2933 results about "Fire prevention" patented technology

The invention relates to a power transmission line forest fire risk assessment early warning system and method, and belongs to the technical field of power transmission line risk assessment. The system comprises an environment DNA power transmission line forest fire risk assessment and early warning system, a quantum sensing power transmission line fire remote monitoring system, a power transmission line forest fire early warning emergency training system, a digital twin power transmission line fire risk dynamic management system, and a multi-node power transmission line fire early warning system of an edge AI and a low orbit satellite. The system comprises a thermal imaging and deep learning integrated power transmission line fire risk distribution prediction system, an AI-driven power load dynamic adjustment and fire prevention system, an intelligent fire-fighting equipment linked power transmission line fire spreading path prediction system and a distributed quantum computing power transmission line fire emergency simulation and optimization response system. According to the method, the inflammable vegetation density and biocenosis change can be dynamically analyzed through a plant and microorganism gene sequencing technology.

A device, system and method comprising computer vision techniques for fire prevention / detection and risk assessment, as well as for determining deviations from an ideal operational state. The present invention includes for example systems and methods which leverage data collected by camera systems composed of infrared and visible light sensors to detect and / or prevent a fire from starting, and additionally, use this data to determine a risk assessment for the building. The present invention also provides for example a system and method for monitoring and controlling safety risks in indoor industrial environments by determining deviations from an ideal operational state using computer vision techniques and game-theoretic competitive ranking frameworks.

The invention discloses a forest fire detection and disappearance method and system, and relates to the technical field of forest fire prevention and data processing, and the method comprises the steps: carrying out the fire inspection of a planned forest region through a plurality of inspection unmanned planes, and transmitting an image and airborne sensor data back to a command center in real time; identifying a fire behavior by using an artificial intelligence method, and performing fire behavior judgment and fire hazard grade division; the command center screens fire points according to the thermal images and updates the positions of the fire points in real time; the fire scene spreading trend is judged according to the returned data, and a basis is provided for the dispatching task of throwing the fire extinguishing bombs by the fire extinguishing unmanned aerial vehicle; using an MAPPO multi-agent reinforcement learning algorithm to train an optimal dynamic fire point collaborative disappearance regulation strategy; and after the open fire is extinguished, the inspection unmanned aerial vehicle continues to inspect to identify a re-ignition point or a smoke point, and secondary large-area fire is prevented. According to the method, efficient collaborative fire extinguishing, dynamic environment adaptation and resource optimization configuration can be achieved, and robustness is kept when part of intelligent agents fail.

The invention discloses an electric vehicle parking lot fire prevention and control system based on multi-source information fusion. A sensing layer captures the environment state of an electric vehicle parking lot and the operation parameters of a vehicle battery in real time through a multi-mode sensor network; the network layer adopts an intelligent fusion network architecture to realize communication between the sensing layer and the data processing layer; the data processing layer is used for performing fusion analysis on the environment state and the operation parameters, judging a fire risk level and generating a dynamic control instruction; the data processing layer is composed of a multi-dimensional analysis engine, and a hierarchical progressive intelligent analysis architecture is adopted to realize multi-level research and judgment of fire risks; and the application layer receives the dynamic control instruction generated by the data processing layer, and realizes full-process closed-loop management of fire prevention and control through a multi-mode terminal device. The method not only improves the fire prevention and control precision of the electric vehicle parking lot, but also has good interpretability, and can be directly applied to a fire prevention and control system of the electric vehicle parking lot.

The invention provides a forest fire prevention unmanned aerial vehicle patrol early warning system and method based on edge calculation, and relates to the technical field of unmanned aerial vehicle patrol early warning. The patrol early warning system comprises an unmanned aerial vehicle platform, a data acquisition module, a data processing and analysis module, a communication and early warning module and a ground control and scheduling platform. According to the invention, calculation and data storage are close to a data source through edge calculation, the data collected by the unmanned aerial vehicle is processed in real time at an edge calculation node without being transmitted to a remote cloud and returning a result, the data processing and response time is greatly shortened, fire hazards can be found in seconds or even shorter time, and early warning can be given out. And the system can analyze data in real time in the flight process of the unmanned aerial vehicle, provides real-time decision support for a ground control center, and can quickly formulate a fire-fighting scheme according to information such as fire behavior and wind direction when a fire occurs, so that the fire-fighting efficiency is improved.

