4995 results about "Point data" patented technology

The invention relates to the technical field of the Internet of Things, and discloses a gas leakage monitoring method, device and equipment based on the Internet of Things and a storage medium, and the method comprises the steps: carrying out the data collection of a gas storage device, and obtaining an initial multi-dimensional data set; performing filtering and edge detection processing to obtain potential abnormal point data; inputting the potential abnormal point data into a mixed deep learning model for gas leakage detection to obtain initial leakage source position data and a leakage intensity value; executing multi-sensor cooperative accurate leakage positioning to obtain target leakage source position data; performing gas diffusion simulation to obtain gas concentration distribution data and risk indexes; based on the gas concentration distribution data and the risk index, dynamic reconstruction of the sensor network is carried out, an emergency response strategy and sensor network configuration information are output, and the method realizes whole-process management from leakage detection and source positioning to risk assessment and emergency response.

A method of constructing a digital twin physical field of a computer numerical control machine tool with double order reduction is provided. A physical field finite element simulation sample data set in a working state sampling space has an order preliminarily reduced to sparse grid node data via an improved K Nearest Neighbor (KNN) algorithm, so that the number of point sets is kept within the real-time rendering capability range of graphic rendering software. A digital twin physical field proxy model is trained using a radial basis function (RBF) to realize reconstruction of the physical field. The proxy model is iteratively optimized using a genetic algorithm to solve the problem of deviation between a proxy physical field model and a real physical model. Finally, the proxy model is deployed to a digital twin system, and the physical field distribution is solved in real time.

The invention discloses an underground cable pipeline abnormity monitoring method and system, and the method comprises the following steps: S1, building a three-dimensional grid based on the regional soil distribution condition, and obtaining a collection point data set based on the three-dimensional grid and underground cable pipeline data; s2, acquiring acquisition distribution corresponding to each to-be-arranged sensor type based on the to-be-arranged sensor type and the acquisition point data set; s3, acquiring underground cable pipeline time sequence data acquired by each sensor based on acquisition distribution, and performing clustering analysis on the underground cable pipeline time sequence data to acquire abnormal data points; and S4, obtaining an abnormal position according to the abnormal data points and the collection distribution, and outputting abnormal information of the underground cable pipeline according to the abnormal position. The method has the beneficial effects that the physical position of the cable pipeline is considered, meanwhile, the soil environment characteristics are considered, the accuracy of sensor arrangement in the pipeline is improved, the abnormal data point analysis efficiency is improved, and the underground cable pipeline abnormity can be conveniently and timely checked.

The invention relates to the technical field of process automation, in particular to an RPA service automation method capable of enabling a large language model MCP. The method comprises the following steps: acquiring user input data, and performing intention understanding through a multi-round intention recognition model according to the user input data to obtain semantic instruction data; performing standard semantic protocol conversion according to the semantic instruction data to obtain standard semantic protocol data; performing flow chart node matching according to the standard semantic protocol data to obtain flow chart node data; and generating a business process according to the flow chart node data to obtain business process data. According to the method, the multi-round intention recognition capability of the large language model and the standard semantic protocol conversion are fused, so that the accurate mapping from the natural language to the business process is realized, and the intelligent level and the user friendliness of the RPA system are improved. Through process node matching and process logic verification, the execution effectiveness of the automatic generation process is ensured, and the labor cost and configuration errors are reduced.

The invention discloses a human body motion posture evaluation method and system. Low-cost and high-precision human body posture analysis is realized through common camera equipment. The method comprises the following steps: acquiring a human body motion video through camera equipment; detecting key points of a human body by using a deep learning model, and extracting two-dimensional or three-dimensional coordinates of the human body; calculating joint angle, speed and acceleration based on the key point data; and analyzing attitude stability, motion symmetry and fluency characteristics, and generating a comprehensive evaluation result. The system evaluates the motion quality of the user through comparison between dynamic feature extraction and a standard motion template, and generates personalized improvement suggestions based on specific indexes, including direction adjustment and amplitude range adjustment. The method can be widely applied to scenes such as exercise and fitness, rehabilitation medical treatment and competitive training, and the comprehensiveness, the accuracy and the guiding effect of action evaluation are improved. By means of the method, the user can obtain motion quality feedback in real time, the posture is effectively optimized, and the exercise performance or rehabilitation effect is improved.

