level and efficiency of earthquake disaster emergency monitoring are improved.

The invention belongs to the technical field of building engineering intelligent detection, and particularly relates to an unmanned aerial vehicle-based fabricated building defect automatic identification system, which comprises a data acquisition module, a data registration module, a sleeve positioning module, a feature extraction module, a defect identification module and a report output module. A sequential three-dimensional data field is constructed, environmental interference influences are eliminated through preprocessing, visible light image features, infrared thermal imaging features and three-dimensional point cloud geometric features are extracted in parallel, internal and external comprehensive diagnosis of the grouting full state is achieved, grouting insufficient cavities, later-stage contractile cavities and grouting vertical depth deficiency defects are accurately recognized, and the grouting quality is improved. Dynamic tracking, accurate quantification and traceability are synchronously carried out, the problems of single dimension, poor space-time continuity, weak environmental adaptability, difficulty in accurate quantification and traceability of defects and the like of existing prefabricated building sleeve grouting fullness state detection are effectively solved, and the intelligent reliability of quality control is improved.

The invention discloses a gallium oxide substrate surface grinding morphology prediction method and system, and relates to the technical field of nondestructive testing, and the method comprises the steps: collecting a surface fluorescence distribution image, matching the surface fluorescence distribution image with a pre-constructed fluorescence-crack mapping database, and generating a two-dimensional crack depth distribution cloud picture; marking a risk area coordinate set in the two-dimensional crack depth distribution cloud picture based on a preset crack depth threshold value; inputting the risk area coordinate set into a Bessel beam chromatography scanner for multi-angle transmission type scanning, synchronously collecting scattering images generated by scanning, and fusing the scattering images to generate a subsurface damage three-dimensional model; and performing data registration and weight superposition on the two-dimensional crack depth distribution cloud picture and the subsurface damage three-dimensional model, and outputting a morphology prediction report and grinding process optimization parameters. The subsurface damage three-dimensional model is constructed through the Bessel beam tomography scanning and multi-angle scattering image fusion technology, and high-resolution visual reconstruction of microcracks and damage distribution in the gallium oxide substrate is achieved.

Example embodiments of the present disclosure relate to dynamic authorization. According to embodiments of the present disclosure, a solution for dynamic access control to data is proposed. On receiving data registration from a data source, a first device checks the data types to be produced by the data source and adds policies for the data or updates existing policies for the data according to its property. It also serves as access control decision point to determine consumers' access rights based on centrally managed policies. Authorization for data access is granted / denied according to local attributes / policies. In this way, it achieves a dynamic, context-aware and risk-intelligent access control to different kind of data from various data sources (i.e., service producers).

The invention belongs to the field of power transmission line tree obstacle detection, and particularly relates to a power transmission line tree obstacle detection method based on thunder-vision fusion, and the method comprises a data collection module which comprehensively collects the original data of the surrounding environment of a power transmission line through a laser radar, a camera and POS equipment. The advantages are obvious. Laser radar and camera data are fused, data registration and feature extraction technologies are applied, tree obstacles are accurately identified and positioned, the danger degree is accurately judged, and the detection precision is far higher than that of a single sensor. Quasi-real-time data transmission is achieved through the point cloud technology, and hidden dangers can be found in time in cooperation with real-time modeling and rapid distance measurement. The intelligent device integrates multiple sensors, automatically analyzes tree barrier risks, reduces manual intervention and reduces cost. In addition, detailed detection reports can be generated, support is provided for operation and maintenance, multi-element perception fusion enhances environmental adaptability, and stable operation of the power transmission line is guaranteed.

The invention discloses a glacier movement speed calculation method, system and device based on three-dimensional laser scanning point cloud data, a medium and a product, and relates to the field of geographical environment survey, and the method comprises the steps: carrying out the three-dimensional laser scanning of the surface of a glacier, and obtaining the point cloud data at different moments; adopting a Cosi-Corr algorithm to calculate a displacement field between the point cloud data at different moments; and calculating the glacier movement speed based on the displacement field. According to the invention, the point cloud data is obtained based on three-dimensional laser scanning, automatic data registration and analysis are carried out through the Cosi-Corr algorithm, and the glacier movement speed can be accurately calculated.