57 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 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 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 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 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.

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.

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.

The invention relates to the technical field of target detection, in particular to an adaptive detection method based on regional dynamic reasoning optimization, and the method comprises the steps: 1, presetting an abnormal target; step 2, carrying out risk level division on each abnormal target; step 3, setting a detection result without an abnormal target and contribution weights of the abnormal target of each risk level to risk scores, and determining risk score ranges and detection frequencies corresponding to risk areas of different levels; 4, starting a camera device, and dividing a detection picture obtained by the camera device into AxB grid regions; 5, performing risk area grade division on each grid area according to the corresponding risk score; 6, performing risk detection on each grid region based on the corresponding detection frequency according to the corresponding risk region grade, and outputting a risk detection result; and step 7, updating the risk score and the risk area grade of each grid area according to the risk detection result.

The invention discloses a PET facing intelligent flat pasting system based on deep learning defect detection, and relates to the technical field of data analysis. By constructing a region fragment sequence which is continuously arranged along a fitting path and calculating a coupling risk value between adjacent regions in combination with image information and sensor feedback information, the system can identify a potential interaction risk region caused by material physical property difference before defects are not explicitly exposed, so that the defect detection accuracy is improved. A risk hot area map is generated, and local process intervention operation is executed in real time in a linkage manner, so that the high-order structural defects which are originally difficult to perceive visually and are easy to evolve into cracks, edge warping or stripping in the fitting process are effectively avoided. Compared with a conventional independent discrimination mode based on local visual features, the method achieves the dynamic modeling and risk pre-control of the regional coupling behavior, and remarkably improves the defect prediction capability of the system in a complex material matching scene and the stability of the fitting quality.

An information processing apparatus includes a risk area identification unit configured to identify a risk area outside a moving object and a transmission control unit configured to perform control for transmitting risk area information representing the risk area identified by the risk area identification unit to a server configured to retain information related to the risk area, in which the risk area identification unit is configured to identify an area defined by a plurality of points as the risk area, and the risk area identification unit is configured to identify, based on a boundary between a first area in which the moving object is prohibited from moving and a second area in which the moving object is allowed to move, a shape of the risk area on a side of the first area.

The invention discloses a port storage tank risk prediction method and system based on machine learning, and the method comprises the steps: obtaining the storage tank data and regional data of a preset port, carrying out the logistics transportation analysis and energy density simulation according to the regional data, carrying out the statistics of historical risk factors of a risk region based on the simulation energy density fluctuation range, and obtaining a risk prediction result; and constructing a criticality matrix according to the historical risk factors and the logistics grid point locations, performing risk assessment on the risk region by using the criticality matrix, inputting a risk assessment result and risk factor items of the storage tank data into a random forest for training, and obtaining a storage tank risk prediction model. And outputting a risk prediction result of the storage area according to the storage tank risk prediction model. According to the method, risk prediction is carried out by utilizing logistics grid division, risk area irregular splitting, energy density simulation and quality detection data of the storage tank, a multi-dimensional decision basis is provided for safe operation of a port, and meanwhile, the method has relatively good interpretability.

The present application 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, comprising: at least two twin spaces; wherein the first twin space is used to build a mine digital twin of the current mine entity mapping, and the second twin space is used to map a target dynamic event containing at least one risk factor in the dynamic update of the mine digital twin; at least one reinforcement learning intelligent agent generates a third twin deduction model for predicting the evolution trend of mine risks; a risk warning module is used to receive real-time multi-source data, determine the target dynamic event in the real-time multi-source data through the first twin space, determine whether the target dynamic event has a risk factor through the second twin space, and when the target dynamic event has a risk factor, generate deduction data of the target dynamic event through the third deduction model, and determine the risk area and the risk probability.

The application discloses a laboratory motion line safety early warning method and device, which realizes personnel space-time trajectory reconstruction and accurate identification of abnormal motion line segments by real-time collection of relevant personnel positioning and behavior monitoring data streams, formation of motion line basic data with space-time stamps and identity tags, and combination with standard templates, and provides reliable support for risk assessment. By combining personnel characteristics and basic risk coefficients corresponding to the identity, the personal risk contribution value is calculated and the personal risk field is generated, realizing accurate quantification and differentiated assessment of personnel risk. Superimposing the personal risk field generates a dynamic group risk heat map, which can intuitively present the global risk distribution; building a risk coupling network can predict the conduction effect of high-risk areas, dynamically determine the early warning threshold according to the regional personnel and activity types, effectively improve the early warning accuracy, and reduce false positives and false negatives. In summary, the application realizes accurate control and risk early disposal of the whole process of laboratory motion line safety, and significantly improves the fine and intelligent level of safety management.

The invention relates to the technical field of risk assessment, in particular to a method for providing risk assessment level map information, which comprises the following steps of: acquiring real-time data from a plurality of sources, and performing unified standardization processing on the real-time data to form a fusible data set; analyzing historical event data based on the fusible data set, and generating a historical event mode and a corresponding initialization parameter; and performing uncertainty fusion on the fusible data set in combination with a historical event mode, allocating various data weights, and generating a regional risk level with a confidence index. According to the method, a historical event association graph is constructed, time and space association rules between events are mined, and a historical event mode is used for initializing event propagation model parameters, so that prospective space prediction of potential risk events between regions is realized; therefore, the system can predict the propagation trend of the event between the areas and the potential high-risk area.

