767 results about "Emergency plan" patented technology

The invention discloses a coal mine ventilation system intelligent regulation and control method based on environmental perception, and relates to the technical field of coal mine ventilation system intelligent regulation and control, and the method comprises the following steps: collecting environmental data of different areas of a mine, including gas concentration, temperature and humidity, air velocity and oxygen content, and monitoring the operation state of ventilation equipment in real time; data of all the sensors are synchronously processed through the data fusion technology, and a unified environment sensing model is generated. According to the invention, through cooperative work of the expert system and deep learning, real-time monitoring and emergency plan execution under extreme working conditions are realized, and the response speed and decision accuracy are improved. A reinforcement learning algorithm is adopted to optimize ventilation line switching, the adaptive capacity and redundancy guarantee of the system are enhanced, and it is ensured that airflow is not interrupted at the critical moment. Through abnormality comparison of the knowledge base and the rule base, false alarm and misjudgment are reduced, system efficiency is improved, operation burden is relieved, and safety and stable operation of mine personnel are guaranteed to the maximum extent.

The invention provides a road network safety early warning method and device based on a hologram and a storage medium. The method comprises the following steps: collecting original traffic data flow of traffic participants through multi-element perception equipment; and processing a data stream by using edge calculation and a multi-source data fusion technology, and generating a digital twinning hologram of a road network through a deep learning algorithm and a Leiye fitting technology. And combining the hologram with the road network analysis and prediction model to carry out space-time analysis and situation prediction on the original data to generate a road network digital portrait. According to the road network digital portrait, a traffic management scheme and an emergency plan set are retrieved and matched from a preset traffic control strategy library, and safety early warning is provided in the digital twinning hologram. According to the method, multi-source data fusion and situation prediction are carried out on key nodes of a target road network, a digital twin hologram and a road network digital portrait are generated, the road network state is analyzed in real time, a corresponding traffic management scheme and a corresponding emergency plan are matched, and thus a more accurate safety early warning service is provided.

The invention relates to the technical field of data processing, and discloses a BIM-assisted steel structure construction safety management early warning method, device and equipment. The method comprises the following steps: carrying out multi-source heterogeneous data acquisition and space-time alignment processing on a steel structure construction site to obtain an engineering data stream; carrying out dynamic noise reduction and feature extraction on the engineering data flow to obtain a standardized multi-modal feature vector, and carrying out time sequence correlation analysis and mode mining to obtain a potential risk index set; performing multi-level threshold calculation and context sensitivity evaluation on the potential risk index set to obtain an adaptive early warning trigger matrix; performing multi-dimensional cross validation and causal chain analysis on the self-adaptive early warning trigger matrix to obtain a graded and classified risk assessment report; and carrying out scene analysis and response scheme generation on the graded and classified risk assessment report to obtain an executable multi-step emergency plan. According to the invention, the efficiency and accuracy of BIM-assisted steel structure construction safety management early warning are 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 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 forest defense method and system based on digital twin and intelligent sensors, and the method comprises the steps: collecting and monitoring environment data, and carrying out the integration processing through data fusion; a big data analysis technology is adopted to analyze a potential risk area where a fire occurs, a high-risk area is identified by evaluating a risk level, and a fire spreading trend is predicted; constructing a three-dimensional virtual model of the forest region by adopting a digital twinborn technology; the spreading conditions of the fire at different time and spatial positions are simulated; according to the predicted fire spreading trend, when a potential fire risk is detected, an early warning mechanism is started; automatic generation and screening of the emergency plans are realized, and the emergency plans are generated. The invention provides an emergency protection system integrating the functions of multi-source sensing data fusion, intelligent early warning analysis, dynamic emergency plan generation and the like. In addition, the problem that a traditional emergency plan is static and cannot be dynamically adjusted according to actual conditions is solved.

The invention relates to the technical field of electrical system evaluation, in particular to a toughness evaluation method of a novel power system in extreme weather, which comprises the steps of constructing a digital twin model, collecting data in real time, performing fusion processing on multi-source data, identifying potential risks of the power system in extreme weather, determining system vulnerability and constructing an extreme weather scene. The method comprises the following steps: simulating weather events possibly occurring in the future, predicting the occurrence probability and influence range of extreme weather, analyzing system vulnerability, identifying key nodes and vulnerable equipment, defining and calculating a toughness index, optimizing an operation strategy, executing an emergency plan when the extreme weather occurs, evaluating a plan execution effect, and continuously optimizing the plan through feedback; according to the method, the dynamic response capability and the toughness evaluation accuracy of the power system in extreme weather are remarkably improved, the actual operation state of the power system is reflected in real time, the model parameters can be dynamically adjusted through the digital twin technology, and the real response of the system under the changing condition is simulated.

