80 results about "Risk zone" patented technology

The invention belongs to the technical field of coal mine risk early warning, and particularly relates to a coal mine goaf multi-risk comprehensive early warning method and system based on machine learning, and the method comprises the steps: collecting mine pressure, gas and hydrological real-time data in real time through a multi-temporal-spatial-scale sensor, and obtaining a dynamic coupling relation basic data set based on the real-time data; preprocessing noise and missing values according to the dynamic coupling relationship basic data set, and modeling node connection between a geological structure and mine pressure change by adopting a graph neural network to obtain space-time heterogeneous feature representation; non-linear features are analyzed through spatial-temporal heterogeneous feature representation, and a multi-scale dynamic mode is determined; acquiring a risk conduction path in the multi-scale dynamic mode, and acquiring an early recognition signal of a potential disaster chain; based on the early recognition signal, a long-short-term memory network is used for processing a sequential sequence, and the probability of the compound disaster is judged; a high-risk area is extracted from the composite disaster probability, and real-time early warning model parameters are obtained; and generating alarm output according to the real-time early warning model parameters.

The invention relates to the technical field of deep learning, in particular to a stress distribution monitoring and crack risk prediction method for a silicon carbide part, which realizes comprehensive sensing of the stress state of the silicon carbide part, accurate positioning of a risk area and advanced early warning of a crack fault. The method comprises the following steps: synchronously acquiring multi-modal data through multiple types of sensors, and realizing cross-modal time sequence synchronization through feature alignment; designing a crack risk multi-branch feature extraction module, and respectively extracting general depth features and risk features oriented to thermal stress mismatch, microcrack evolution and structural instability through a shared backbone network and a special branch network; constructing a stress nephogram generation and risk area positioning module based on a graph neural network, and realizing visual reasoning and risk area marking from discrete features to full-field stress distribution; and designing a crack risk comprehensive prediction module based on multi-dimensional risk feature fusion, fusing an instantaneous state and an evolution trend, outputting a multi-risk confidence vector and triggering graded early warning.

The invention provides an intelligent inspection risk assessment method and system based on multi-sensor fusion, and the method comprises the steps: obtaining original multi-source data of a transformer substation, the original multi-source data comprises a binocular vision image, infrared thermal image data and environment sensor data, and carrying out the time-space calibration and preprocessing of the original multi-source data, obtaining a multi-source sensor data stream; carrying out feature extraction on the multi-source sensor data stream to obtain a multi-modal feature set, generating a refined semantic mask based on the multi-modal feature set, constructing an initial scene relation graph, calculating a risk level based on a multi-layer perceptron classifier, and generating a risk level evaluation result and a risk distribution graph; outputting a safety distance violation warning and a risk area identifier; and generating comprehensive risk early warning information based on the risk level assessment result, the risk distribution diagram, the safety distance violation warning and the risk area identifier.

The invention discloses a test risk identification method and system based on artificial intelligence, and particularly relates to the technical field of data processing. By introducing an adaptive learning rate and a feedback mechanism, the improved test risk identification method is provided for the problem of data imbalance existing in the test process of an existing machine learning model, and the test risk identification method is provided by comprehensively collecting test data including test case execution results and test environment states. Extracting a dependency relationship abnormal value and a network delay high-frequency value between code modules, constructing a data prediction model, and comprehensively calculating a deviation of a prediction result based on the data; by dynamically adjusting the deviation of the model to the successful use case, the identification capability of the failed use case and the high-risk area is enhanced, and the high-risk area is effectively prevented from being underestimated or the normal use case is misjudged to be high in risk, so that the prediction precision is improved, the configuration of test resources is optimized, the invalid test reexamination is reduced, the test efficiency is remarkably improved, and the cost and the period are reduced.

The invention relates to the technical field of risk assessment, and particularly discloses an artificial intelligence-based hydrogen leakage risk assessment model, which comprises a dynamic monitoring data module, a risk feature extraction module, a leakage rate prediction and source point inversion module and a leakage evolution module, the dynamic monitoring data module collects real-time parameters and preprocesses the real-time parameters; the risk feature extraction module introduces a 1D-CNN and CNN-LSTM fusion neural network to generate a leakage risk factor sequence; the leakage rate prediction and source point inversion module predicts the leakage rate by using a sliding window and a dynamic correction mechanism, and inverts the position of a hydrogen leakage source in combination with a Gaussian plume model and sensor data; and the leakage evolution module displays a high-risk area heat map and a leakage source position. According to the method, the multi-source sensing data and the physical model are fused, and the defect that the leakage risk cannot be dynamically predicted through a traditional method is overcome.

