4120 results about "Emergency response" patented technology

An emergency response and crisis management system comprises a computing system which performs steps comprising: aggregating real-time and historical data from multiple data sources; processing the data using artificial intelligence techniques; analyzing the processed data to detect and classify active crises to predict the likely development and trajectory of the ongoing crises, and assess potential short-term and long-term impacts across one or more sectors comprising one or more of public health, economy, infrastructure, environment, and social stability; generating multi-dimensional, interactive visualizations of the analysis results comprising one or more of geospatial maps, time series charts, and network diagrams; providing recommendations for crisis management and response tailored to different stakeholder roles; facilitating integration with external systems and applications through a comprehensive application programming interface (API) layer; and continuously adapting the system's models and algorithms based on one or more of real-world outcomes, expert input, and user feedback.

An Internet-of-Things-based intelligent safety monitoring method and system for power plant operation. The method comprises: deploying sensors and collecting monitoring data in real time; transmitting the collected data to a central server, performing data processing, integrating the monitoring data, calculating a comprehensive environmental index, and preliminarily assessing an environmental monitoring condition; and comprehensively determining a device fault condition, remotely monitoring device status, starting a device emergency response mechanism, and performing intelligent safety monitoring of power plant operation. Power plant devices and environment parameters are monitored in real time, so that device anomalies and potential safety hazards are promptly captured to achieve rapid response. The stability of the power plant is improved, and a video monitoring system is integrated in combination with image recognition technology to monitor anomalous events in the power plant area. The level of safety management is enhanced, and decisions can be quickly made in emergency situations, improving flexibility and efficiency and enhancing management effectiveness and the accuracy and stability of the monitoring system.

The invention discloses a reservoir flood control monitoring system and method based on digital twinning, and relates to the technical field of reservoir flood control monitoring, and the system comprises a multi-source data collection module which is used for building a digital twinning model according to the real-time monitoring data of the multi-source data collection module, and transmitting the digital twinning model to a reservoir flood control monitoring module. The coupling module is used for receiving the fusion data set provided by the digital twin module, performing dynamic simulation on the fusion data set, and providing dynamic prediction results of dam seepage, deformation and flood routing; the dynamic early warning module comprises a dynamic threshold adjusting unit and an early warning unit; compared with a traditional early warning system with a fixed threshold value, the system can adapt to changes of dam body states and environmental conditions, identify potential abnormal events in advance, provide sufficient time for flood control of a reservoir to perform emergency response and scheduling decision making, and improve the flood control efficiency. Therefore, the disaster risk is effectively reduced, and the safety of reservoir operation is ensured.

The invention relates to a network public opinion intelligent classification and emergency decision-making system based on multi-modal fusion and dynamic evolution, and belongs to the field of network public opinion monitoring and big data analysis and artificial intelligence. The system comprises a multi-source data acquisition and preprocessing module used for crawling multi-modal data, constructing a propagation path map after preprocessing, and identifying key propagation nodes; the multi-dimensional classification engine module is used for carrying out conflict intensity quantification on public opinion events and dynamically updating a rule word bank to keep the adaptability of a conflict intensity quantification model; the event graph construction and anomaly detection module is used for constructing a public opinion propagation path and public opinion event generality logic chain mode, monitoring public opinion propagation speed and giving an alarm; the stakeholder dynamic risk assessment module is used for finely classifying network public opinion participants, providing a basis for differential propagation intervention and simulating public opinion evolution to carry out risk simulation; and the intelligent decision-making and emergency response module executes different levels of emergency measures based on the risk index according to the hierarchical response strategy.

