517 results about "Emergency plan" patented technology

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 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 community intelligent monitoring and emergency linkage method and system fusing BIM spatial semantics, and the method comprises the steps: constructing a BIM scene map, and obtaining the attributes and mutual relationships of components and spatial regions in a BIM model; mapping a dynamic target detected in video monitoring into the BIM model, and obtaining spatial semantic information of the dynamic target; based on BIM spatial semantic information of a dynamic target, a target-environment interaction graph is constructed, a graph neural network model is used for training and reasoning, and specific complex events related to spatial contexts are recognized; taking the BIM model as a space-time reference, fusing multi-source heterogeneous data, and reconstructing by adopting a graph-based event association algorithm to form a complete event chain containing an atomic event sequence and an association relationship; and when an emergency event or an event chain is detected to indicate an emergency state, combining BIM preset information and real-time sensor data, dynamically generating an optimal emergency plan, and performing visual commanding and dispatching through a BIM three-dimensional scene and augmented reality.

The invention relates to the technical field of emergency command, and discloses an emergency command system and method based on a BIM digital base, and the method comprises the steps: building a unified data model through expanding an IFC standard, carrying out the semantic mapping and entity alignment of BIM building information, GIS geographic space data, monitoring data and an emergency plan document, and carrying out the construction of a unified data model. The method comprises the steps of generating an emergency knowledge graph with BIM building information as nodes, coupling physical simulation and an LSTM prediction algorithm, injecting monitoring data into a BIM model to deduce a disaster process, outputting a risk thermodynamic diagram and an evacuation forbidden zone, and calculating space-time accessibility of emergency resources through a reinforcement learning model according to a deduction result. An optimization scheme including an unmanned aerial vehicle path, a personnel evacuation route and an equipment scheduling instruction is generated, the instruction is synchronized to an augmented reality individual soldier terminal and a mobile command shelter through an edge computing node, a resource deployment state is labeled in real time in a BIM model, multi-terminal operation is synchronized, the timeliness and accuracy of disaster situation sensing are remarkably improved, and the disaster situation sensing efficiency is improved. And disaster loss is effectively reduced.

The invention relates to the technical field of abnormal monitoring of water-turbine generator sets, in particular to an abnormal monitoring system and method for a water-turbine generator set, and the system comprises a sensor module, an edge computing node, a cloud analysis platform and a scheduling decision center. The edge computing node performs data preprocessing, the cloud analysis platform realizes accurate early warning by applying technologies such as multi-source data fusion, a dynamic early warning model library and adaptive threshold adjustment, the scheduling decision center optimizes scheduling decisions through a resource matching engine and an emergency plan generation module, and the industrial communication network guarantees reliable transmission of data. In addition, the system is also provided with a visual interaction interface and a state evaluation report generation module. According to the invention, comprehensive and accurate monitoring and intelligent early warning of the water-turbine generator set are realized, the fault processing efficiency is effectively improved, the probability of false alarm and missing alarm is reduced, resource allocation is optimized, the operation safety is enhanced, and a powerful guarantee is provided for stable operation of the water-turbine generator set.

The invention relates to the technical field of computer science and artificial intelligence, and discloses a digital twin basin flood disaster early warning plan generation system based on deep learning, and the system comprises a data collection and processing module which obtains and standardizes multi-source data of basin meteorology, hydrology, terrain, remote sensing and the like in real time, and provides real-time data input; the digital twinborn modeling module is used for constructing a digital twinborn model of a watershed hydrological dynamic process according to the real-time data and outputting a simulation result; the deep learning prediction module is used for receiving a simulation result, training a prediction model and outputting space and time distribution prediction of flood disasters; the emergency response generation module is used for generating an emergency plan of the flood disaster according to the prediction result; and the system feedback module is used for evaluating an emergency plan and a prediction effect and providing an adjustment basis for future disaster early warning. According to the invention, accurate flood disaster prediction based on real-time data can be realized, the accuracy and response efficiency of an emergency plan are improved, and disaster loss is reduced.

The invention relates to the technical field of dispatching and emergency response, in particular to a supply chain multi-node real-time collaborative dispatching and emergency response system and method, and the system comprises a distributed data collection module which is used for obtaining the inventory data, logistics state and equipment operation parameters of each node in real time; the digital twin modeling engine is used for constructing a dynamic virtual mapping model of the supply chain network; the collaborative decision center generates a multi-objective optimization scheduling scheme based on a reinforcement learning algorithm; the emergency response trigger is used for automatically starting a graded emergency plan through abnormal mode recognition; according to the method, second-level response is realized through millisecond-level data synchronization and edge calculation, so that decision timeliness is improved, cross-node cooperation efficiency is improved by adopting multi-agent game and federated learning, and the punctuality rate of orders and the toughness index of the network are improved through multi-target Pareto optimization on the premise of controllable cost.