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 discloses a safety management risk dynamic monitoring system for safety production, and belongs to the technical field of industrial safety management. The system collects monitoring parameter data in an enterprise in real time by integrating toxic gas, temperature, pressure and other sensors and video monitoring equipment, dynamically displays risk distribution of major hazard sources and key areas by combining a three-dimensional map labeling function, identifies high-risk areas and potential hazards through risk grading evaluation and hidden danger correlation analysis, and improves the safety of the enterprise. A response mechanism is dynamically adjusted according to the risk level, an emergency plan and a remote equipment control function are combined, major risks are handled in a rapid linkage mode, the possibility of accident expansion is remarkably reduced, an intelligent inspection module achieves efficient and accurate inspection tasks, inspection data are collected and analyzed in real time, and an inspection report containing the task completion rate and abnormal conditions is generated. Through closed-loop management, hidden danger rectification and continuous optimization are realized, real-time monitoring of safety risks is realized, and the occurrence rate of safety production accidents 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 discloses a management system for real-time monitoring and early warning of mine coal and gas outburst parameters, and belongs to the field of safety production, and the management system specifically comprises the steps: collecting gas and ground stress data in a mine in real time through a gas sensor and a microseismic sensor, and carrying out the real-time monitoring and early warning of the mine coal and gas outburst parameters through a time sequence analysis and spatial data mining technology and a multi-scale coupling strategy; identifying the change rule and potential correlation of the crustal stress and the gas parameters; a comprehensive outburst analysis and prediction model is established, the coal and gas outburst risk level is evaluated, and a three-level early warning mechanism and an emergency plan are designed; the method can be flexibly adjusted according to actual conditions of a mine, coal and gas outburst accidents are effectively prevented and controlled, and the safety of the mine is improved.

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 air-ground cooperative monitoring and early warning method and system for a buried gas pipeline in a landslide hidden danger area, and the method comprises the steps: carrying out the ground monitoring through employing distributed optical fiber sensing, and enabling an optical fiber sensor to be embedded in the outer wall of the buried gas pipeline or to be laid with an accompanying optical fiber pipeline; collecting multiple physical quantity data of strain, temperature and vibration of the pipeline; in-air monitoring, a multi-sensor platform carried by an unmanned aerial vehicle is used for carrying out in-air inspection; the air monitoring data and the ground monitoring data are fused, and a landslide hidden danger area pipeline safety state evaluation model is constructed; performing real-time intelligent analysis on the fusion data by using an advanced mixed attention convolutional neural network, and identifying a potential catastrophe hidden danger area of the pipeline; and according to a risk assessment result, early warning grading is carried out, and a corresponding emergency plan is formulated. According to the method, real-time and accurate monitoring of the pipeline state can be realized, the landslide hidden danger can be found in time, accurate early warning can be realized, and safe operation of the buried gas pipeline in the landslide hidden danger area can be effectively guaranteed.

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 seepage monitoring, and discloses a seepage monitoring system based on a hydraulic engineering project. The system collects water level, flow velocity, seepage flow, soil humidity, soil temperature and soil pressure data through a data collection module, a data analysis module calculates standard soil humidity, standard water flow velocity, standard soil pressure, standard soil temperature and standard seepage flow according to the data, establishes a mathematical model, calculates seepage abnormal indexes, and calculates the seepage abnormal indexes according to the standard soil humidity, the standard water flow velocity, the standard soil pressure, the standard soil temperature and the standard seepage flow. The risk assessment and early warning module performs threshold judgment on the seepage abnormal indexes, sends out an overrun signal when an overrun condition occurs, predicts a future seepage trend by using a moving average method at the moment, generates a future seepage trend broken line graph and a seepage risk assessment report, and uniformly transmits the future seepage trend broken line graph and the seepage risk assessment report to the emergency response module; and when the emergency response module receives the overrun signal, an emergency plan is started, and related managers are informed of the emergency plan, the future seepage trend broken line graph and the seepage risk assessment report in a short message mode.

The present invention discloses a coal mine disaster simulation prediction system based on multi-dimensional data collection, which relates to the field of coal mine safety technology. A variety of monitoring equipment such as geological radar and microseismic monitoring are integrated to build a real-time monitoring network covering key areas of coal mines, and comprehensively collect multi-dimensional data. The system includes modules such as data preprocessing, model construction and training, prediction and decision-making, early warning and emergency response, and user interaction interface. Data is processed through data cleaning, filtering, standardization and fusion technology, and disaster prediction is performed using a CNN and LSTM hybrid model to output disaster probability and risk areas, and automatically trigger early warning and emergency plans. The user interaction interface displays real-time monitoring, prediction results and early warning information, providing a comprehensive and intelligent solution for coal mine disaster prevention and control.

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 a substation operation risk analysis method, and relates to the technical field of information, and the method comprises the steps: recognizing whether a person correctly wears a protection device or not in real time through a radio frequency identification reader on the protection device, triggering an alarm if the person does not wear the protection device, and synchronizing the alarm information to a block chain node; the block chain is used for carrying out whole-process recording on the operation behavior of personnel, the operation state of equipment and the disposal condition of a security event to form auditing evidence of security operation, and meanwhile, according to the behavior of the personnel and the state of the equipment, a block chain node dynamically optimizes a security management strategy and an emergency plan to form closed-loop management, so that the security accident is effectively prevented, and the security management efficiency is improved. And the overall safety management level of the transformer substation is improved.

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 discloses a reservoir dam safety monitoring abnormity early warning method and system, and belongs to the technical field of safety monitoring, and the method specifically comprises the steps: installing a sensor array at a spillway, a dam body, a dam foundation and a dam shoulder part of a reservoir dam, collecting dam physical parameter data in real time, and transmitting the data to a monitoring center in a wireless manner; after receiving the data, the monitoring center preprocesses and analyzes the data, calculates parameters such as deformation, settlement and displacement of the dam, and compares the parameters with preset safety thresholds; if the dam is in an unsafe state, the system automatically sends out an early warning signal, and a worker acts according to an emergency plan; the method further comprises the steps of field inspection, treatment measure implementation and result recording and feedback, safe operation of the dam is ensured, and the early warning and emergency response efficiency is improved.