A chemical industrial park hazardous chemical substance vehicle real-time dynamic scheduling system based on multi-dimensional data fusion and intelligent decision-making comprises a vehicle monitoring module used for collecting dynamic information of transport vehicles in real time and monitoring the running state of each vehicle; the risk assessment module is used for calculating the current risk level of the transportation task based on the data collected in real time in combination with a risk assessment model; the path optimization module is used for calculating and adjusting an optimal driving path through a dynamic A * path planning algorithm; and the emergency response module is used for automatically starting when the high-risk condition is detected, so that the safety in the dangerous chemical transportation process is ensured. The invention aims to realize real-time and dynamic scheduling of the hazardous chemical vehicles in the chemical industrial park through multi-dimensional data fusion and intelligent algorithm design. By integrating multi-dimensional data of vehicles, goods, external environment, time, space and the like, the system can be ensured to automatically adjust a scheduling scheme in an emergency, and an efficient emergency response mechanism is provided.

The invention relates to the technical field of information data processing, in particular to a mountain area engineering slope real-time monitoring and early warning method and system based on digital twinning. The method comprises the following steps: acquiring original slope real-time monitoring data, and carrying out standardization processing to obtain standardized slope real-time monitoring data; the method comprises the following steps: constructing a digital twinborn model by using standardized slope real-time monitoring data to obtain a preliminary slope digital twinborn model; and dynamically updating the preliminary side slope digital twinborn model by using the standardized side slope real-time monitoring data and the side slope historical multi-source monitoring data to obtain a real-time updated side slope digital twinborn model. According to the method, the digital twinborn model is updated in real time, multi-physics coupling analysis and nonlinear stability prediction are combined, it is ensured that dynamic monitoring, stability evaluation, risk prediction and emergency response of the slope can reflect the actual condition in time, and therefore the scientificity and timeliness of disaster prevention and control are improved.

The invention discloses a clean workshop production environment quality control method and system, and relates to the technical field of environment control, and the method comprises the steps: constructing a multi-layer sensing network, deploying temperature and humidity, particle concentration, pressure difference and VOC gas sensors, and carrying out the preprocessing data of each node through edge calculation; fusing the data based on a dynamic weight distribution algorithm, adjusting the weight according to the confidence score, and generating an environment quality comprehensive index; an LSTM pollution diffusion prediction model is established, a diffusion path is calculated in combination with airflow field simulation when pollution suddenly occurs, and an emergency response partition strategy is generated; fresh air system control parameters are optimized through reinforcement learning, a dynamic ventilation frequency adjusting model is established based on a real-time environment quality index and historical energy consumption data, and energy consumption is minimized through a Q-learning algorithm on the premise that cleanliness is guaranteed; a double-threshold early warning mechanism is set, local supercharging purification is started when a comprehensive index exceeds a first-level threshold, a second-level threshold is linked with adjacent areas to form a dynamic isolation barrier, and an intervention scheme effect is simulated through a digital twin system.

The invention discloses a data analysis platform management method and system, and belongs to the technical field of computer data processing, and the method comprises the steps: obtaining multi-source heterogeneous data from a medical information system; establishing a cross-system data association model, performing dynamic feature extraction and weight distribution on multi-source heterogeneous data, and outputting a resource supply-demand relationship graph; analyzing the resource supply-demand relation graph, and identifying business conflicts in medical resource scheduling; generating a conflict solution of the business conflict based on a multi-objective optimization algorithm, and generating an execution instruction; and issuing an execution instruction to the target medical system, verifying an execution result of the execution instruction, and updating system parameters. According to the method, a cross-system dynamic modeling and multi-objective optimization strategy is adopted, and a closed-loop verification mechanism and a multi-source heterogeneous data fusion technology are combined, so that intelligent prediction, accurate scheduling and self-learning optimization of medical resource conflicts can be realized, full-process traceable execution of high-value consumables is ensured, and the resource utilization efficiency and the emergency response capability are comprehensively improved.