The invention discloses an expressway tunnel monitoring method, system and device and a medium, and relates to the technical field of tunnel monitoring. Tunnel environment data are collected in real time through a multi-source heterogeneous sensor array, and the data at least comprise video images, millimeter wave radar point cloud, laser radar three-dimensional coordinates, temperature and humidity and CO concentration parameters. According to the system, an improved YOLOv7 algorithm is used for carrying out target real-time detection, an optical flow method is combined to predict the motion trail of a target object, a tunnel dynamic characteristic spectrum is constructed, real-time monitoring of the tunnel environment is achieved, by deploying a risk level assessment engine, the system can extract space-time correlation characteristics, and the risk level assessment efficiency is improved. A comprehensive risk index is generated in combination with a fuzzy logic decision tree, and when the risk index exceeds a dynamic threshold value, the system triggers a grading early warning mechanism, links tunnel emergency equipment, synchronously generates an emergency plan and pushes the emergency plan to an operation and maintenance terminal to guarantee tunnel operation safety.

The invention relates to the technical field of offshore area monitoring, in particular to an offshore area pollutant diffusion real-time tracking and traceability monitoring system. The method comprises the following steps: a data perception and fusion unit collects and preprocesses data, carries out space-time calibration and multi-source fusion, and outputs a standardized fusion space-time sequence; the pollutant diffusion simulation and prediction unit calculates a flow velocity field, simulates a diffusion process and predicts a trend; the pollutant traceability positioning unit tracks a pollution path and inverts pollution source parameters; the emergency decision support unit determines a risk level and generates an emergency plan. And through a space-time alignment algorithm and a multi-source fusion mechanism, the data consistency and reliability are improved, a high-quality data basis is provided for subsequent processing, and the monitoring accuracy is guaranteed.

The invention relates to the technical field of natural language processing, in particular to a knowledge graph fused reservoir emergency plan large language model review method. The method comprises the following steps: acquiring a to-be-examined reservoir emergency plan, and performing data structured processing; a first neural network model is combined with a multi-scale context sensing mechanism to comprehensively capture local and global semantic information, and knowledge graph representation of a reservoir emergency plan text is constructed; a second neural network model is adopted to process the chart type information, and a reservoir emergency plan multi-modal knowledge graph is constructed; and meanwhile, an open source large language model of a Transform architecture is finely adjusted by adopting comparative learning, a special reservoir emergency plan large language model is constructed, and complementary enhancement of language model semantic embedding and knowledge graph structure representation is realized through a bidirectional enhancement mechanism. And finally, in combination with a graph attention mechanism, embedding the multi-modal knowledge graph into the fine-tuned large language model for comprehensive review, and generating a review report.

The invention provides an intelligent early warning system fusing Internet of Things sensing data and a domain knowledge graph, aiming at the problems of data islands, high false alarm rate, response lag and the like of a traditional park early warning system, and is suitable for park safety prevention and control in industries such as chemical industry, logistics, manufacturing and the like. The knowledge graph is an ideal tool for modeling connection between objective objects in the real world, the data island problem can be effectively solved by constructing the knowledge graph oriented to the smart park safety management field and fusing an intelligent reasoning algorithm, and the accuracy and timeliness of park risk early warning are remarkably improved. Specifically, a whole set of pre-warning system is designed from bottom to top in three aspects of multi-modal knowledge graph modeling, a three-level pre-warning inference engine and a self-adaptive optimization mechanism, and the park pre-warning requirements which meet current intellectualization and manpower cost saving are constructed. The multi-modal knowledge graph relates to six types of ontology concepts, comprises different data types, and comprises an equipment topological relation, environmental parameter association, an emergency plan, risk analysis, attack behavior simulation and an asset attribute model. The third-level early warning reasoning comprises rule reasoning, sub-graph matching reasoning and link prediction reasoning. The self-adaptive optimization technology aims at constructing a feedback learning mechanism, incorporating each early warning processing result into a knowledge graph, and continuously optimizing the object relation weight. In an early warning analog simulation experiment, the scheme of the invention realizes the effects of reducing the false alarm rate by 42% and improving the emergency response speed by 60%, and the feasibility and effectiveness of the scheme are proved.