The invention discloses an AFIS intelligent fire-fighting monitoring system based on the Internet of Things technology, and relates to the technical field of fire-fighting safety, and the AFIS intelligent fire-fighting monitoring system comprises a front-end module, a central monitoring module, a data analysis module, an early warning module and a fault diagnosis module. The front-end module is used for monitoring fire-related environment data and fire-fighting equipment states through a plurality of sensor nodes; installing and deploying various types of sensor nodes in the key area; the collected original data is subjected to primary processing, including data cleaning, denoising, standardization and the like, so that the accuracy and reliability of the data are ensured; according to the invention, through integration of various sensor nodes, real-time monitoring of environment parameters of a key area and the state of fire-fighting equipment is realized, so that the fire prevention and response efficiency is obviously improved, the system can wirelessly transmit data by using the Internet of Things technology, the complexity of installation and maintenance is reduced, and the monitoring range is expanded.

The invention relates to the technical field of communication machine room safety protection, and discloses a communication machine room intelligent fire monitoring and fire extinguishing system based on edge computing, which comprises a fire parameter acquisition module, a temperature characteristic analysis module, a smoke dynamic detection module, a multi-source fusion judgment module, a regulation and control parameter generation module and the like. The fire parameter acquisition module acquires parameters such as temperature gradient, smoke concentration and equipment current, judges fire risk and triggers cooperative monitoring; the temperature characteristic analysis module extracts temperature distribution characteristics and quantitatively evaluates hot spot abnormity; the smoke dynamic detection module analyzes the smoke diffusion trend; the multi-source fusion judgment module is combined with multiple parameters to generate a fire extinguishing or optimizing signal; and the regulation and control parameter generation module is matched with the strategy to generate injection and ventilation parameters. The system also corrects a risk index through an equipment coupling analysis module, and forms closed-loop control by using a dynamic feedback execution module. According to the system, multi-source data fusion monitoring and intelligent fire extinguishing are realized, and the fire prevention and control accuracy of the communication machine room is improved.

The invention relates to the technical field of fire-fighting early warning, and discloses a fire-fighting early warning system based on image data relevance. The system comprises a multi-source image acquisition module used for acquiring fire-fighting scene multi-source heterogeneous image data; the correlation feature analysis module is used for performing cross-data-source correlation analysis on the data to generate feature vectors; the spatio-temporal dynamic modeling module is used for constructing a multi-dimensional feature fusion space to generate an associated spatio-temporal feature tensor; the resource optimization scheduling library is used for constructing a double-layer collaborative library; and the intelligent early warning control module generates a real-time fire early warning instruction and an emergency response decision through a hierarchical reinforcement learning framework. The system also has the functions of building structure deformation detection, smoke diffusion prediction and the like. Through cooperation of multiple modules, accurate fire-fighting early warning and efficient emergency response are realized, the fire prevention and control capability is improved, and life and property safety is effectively guaranteed.

The invention relates to a fire prevention and control smart network method and system based on Beidou satellite navigation. The method comprises the following steps: firstly, acquiring real-time operation data of fire-fighting equipment in a building, and scanning a building structure through a three-dimensional imaging device to generate a three-dimensional digital model; secondly, fusing the real-time operation data, the three-dimensional digital model and preset environment parameters to form a fire prevention and control reference data set; thirdly, carrying out multi-dimensional data comparison on the reference data set based on a preset fire-fighting standard program, and generating an alarm signal with spatial positioning information; then, determining a fire-fighting facility failure area according to the alarm signal, and calculating a fire risk diffusion parameter; and finally, generating a grading early warning instruction according to the risk diffusion parameters, and constructing an electronic combat map containing the optimal rescue path. The method provides global and accurate basic data support for fire prevention and control, improves the timeliness and accuracy of early warning, optimizes the rescue path, and improves the fire disposal efficiency.