The invention provides a drainage basin digital twinning construction method and system based on multi-source data fusion. The method is applied to the technical field of drainage basin digital twinning construction, and comprises the following steps: acquiring water level monitoring data, surface flood routing path data, weather forecast rainfall data and terrain slope change data; rainfall and topographic slope data are fused to generate a runoff distribution relation, and a high-risk area is identified in combination with a water level sudden change amplitude. A digital twinning preliminary framework is established, and related characteristic parameters are generated by utilizing reverse feedback of a flood path and water level data. And inputting meteorological data, constructing a twinborn model based on the boundary condition of the catchment area, and simulating flood direction abnormity and flooding early warning signals. And according to the early warning space-time distribution and the terrain elevation data, generating emergency schemes such as flood dividing gate scheduling and flood storage and detention area starting, and realizing risk hierarchical management and control. According to the technical scheme provided by the invention, the flood inundation prediction precision and the emergency response efficiency are improved, and precise decision support is provided for flood control of a drainage basin.

The invention discloses an oil and gas pipe network operation state risk perception early warning method and system, and the method comprises the steps: carrying out the fusion of the multi-dimensional features of the operation state of a pipe network according to the real-time operation data of the oil and gas pipe network, and generating a spatial-temporal feature matrix; inputting the spatial-temporal feature matrix into a deep Gaussian process model, performing probabilistic modeling on abnormal features in the pipe network operation state, and outputting a risk feature vector with confidence; constructing a three-dimensional risk concentration distribution field according to the risk feature vector, and predicting a propagation path and a diffusion trend of the risk concentration distribution field; and for the prediction result, constructing a self-adaptive early warning threshold generation model based on the generative adversarial network, generating a multi-stage early warning threshold changing along with the operation state of the pipe network, and realizing risk perception early warning of the operation state of the oil and gas pipe network according to the comparison result of the real-time risk characteristics and the early warning threshold. According to the embodiment of the invention, dynamic risk assessment and self-adaptive early warning can be realized, and the safety and emergency response capability of the pipe network are enhanced.

The invention relates to the technical field of data processing, in particular to a distributed multi-source heterogeneous sensor data processing method and system. The method comprises the following steps: collecting environmental parameters of a leakage area in real time through a distributed sensor array; performing coordinate system unification and timestamp alignment on the environmental parameters of the leakage area to generate a standardized leakage situation data set; extracting gas diffusion characteristics and thermal imaging characteristics of the standardized leakage situation data set to calculate a gas cloud cluster diffusion path probability; performing risk decision instruction generation on the gas cloud cluster diffusion path probability based on a preset leakage level classification neural network to obtain a risk decision instruction; calling a matched emergency plan based on the risk decision instruction; and analyzing the implementation steps of the emergency plan and performing instruction conversion to generate an emergency plan instruction. According to the invention, through real-time data acquisition, intelligent risk assessment and automatic emergency response, the timeliness responsiveness of data processing is improved.

The invention discloses a traffic transportation operation monitoring early warning and decision analysis method based on vehicle infrastructure cooperation, and relates to the technical field of intelligent traffic. According to the method, real-time collection of dynamic traffic elements is realized through a multi-dimensional sensing network of a vehicle end, a road side and an environment and V2X communication, time-space reference unification of multi-source data is ensured, a road-vehicle-environment-event semantic network is constructed, multi-dimensional recognition of abnormal events such as accidents, congestion and severe weather is realized in combination with hierarchical feature extraction, and the method has the advantages of being high in practicability and high in practicability. The method is advantaged in that identification accuracy is improved, abnormal event propagation paths can be predicted, global road network situation prediction capability is realized, differential early warning is generated based on a comprehensive risk index, multi-level responses such as traffic signal adjustment and path planning are triggered, response time is greatly shortened, emergency response efficiency is optimized, a decision execution effect real-time feedback mechanism is established, and the method is suitable for popularization and application. The system performance is continuously optimized along with data accumulation, and the defect that a big data platform lacks an intelligent decision closed loop is avoided.