The invention relates to the technical field of fire safety, and provides a smart campus multi-dimensional fire safety monitoring method and system. The method comprises the following steps: collecting multi-source security data of a campus building; extracting features based on a preset risk dimension, and generating a fire risk feature vector; constructing a risk model according to historical fire cases, and scoring the feature vectors to obtain a real-time risk score; when the score exceeds a dynamic threshold value, triggering a grading early warning instruction; an emergency scheme is generated according to the early warning, and fire fighting equipment and a personnel evacuation system are linked to execute operation. The technical problem that an existing campus fire safety monitoring system lacks a multi-dimensional, real-time and comprehensive risk assessment means and is difficult to accurately judge different fire risks and deal with the fire risks in real time is solved, and accurate fire risk early warning and efficient emergency response are achieved through multi-source data fusion and intelligent analysis. And the campus fire safety management level is improved.

The invention discloses an emergency plan making method based on an industrial park, and the method comprises the steps: achieving the data collection and preprocessing through a distributed sensor network and edge calculation, building a park dynamic model through the multi-source heterogeneous data fusion and digital twinning technology, analyzing a risk propagation path in combination with a graph neural network, and obtaining a risk plan. And a preliminary emergency suggestion is generated based on a knowledge graph matching historical case. According to the invention, a resource scheduling optimization and path planning algorithm is further adopted to formulate a dynamic response scheme, effects of different schemes are evaluated through a situation evolution prediction model, and finally an optimal emergency disposal scheme is generated and a structured instruction is issued. According to the method, intelligent formulation of the emergency plan of the industrial park is realized, the accuracy of risk identification and the timeliness of emergency response are improved, and powerful support is provided for safety management of the park.

The invention provides a method and a system for intelligently generating and dynamically adjusting a public emergency plan, which are oriented to the field of public safety emergency. The method comprises the following steps: fusing multi-source heterogeneous data to construct an extensible domain knowledge graph, establishing a standardized emergency instruction library, and carrying out multi-dimensional information labeling; automatic extraction of disaster elements is realized based on an entity recognition model of deep learning; analyzing association rules among the emergency entities through a semantic relationship mining technology; key information such as a disaster chain and resource distribution is rapidly obtained by using a map reasoning mechanism; carrying out cross-department resource collaborative allocation by adopting a multi-objective optimization algorithm to generate an optimal disposal scheme; a high-dynamic adjustment mechanism is constructed, and an emergency plan is dynamically optimized based on situation evolution prediction and real-time monitoring data; and finally, automatic generation and versioning management of the plan are realized. Through multi-mechanism cooperation of knowledge modeling, intelligent element analysis, semantic reasoning, dynamic optimization and predictive adjustment, the emergency plan generation efficiency, situation adaptability and resource allocation rationality are remarkably improved, and support is provided for quick response and scientific decision under emergencies.

The invention discloses an optimal scheduling and safety early warning intelligent aid decision-making method based on tourist attraction people flow prediction, and the method employs an advanced machine learning model to carry out the people flow prediction through integrating historical tourist data, holiday and festival information and other data sources. Based on the prediction result, the dynamic resource optimization scheduling is realized, such as reasonable arrangement of workers, facility opening and traffic guidance, so that the tourist experience is improved. Meanwhile, a safety early warning mechanism is established, and when the predicted visitor flow rate exceeds a set threshold value, management personnel can receive a monitoring result in time, issue early warning information and start an emergency plan, so that the safety of tourists is ensured. The invention aims to improve the management level and service quality of the tourist attractions through an intelligent means, and is suitable for management and operation of various tourist attractions.

The invention relates to the field of bill auditing, and particularly provides a financial bill auditing decision-making method and system, a terminal and a medium, and the method comprises the steps: collecting multi-mode finance and tax data including texts, images and structured data, and carrying out the preprocessing and alignment; carrying out feature extraction on the preprocessed data by using a multi-modal large model, carrying out feature fusion by using a dynamic weight distribution algorithm based on the credibility of each modal, the service priority and historical feedback, and obtaining a risk probability through a risk identification neural network; the risk probability is compared with a preset threshold value and rule in an auditing rule base, automatic passing is triggered, after risk abnormity is recorded, passing is conducted, and auditing actions such as manual auditing or starting of a high-risk emergency plan are pushed; and finally, adjusting model weight parameters according to manual feedback information to realize system self-optimization. The auditing decision-making efficiency is improved, and the accuracy and the service adaptability are improved.

