360 results about "Emergency response systems" patented technology

The invention relates to the technical field of artificial intelligence, and discloses an anomaly detection emergency processing system and method based on artificial intelligence, and the system comprises a data collection module; a data preprocessing module; an anomaly detection module; an emergency decision module; an emergency execution module; a real-time monitoring and state feedback module; a multi-mode communication and coordination module; a man-machine interaction and visualization module; and a knowledge updating and model iteration module. The method is reasonable in design, the accuracy and timeliness of anomaly detection are remarkably improved through a multi-source heterogeneous data fusion and dynamic threshold adjustment technology, and the model robustness is enhanced in combination with incremental learning and an adversarial training mechanism; the intelligent decision-making module realizes multi-objective optimization processing by relying on a knowledge graph and a digital twinborn pre-judgment risk; redundant fault-tolerant execution and distributed consistency guarantee ensure high reliability of the system, and a man-machine cooperation mechanism considers both automation efficiency and manual intervention accuracy.

The invention discloses an energy-saving intelligent street lamp automatic emergency response system and a control method thereof, relates to the technical field of industrial Internet of Things control, and solves the problems that an existing intelligent street lamp system is poor in dynamic scene adaptability, single in emergency response strategy and insufficient in communication stability. According to the method, a dynamic priority scheduling matrix is generated through multi-source data fusion and an adaptive weighted decision tree, and an intelligent dimming strategy is trained in combination with improved fuzzy reinforcement learning; a multi-level fuzzy control and fuzzy reasoning system is used for generating emergency parameters driven by accident levels; dynamically selecting an optimal communication link transmission instruction based on a multiple access protocol and multi-scale channel sensing; an IEEE 1588PTP protocol and Bayesian clock drift correction are adopted to guarantee time sequence consistency, and energy consumption and safety balance are optimized through multi-target reinforcement learning; the dynamic adaptive capacity, the emergency response accuracy and the communication reliability of a complex scene are remarkably improved, and collaborative optimization of energy-saving efficiency and road safety is realized.

The invention provides a logistics support dynamic scheduling and emergency response system based on intelligent data fusion, and relates to the technical field of logistics support. Comprising a data acquisition module, a data fusion and processing module, a scene prediction and simulation module, a privacy protection collaborative scheduling module, a game optimization scheduling module, a transparent resource tracking module, a self-adaptive network optimization module and a moral priority scheduling module, according to the logistics resource dynamic scheduling system, global efficient optimization of logistics resource dynamic scheduling and intelligent analysis and strategy optimization of real-time data are realized through advanced technologies such as a variable component optimization algorithm, fusion of quantum annealing and a genetic algorithm, deep reinforcement learning and combination of Transform and a genetic algorithm; and the rationality of resource allocation and the scheduling efficiency are greatly improved.

The invention relates to the technical field of artificial intelligence rescue, and discloses an unmanned aerial vehicle and robot dog rescue emergency system and method, and the system comprises an unmanned aerial vehicle cluster module which is used for air data collection, communication guarantee and emergency material distribution; the robot dog cluster module is used for ground environment detection, sample collection and special task execution; the communication cooperation module is used for ensuring information intercommunication and task cooperation between air-ground equipment; the central control module provides task instructions for the unmanned aerial vehicle cluster and the robot dog cluster; the human-computer interaction interface module is used for realizing effective communication between rescue workers and the system; the modules work cooperatively, multi-source data acquisition, three-dimensional scene reconstruction, semantic segmentation, risk assessment, fire spreading prediction and decision support information generation of a disaster site are realized, and a reliable basis is provided for rescue command decision.

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 AI-based bridge construction intelligent monitoring method, particularly relates to the field of data analysis, and comprises the steps of multi-modal sensing network construction, cross-domain characteristic parameter extraction, comprehensive state evaluation modeling and intelligent decision and regulation. According to the method, deformation, corrosion, vibration and other multi-physical field data are synchronously collected through a multi-modal sensing network, a cross-scale feature extraction algorithm is adopted to construct creep-corrosion collaborative factors and other composite indexes, the limitation of traditional single-dimensional parameter analysis is broken through, a dynamic evaluation model is fused with weighted integral and a nonlinear function, and the dynamic evaluation accuracy is improved. Precise early warning of risks such as bridge pier instability and cantilever cracking is achieved, the multi-field coupling effect quantification capacity is remarkably improved, an intelligent joint control mechanism automatically triggers instructions such as jacking reinforcement and damping tuning through a three-level emergency response system, a monitoring-analysis-regulation closed-loop system is constructed, the hysteresis of manual decision is effectively eliminated, and the safety and reliability of the system are improved. And all-weather self-adaptive safety guarantee is provided for bridge construction.