The invention provides a micro-grid fault diagnosis and dynamic recovery method based on deep reinforcement learning, and the method focuses on the multi-modal features of key nodes through a graph attention mechanism, extracts fault features through a multi-layer graph attention layer, and captures the spatial dependence relation of micro-grid nodes to recognize a fault propagation path. Meanwhile, spatial features and historical multi-modal data are fused with the help of a gating circulation unit, space-time joint feature representation is constructed, pre-fault symptom time sequence evolution is captured, intermittency and early fault detection capacity are enhanced, the multi-modal feature data fusion problem is solved, and high-precision fault diagnosis is achieved. A knowledge distillation technology is adopted to deploy a lightweight student model at edge equipment, millisecond-level emergency response is realized, fault diffusion is prevented, and meanwhile, the accuracy of diagnosis and repair strategies is guaranteed. The optimal repair strategy is generated at the cloud through the teacher model by using the global data, the system can adapt to the topological change of the micro-grid and novel faults, and the fault processing capability is continuously improved.

The invention relates to a geological disaster automatic identification method and system based on multi-source remote sensing data, and belongs to the technical field of geological disaster monitoring, and the method comprises the steps: collecting the multi-source remote sensing data of a to-be-detected region, carrying out the cross-modal registration, and generating a registered multi-source data set; carrying out multi-modal feature extraction based on the multi-source data set and carrying out space-time correlation analysis to obtain a multi-modal feature map; performing dynamic weight distribution on the multi-modal feature map, generating a fusion feature vector, inputting the fusion feature vector into a pre-trained geological disaster prediction model, and outputting a geological disaster probability map; performing binarization segmentation on the geological disaster probability graph to obtain a potential disaster area mask; and according to the geological disaster probability map and the potential disaster area mask, based on a preset joint determination rule of the surface deformation rate and the gradient characteristics, carrying out risk grade division on the potential disaster area to obtain a risk grade distribution map of the to-be-detected area. The disaster prediction precision can be improved, and the emergency response capability is enhanced.

The invention relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock grading method and system, comprising intelligent sensing and data acquisition, multi-source data fusion and modeling, hybrid model dynamic grading, real-time decision and support optimization, online learning and dynamic feedback, and risk early warning and emergency response. Compared with the prior art that a geological data acquisition mode combining manual drilling coring and low-resolution geophysical prospecting is adopted, efficiency is low, subjective errors are large, and a complex geological structure is difficult to cover, unmanned aerial vehicle LiDAR scanning, intelligent rock core image analysis and a high-density IoT sensor network work cooperatively, and the working efficiency is greatly improved. Real-time dynamic acquisition of full-section geological information is achieved, manual intervention errors are eliminated in combination with a multi-source data fusion algorithm, the automation level and three-dimensional space representation precision of data acquisition are remarkably improved, and a high-resolution holographic data base is provided for surrounding rock classification.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine safety data comprehensive analysis and early warning system which comprises a data integration module, a three-dimensional visualization module, a risk assessment module, a linkage control module, a model training module and a central processing unit and can further comprise a decision support module, a storage cluster and a communication gateway. The data integration module constructs a multi-source heterogeneous data acquisition channel and performs dynamic topology modeling; the three-dimensional visualization module dynamically renders the monitoring data based on the space-time reference axis; the risk assessment module generates a danger situation map through space-time correlation analysis; the linkage control module establishes a multi-level response mechanism; the model training module optimizes the risk prediction model; and the central processing unit schedules each module to operate. According to the system, integrated analysis, dynamic visualization, risk prediction and cross-system linkage disposal of coal mine safety data are achieved, and the intelligent level and emergency capacity of coal mine safety monitoring are improved.

The invention relates to the technical field of mine ventilation, in particular to a mine ventilation dynamic regulation and control method and system based on a space-time diagram convolutional network, and the regulation and control method comprises the following steps: 1, carrying out the modeling and data collection of a ventilation system; step 2, modeling and anomaly prediction of the graph convolutional network; step 3, generation and execution of a ventilation equipment coordinated regulation and control strategy; step 4, edge node calibration and model optimization; and 5, performing full-process control and emergency response. A ventilation network topological structure and multi-modal time series data are fused through a space-time diagram convolutional network, a joint model containing spatial feature extraction and time series modeling is constructed, 30-minute early warning of ventilation abnormity and propagation path prediction are achieved, the lag response limitation that a traditional system only depends on a single parameter threshold value is broken through, and the risk pre-judgment capability is improved.