The invention discloses a real-time three-dimensional modeling method and system based on 5G flight control and a medium, and belongs to the field of three-dimensional modeling, and the method comprises the steps: obtaining a modeling demand and task environment parameters of a user, and planning a route; controlling the unmanned aerial vehicle to fly according to the planned route; judging whether the position data and the attitude data of the unmanned aerial vehicle at each waypoint are within a preset threshold range, and if so, controlling the unmanned aerial vehicle to collect image data of the target area; the collected image data is preprocessed; extracting feature point data of the target area by utilizing machine learning according to the preprocessed image data; converting the feature point data of the target area into sparse point clouds by using a triangulation algorithm; converting the sparse point clouds into dense point clouds through an MVS algorithm; and according to the pre-processed dense point cloud, a Poisson surface reconstruction algorithm is adopted to establish a three-dimensional grid model of the target area. Aiming at the low precision of unmanned aerial vehicle three-dimensional modeling in the prior art, the method improves the precision of unmanned aerial vehicle three-dimensional modeling.

The invention discloses a vision-based composite robot hand-eye calibration and workpiece positioning device and method. The method comprises the following steps: S1, performing camera calibration and end tool TCP calibration on two cameras according to a preset operation height; s2, the mechanical arm is controlled to reach a calibration position, and image data of hand-eye calibration are collected; the tail end of the mechanical arm tool is controlled to reach the characteristic point position of the checkerboard calibration plate, and characteristic point data of hand-eye calibration are collected; calculating hand-eye calibration parameters based on the image data and the feature point data; and S3, the mechanical arm is demonstrated to reach the workpiece grabbing pose, the camera in the box collects the checkerboard image and the Aruco two-dimensional code image before and after demonstration, and station calibration parameter calculation is carried out. Hand-eye calibration is completed in a simpler, more convenient and quicker mode, the problem that the vision-guided workpiece grabbing precision is reduced due to movement of the chassis is solved, and meanwhile post-processing is conducted on subsequent workpiece grabbing poses.

The embodiment of the invention provides a kafka storage and calculation separated data processing method and device, and the method comprises the steps: constructing a multi-layer data processing architecture which comprises four layers: network communication, task distribution, processing scheduling and storage. Cold and hot data separation is realized through hierarchical design of a log pre-writing area and a main storage area, and intelligent scheduling of system resources is realized in combination with a copy manager, a coordinator and a load predictor. An adaptive compression algorithm and a verification mechanism are adopted to ensure the data transmission efficiency and integrity, a message cache region is created to store hotspot data, and flexible access of cold and hot data is realized based on message sites. According to the method, the defects of the traditional technology in storage and calculation coupling, storage efficiency, load balancing and the like are effectively overcome, and the performance and expandability of a message queue system are remarkably improved.

The invention relates to the field of artificial intelligence and computer vision, and discloses an artificial intelligence physical training exercise system which comprises a data acquisition module, a data preprocessing module, an action modeling module, an action evaluation module and a user feedback module. The data acquisition module acquires real-time video data of a moving target and extracts skeleton key point data; the data preprocessing module performs optimization processing on the skeleton key point data; the action modeling module is used for modeling and predicting the action state of the moving target by utilizing a target constraint dynamic optimization model based on the optimized skeleton data; the action evaluation module generates action scores and optimization suggestions through local action decomposition and action matching analysis; and the user feedback module provides real-time feedback information and generates a training summary report according to the action score. The method can achieve the efficient analysis and precise evaluation of the motion of the moving target in a complex scene, provides personalized optimization guidance, and is widely suitable for physical training and teaching scenes.

The invention relates to a synchronous line loss intelligent diagnosis and analysis system based on an electric power knowledge graph. The synchronous line loss intelligent diagnosis and analysis system comprises a data acquisition layer, a data access layer, a data storage layer, a data analysis layer and a visualization layer, the data acquisition layer is used for acquiring power grid node data; the data access layer is used for carrying out dynamic synchronization on power grid node data based on Apache Kafka; the data storage layer comprises a time sequence database and a graph database; the data analysis layer comprises a detection module and an inference engine; and the visualization layer is used for dynamically displaying a propagation path and node characteristics of an abnormal region based on a topological structure of the knowledge graph, and querying an abnormal reason of the node and the influence on the whole system by clicking the node of the device. According to the invention, intelligent detection and diagnosis of the line loss abnormal area are realized, and the problem root cause is rapidly identified.