The invention relates to the technical field of smart park data monitoring, and discloses an artificial intelligence smart park multi-source data monitoring and analysis method. The method comprises the following steps: constructing a park operation state basic model; when the model is identified to be abnormal, data dynamic change characteristics are extracted by utilizing a spatio-temporal data fusion technology, and an abnormal event mode in a specific region is identified by combining deep belief network deep analysis; calculating data complexity by using permutation entropy, optimizing and analyzing an abnormal propagation path in combination with an A star algorithm, and evaluating a diffusion range and an influence path; recognizing a comprehensive risk area, analyzing the environment and equipment state characteristics of the area through a multispectral imaging technology, and evaluating the operation abnormity in combination with real-time multi-source data; and performing accurate intervention on the comprehensive risk area based on the abnormal condition. According to the method, effective integration and analysis of multi-source data of the smart park are realized, abnormity is accurately identified, risks are mastered, a scientific means is provided for park management, and stable and efficient operation of the park is guaranteed.

The invention relates to the technical field of risk assessment optimization, and discloses a risk assessment method based on statistical model optimization. The method comprises the following steps: acquiring a running state data set of a target object containing multi-dimensional monitoring index time sequence data; and inputting the operation state data set into a pre-trained reference risk assessment model, and generating an initial risk score and risk distribution characteristics. And iteratively adjusting parameters of the reference risk assessment model through a dynamic correction algorithm according to the generated risk distribution characteristics, and generating an optimized risk assessment model. And adopting the optimized risk assessment model to re-assess the same operation state data set, and outputting a corrected risk score and a key risk area identifier. According to the method, the risk distribution characteristics of the specific data of the target object under the reference model are analyzed, and the model parameters are dynamically adjusted, so that the risk assessment standard better fits the actual risk mode, the accuracy and pertinence of risk identification are improved, and a more reliable basis is provided for a risk management and control decision.

The invention provides a personalized safety risk early warning method and system integrated with rapid help calling and a medium, and relates to the technical field of safety early warning, and the method comprises the steps: collecting and obtaining an information platform data set through employing a crawler technology, and carrying out the safety risk recognition, and obtaining a risk region feature set and a risk region position set; calculating to obtain a real-time distance information set of the risk area position set and the current position information of the target user; performing multi-dimensional risk assessment on the risk area feature set and the real-time distance information set to obtain risk area hidden danger factors; and constructing a dynamic safety early warning strategy library, carrying out strategy analysis on the hidden danger factors of the risk area, determining a target safety risk early warning strategy, and carrying out dynamic safety early warning on the target user. The technical problems of incomplete safety risk identification and insufficient early warning accuracy caused by limited data sources in the prior art are solved, and the technical effects of improving the real-time performance and accuracy of risk early warning and the flexibility of emergency response are achieved.

The invention relates to the technical field of mine safety monitoring and early warning, and provides a non-coal mine risk situation deduction system based on digital twinning and reinforcement learning, which comprises at least two twinning spaces; wherein the first twinborn space is used for building a mine digital twinborn body mapped by the current mine entity, and the second twinborn space is used for mapping a target dynamic event containing at least one risk factor in dynamic update of the mine digital twinborn body; the at least one reinforcement learning agent generates a third twinborn deduction model used for predicting the risk evolution trend of the mine; the risk early warning module is used for receiving the real-time multi-source data, determining a target dynamic event in the real-time multi-source data through the first twinborn space, judging whether the target dynamic event has a risk factor through the second twinborn space, and if the risk factor exists in the target dynamic event, performing early warning on the target dynamic event; and generating deduction data of the target dynamic event through a third deduction model, and determining a risk area and a risk probability.