The invention discloses a fire-fighting monitoring and generator set collaborative optimization multi-target constraint solving method, which comprises the following steps: carrying out real-time acquisition and structured processing on multi-source data, acquiring safety monitoring and equipment operation data through a sensor network, and storing the data in a distributed real-time database after preprocessing; carrying out dynamic thermodynamic field modeling and risk area division, discretizing a plant area as a grid unit, solving a heat conduction equation in combination with heat source modeling and boundary conditions, and dividing three levels of risk areas; building a multi-objective optimization model, defining three types of core constraints, building a normalized objective function, and realizing multi-objective decoupling through dynamic weight adjustment; according to an asymmetric load migration strategy, differential load adjustment is implemented according to an optimization result, an auxiliary system is linked, constraints are verified in real time, and closed-loop control is formed. According to the technology, the collaborative goals of safety risk controllability, system efficiency maintenance and equipment redundancy optimization are achieved, and the method is suitable for fire prevention and efficient operation in an energy production scene.

The invention discloses a community fire early warning method combined with a knowledge graph, and relates to the technical field of fire early warning, and the method comprises the steps: carrying out the traversing of community region information, carrying out the data collection, and constructing a community fire knowledge graph according to a data collection result; performing feature extraction based on the community fire knowledge graph to obtain a plurality of fire features; constructing a fire risk assessment model to perform fire risk assessment on the community, and generating a plurality of risk assessment results; monitoring the community in real time, and generating a trigger instruction; and starting a fire early warning mechanism according to the trigger instruction, and generating a fire response decision. The technical problems that in the prior art, a traditional fire early warning method is inaccurate in evaluation and cannot effectively prevent fire in advance are solved, and the technical effects of improving the accuracy of fire risk evaluation, improving the accuracy of fire prevention and effectively reducing fire hazards are achieved by combining the knowledge graph.

The invention provides a forest fire prevention early warning method and system based on a multi-source sensor, and the method comprises the steps: firstly obtaining a multi-source sensing monitoring data set of a target forest region, including data subsets of temperature, humidity, smoke concentration, visible light images and the like, and collecting each subset by at least two heterogeneous sensor nodes; performing space-time alignment processing on the multi-source sensing monitoring data set to generate a space-time distribution characteristic set containing various characteristics such as temperature gradient, calling a dynamic anomaly detection model to compare fire danger indexes, generating a fire danger grade quantitative score and an anomaly fluctuation index set, and performing data processing on the fire danger grade quantitative score and the anomaly fluctuation index set; the method comprises the steps of obtaining a fire hazard grade quantitative score, matching a multi-level early warning strategy based on the fire hazard grade quantitative score and a preset early warning threshold interval, generating a dynamic fire hazard early warning instruction, triggering fire extinguishing resource scheduling according to the dynamic fire hazard early warning instruction, generating a forest region escape path optimization strategy in combination with an abnormal fluctuation index, and achieving efficient and accurate forest fire prevention early warning and coping.

The invention relates to the technical field of regional fire-fighting analysis, in particular to a regional fire-fighting risk analysis method and system based on multi-modal AI analysis. The method comprises the following steps: collecting a multi-modal fire-fighting related sensing source in an area, carrying out cross-modal fusion processing and abnormal event detection, and identifying a fire-fighting risk mutation point; carrying out risk propagation trend evolution on the fire risk mutation points, and carrying out fire source high-dimensional traceability analysis to obtain regional fire risk traceability points; performing risk evolution time estimation on the regional fire-fighting risk tracing point, and then performing fire risk situation evolution prediction to obtain a fire-fighting simulation situation map; and performing dynamic risk level change tracking according to the fire-fighting simulation situation map, and performing comprehensive emergency demand adjustment to obtain comprehensive risk intervention decision parameters. Through refined resource matching, the utilization rate of fire-fighting resources is improved, and the regional fire prevention and control quality and safety efficiency are improved.

The invention discloses a fire-fighting early warning system and method based on artificial intelligence, and relates to the technical field of intelligent fire-fighting early warning, and the system comprises an environment sensing module which comprises a multispectral sensor, a temperature sensor, a smoke sensor and a high-definition camera, and is used for collecting fire characteristic spectral data and transmitting the data to a data analysis module; the data analysis module comprises an edge calculation unit and a data fusion unit, and is used for performing feature extraction and anomaly detection on the fire feature spectrum data through a deep learning model to generate a fire risk assessment result; the early warning decision module comprises a risk assessment unit, an early warning grading unit and a communication unit, and is used for determining an early warning grade according to a fire risk assessment result and triggering equipment of the execution feedback module; and the execution feedback module comprises a spraying system, an emergency lighting system and an alarm and is used for executing fire emergency measures according to the instruction of the early warning decision module. According to the invention, comprehensive improvement of fire prevention and control efficiency is realized through cooperative work of multiple modules.