The invention relates to the technical field of data processing, in particular to a real estate surveying and mapping data processing method and system based on intelligent data. The method comprises the following steps: acquiring multi-source real estate surveying and mapping data; performing space-time synchronization on the multi-source real estate surveying and mapping data to generate multi-source real estate surveying and mapping space alignment data; real estate space feature extraction is carried out on the multi-source real estate surveying and mapping space alignment data to obtain real estate space feature data; performing cross-domain feature fusion on the multi-source real estate surveying and mapping data based on the real estate spatial feature data to generate real estate surveying and mapping fusion spatial feature data; public surveying and mapping point confirmation is carried out on the multi-source real estate surveying and mapping data through the real estate surveying and mapping fusion spatial feature data, and real estate surveying and mapping public surveying and mapping point data are obtained. Through multi-source data space-time synchronization, feature fusion, distortion correction, high-dimensional space reconstruction, semantic tag association and chain storage, the efficiency and accuracy of real estate surveying and mapping data processing are improved.

The invention provides a wharf facility health monitoring method and system based on digital twinning and machine learning, and relates to the field of wharf facility health monitoring, and the method comprises the steps: building a weight coefficient of a monitoring point network topological graph according to a synchronism change value of the same type of sensor data corresponding to adjacent monitoring points; determining an abnormal transmission path based on the weight coefficient; determining an abnormal state propagation rule of each abnormal transmission path in each preset abnormal state according to the historical monitoring data corresponding to each preset abnormal state; after abnormal sensor data of any monitoring point is detected, parameter predicted values of other monitoring points in the corresponding abnormal transmission path are calculated based on an abnormal state propagation rule; and if the parameter prediction values of the preset proportion number deviate from the corresponding actual monitoring values, generating early warning information. By implementing the method, false alarms caused by single-point data fluctuation summarizing in a traditional health monitoring mode can be dealt with, and the accuracy and reliability of abnormal state early warning are improved.

The invention relates to the technical field of image recognition, in particular to an image processing method and system for rehabilitation training action analysis. According to the method, a multi-view image sequence is collected based on a binocular camera device, skeleton key point data of a user in a training process is extracted by utilizing a three-dimensional attitude reconstruction technology, and an action three-dimensional time sequence data set is constructed. The method comprises the following steps: firstly, constructing an individual standard power generation characteristic model of a user, modeling a skeleton driving path of a main muscle group, and forming a personalized power generation reference structure; and then a standard rehabilitation action path is matched through a dynamic time warping algorithm, and the attitude deviation under the key frame is identified. And the system dynamically compares the identified non-standard motion mode with the individual model, judges whether abnormal force generation exists or not and outputs the type and the part of the muscle compensation behavior. And finally, multi-modal feedback information with highlighted graphs, voice prompts and character suggestions is generated in combination with an identification result, so that the identification precision and personalized guidance capability of rehabilitation training are remarkably improved.

The invention discloses a self-adaptive three-dimensional scene reconstruction method and system based on a single panorama, and belongs to the field of computer vision processing. The method comprises the following steps: firstly, generating a depth map through an indoor scene panorama and constructing an initial three-dimensional grid; then generating a multi-view image and a shielding mask thereof based on view conversion, forming a training sample pair for fine tuning of the diffusion completion model, and injecting scene prior information for the model; then extracting a grid boundary contour and calculating a central axis, and adaptively constructing a camera pose set; after the integrity of the grid is optimized through iteration completion, the grid is converted into a 3D Gaussian sputtering field, and Gaussian point parameters are optimized through multi-view data; in the optimization process, an up-sampling strategy of rendering error feedback and edge Gaussian points is adopted, and finally a scene model with complete geometric textures is output. According to the method, a high-quality three-dimensional scene is reconstructed from a single panorama through a structure self-adaptive completion and Gaussian refining technology, and the method is particularly suitable for immersive roaming reconstruction of indoor scenes.

The invention relates to the technical field of big data and the field of financial science and technology, and discloses a database load optimization method based on dynamic fragmentation, which comprises the following steps: establishing an association relationship between fragments and storage nodes by configuring basic logic fragments and dynamic logic fragments; monitoring a data access record, identifying hotspot data and marking the hotspot data as a to-be-migrated data block; when the fragment load exceeds a threshold value, migrating the high-load data block to the dynamic logic fragment; and when the access frequency is reduced, moving the low-load data block back to the basic logic fragment. According to the method, through configuration and management of the dynamic logic fragments, in combination with real-time monitored access records and load state analysis, the high-load data blocks are recognized and migrated to the dynamic logic fragments for storage, and the overload problem of the basic logic fragments is relieved. Meanwhile, the low-load data blocks are moved back to the basic logic fragments, dynamic adjustment and balance of storage resources are further achieved, the load balancing capacity of the system is improved, and the requirements for high concurrency and real-time load changes in a financial scene are met.