The invention discloses a debris flow disaster early warning method and an emergency response system based on big data analysis, and belongs to the technical field of geological monitoring, and the method specifically comprises the steps: collecting and preprocessing watershed terrain, weather, geology, vegetation coverage and historical debris flow event record data, and obtaining image data through combining a remote sensing technology; extracting and integrating the data and image features to form a debris flow feature data set; a debris flow early warning model is constructed by using a finite volume model, a deep learning algorithm and an Internet of Things detection technology in combination with an integrated learning method, and training and verification are performed through historical data; when the model predicts a risk, issuing early warning information and starting a dynamic response mechanism; and finally, the early warning model is optimized and adjusted according to response effect feedback. According to the invention, the accuracy and timeliness of debris flow early warning are improved, and support is provided for disaster prevention and reduction.

The invention relates to the technical field of low-altitude safety emergency, and discloses a low-altitude safety emergency system based on large model application. The system comprises a low-altitude situation awareness module which constructs a dynamic situation prediction model based on historical flight data, collects a low-altitude target track coordinate sequence, radar echoes and communication signal characteristics in real time, and outputs a theoretical security situation value. The multi-source data fusion analysis module performs three-dimensional difference analysis on theoretical and actually measured situation values, which relates to time domain trend deviation, space domain coverage offset and event sequence matching degree, and generates a target-level difference feature matrix; and the risk propagation path deduction module inputs the matrix into a geographic topology analysis network, generates a probability thermal distribution diagram of an abnormal risk propagation path in combination with airspace node communication parameters and a target position, and locates a risk influence area. And the intelligent disposal strategy generation module configures parameters according to the graph, starts high-frequency monitoring for a high-probability area, and performs communication disturbance test on an adjacent target.

The invention provides an automatic early warning method for sudden weather in a target area, which belongs to the technical field of weather early warning, and comprises the following steps of: establishing a primary dense matrix by adopting adaptive filtering processing and a frequency domain signal separation technology, and generating a secondary dense matrix by applying a marine meteorological recognition model of a spiral progressive network structure; a dynamic statistical equation is used to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, a maximum flow and minimum cut algorithm is used to optimize a weather system coupling relationship network to calculate a coupling degree matrix, and a dynamic threshold adjustment mechanism is established according to coupling strength parameters to adjust the early warning detection frequency. And based on a comparison result of the coupling degree moment order maximum characteristic value and a preset risk threshold value, establishing a grading early warning system and outputting a corresponding early warning signal to control an offshore oil and gas platform emergency response system. The technical problem that a multi-time scale weather process coupling relationship cannot be effectively processed is solved.

The invention relates to the technical field of medical emergency monitoring, in particular to an ICU emergency response system which comprises a preliminary risk assessment module, an emergency recognition module, a treatment scheme optimization module, a vital sign monitoring module, an emergency response coordination module, a disease evolution prediction module, a resource allocation management module and a medical decision support module. According to the method, feature engineering and logistic regression analysis are combined with medical history and real-time vital signs of a patient, emergency medical events are recognized by applying pattern recognition and natural language processing, a treatment scheme is optimized by using a support vector machine and a decision tree, personalized treatment suggestions are provided, and the time sequence and a biometric method are used for finely monitoring the vital signs; health risks are early warned, resources are reasonably allocated in emergency response through a resource scheduling algorithm and a priority queue, illness state prediction is assisted through machine learning and probability statistics, resource utilization is improved through linear programming and a resource optimization strategy, and waste is reduced.