The invention discloses a new energy vehicle underground garage fire treatment method and system based on the Internet of Things. The system comprises a sensor data acquisition module, an edge calculation and risk assessment module, an early warning and emergency response module and a data storage and cloud platform module. The sensors are arranged at positions such as parking spaces and ventilation systems in a garage, monitor environmental parameters such as temperature, smoke and gas in real time, and transmit data to the edge computing device through the wireless gateway. And the edge calculation module evaluates the fire risk in combination with a fuzzy Bayesian network. And the early warning and emergency response module triggers different levels of alarm signals according to the risk levels, and controls ventilation equipment, fire-fighting equipment and the like in a linkage manner. The data storage module is used for storing monitoring data. The system has the advantages of real-time monitoring, intelligent evaluation, multistage early warning and quick response, is suitable for fire prevention and safety management of closed environments such as underground garages, and effectively improves the accuracy of fire risk prediction and the efficiency of emergency response.

The embodiment of the invention provides a method and device for identifying fire protection hidden dangers through an AI visual analysis technology, and the method and device achieve the integration of image video streaming, three-dimensional space, thermal imaging, environment perception and other multi-modal data through the innovative construction of a multi-source data collection and fusion mechanism. And designing a feature extraction model based on transfer learning, and realizing high-precision hidden danger feature recognition through integrated learning in combination with a hierarchical classification structure and an attention mechanism. Time sequence analysis and space positioning technologies are introduced, a hidden danger feature association network and an evolution model are constructed, and hidden danger development trend prediction and common hidden danger discovery are achieved. According to the method, the defects of the traditional technology in the aspects of multi-modal data processing, feature recognition, trend prediction and the like are effectively overcome, and the intelligent level and the early warning capability of fire protection hidden danger recognition are remarkably improved.

The invention relates to the technical field of fire prevention and control, in particular to a BIM and AI algorithm-combined fire spreading path evaluation system, which comprises a multi-source data fusion module, a fire source positioning module, a spreading path prediction module, a risk evaluation module and an intelligent linkage module. A three-dimensional dynamic model is generated by fusing sensor data and BIM data, accurate fire source positioning and fire spreading path prediction are achieved, the regional risk level is quantified in combination with building information, and a control instruction is sent to fire fighting equipment to isolate a high-risk region and delay fire spreading. The fire source positioning precision, the fire behavior prediction reliability and the emergency response efficiency can be improved, and scientific decision support is provided for building fire prevention and control.

The invention discloses a multispectral satellite fire prevention method and system based on data identification, a multispectral satellite optical sensor array scans a target area for 30 minutes, the acquisition precision is guaranteed through a high-precision attitude system and a formula to obtain data, and the quality is guaranteed through adaptive filtering and multi-source data correction. A dynamic threshold value and wavelet transform are used for extracting fire features to construct vectors, the vectors are input into a fusion model to discriminate a fire in real time, early warning information including positions, fire behaviors and the like is transmitted to a ground command center within 8 minutes, data are updated every 10 minutes during the fire, the fire behaviors are evaluated according to multiple indexes in combination with a GIS and a time sequence model, strategies are adjusted, and the data are stored. The system is fast in data acquisition and processing, early warning is performed within 8 minutes, the accuracy rate exceeds 92%, the fire behavior is pre-judged 15 minutes in advance, the system is stable, data is efficiently processed, and ecological and life and property safety is protected.

The invention belongs to the technical field of forest fire early warning, and discloses a forest fire prevention method based on smoke recognition, which comprises the following steps: acquiring heterogeneous image data from different monitoring devices, performing format unification and quality enhancement through a data preprocessing module, eliminating the difference of data from different sources, and obtaining preprocessed image data; a multi-scale feature extraction network based on deep learning is adopted, key features such as the color, texture and form of smoke are automatically learned and extracted from the preprocessed image data, and feature representation is obtained; according to the method, corresponding early warning threshold values are set according to different grades of fire judgment results, when the fire grade judgment results meet early warning conditions, early warning signals are triggered automatically, and early warning of the fire is achieved in the modes of message pushing, sound-light alarm and the like.