The invention provides a cross-platform burying-point-free data collection and intelligent circle selection rule generation method and system, and relates to the technical field of intelligent algorithms, and the method comprises the steps: obtaining view hierarchy tree information and interaction event information of page elements through injecting a data collection script, and training a page element recognition model based on a graph neural network. The model is combined with an adaptive feature fusion network to realize cross-platform element alignment and recognition. And classifying the newly added elements by utilizing the element similarity information, and generating and correcting the circle selection rule based on a preset rule template and the context feature vector. And finally, the rule execution engine realizes continuous monitoring and dynamic acquisition of the target element in the cross-platform application program according to the target circle selection rule.

The invention discloses a self-adaptive data caching and synchronizing method, system and device for an edge gateway of the Internet of Things and a storage medium, and the method comprises the steps: obtaining network quality parameters, such as link bandwidth, time delay and packet loss rate, between nodes, detecting and solving instruction conflicts based on a vector clock mechanism, generating an instruction execution sequence, and carrying out the self-adaptive data caching and synchronizing of the edge gateway of the Internet of Things; local caching and incremental synchronization strategies are adopted for hotspot data, a data access mode is dynamically predicted in combination with a machine learning algorithm, and a cache updating period is optimized. The future data is predicted according to the data characteristics of the Internet of Things application, and the data cache updating period of each node is dynamically adjusted, so that each node can keep a relatively consistent updating rhythm when caching a hotspot data copy; therefore, the hot spot data copies of different nodes can be ensured to have similar accuracy and timeliness at the same time point, so that the data consistency is maintained.

The invention relates to the technical field of river section measurement, in particular to a river measurement data preprocessing method based on artificial intelligence. The method comprises the following steps: obtaining river section measuring point data, and carrying out measuring point density evaluation on the river section measuring point data based on different scales and section elevation changes so as to obtain high-density measuring point data; performing section thalweg point and turning point identification based on adjacent measuring point slope change on the high-density measuring point data, and performing measuring point thinning processing of retaining thalweg points and turning points so as to obtain a retaining measuring point data set; according to the preset section line and the reserved measuring point data set, the measuring points exceeding the preset measuring point deviation distance threshold value are automatically detected and calibrated, coordinate reconstruction and position correction are conducted on the measuring points deviating from the preset section line, and therefore calibrated measuring point data are obtained. The problems of uneven data, missed measurement and deviation in traditional measurement can be effectively solved, and river channel measurement data are more accurate and complete.

The invention discloses a building construction process risk assessment method and system based on a fault tolerance mechanism, and particularly relates to the field of building construction process risk assessment, and the method comprises the steps: building a measurement point hierarchical index of a building construction process, synchronously arranging time sequence displacement data, and executing difference calculation to obtain a space gradient evolution sequence; mapping a trend change interval set extracted by sliding window processing into a path node set, and constructing a topological connection relation of the path node set to form an initial risk path map; the data state of each path node in the risk path map is subjected to anomaly identification, the access control effect of the abnormal node in the path is analyzed, and whether the path fracture risk is formed or not is judged in combination with the sensitive monitoring area. By constructing a risk map structure based on a trend path and combining a path interruption node identification and alternative path dynamic deduction mechanism, continuous assessment and map self-correction of the risk trend under the condition of measuring point data failure are realized.

The invention relates to the technical field of obstacle avoidance early warning, in particular to an obstacle avoidance early warning method and system for a robot. The method comprises the following steps: acquiring motion data and moving target point data of a robot; robot motion mode classification is conducted on the robot motion data, and a robot motion mode and a robot static mode are generated; performing environment detection on the moving target point data by using a built-in laser radar of the robot according to the static mode to obtain standard static environment detection data; and planning the path according to the standard static environment detection data, and generating initial running path planning data of the robot. According to the invention, the obstacle avoidance early warning precision and real-time performance of the robot in a complex environment are improved by accurately distinguishing static and dynamic objects, reconstructing the motion trail and planning and adjusting the path.