The invention discloses an early warning and emergency response system for agrometeorological disasters, and relates to the technical field of intelligent agriculture, and the system comprises the following components: a meteorological data acquisition and early warning module, a soil humidity and rainfall monitoring module, an automatic irrigation and drainage control module, and a remote monitoring and management platform. By accurately monitoring meteorological data and soil humidity in real time and combining an early warning algorithm, agricultural meteorological disasters such as drought and flood can be predicted and responded in advance, before the disasters occur, the system can automatically adjust an irrigation strategy to ensure that crops obtain a proper amount of water, and when the disasters occur, the system can quickly start a drainage pump to prevent water accumulation in farmland, so that the system is safe and reliable. Therefore, crops are effectively protected from being affected by extreme weather, the risk resistance of agricultural production is remarkably improved through an automatic emergency response mechanism, and yield loss caused by disasters is reduced.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic accident prediction and emergency response system and method based on vehicle-mounted data. The method comprises the steps of collecting vehicle-mounted data, and then performing denoising processing on the vehicle-mounted data by using an adaptive convolutional neural network; constructing an intelligent traffic accident prediction model based on the de-noised data, capturing key features in time sequence data by using a bidirectional LSTM, an attention mechanism, residual connection and a Dropout layer, and predicting an accident occurrence probability; according to a prediction result, an early warning threshold value is dynamically adjusted, alarms of different levels are triggered, and corresponding measures are taken through automatic emergency response or driver reminding; through collecting data before an accident, a prediction model and an emergency response strategy are optimized, prediction precision is continuously improved, and traffic management efficiency is enhanced. According to the invention, vehicle-mounted data and an intelligent algorithm are comprehensively utilized, accurate accident prediction and efficient emergency response are provided, and traffic safety and traffic efficiency are improved.

The invention provides a coal mine multi-disaster intelligent sensing early warning and linkage emergency system, and relates to the technical field of coal mine safety, multi-source data collected by a hollow probe sensor group is uploaded to an edge gateway through an industrial ring network for primary processing, and the multi-source data after primary processing by the edge gateway is uploaded to a data layer through the industrial ring network; the processed multi-source data is classified by a time sequence database and a space database of the data layer, the classified multi-source data is analyzed by the analysis layer, and an analysis result of the analysis layer is uploaded to the early warning layer. According to the invention, three-in-one monitoring of deep part-surface-environment is realized through the hollow probe sensor group, and real-time and reliable data is guaranteed by combining edge calculation and dual-channel redundancy transmission; and the space-time database and the disaster chain rule base support analysis layer are fused with long and short-term memory network time sequence modeling and graph convolutional network space correlation and dynamic coupling correction to realize accurate quantification of the composite disaster probability.

The invention discloses a fusion reward-based reinforcement learning sparse graph reasoning method and system, which ensure accurate and timely response in emergency response decision by introducing a rule set and a fusion reward mechanism. Meanwhile, a fusion embedding module is constructed, semantic association information and graph structure information in the dam emergency response knowledge graph are expressed in a combined mode, more complete and accurate knowledge expression is provided for reasoning of an intelligent agent, and a complex emergency response scene can be dealt with. Besides, through a rule-based reward shaping mechanism, a terminal reward is combined to realize a fused reward function, the reinforcement learning model is further guided to generate a high-quality explainable reasoning path, and it is ensured that the generated decision path can be rapidly understood and executed in an emergency. According to the method, the accuracy and practicability of the sparse knowledge graph reasoning task in the dam emergency response system are remarkably improved, and application in the fields of knowledge graph completion, disaster emergency decision making, intelligent early warning and the like can be effectively supported.

The invention provides an intelligent blind person navigation system, and relates to the technical field of intelligent wearable blind person navigation, the intelligent blind person navigation system comprises a multi-mode sensing unit, an A I decision center, a bidirectional feedback module and an emergency response system.According to the intelligent blind person navigation system, through a three-layer sensing architecture of a dual-light fusion camera, a laser radar and an ultrasonic array, 94% of obstacle detection rate is still kept under 5lux low illumination, and the blind person navigation efficiency is improved. And the effective detection distance reaches 8 meters. The semantic segmentation algorithm enables the detection rate of the suspended obstacle to rise from 12% to 92%, and the recognition error of the glass door in the underground garage scene is only 2.1 cm. And a multi-sensor space-time alignment technology is adopted, so that the false alarm rate in a complex scene is reduced from 25% to 3.2%, and all-weather and full-height accurate sensing of obstacles is realized. A biological characteristic + environmental risk multi-stage triggering mechanism is adopted, automatic alarming and shared positioning are performed within 2.1 seconds when a user falls down, and the success rate of rescue for falling down of the underground garage is increased from 62% to 100%. An environment video with positioning is automatically recorded without operation timeout, and a'prevention-early warning-rescue 'closed-loop safety net is constructed.