The invention discloses a building pedestrian simulation statistics and analysis method, and belongs to the technical field of simulation. According to the invention, a comprehensive pedestrian behavior evaluation and optimization scheme is provided through normal pedestrian circulation simulation and emergency evacuation simulation in combination with statistical analysis, data visualization and an optimization algorithm. The system manages pedestrian density through a KD tree, performs real-time data visualization by using ScottPlot, generates one-dimensional, two-dimensional and three-dimensional charts, and visually displays pedestrian distribution and flow conditions in a building. In an emergency evacuation scene, the system dynamically plans a path through a # imgabs0 # algorithm, and identifies and relieves a potential bottleneck area in combination with a heuristic function of congestion measurement. The simulation system can collect abundant statistical data including evacuation time, people flow distribution, congestion point analysis and the like, and provides a scientific basis for building safety design improvement and emergency plan making.

The invention discloses a hospital project intelligent management and collaborative operation optimization system based on an AI algorithm technology, and the system comprises a multi-modal data collection module which is used for obtaining hospital operation data, medical equipment data, patient vital sign data and medical staff operation records in real time; the data fusion engine, the dynamic resource scheduling module, the risk prediction unit and the collaborative work platform provide intelligent task distribution, conflict detection and emergency plan generation functions; the self-adaptive optimization controller is used for continuously updating system parameters through an online learning mechanism; the method breaks through the limitation of traditional single-mode data processing, constructs a cross-department data association model through a graph neural network, integrates heterogeneous data sources such as radiation images, inspection reports and equipment logs, realizes dynamic mapping of diagnosis and treatment paths and resource states, introduces a space-time attention mechanism, and improves the diagnosis and treatment efficiency. Equipment operation cycle characteristics and patient movement tracks are subjected to combined modeling, so that the sensing precision of the system is improved to a minute level.

The invention provides a production operation dynamic supervision method, which comprises the following key steps: S1, data integration: widely collecting data of different production links, transmitting the data to a database through a network interface, and constructing a comprehensive data basis; s2, establishing a visual monitoring platform to integrate data to drive development, vividly displaying a production state by means of a three-dimensional model, a chart and a report, and helping a manager to intuitively insight; s3, intelligent analysis and early warning: deeply mining a data rule trend by using big data and an artificial intelligence algorithm, accurately setting an early warning threshold value, and giving an alarm in time when the threshold value is exceeded, so as to effectively prevent risks; and S4, collaborative management and decision support are carried out, department information barriers are broken, efficient sharing and collaboration are realized, scientific decision support is provided for managers, and production plans, maintenance strategies and emergency plans are optimized. According to the method, through multi-link collaborative operation, the production operation dynamic management and control capability is comprehensively enhanced, and efficient, safe and robust development of enterprises is powerfully promoted.

The invention relates to the technical field of underground pipe gallery asset management, and discloses a BIM-driven underground pipe gallery asset dynamic management and emergency decision-making platform, and the platform comprises a dynamic topology mapping module which achieves the dynamic grid density adjustment of a BIM model through monitoring the change rate of environment parameters, and generates a logic topology network synchronized with a physical pipe gallery structure in real time; the asset state reverse derivation unit is used for reversely deducing asset degradation based on environmental data by using a physical rule library dynamically loaded in a time-sharing domain; the logic rule self-triggering engine constructs a multi-level response rule chain containing space-time prediction and optimizes an emergency plan; and the edge-cloud collaborative architecture synchronously realizes real-time decision-making of low bandwidth dependence through intelligent cache and asynchronous increment. When dynamic topological mapping is driven by the change rate of environmental parameters, the real-time state of the underground pipe gallery can be represented more finely, and a high-reliability data basis is provided for subsequent accurate analysis and decision making.

The invention provides a water affair informatization safety monitoring system, method and device based on the Internet of Things and machine learning, and belongs to the technical field of water affair monitoring. Through real-time data collection, intelligent early warning of abnormal operation of a water affair system, device health assessment and water volume scheduling optimization are carried out; through combination of multi-source data fusion analysis and a machine learning model, water resource optimization scheduling and equipment energy efficiency management and control are realized. Through a multi-level early warning threshold setting mechanism, an intelligent matching emergency plan library, a multi-mode notification unit and an emergency command decision support unit, and in combination with a historical early warning event tracing analysis interface, identification and response of abnormal events of a water affair system are realized. Classification statistics according to departments, personnel or equipment is supported to generate a visual chart; a machine learning algorithm is utilized to predict future trend change, and hydrological data distribution conditions are displayed on a map based on GIS integration. And the level and efficiency of water affair management are improved.