The invention relates to the technical field of power plant supervision, and particularly discloses a BIM-based power plant safety supervision method and platform, and the method comprises the steps: obtaining a BIM model of a power plant, and building a three-dimensional power plant model based on the BIM model; operating parameters of equipment are collected, and a risk area with the equipment model as the center is created in the three-dimensional power plant model according to the operating parameters of the equipment; obtaining a position relationship between the human body model and the risk area, and generating prompt information pointing to the human body model according to the position relationship; and acquiring the real-time position of each human body model in a work cycle, and performing work rating on the human body models according to the relationship between the risk area and the real-time position. According to the method, the recognition result of the camera system is counted by means of the BIM model and then displayed, the risk area with a prediction function is introduced in the display process, risk recognition is performed on the staff according to the predicted risk area, the prompt information is generated, and the safety degree of the staff is improved.

The invention discloses a mountainous area debris flow dynamic early warning method based on deep learning, and relates to the technical field of geological disaster early warning, and the method comprises the steps: S1, marking a rainwater collection conventional region covered by a rainfall region as a rainwater collection risk region; s2, recording a real-time influence factor set of potential debris flow in each rainwater harvesting risk area; s3, inputting the real-time influence factor set as an iteration input value into the deep learning network for iteration, and setting an iteration output value as a prediction influence factor set; and S4, carrying out risk assessment on the recorded data of the prediction influence factor set by using an abnormal assessment strategy, screening out all rainwater collection risk areas meeting the risk assessment level, and marking the rainwater collection risk areas as debris flow risk areas. Compared with the prior art, the method utilizes the deep learning network to process the complex data set, covers more potential risk areas, and has the advantages of improving the accuracy of mountain debris flow early warning and recognizing the debris flow risk mountain areas more accurately.

The invention relates to the technical field of natural resource supervision, and particularly discloses a cultivated land non-agrochemical high-risk area identification method and device based on a three-dimensional feature space, and the method comprises the steps: obtaining basic data, and carrying out the preprocessing and time-space alignment processing; based on the processed data and an integrated learning model, determining target features corresponding to the cultivated land non-agrochemical degree and contribution values of the target features to the cultivated land non-agrochemical degree so as to determine weight values of the target features; calculating a natural score and a social economic score of the target area; the natural score of the target area is used as an X coordinate value of the target area, the social economic score is used as a Y coordinate value, and the cultivated land non-agrochemical degree value is used as a Z coordinate value; constructing a three-dimensional feature space model based on the coordinate information of all the target areas; and based on a tip mutation theory, determining a mutation threshold value of the three-dimensional feature space model, and based on the mutation threshold value, determining a cultivated land non-agrochemical high-risk area. Therefore, the identification accuracy of the non-agrochemical high-risk area of the cultivated land can be greatly improved.

The invention provides an engineering safety management method and system based on a BIM model, and relates to the technical field, and the method comprises the steps: obtaining engineering information, constructing an engineering BIM model, carrying out the monitoring region division of the engineering BIM model, obtaining an engineering model monitoring region, and obtaining the region group association information; calculating an area scheme influence coefficient to obtain a risk area, and calculating a type influence coefficient of each engineering monitoring type to obtain a risk type; calculating an engineering cumulative risk influence coefficient, comparing the engineering cumulative risk influence coefficient with a preset cumulative influence threshold value, obtaining cumulative risk judgment of the engineering BIM model, and further obtaining risk influence early warning information; according to the risk early warning information, the abnormal risk monitoring type and the risk area information, reversely obtaining the wind direction source information, and adjusting the risk source information until the risk early warning information does not exist, the method can comprehensively and accurately identify and evaluate the potential risk in the project, and realizes real-time monitoring and effective early warning.

The invention discloses a pipeline siltation risk assessment method and system based on desilting detection, and relates to the technical field of image processing. The method comprises the following steps: acquiring detection images of a plurality of positions in a to-be-dredged pipeline, and performing feature extraction on each detection image to obtain siltation target features in each detection image; for each detection image, determining a pipeline siltation type and a risk value of a corresponding position of the detection image according to each siltation target feature in the detection image; generating a risk unit corresponding to the pipeline siltation type and the risk value of each position, and generating a target three-dimensional map of the to-be-dredged pipeline in combination with each risk unit; and determining a first risk area and a second risk area which do not conform to the siltation risk standard in the target three-dimensional map, and generating a pipeline siltation risk assessment report of the to-be-dredged pipeline based on risk units in the first risk area and the second risk area. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of pipeline siltation risk assessment based on desilting detection is achieved.