The invention discloses a live-action three-dimensional model construction method and system, a storage medium and equipment, and belongs to the technical field of three-dimensional modeling, and the method comprises the steps: laying control points in a region needing collection, and collecting ground control point data; acquiring inclined image data by using the unmanned aerial vehicle, and performing aerial triangulation calculation on the inclined image data in combination with the ground control point data to obtain aerial triangulation encrypted point cloud data; acquiring point cloud data by using a laser scanner, registering the point cloud data in combination with ground control point data, and then preprocessing the point cloud data to obtain laser point cloud data; carrying out fusion reconstruction on the aerial triangulation encryption point cloud data and the laser point cloud data to generate a three-dimensional model; and performing fine processing on the three-dimensional model to generate a high-precision model. According to the method, the three-dimensional laser scanning technology is used for assisting oblique photogrammetry, ground control points are used for unifying coordinates and repairing missing point clouds of a blind area, the integrity of the point clouds of an oblique photogrammetry shielded area can be guaranteed, and the fineness of the model is improved.

The invention discloses a data enhancement method and system based on multi-agent self-evolution and hybrid evaluation, and relates to the technical field of artificial intelligence, and the method comprises the steps: employing a teacher model as a multi-agent cooperation system, and carrying out the interaction of a reasoning agent, an evaluation agent, a reflection agent and a memory management agent through a reasoning agent, an evaluation agent, a reflection agent and a memory management agent; iteratively generating a high-quality reasoning data set with a traceable reasoning path and a self-verification label, and performing multi-task supervision fine tuning on a learning model; a diversity sampling strategy based on reinforcement learning is adopted to generate multiple groups of outputs, and a weak point data set is screened by using a consistency score; and for the weak point data set, the multi-view instruction rewriting agent performs diversity rewriting on the instruction and then returns to perform deep reasoning distillation to generate new enhanced data and combine the new enhanced data to high-quality reasoning data. According to the method, multiple agents are arranged, so that the model is iteratively synthesized, evaluated, reflected and modified, the obtained enhanced data can be directly used, a manual auditing step is omitted, and the synthesis efficiency is improved.

The invention discloses an intelligent water affair monitoring and management system based on digital twinning, and relates to the technical field of intelligent water affair. The intelligent water affair monitoring and management system comprises a water affair monitoring and management platform, and the water affair monitoring and management platform is in communication connection with the following modules: a multi-source data acquisition module; the water affair monitoring system is used for collecting and preprocessing water affair monitoring data from a plurality of monitoring points of the water affair system, monitoring changes of a pipe network topological structure and obtaining dynamic data of a pipe network connection relation and geometric parameters. Through the digital twinborn technology, data of multiple monitoring points can be integrated in real time, abnormal events such as water quality pollution, equipment faults, water shortage and hydraulic change can be rapidly recognized in combination with the abnormal trend analysis module, early warning signals are automatically generated, the response time is remarkably shortened through an instant early warning mechanism, and the early warning efficiency is improved. Therefore, the management personnel can take measures at the initial stage of the abnormal event, the problem expansion is effectively prevented, and the timeliness and accuracy of water management are improved.

The invention discloses a sensor fault detection and reconstruction method based on multi-sensor fusion, and the method comprises the steps: collecting data in a production process, and obtaining the data information of a detection point; carrying out anomaly detection on the collected data information through an LOF algorithm; classifying the detected abnormal data into normal data and abnormal data; inputting the abnormal data into a CNN-LSTM network sensor fault diagnosis model for principal component analysis dimensionality reduction, and reducing the input abnormal data from high-dimensional data to low-dimensional data by using principal component analysis; inputting sensor abnormal data subjected to principal component analysis dimensionality reduction into a CNN-LSTM-based sensor fault classification model, and determining the type of a sensor fault; and reconstructing the data of the fault sensor.

The invention relates to a power distribution network distributed energy bearing capacity assessment method and device. The method comprises the following steps: carrying out topology analysis on a network structure of a power distribution network to obtain an initial network topology model; obtaining a node dynamic feature data set based on the initial network topology model and the distributed energy access point data of the power distribution network; wherein the node dynamic characteristic data set comprises operation parameters of each node of the power distribution network in different load scenes; generating a parameter incidence matrix according to the node dynamic characteristic data set, and obtaining a bearing capacity reference model of the power distribution network according to the parameter incidence matrix and real-time data of the power distribution network in an operation state; wherein the parameter incidence matrix is used for quantifying the coupling degree between the operation parameters; and obtaining a risk distribution mapping graph according to the bearing capacity reference model, and identifying a potential overload area of the power distribution network based on the risk distribution mapping graph. According to the invention, power distribution network operation risk assessment can be accurately realized.