This invention provides a construction AI-based risk analysis and decision-making method for safety management. Through a knowledge base construction mechanism involving intelligent parsing, vectorized storage, and incremental updates, it transforms scattered safety knowledge from projects into digital assets, enabling the continuous accumulation of enterprise safety knowledge assets. A RAG-enhanced large language model question-answering system equips each on-site personnel with a personal AI expert, providing near-instantaneous responses to on-site issues and improving efficiency. By employing a performance quantitative analysis model, an Attention-LSTM time-series risk prediction model, and a hazard clustering analysis algorithm, it uncovers management shortcomings and predicts risk trends from massive amounts of data, making safety management decisions more data-driven and moving away from experience-based, extensive management. AI time-series prediction identifies high-risk areas and hazard trends seven days in advance, generating actionable control recommendations, shifting safety management from post-event remediation to pre-event prevention, significantly reducing the probability of accidents.

This application relates to the field of smart city technology, and particularly to a risk prevention and management method and system based on map data fusion. The method includes: acquiring multi-dimensional geographic attribute data from a multi-source geographic information database; analyzing the multi-dimensional geographic attribute data to obtain multiple grid areas with risk coupling effects, which are then designated as risk areas to be investigated; retrieving refined geographic monitoring data and regional response data corresponding to each risk area to be investigated to analyze the geographic correlation of the regional responses; analyzing the refined geographic monitoring data and regional response data of each risk area to be investigated to identify specific impact information instances contained in each risk area; and determining risk management plans for the specific impact information instances based on these instances, thereby weaving people, places, events, things, and organizational elements into a comprehensive security and prevention network to enhance risk prevention capabilities.

This invention discloses a method, system, and electronic device for personnel operation assessment based on video temporal joint analysis, relating to the field of power safety technology. This application involves acquiring video from the work site and extracting frames; extracting the sequence of key human points from each frame; dynamically defining risk areas for the head, torso, and hands based on these key points; calculating hand movement speed, distance from the risk area, and movement consistency characteristics; triggering refined behavior recognition when hand movement characteristics indicate a tendency to enter the risk area; extracting multi-scale temporal features before and after the trigger; and judging specific risk behaviors using a pre-trained action recognition model. This invention, through a two-level mechanism of "trend triggering + refined recognition," achieves early warning and accurate identification of risk behaviors such as removing helmets and gloves, effectively improving the real-time performance and preventative capabilities of work safety monitoring. This application realizes a shift from post-event identification to pre-event warning, resulting in better safety monitoring performance.

The invention belongs to the technical field of intelligent water affair and urban disaster prevention, and provides a drainage pipeline risk early warning method, system and device and a medium, and the method comprises the following steps: dividing risk areas through combining pipeline GIS data and historical faults, and carrying out the differentiated arrangement of temperature and strain sensing optical fibers to form a double-parameter network; synchronously acquiring data by using a demodulator, processing the data by using a dynamic self-adaptive sliding window of a preset threshold value, and identifying abnormal segments; after multi-source fusion correction, inputting a pre-trained CNN-LSTM model to preliminarily evaluate the event type and risk; carrying out digital twinning simulation on consequences of moderate and above risks by using pipelines, and judging fourth-level risks; early warning is pushed according to grades, response is triggered, disposal feedback is collected, and model parameters and risk thresholds are dynamically adjusted through incremental learning; according to the invention, accurate capture of micro-anomaly of the drainage pipeline and real-time quantitative assessment of risk are realized, the life cycle of the system is prolonged, the operation and maintenance cost is reduced, and intelligent and safe operation and maintenance of the drainage pipeline are achieved.

This invention discloses a method and system for dynamic management of mine risk areas based on the Industrial Internet of Things (IIoT), relating to the technical field of mine management. The method includes: constructing a network of sensing devices to collect multi-source mine area data streams and transmitting them to a cloud processing center; performing data fusion modeling and situation analysis to construct a three-dimensional real-time mine situation map, while simultaneously pre-setting a risk rule base; predicting and marking risk areas to obtain a set of potential mine risk areas, while simultaneously retrieving multi-physics monitoring data; verifying and reviewing the set of potential mine risk areas to determine the target mine risk area set and perform risk quantification assessment and collaborative dynamic control. This invention solves the technical problems of lagging mine risk identification, severe information silos, and a lack of collaborative risk control methods in existing technologies, achieving comprehensive real-time perception of mine risk areas, dynamic and proactive prediction of risk situations, and improving the accuracy and reliability of risk identification and control.

The invention relates to the technical field of industrial machine vision and group robot cooperative control, in particular to a group vision robot quality inspection method based on multi-agent reinforcement learning. A robot pose identifier and a camera imaging parameter identifier are written into each frame of image to form a quality inspection observation set; carrying out N times of forward reasoning on the quality inspection observation set under a defect detection network containing a random inactivation layer, generating an uncertainty prediction value by a defect confidence coefficient variance, and calculating a risk scoring matrix to determine a risk region set; inputting the risk score matrix, the risk region set and the robot pose identifier into a multi-agent reinforcement learning model to output a candidate observation action set; and acquiring supplementary images according to the candidate observation action set, performing weighted voting by using an uncertainty predicted value to update the risk scoring matrix, writing a risk region meeting a threshold value into a recheck task set, and rolling to bring the risk region into a next sampling period.

The invention relates to the control of a vehicle (1) based on risk area information (13.1, 13.2). In this context, a method is proposed, among other things, which may comprise the following steps: - Recording of risk area information (13.1, 13.2), - Transmitting the risk area information (13.1, 13.2) to a vehicle (1) and - Controlling the vehicle (1) based on the risk area information (13.1, 13.2), wherein a local arrangement, geometry and type of risk area (12) are determined based on the risk area information (13.1, 13.2).