The invention discloses an intelligent contract risk perception fuzzy testing method based on a large language model, and the method comprises the steps: introducing a risk perception mechanism based on the large language model, testing a resource scheduling strategy and semantic knowledge modeling, precisely guiding a fuzzy tester to focus on a high-risk region in an intelligent contract, and achieving the intelligent contract risk perception fuzzy testing. Therefore, the efficiency and accuracy of vulnerability detection are remarkably improved. According to the method, a risk strategy is generated by using a large language model, and the method comprises the steps of identifying a high-risk function and outputting a structured variation parameter suggestion; test resource allocation is optimized for the three key stages, and a fuzzy tester can test a high-risk code path preferentially; a knowledge graph is constructed by analyzing a function call relationship and a data dependency relationship in an intelligent contract, and the knowledge graph is used as a context semantic basis to assist a large language model in more accurately identifying a potential risk function and positioning a key mutation point.

The invention relates to the technical field of risk identification, in particular to an intelligent automobile operation high-risk scene identification method and device based on deep learning and a storage medium, and the method comprises the steps: collecting multi-modal data of an intelligent automobile operation scene; determining the space-time evolution of the risk features based on the fused feature sequence, and updating the global risk feature map based on the space-time evolution; and determining a risk mark of the operation scene based on the updated global risk characteristic spectrum, and distinguishing a static risk region and a dynamic risk region in the risk mark based on the unupdated global risk characteristic spectrum and the fusion characteristic sequence to obtain a high-risk candidate region corresponding to the operation scene. According to the method, the multi-modal data is collected in real time, and the data is fused and coded in combination with deep learning, so that a high-risk scene in the operation of the intelligent automobile can be accurately identified, and potential risks can be dynamically evaluated and predicted, thereby providing more accurate decision support for the intelligent automobile and avoiding possible accidents.

The invention provides a geological disaster risk visualization early warning platform based on a GIS and a meteorological cloud picture, and relates to the technical field of geological disaster monitoring and early warning, and the platform comprises a data preprocessing module which carries out the normalization processing of data, extracts key feature parameters, and stores the normalized data in a distributed database; the risk region division module is used for generating a plurality of risk regions by adopting a self-adaptive clustering algorithm and calculating boundary coordinates of each risk region; the dynamic weight distribution module is used for distributing dynamic weight values for the key characteristic parameters of each risk area according to the historical disaster data and the real-time meteorological change trend; the risk assessment module is used for calculating a risk index of each risk area by adopting a multi-factor overlay analysis model; and the visual presentation module is used for generating a hierarchical thermodynamic diagram according to the risk index and displaying the thermodynamic diagram and the GIS map in an overlapping manner. The problem that the effect is poor during multi-source data fusion in the prior art is solved.

A method for safety detection, an electronic device, and a storage medium are provided, relating to the field of computer technologies. The method includes: obtaining an actual environmental parameter of each of multiple first detection areas in a first time period; obtaining a first environmental parameter prediction result of each first detection area in a second time period based on the actual environmental parameter; obtaining multiple second detection areas based on an area position of each first detection area in the production workshop; obtaining a second environmental parameter prediction result of each second detection area in the second time period based on the first environmental parameter prediction result; and generating safety pre-warning information corresponding to a risk area when determining that the risk area is present in the multiple second detection areas based on the second environmental parameter prediction result.

The invention discloses a geological disaster identification monitoring early warning system and method based on an AI technology, and relates to the technical field of geological disaster monitoring. The method comprises the following steps: collecting a multi-source data set in a target area, preprocessing the multi-source data set, and carrying out hidden danger labeling to obtain a plurality of mark points; the technical key points are as follows: intelligent benchmarking of a current high-risk area and a historical disaster case is realized by adopting a technical scheme of comprehensive similarity calculation and a KNN algorithm, an effect of intelligent grade mapping is achieved, scientificity and reliability of disaster grade judgment are improved, and by comparing the similarity of a current characteristic value and a historical disaster sample, the judgment accuracy of the disaster grade is improved. According to the method, the closest historical case can be quickly positioned, the risk level of the current area is determined accordingly, the adaptive capacity and the response speed of an early warning system are further enhanced, and meanwhile, the method can effectively balance the accuracy and the response speed of risk judgment.