The invention relates to the technical field of subtitle translation, in particular to a multi-modal time sequence alignment AI video translation method and system, and the method comprises the steps: 1, carrying out the multi-modal analysis of a to-be-translated video, and obtaining audio separation data, voiceprint feature data and visual time sequence data; 2, performing cross-language translation and context optimization on the basis of the voice of the audio separation data to generate a target language text, and synthesizing target language voice retaining the original voice color in combination with the voiceprint feature data and the target language text; generating a mouth shape animation matched with the target language voice based on the lip key point data and the limb action time sequence data; and step 3, performing four-dimensional alignment on the target language voice, the translated text, the mouth shape animation and the limb action sequence through a cross-modal time sequence encoder, and dynamically adjusting the layout of the bilingual subtitles to adapt to a video picture. According to the method and the device, multi-mode synchronization can be taken into consideration during video translation, so that the body actions such as voice, subtitles and mouth shapes are kept aligned.

The invention discloses an integrated circuit junction temperature and thermal resistance evaluation system and method based on a digital twinning technology, and the method comprises the steps: extracting a temperature cloud picture from a simulation calculation result, and extracting a thermal flow line diagram from the temperature cloud picture based on a heat dissipation path, carrying out discrete sampling on the heat flow line diagram by using a deep learning data analysis and coding technology, and then carrying out embedded coding on each piece of sampled heat flow point data (a flow point position and a heat flow point temperature); in this way, the rectangular frame position data of the hot spot region is automatically identified according to the graph walk significant aggregation representation between the data embedding coding features of each heat flow point. Through the mode, the influence of a fine structure (such as a connecting wire, a welding spot and the like) on a heat transfer path can be captured, the complex heat dissipation requirement can be better met, and it is ensured that the heat dissipation requirement is effectively met.

The invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, and belongs to the technical field of data processing, and the method comprises the steps: 1, importing a preset BIM model into a preset intelligent monitoring platform, and carrying out the analysis of the preset BIM model; 2, creating a monitoring project in a preset intelligent monitoring platform based on a model analysis result, and configuring monitoring project information; step 3, acquiring a monitoring demand matched with the monitoring project, determining the type of a monitoring point, analyzing the information of the monitoring project, and creating a plurality of monitoring points; 4, the monitoring equipment based on the monitoring points collects monitoring data in real time, uploads the monitoring data to a preset intelligent monitoring platform through a network, and automatically associates the uploaded real-time data with the corresponding monitoring points; and 5, analyzing the real-time data, and optimizing the deployment of the monitoring points and the data of the monitoring points. And an efficient and intelligent solution is provided for application scenes such as engineering monitoring and environment monitoring.

The invention provides a human body skeleton point positioning identification method and system based on OpenPose, and the method comprises the steps: carrying out the preprocessing of an input human body image, and obtaining the preprocessing image data; performing skeleton point detection on the preprocessed image data by adopting a double-domain multi-path self-supervised diffusion model in combination with a convolutional neural network to obtain two-dimensional human skeleton point coordinate data; performing signal-to-noise ratio evaluation and noise reduction processing on the two-dimensional skeleton point coordinate data to obtain two-dimensional skeleton point data after noise reduction; performing two-dimensional to three-dimensional conversion through a triangulation principle and a feature matching technology to obtain preliminary three-dimensional skeleton point coordinate data; performing skeleton point optimization through rate perception analysis and a three-dimensional Gaussian compression algorithm; and carrying out shielding prediction and completion processing on the optimized three-dimensional skeleton points to obtain a complete human skeleton point positioning identification result. According to the invention, the problems of accuracy and stability of skeleton point positioning in a complex dynamic scene are solved.

The invention relates to the technical field of data expression, in particular to a GIS-based geological disaster monitoring point data expression method and system, and the method comprises the following steps: obtaining the detection data of geological disaster monitoring points, calling the space coordinates of the monitoring points and the corresponding monitoring data, calculating the spatial correlation according to the spatial distance between the monitoring points, and calculating the spatial correlation; and calculating monitoring data of the uncollected points according to the spatial correlation, and generating an interpolation monitoring value. According to the method, the monitoring data of the non-collection points are calculated according to the spatial correlation among the monitoring points, so that the integrity of the spatial data is improved, monitoring information loss caused by incomplete data collection is avoided, the local density of the monitoring points is analyzed, different grid scales are divided according to density characteristics, and the partition expression precision of the data is improved; based on the space coordinates, the timestamps and the disaster intensity data of the monitoring points, the multi-level visual expression is constructed, so that the monitoring information is intuitive, and the interpretability of the disaster monitoring data is improved.