The invention discloses an early warning emergency processing system and method based on electric power operation, and relates to the technical field of monitoring analysis, and the method comprises the steps: obtaining an electric power equipment operation state data set, and generating an electric power operation three-dimensional dynamic model; performing abnormal operation feature extraction on the power equipment operation state data set to obtain an equipment abnormal feature map, performing dynamic risk prediction on the equipment abnormal feature map in combination with environment monitoring data, and generating a power equipment risk level distribution map; performing potential safety hazard correlation analysis according to the power equipment risk level distribution diagram and the power operation three-dimensional dynamic model, and generating a power safety early warning optimization model; and performing emergency plan matching degree calculation based on the electric power safety early warning optimization model, generating a multi-stage emergency response scheme set, performing dynamic priority adjustment on the multi-stage emergency response scheme set, executing electric power operation emergency processing operation, and outputting an alarm signal to an alarm device. The method has the effect of improving the emergency processing efficiency.

The invention relates to a subway station large passenger flow emergency command linkage method and device and a storage medium. The method comprises the steps that station passenger flow information is received in real time; the station passenger flow information is preprocessed and analyzed, and real-time passenger flow data are extracted; the historical passenger flow data and the real-time passenger flow data are utilized, the future passenger flow condition and the number of passengers getting off a train about to arrive are predicted through a machine learning algorithm, if it is predicted that the future passenger flow exceeds a station bearing capacity threshold value, the abnormal passenger flow condition is judged, and if it is judged that the abnormal passenger flow is generated, a large passenger flow alarm is generated; alarm information is automatically generated according to large passenger flow alarm, and is pushed to an operation control center for confirmation; and after the alarm information is confirmed, an emergency plan is started for station management and control. Compared with the prior art, real-time monitoring, early warning, evaluation and emergency command of the large passenger flow of the subway station are realized, and the emergency response speed and the disposal efficiency are improved.

The invention discloses an emergency plan generation method and device based on artificial intelligence, and a medium, belongs to the technical field of artificial intelligence, and is used for solving the technical problems of sensing one-sided lag, poor static plan adaptability, insufficient situation deduction and decision support and low whole process recovery efficiency caused by information islands in an existing emergency processing system. The method comprises the following steps: acquiring multi-source event situation data, identifying an emergency event type, and constructing a dynamic event situation map; matching a basic plan based on the dynamic event situation map, and generating a plurality of preliminary emergency plans based on a preset constraint condition; simulating and deducing each preliminary emergency plan in the digital twin scene model, and determining a comprehensive evaluation value of each preliminary emergency plan; determining an optimal emergency plan based on the comprehensive evaluation value, and sending the optimal emergency plan to an execution end; and capturing key data in the execution process of the optimal emergency plan, generating an event handling process report, and optimizing the optimal emergency plan based on the event handling process report.

The invention belongs to the technical field of extreme weather emergency management, and particularly relates to an extreme weather event prediction and emergency response system. The problems of multi-source data splitting, disaster chain prediction missing, static plan stiffness and lack of closed-loop evolution in the prior art are solved. A global disaster map is constructed through real-time fusion of multi-source data, a physical mechanism and machine learning are coupled to realize refined deduction of extreme weather and secondary disasters, a self-adaptive emergency scheme is generated based on a dynamic resource library and an optimization algorithm, and disaster feedback data is utilized to drive model and strategy iteration, so that the disaster situation feedback data can be used for driving the model and strategy iteration. And a'prediction-decision-feedback-evolution 'intelligent closed loop is formed. The method has the advantages that information islands are eliminated, the disaster chain modeling bottleneck is broken through, resource scheduling is dynamically optimized, a self-evolution mechanism is established, and the extreme weather response efficiency is improved.