The invention provides a method and system for removing multiple obstacles of a feeding bin based on a prediction model, and relates to the technical field of obstacle removal, and the method comprises the steps: carrying out the weighted fusion of material parameters and equipment state data in the feeding bin through an attention mechanism, and outputting an obstacle risk index. Dividing a risk interval according to the index, and triggering a preventive control strategy to reduce the risk. And when the index reaches a first risk interval or the sensor signal is abnormal, recording working condition data and starting a deep reinforcement learning control system, generating a state vector and planning an optimal release path, and finally executing operation and recording empirical data.

The invention discloses a big data visualization method and system for an intelligent construction site, and relates to the technical field of intelligent construction site management, and the method comprises the steps: carrying out the standardization processing of construction site data through a data fusion module by employing a federated learning technology and a distributed calculation architecture, and calculating a data fusion efficiency index Df; the data compatibility is optimized by combining an adaptive data cleaning algorithm and a heterogeneous data matching technology, the problem of data islands is solved, and the information sharing efficiency is improved; the environment monitoring module calculates and evaluates an environment risk index Er, and combines BIM, GIS and AR technologies to realize visual early warning of a risk area, so that the accuracy and timeliness of environment monitoring and safety early warning are improved; and the intelligent scheduling module is combined with the construction progress deviation rate Sp, adopts a quantum computing optimization algorithm to intelligently distribute personnel, machinery and materials, and dynamically corrects a scheduling scheme through an adaptive adjustment mechanism, so that intelligent scheduling of construction site resources is realized, and the construction efficiency and the resource utilization rate are remarkably improved.

A method for creating a personalized immersive safety simulator includes collecting demographic information relating to a first individual, identifying existing information relating to a work environment of the first individual, and combining the demographic information and the existing information into a semantic model for predicting risk to individuals in the work environment. Training scenarios displayable in an augmented reality or virtual reality device may be created using the combined information. The semantic model may be queried based on the individual and a real-world incident and near miss report related to the work environment or information in a safety protocol or safety standard. The semantic model may be enhanced by integrating information about high-risk zones, high-risk activities, and high-risk demographics into the semantic model, the integrated information being unique to the work environment associated with the semantic model. The semantic model may be updated based on observed tendencies or scene changes.

The invention provides a power plant risk prevention and decision-making method and system based on a multi-modal knowledge graph. The method comprises the following steps: constructing a time-space associated production field risk data set; establishing a power plant risk ontology model based on an equipment topological relation and safety regulations, and defining an equipment entity-risk event-rectification scheme three-level category system; performing dynamic target detection on the field image, and outputting a risk area image block with confidence; performing entity relationship extraction on the equipment maintenance report, and generating an equipment-fault mode-processing measure structured triple; after carrying out text-image cross-modal alignment on the risk area image blocks and the structured triples, storing the risk area image blocks and the structured triples into a Neo4j graph database, and constructing a knowledge graph in combination with a three-level category system; according to the risk detection method, the equipment abnormity alarm is responded, the knowledge graph and the DeepSeek large model are called to carry out joint decision making, the knowledge illusion of the DeepSeek large model is compensated by the knowledge graph, and the risk detection efficiency is effectively improved.

The invention discloses a risk number management and control method and electronic equipment, and belongs to the technical field of communication, and the method comprises the steps: determining a plurality of regions, and calculating a region risk score corresponding to each region; determining a regional risk score threshold, and determining a regional risk level corresponding to each region according to the regional analysis score and the regional risk score threshold; determining a silent remote starting risk area, and detecting a target number in the silent remote starting risk area in real time according to a silent remote starting model; determining a track risk coefficient of the target number through a historical region of the target number; judging a number risk level corresponding to the target number through the track risk coefficient; and performing different management and control according to different number risk levels corresponding to the target number. According to the invention, the accuracy and timeliness of data analysis are improved, so that the technical effect of effectively preventing risks is achieved.

The invention discloses a risk area optimization method, and belongs to the technical field of geological disasters. The method comprises the steps of 1, preparing basic data of each city; step 2, acquiring a whole province slope unit database based on the basic data; inputting the basic data into a whole province DEM, and adjusting the basic data by adopting a slope unit algorithm program to obtain a whole province slope unit result; step 3, acquiring a framework database based on a whole province slope unit; 4, acquiring a risk area database based on the framework database; the method comprises the following steps: marking a house-containing Thiessen polygon based on Thiessen polygon data and prepared house data to obtain a house-containing Thiessen polygon, and then carrying out risk division on the house-containing Thiessen polygon to obtain a house-containing risk area database. And the scale and the framework can be unified in the whole province, and seamless replacement with the subsequently completed 1: 10000 risk area achievements can be realized.