The invention provides an intelligent fire-fighting fire prediction method and system, a medium and a program product, and relates to the technical field of electric digital data processing. The method comprises the following steps: firstly, acquiring real-time smoke monitoring data of a smoke alarm sensor in a target building, and once the data reaches a preset threshold value, immediately determining fire occurrence and locking a target fire source range; thirdly, calculating a fire risk value of the range by means of a fire risk assessment model trained based on multiple factors of a target building and a machine learning algorithm, and determining that a high-risk fire source point exists when the fire risk value is higher than a threshold value; then, combining meteorological data to obtain multi-dimensional environment characteristic data, and inputting the high-risk fire source point and the meteorological data into a fire spreading prediction model trained by a deep learning algorithm to obtain a fire spreading prediction condition; and finally, the situation is sent to a management end for alarming, and according to the method, rapid and accurate pre-judgment of the fire situation is achieved by means of multi-source data and an intelligent model.

The invention relates to the technical field of fire-fighting engineering, and discloses a real-time fireproof monitoring data transmission system for fire-fighting engineering construction, which comprises a fire parameter analysis module, an environmental data analysis module, a fire source positioning module, a model construction module and a safety database. The fire parameter analysis module generates a temperature change record table through gridding temperature measurement nodes, estimates the smoke diffusion rate, and evaluates the thermal stress and deformation risk of the building structure; the environment data analysis module associates the air velocity with the smoke diffusion rate and corrects the parameter weight of the evaluation model; the fire source positioning module generates a fire source three-dimensional positioning map by using multispectral imaging equipment; the model construction module constructs a dynamic fire assessment model according to multi-dimensional data and outputs a comprehensive threat level; the security database stores building material parameters, historical case characteristics, and dynamic threshold rules. According to the system, through multi-module cooperation and multi-data fusion, the accuracy and timeliness of fire-fighting monitoring are improved, and the system is suitable for safety management of fire-fighting engineering.

The invention relates to the technical field of forest fire prediction and simulation, in particular to a forest fire spreading prediction simulation system under complex mountain terrain conditions, which comprises a data acquisition module, a data preprocessing module, a terrain analysis generation module, a vegetation analysis module, a meteorological analysis module, a fire spreading simulation module and a result display module. According to the forest fire spreading prediction simulation system under the complex mountainous terrain condition, the influence of multiple factors such as the complex mountainous terrain, vegetation and weather on forest fire spreading is comprehensively considered, the prediction accuracy and reliability are improved, a user can visually know the forest fire spreading condition through visual result display, and the forest fire spreading prediction simulation system has the advantages of being convenient to use and high in practicability. According to the invention, scientific and reasonable fire prevention and extinguishing strategies can be formulated conveniently, latest data can be acquired and processed in time, powerful support is provided for monitoring and early warning of forest fire, scientific decision basis can be provided for forest management departments, fire departments and the like, and improvement of the prevention and control capability of forest fire is facilitated.

The invention relates to the technical field of intelligent forestry management, and particularly discloses a forest farm intelligent comprehensive management and control system, which comprises a space-air-ground collaborative sensing module, a fire danger dynamic modeling module and an intelligent decision execution module. The method comprises the following steps: acquiring temperature field data through multispectral satellite remote sensing, constructing a high-precision terrain model through a laser radar, extracting vegetation features through hyperspectral imaging, extracting a thermal anomaly structure by adopting topological continuous coherence analysis and a Morse-Smal complex method, and establishing an adaptive fire danger model in combination with a quantum annealing optimizer. The system can generate a fire danger thermodynamic diagram in real time and divide risk levels, and drives the unmanned aerial vehicle to perform priority patrol and obstacle avoidance path planning. The problems of difficulty in pseudo hot area identification, low fire danger prediction precision, non-intelligent path planning and the like in a complex environment are solved, and the intelligent level of forest farm fire prevention and control is improved.

The invention relates to the technical field of intelligent fire early warning, in particular to an urban fire intelligent early warning system which comprises an urban structure intelligent analysis module, a fire dynamic early warning simulation module, a fire prevention strategy optimization module and an environment influence intelligent analysis module. According to the method, the urban graph model is generated by using the geographic positions, materials, fire data and building age data of the urban buildings, the relevance and characteristics between the buildings are deeply analyzed, and the fire diffusion influence is simulated through the graph convolutional network, so that the prediction precision of the fire propagation path and speed is remarkably improved; the evacuation difficulty and risk level of a building under multiple fire source points are calculated in an auxiliary manner, a basis is provided for scientific planning of evacuation routes and fireproof isolation areas, emergency response and resource allocation are optimized, meteorological, population dynamics and holiday activity data analysis are integrated, the effect of environmental factors in fire early warning is enhanced, the accuracy and timeliness of early warning are ensured, and the early warning efficiency is improved. And the overall efficiency of an urban fire prevention system is improved.