The invention discloses a key infrastructure risk identification method and device in a flood disaster chain scene. The device comprises an acquisition module used for acquiring multi-source data and performing time-space alignment and fusion processing; the extraction module is used for extracting spatio-temporal evolution characteristics of a flood disaster chain based on the multi-source data; the construction module is used for constructing a key infrastructure coefficient according to the prior infrastructure information; the coupling module is used for coupling the spatio-temporal evolution characteristics and the key infrastructure coefficient, and calculating a regional risk index through a dynamic coupling coefficient and a space sensitivity factor; and the early warning module is used for generating a risk space distribution map and triggering dynamic early warning. According to the technical scheme, through multi-source data collection and space-time fusion, the space-time evolution characteristics of the flood disaster chain are extracted, key infrastructure coefficients based on information of buildings, roads, population and the like are constructed, the regional risk index is calculated through dynamic coupling, risk distribution is visually reflected, accurate early warning and emergency response are achieved, and the reliability of the system is improved. The urban disaster prevention and reduction level is effectively improved, and the public safety guarantee efficiency is remarkably enhanced.

The invention provides an emergency broadcast intelligent triggering system based on multi-source seismic data fusion, and relates to the technical field of seismic monitoring and early warning, and the system comprises a data receiving module which is used for receiving multi-source seismic early warning information in real time, including seismic source parameter data, strong vibration observation data and real-time early warning parameters, and transmitting the multi-source seismic early warning information through a built-in communication protocol; and outputting the standardized multi-source seismic data. And the localization calculation module is used for carrying out real-time analysis on the standardized multi-source seismic data, extracting a P-wave initial parameter, a seismic motion acceleration predicted value and a seismic source fracture feature, carrying out dynamic calibration on an analysis result by combining actual measurement data of a built-in intensity meter, and outputting a multi-dimensional seismic data stream verified by the intensity meter. Through multi-source earthquake data fusion, real-time analysis and calibration, dynamic risk modeling and intelligent broadcast scheduling, generation and efficient propagation of earthquake early warning information are realized, emergency response timeliness and pertinence are improved, and earthquake disaster risks are reduced.

The invention relates to a network information security protection method and system based on artificial intelligence dynamic defense. The method comprises the following steps: constructing a multi-level security policy library containing basic defense, scene defense and emergency response based on preprocessed network data features (including protocol features, behavior sequences and abnormal indexes); then, combining real-time threat intelligence and a network environment state, and utilizing a strategy combination optimization algorithm to generate a weight matrix reflecting strategy priorities and dependency relationships; dynamically extracting and fusing various defense strategies from a strategy library according to the weight matrix to form a customized defense scheme; and eliminating resource competition and logic contradiction between strategies through a conflict detection mechanism, and finally deploying a conflict-free optimization scheme to a target node. By adopting the method, collaborative deployment of multi-dimensional security policies can be realized, and the accuracy and real-time performance of security protection in a complex network environment are remarkably improved.

A control method and system for precise landing of an unmanned aerial vehicle. The method comprises the following steps: on the basis of a multi-sensor fusion technique, using a lidar and a stereo vision algorithm to collect environmental data and perform primary processing, and generating comprehensive environmental perception data (step S1); on the basis of the comprehensive environmental perception data, using a digital elevation model algorithm to re-construct a three-dimensional terrain of a landing area, and generating a three-dimensional terrain model (step S2); on the basis of the three-dimensional terrain model, using an A* search path-planning algorithm to perform risk assessment and plan a safe landing path, and generating an optimized landing path (step S3); on the basis of the optimized landing path, using an adaptive control algorithm to adjust a flight attitude in real time by means of a fuzzy logic controller, and generating flight parameter adjustment (step S4); on the basis of the flight parameter adjustment, using machine vision and a decision tree algorithm to execute autonomous obstacle avoidance and emergency response, and generating a safe landing execution scheme (step S5); and on the basis of the safe landing execution scheme, using an ultrasonic sensor and a ground feedback system to confirm and fine-adjust a landing point, and completing final landing confirmation (step S6).