This invention relates to the technical field of industrial machine vision and collaborative control of swarm robots, and particularly to a quality inspection method for swarm vision robots based on multi-agent reinforcement learning. The method includes: acquiring images of the workpiece to be inspected at a preset sampling period, and writing robot pose identifiers and camera imaging parameter identifiers into each frame of the image to form a quality inspection observation set; performing N forward inferences on the quality inspection observation set under a defect detection network with a random deactivation layer, generating uncertainty prediction values ​​using the defect confidence variance, and calculating a risk score matrix to determine a risk region set; inputting the risk score matrix, the risk region set, and the robot pose identifiers into a multi-agent reinforcement learning model to output a candidate observation action set; acquiring supplementary images based on the candidate observation action set and updating the risk score matrix using weighted voting based on the uncertainty prediction values; writing risk regions that meet the threshold into a review task set and rolling them into the next sampling period.

The present invention discloses a method for assessing the risk of electric power field operations based on a multi-dimensional operation risk assessment model. The method collects real-time data through sensors and IoT devices to obtain historical operation data, accident records, maintenance logs and operator information; constructs a multi-dimensional operation risk assessment model through a fuzzy comprehensive evaluation algorithm based on a support vector machine; inputs the collected real-time data and historical data into the multi-dimensional operation risk assessment model to obtain risk information of current electric power field operations; integrates a risk response rule engine to output corresponding response strategies according to risk information; creates a risk heat map through a visualization tool to intuitively display high-risk areas; and regularly generates and stores risk assessment reports and risk control effect reports. The present invention realizes multi-dimensional and high-precision real-time risk assessment of electric power field operations.

This invention discloses a method for identifying geological hazard risk zones based on a local search strategy, belonging to the field of geological hazard monitoring and early warning technology. The method includes the following steps: locating seed points within a target area; finding similar developmental condition r grid cells within the local search window Ω of the seed point s, forming a similar developmental condition region; then, using the raster area where the seed point s is located as the initial region, iteratively performing morphological dilation, expanding the current region by a certain number of pixels during each iteration and intersecting it with the similar developmental condition region, updating the current region with the intersecting region; after iteration terminates, obtaining the similar neighborhood of the seed point; converting the similar neighborhood region raster of the seed point into vector polygons, and optimizing the boundary smoothing to obtain the risk zone identification range. This invention focuses on core area calculation, avoiding the low efficiency problem of full-domain analysis and identification, and is suitable for rapid screening and dynamic updating of large-scale risk zones.

The application relates to a danger judgment and active guidance method and system for power construction safety supervision, and belongs to the technical field of power construction safety. The method comprises the following steps: collecting multi-modal data of a construction scene through intelligent glasses; analyzing the data at an edge computing end to obtain a scene semantic category and an interactive behavior of a worker; according to the scene semantic category, dynamically scheduling and hot loading a matched risk identification model set from a model library to identify a risk factor and a three-dimensional position thereof; acquiring a three-dimensional voxelized static danger level base map; mapping the risk factor into a dynamic risk observation point cloud in a voxel space; taking the static base map as a priori and taking the dynamic point cloud as observation evidence, performing recursive Bayesian filtering through a dynamic Bayesian network to generate a dynamic danger level three-dimensional distribution; generating and rendering graded AR early warning information; when a violation trend occurs, generating and rendering a three-dimensional safety path of a risk avoidance area. The method realizes adaptive, accurate and active construction safety supervision.

The invention provides a multi-party cooperative progress visualization monitoring method for intelligent building facilities, and relates to the technical field of intelligent building construction monitoring, and the method comprises the steps: collecting data, and building a three-dimensional model; establishing a causal relationship identifier based on the time sequence and the associated features to obtain a causal map, calculating an abnormal propagation probability and labeling a risk region; performing information mapping to generate a monitoring data stream and extracting features; calculating feature correlation, and mapping the feature correlation into a scene semantic segmentation map; and rendering in combination with the risk area and the progress information to obtain a visual three-dimensional dynamic construction progress. According to the invention, risk early warning and progress visual monitoring of the construction process are realized.