The invention provides a disaster early warning information issuing method and system based on cooperation of a multi-modal product and an intelligent agent. The method and system are used for improving comprehensiveness and accuracy of disaster early warning and timeliness and effectiveness of emergency response. The method comprises the following steps: acquiring a multi-source monitoring data set comprising a satellite cloud picture data unit and a ground observation data unit, wherein the multi-source monitoring data set is acquired and generated by monitoring nodes distributed at different spatial positions in real time; joint feature analysis processing is performed on the multi-source monitoring data set through a multi-agent cooperative processing network, disaster risk trend features are generated, and the disaster risk trend features reflect a time evolution rule and a spatial diffusion mode of a disaster occurrence probability; calling a reinforcement learning algorithm to carry out early warning strategy optimization processing on the disaster risk trend characteristics, and generating an optimized early warning strategy containing early warning content types and delivery opportunities; and executing early warning content delivery operation according to the optimized early warning strategy, and linking an emergency response system to complete emergency response cooperative operation.

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 relates to the technical field of chemical safety emergency, in particular to a quick plugging emergency system for leakage of a chemical storage tank. The system comprises a leakage sensing data acquisition module, a leakage feature extraction module, a plugging decision generation module, a leakage trend prediction module, a plugging decision optimization module and a plugging execution control module. The leakage sensing data acquisition module acquires multi-sensor leakage data flow in real time; the leakage feature extraction module combines preset leakage physical constraint information and analyzes data to determine a leakage source and diffusion feature information; the plugging decision generation module generates an initial plugging scheme according to a built-in decision space and a fitness constraint condition; the leakage trend prediction module predicts leakage development to obtain prediction feature information; a plugging decision optimization module optimizes the initial scheme according to the initial scheme to form an optimization scheme; and the plugging execution control module drives the execution device to complete plugging. The system can improve the leakage detection accuracy, decision scientificity and plugging effectiveness, and guarantees the operation safety of the chemical storage tank.

The invention relates to the technical field of smart park integrated management, and discloses a smart park comprehensive management and control system and method based on multi-modal data analysis, and the method comprises the steps: obtaining a park multi-dimensional real-time data flow, and extracting a current situation feature vector and a personnel distribution thermodynamic diagram; analyzing the historical emergency event data sequence by using a space-time convolutional neural network, and outputting an event type identification vector; and generating a dynamic evacuation path planning and resource scheduling scheme based on a graph optimization algorithm, constructing a minimum cost flow optimization problem taking the weighted sum of time cost and waiting time as a target function, and solving to obtain an optimal people flow allocation scheme. The problem that emergency response cannot adapt to dynamic environment changes is solved. Experiential knowledge cannot be effectively inherited and utilized. And the emergency response system is slow in response and improper in resource allocation.

The invention discloses an AI multi-agent layered hierarchical emergency response system supporting an elastic power grid, and relates to the technical field of substation power response, the AI multi-agent layered hierarchical emergency response system comprises a global situation awareness module, a regional autonomous control module and an equipment toughness execution module, the global situation awareness module integrates multi-source disaster monitoring information, establishes a disaster chain deduction model, and sends the disaster chain deduction model to the regional autonomous control module; quantifying regional disaster intensity and generating a risk map; the regional autonomous control module extracts a high-risk region to divide a balanced island, realizes second-level splitting and black start through an intelligent agent, and constructs an autonomous stability coefficient to evaluate the stability of the island; and the equipment toughness execution module identifies a potential fault area, drives an intelligent agent to dynamically reconstruct a power grid topology, links distributed energy to perform differential energy supplementation according to a load gap, and calculates a recovery speed coefficient to judge the core load recovery quality. The system improves disaster pre-judgment precision and emergency response efficiency, ensures reliable power supply of medical and industrial core loads, and enhances disaster resistance elasticity of a power grid.

The invention discloses a multi-source environment monitoring and intelligent alarm system and method based on a Palladium platform. The method comprises the following steps: S1, obtaining preprocessed environment monitoring data; s2, forming transmission environment monitoring data; s3, carrying out distributed aggregation and fusion on the transmission environment monitoring data on the central Palladium platform; s4, identifying abnormal fluctuation in the data; s5, generating adjusted alarm threshold data by using a self-learning mechanism; s6, according to the adjusted alarm threshold data, carrying out grading evaluation on the detected environment abnormal events, forming multi-grade risk determination, and generating corresponding intelligent alarm signals; and S7, transmitting the intelligent alarm signal to each edge computing node and a related monitoring terminal, issuing alarm information in a short message, mail, voice broadcast or system interface display mode, and starting a corresponding emergency processing plan. According to the invention, the transmission efficiency of the alarm information can be effectively improved, and the emergency response system can make proper response according to different risk levels.