The invention discloses a multi-parameter fusion pipe network overflow prediction system and method, relates to the technical field of pipe network overflow prediction, and aims at overflow risks caused by equipment faults, unstable communication and extreme weather in a combined drainage system. A system scheme integrating five steps of predictive maintenance, reliable real-time control, digital twinning check, layered emergency plan and cloud edge cooperation is provided; in the first step, equipment health is monitored and faults are predicted to ensure that key pump stations and gates are available; 2, generating a fault-tolerant scheduling instruction by adopting reliable optimization and multi-agent reinforcement learning; 3, pre-judging and correcting the deviation by utilizing digital twinning check before execution; 4, dealing with a single-point fault and communication paralysis by using a layered distributed emergency mechanism; 5, cross-regional parallel simulation and self-adaptive scheduling are realized through cloud edge collaboration and federated learning; overflow can be reduced, environmental pollution and waterlogging loss under the rainstorm condition can be reduced, and efficient collaborative management of the drainage system is achieved.

The invention discloses an emergency response decision optimization system and method for an intelligent refrigerant system, and the method comprises the steps: carrying out the automatic data collection, crawling and collecting the text data of an emergency plan, a historical emergency event and a risk factor related to the refrigerant system, carrying out the intelligent text preprocessing, and carrying out the decision optimization. Potential association relationships in the data are mined through an association rule mining algorithm, and a multi-layer complex network model is constructed. The multi-layer complex network model is input to a convolutional neural network through an embedded layer, key features are automatically extracted, and the model is trained, so that optimal emergency response strategies in different risk scenes are learned. The system receives risk input data in real time through an application program interface, and rapidly outputs optimized emergency resource configuration and coping measure combination to assist a decision maker to make an optimal decision in an emergency.

The invention is applicable to the technical field of coal mine tunneling, and provides a coal mine tunneling working method based on a digital twinning system, which comprises the following steps: S1, constructing a three-dimensional dynamic geological twinning model integrating geological radar, measurement while drilling and optical fiber sensing; s2, in the three-dimensional dynamic geological twinborn model, rock stratum characteristic changes are predicted through a digital twinborn body, and a self-adaptive cutting control instruction is generated; s3, simulating gas diffusion and roof stress distribution in real time while the control instruction is sent out, and triggering a grading early warning mechanism; s4, optimizing cutting parameters and emergency plans in a virtual environment according to early warning conditions, reversely controlling physical equipment, setting geological-equipment two-way closed-loop control, breaking through a traditional one-way data acquisition mode, realizing real-time two-way interaction between a geological model and tunneling equipment, establishing a digital twinning driven tunneling process parameter optimization standard library, and realizing real-time two-way interaction between the geological model and the tunneling equipment. Data support is provided for iteration of the technology, and improvement of coal mine tunneling work is facilitated.

The invention discloses an urban traffic intelligent management method based on multi-source data, and relates to the technical field of traffic data management, and the method comprises the steps: collecting multi-source traffic data, firstly carrying out the preprocessing, then carrying out the matching and integration, and building a unified traffic data warehouse; carrying out batch processing and stream processing on warehouse data by applying a distributed computing framework and a stream processing technology; on the basis of multi-source data, various traffic models are constructed and optimized, and a visual tool is developed to visually display an analysis result; by combining real-time traffic data, a model prediction result and historical data, traffic signals are dynamically adjusted, route planning and navigation services are provided for users, and routes and scheduling of public traffic are optimized; a traffic monitoring center is constructed, and real-time monitoring and timely early warning are carried out based on the traffic signal dynamic adjustment result and the real-time traffic data; establishing an emergency plan; and establishing a data sharing platform, and sharing the related traffic data. The system is used for monitoring, analyzing, managing and applying the traffic data.

The invention discloses a dam leakage monitoring system and method based on optical fiber sensing and time-space fusion. The dam leakage monitoring system comprises an optical fiber sensing system, a data processing and analyzing system, a dam leakage data management system and a real-time monitoring platform. According to the invention, the distributed optical fiber realizes continuous strain and temperature monitoring of the whole length of a dam, a leakage area can be roughly positioned, an FBG sensor is deployed at a key position, local tiny deformation is accurately monitored, a cooperative mode is formed by combining wide-area screening of the distributed optical fiber and key diagnosis of the FBG, and the monitoring efficiency is improved; the all-fiber passive design is adopted, the environmental adaptability is high, the anti-interference performance and the long-term stability are high, frequent maintenance is not needed after the fiber is embedded into the structure, and the operation and maintenance cost is reduced; finally, the leakage risk is displayed through a trend curve of a visual data platform, alarm pushing and emergency plan functions are automatically triggered, the closed-loop time of monitoring, decision making and response is shortened, and the real-time performance and decision-making force are improved.