The invention relates to the technical field of traffic management, in particular to a smart city intelligent traffic management system based on big data, which comprises a multi-source data acquisition module, a data fusion module, a dynamic feature generation module, a situation prediction module, a multi-target optimization module and an instruction execution module, the multi-source data acquisition module acquires multi-source data in real time; the data fusion module generates a standardized traffic data flow and outputs a three-dimensional data cube; the dynamic feature generation module outputs a traffic feature matrix; the situation prediction module generates a dynamic situation map; the multi-objective optimization module outputs an optimization instruction set; and the instruction execution module issues a control instruction to the traffic signal controller, the variable information board and the vehicle-mounted terminal. According to the invention, traffic jam can be relieved, road traffic efficiency can be improved, bus priority scheduling can be optimized, vehicle delay can be reduced, and emergency response speed can be improved.

The invention discloses a chemical industry park emergency processing method based on digital twinning. High-precision digital modeling is carried out on the physical environment of the chemical industry park, and dynamic changes in the park are sensed and reflected in real time; the Internet of Things equipment model is fused with the twin scene, the Internet of Things equipment model is matched with equipment information of an actual park enterprise, and collaborative linkage of the model and monitoring equipment is achieved; emergency scene simulation of various chemical industrial parks is provided, and different scenes are selected for drilling according to actual requirements; a chemical industrial park accident emergency resource scheduling model is constructed, a digital twinborn simulation technology and a hybrid intelligent optimization algorithm are fused, and resource allocation in a dynamic and complex environment is realized; and a collision-free safe path from the starting point to the ending point is found by comprehensively considering the necessary points and the bypassing areas. According to the invention, through integration of a high-precision digital twinning technology, a comprehensive emergency simulation drilling system and method are provided for the chemical industry park, so that the emergency response capability and the safety management level of the park are comprehensively optimized and improved.

The invention relates to the technical field of digital twinning, in particular to a high-density urban ponding evolution and early warning method based on digital twinning. The method comprises the following steps: real-time ponding depth, real-time river water level, real-time meteorological conditions and real-time drainage pipe network water level conditions corresponding to a city are collected in real time through ponding depth, river water level, meteorological monitoring and drainage pipe network sensors installed in the city, digital twinborn construction is carried out, and an urban inland inundation digital twinborn model is generated; obtaining drainage pipe network distribution and carrying out drainage grid division to generate an urban drainage grid model; acquiring a historical ponding event rule, and performing ponding area prediction analysis and ponding evolution coupling analysis to generate an urban ponding evolution distribution field; performing ponding space-time evolution analysis and ponding early warning processing on the urban ponding evolution distribution field so as to execute corresponding urban ponding emergency response work. According to the invention, accurate ponding evolution prediction and effective early warning can be provided.

The invention relates to the field of computing power network services, and provides a computing power network ecological digital simulator based on multi-agent self-organizing interaction. Comprising a multi-agent self-organizing interaction module, a resource modeling and dynamic allocation module, a service scheduling and collaborative optimization module, a risk management and emergency response module and a strategy evaluation and optimization regulation module. The digital simulator constructs a refined and virtualized computing network model through multi-agent self-organization interaction, can dynamically reproduce resource interaction behaviors in a complex environment, assesses the effectiveness of a scheduling strategy, and optimizes the adaptive capacity of a system in a high-dynamic environment, thereby providing a scientific basis for intelligent decision-making of computing network ecological operation.