The invention relates to the technical field of path planning, in particular to a multi-agent dynamic nuclear emergency evacuation path planning method and system based on deep reinforcement learning. In particular to a multi-agent optimization path generation and dynamic adjustment method based on a deep reinforcement learning technology, and aims to improve the real-time performance, the safety and the execution efficiency of a public evacuation path in an emergency nuclear event. According to the scheme, the optimal evacuation path is automatically explored through deep reinforcement learning, a path model does not need to be manually preset, complex factors such as pollution dynamic diffusion and traffic jam under the nuclear accident scene can be dealt with, and the efficiency and safety of emergency evacuation path planning are greatly improved. The system is suitable for public emergency response systems in high-risk environments such as nuclear accidents, earthquakes, fire disasters and urban explosions.

The invention discloses a baseboard management function fault emergency system and method, a server and a storage medium, and relates to the technical field of communication, the system comprises a management module, a switching module and a controller, when any function of the management module has a fault, the fault of any function of the management module is switched through a link of the switching module; the task of the fault function is redistributed to the device capable of replacing the fault management function to achieve the corresponding management function, timely replacement of the fault management function is achieved, the reliability of the system is improved, the management function is achieved through the device on the substrate, a redundant management module does not need to be added, the device of the substrate is used, and the cost is reduced. The hardware cost is effectively reduced, meanwhile, the number of the management modules is reduced, the analysis complexity can be reduced when the fault occurs, and the fault analysis and recovery efficiency is improved. Therefore, the problems that the cost is increased, resources are wasted and the maintenance complexity is increased due to the fact that double-BMC configuration is adopted to improve the running stability of the server in the related technology are solved.

The invention discloses a help-calling emergency response system based on an intelligent communication network, particularly relates to the field of intelligent communication, and comprises a multi-source data acquisition layer, a preprocessing and feature extraction layer, a progressive analysis engine, a decision fusion center and an execution control layer. According to the method, the data integrity and the anti-interference capability are remarkably improved, the time-space reference unification module adopts a PTP protocol and a coordinate system conversion technology, accurate alignment of multi-source data is achieved, a multi-dimensional feature matrix is generated through time-frequency conjoint analysis, and the problem that the feature extraction dimension of a traditional system is single is solved; the progressive analysis engine triggers a hierarchical response strategy based on dynamic fusion of a transmission robustness index, a space confidence coefficient and an environmental risk value, combines terrain refraction compensation and a timeliness attenuation mechanism to optimize decision logic, breaks through the limitation of a fixed threshold response mode, adapts to communication quality fluctuation and environmental sudden change, reduces rescue delay risk, and improves rescue efficiency. And the resource scheduling accuracy and the system intelligence level are improved.

The invention provides an emergency situation understanding system based on a multi-modal large model. The emergency situation understanding system comprises a multi-modal data acquisition module, a data preprocessing module, a multi-modal large model deployment module, a situation understanding and question and answer generation module and a tool registration and management module. Through four core innovations of multi-modal fusion, edge intelligence, dynamic optimization and cross-domain collaboration, the bottleneck problems of a traditional emergency system in the aspects of data utilization, real-time performance, safety, intelligence and the like are solved, and the response speed, decision-making precision and collaboration efficiency of emergency management are remarkably improved; and efficient and reliable technical support is provided for the fields of public safety, disaster rescue, military emergency and the like.

The invention discloses a multi-device cooperative global multi-mode long-endurance emergency system, and belongs to the technical field of emergency safety. The system focuses on a dual-device collaborative architecture, multi-mode communication, multi-stage energy guarantee and intelligent risk assessment technology. The master device integrates a master control module, a multi-mode communication module, an environment sensor group module, an energy management module, an interaction module and the like, and supports a one-dragging-six mode. The system integrates 5G full-band communication and a Beidou independent positioning technology, double-link redundancy of the main device and the wearable device is constructed, and the communication coverage rate is increased from less than 60% to 100%. The beacon module achieves RSSI positioning with the precision smaller than or equal to 5 meters, the near-field search and rescue time is shortened by 40%, the complex terrain positioning efficiency is improved by 50%, the rescue response time is shortened by more than 50%, and the three-break scene requirements are met. The rescue efficiency of remote scenes such as earthquakes, fire disasters, deserts, forests, seaborne and polar regions is remarkably improved, and meanwhile the single-equipment light-weight emergency requirements of scenes such as child monitoring and outdoor exploration are met. The application prospects in the fields of medical treatment, old-age care, camping, chemical engineering and the like are wide.

The invention provides a traffic hub large passenger flow emergency decision-making method and system based on swarm intelligent emergence and a large model, and a readable storage medium, and the system comprises a distributed sensing module which is responsible for carrying out edge intelligent processing at a data source; the group intelligent emerging module is responsible for forming a global control strategy in a self-organizing manner through local interaction among intelligent agents; the large model element agent evaluation module is responsible for carrying out macroscopic monitoring and evolution guidance on an agent group; and an emergency decision linkage execution module. The method has the beneficial effects that the mode conversion from the traditional single center decision to the distributed collaborative autonomous decision is realized, the robustness, the self-adaptability and the response real-time performance of the system in a dynamic uncertain environment are remarkably enhanced, and finally a closed-loop autonomous emergency response system integrating sensing, decision making, execution and optimization is formed; and the intelligent level and the operation safety guarantee capability of high-speed rail station large passenger flow management are comprehensively improved.

The invention discloses an old people fall monitoring and multi-terminal linkage emergency response system, and relates to the technical field of medical treatment, and the system comprises the steps: carrying out the non-inductive collection of human body dynamic information in a weak light visual blind area environment; the voice interaction verification judgment module comprises a far-field voice excitation unit; and setting a priority queue management mechanism, and confirming a behavior perception output tumble instruction. According to the invention, closed-loop management of fall monitoring and emergency treatment of the old people is realized, and the system has remarkable beneficial effects; the privacy of the user is effectively protected, and the false alarm rate and the invalid scheduling of nursing resources are obviously reduced; the event discrimination accuracy and the individual suitability are improved, and it is ensured that high-risk events are not missed; the timeliness and accuracy of emergency response are improved, and safe, intelligent and humanized technical support is provided for intelligent old-age care and home health monitoring.

The invention discloses a disaster emergency system based on big data, which comprises a data collection module, a fire-fighting early warning module and a fire-fighting emergency module, and is characterized in that the data collection module is used for collecting visual data and environmental comprehensive data in a monitored target area in real time, and inputting comprehensive information of all people entering a building into the system; the fire-fighting early warning module is used for detecting whether a fire occurs in a target area or not, further analyzing the spreading trend and the danger degree of the fire and adjusting the operation mode of the spraying system according to an analysis result, and the fire-fighting emergency module is used for obtaining the positions of trapped persons in a building, shunting the trapped persons and sending the trapped persons to the fire-fighting early warning module. The system comprises a data collection module and a distribution module, an evacuation route is planned for each distribution group, the data collection module comprises a data input module, a visual module and a sensor module, the data input module is used for inputting comprehensive information of personnel entering the building into the system, and the system has the advantage of improving evacuation efficiency and accuracy.

The invention relates to the technical field of unmanned aerial vehicle electric power inspection and safety monitoring, and discloses an unmanned aerial vehicle electric power inspection safety supervision and emergency response system which comprises a multi-mode space-time fusion module, a layered anti-interference control module, a federal collaborative decision module, a cross-domain dynamic coupling module and a closed loop verification module. The method comprises the following steps: constructing an environmental perception map through multi-source data fusion, realizing stable flight under electromagnetic interference by adopting layered anti-interference control, dynamically generating an emergency instruction in combination with federal reinforcement learning, optimizing control parameters by utilizing cross-domain coupling, and driving system iteration upgrading through closed-loop feedback. And the inspection safety and the emergency response efficiency in a complex environment are comprehensively improved. According to the method, electromagnetic interference is suppressed through layered anti-interference control, defect positioning precision is improved through multi-modal fusion, emergency response priority is optimized through federal reinforcement learning, system robustness is enhanced through cross-domain coupling, closed-loop iteration driving self-optimization is achieved, and cooperation of unmanned aerial vehicle electric power inspection safety supervision and emergency response is achieved.