The invention, which relates to the technical field of community safety supervision, discloses an artificial intelligence-based community safety environment supervision system comprising a data acquisition module, a data processing and analysis module, an intelligent decision module, an early warning response module and a system management module. The data acquisition module is used as a sensing layer of the system; the data processing and analysis module specifically comprises a feature extraction unit and a behavior recognition unit; the intelligent decision module is used for receiving the risk assessment result output by the data processing and analysis module; and the early warning response module generates the scheme according to the intelligent decision module. According to the community safety environment supervision system based on artificial intelligence, intelligent supervision of a community safety environment is realized through a complete closed loop of data acquisition, data processing, intelligent decision making, early warning response and system management; all the modules are in close cooperation, full-process automation from data collection to emergency response is ensured, and the efficiency and accuracy of community safety management are greatly improved.

The invention discloses a water conservancy reservoir group joint scheduling optimization system based on digital twinning, and the system comprises the following steps: a real-time data collection and preprocessing module which is used for collecting and processing meteorological data; the reservoir group digital twinning modeling module is used for constructing a dynamic digital twinning reservoir group simulation model; the future state prediction module is used for predicting the future state change trend of the reservoir group; the joint scheduling optimization module is used for optimizing a multi-target joint scheduling scheme; the self-adaptive memory mechanism management module is used for maintaining an excellent scheduling strategy memory bank and carrying out intelligent disturbance adjustment; the execution and dynamic adjustment module is used for executing the scheduling scheme and dynamically correcting the scheme; the emergency response module is used for generating an emergency scheduling plan; and the feedback and adaptive optimization module is used for feeding back the real-time execution result to the twin model for dynamic correction. According to the invention, real-time optimization and intelligent emergency response of the reservoir group scheduling system are realized, and the scheduling efficiency and the system stability are obviously improved.

The invention provides a digital port intelligent customs affair method and system based on artificial intelligence, and the method comprises the steps: collecting multi-source data, and carrying out the standardization processing of a data base, and generating customs declaration data flow; predicting the risk by using federal learning, and reinforcing learning to generate a checking scheme; verifying document consistency in combination with a block chain, and verifying document logic through OCR and a knowledge graph; and dynamically adjusting a cargo distribution strategy, performing digital twinborn simulation emergency response, and finally generating a full-process visual supervision view. The customs clearance efficiency, the risk prevention and control capability and the emergency response level can be improved.

The invention provides an edge calculation traffic light emergency control method and device for a sudden traffic event. The method comprises the following steps: acquiring real-time traffic flow information of a target intersection; determining an emergency response area of the target intersection based on the position identifier of the sudden traffic event, and extracting a dynamic offset between a historical traffic flow feature and a current traffic flow feature in the emergency response area through an edge computing node; inputting the dynamic offset into a pre-trained traffic signal adaptive model, generating a traffic signal lamp control parameter set corresponding to the emergency response area, and sending a control instruction sequence to the traffic signal lamp of the target intersection according to the traffic signal lamp control parameter set, and the execution priority of the control instruction sequence is dynamically adjusted based on the real-time change trend of the vehicle density distribution data and the pedestrian movement track data. According to the invention, the intersection passing efficiency and the safety guarantee capability of traffic participants under the sudden traffic event can be improved.

The invention relates to the technical field of power equipment state monitoring and the Internet of Things, in particular to an SF6 gas state intelligent diagnosis and early warning system based on the Internet of Things, which comprises a sensor array module, an edge computing module, an anti-interference communication module, a cloud platform intelligent analysis module, an early warning and linkage control module, an energy management module and a self-checking and fault-tolerant module. The sensor array collects gas state and equipment environment data in real time, and the data are stably transmitted to the cloud platform through the anti-interference communication module after being preprocessed through edge computing. The cloud platform uses a multi-modal data fusion and deep learning algorithm to realize gas leakage trend analysis, leakage source positioning and risk level evaluation; and the early warning and linkage control module triggers graded early warning according to the diagnosis result and is linked with related equipment for emergency response. According to the invention, omnibearing intelligent monitoring and management of the SF6 gas equipment are realized, the monitoring accuracy and the system reliability can be effectively improved, and the equipment fault risk is reduced.