The invention discloses a fire-proof gas concentration detection device for a coal mine goaf, and relates to the technical field of environment detection, the fire-proof gas concentration detection device comprises a cylinder, the side wall of which is provided with an air inlet for introducing to-be-detected air; the air outlet cylinder is connected to the upper end of the cylinder body, fan blades are rotationally arranged in the air outlet cylinder, and rotation of the fan blades drives air to enter from the air inlet and flow through the whole device; the funnel-shaped dust collection cylinder is connected to the lower end of the cylinder body, and a plurality of separation blades are distributed on the inner circumference of the funnel-shaped dust collection cylinder and used for preliminarily filtering and blocking dust impurities in the air, so that the purified air enters the cylinder body to be detected; the dust collection box is connected to the bottom of the dust collection barrel; the plurality of sensor cylinders are uniformly distributed on the inner circumference of the cylinder body, a connecting seat is arranged in each sensor cylinder, and a gas semiconductor sensor is mounted on each connecting seat; the device can improve the precision of gas concentration detection, flexibly adapt to different working conditions and realize intelligent cleaning and maintenance.

The invention discloses an intelligent forest fire prevention method and system, and aims at solving the problems of monitoring failure, information updating lag and low prediction precision of a traditional method in a severe environment by integrating multi-source real-time data and adopting deep learning and a dynamic probability model to realize accurate identification of a fire disaster and fire spreading prediction. Through an intelligent path planning algorithm and an unmanned aerial vehicle autonomous fire extinguishing technology, real-time response to dynamic changes of a fire scene and flexible adjustment of a fire extinguishing strategy are realized, the fire extinguishing efficiency and safety are improved, a fire extinguishing effect data acquisition and feedback mechanism is established, and the defect that a traditional system lacks effect evaluation after fire extinguishing is overcome. And a reliable basis is provided for subsequent model optimization and decision improvement, so that the forest fire early warning and emergency response capabilities are integrally improved.

The invention discloses a porcelainized expansion type fireproof heat-insulating coating and a preparation method thereof, and relates to the technical field of fire prevention, and the fireproof heat-insulating coating comprises an inner coating and an outer coating; the preparation method comprises the following steps: S1, weighing the raw materials of the component A and the component B; s2, preparing the component A; s3, preparing a component B; s4, the surface of the steel structure is coated with the component A, and an inner coating of the fireproof heat-insulating coating is obtained after normal-temperature drying; and continuously coating the surface of the dried inner coating with the component B through a spraying or brushing method, and drying at normal temperature to obtain the ceramifiable expansive fireproof heat-insulating coating. A porcelainized reaction and an intumescent flame-retardant system are combined to protect the steel structure material from two aspects of efficient flame retardance and high-temperature heat insulation, and the effect is excellent; a nitrogen-phosphorus synergistic flame retardant is used for actively inhibiting the flame combustion process, a high-temperature expansion layer formed by expansible graphite and a vitrifiable heat insulation layer formed by ceramic forming filler are used for greatly improving the high-temperature stability and heat insulation efficiency of a carbon layer, and the fire endurance of a steel structure is prolonged.

The invention relates to the technical field of unmanned aerial vehicle inspection, and provides an unmanned aerial vehicle-based forest fire prevention inspection and disposal method, device and system. When an inspection unmanned aerial vehicle equipped with a multi-modal sensor is deployed to cruise according to a preset route, a visible light camera of the inspection unmanned aerial vehicle continuously collects forest region videos and transmits the videos to edge computing equipment of an inspection airport; fire hazards are analyzed in real time through a target recognition algorithm, and when a fire source is detected, position information is reported to a background terminal immediately; the system automatically dispatches the nearest fire extinguishing airport to dispatch the fire extinguishing unmanned aerial vehicle, and secondary accurate positioning is performed on a fire source by using the airborne infrared vision module, so that accurate throwing of the fire extinguishing bomb is ensured. The whole system enhances the real-time analysis capability through the edge computing device, breaks through the single-machine computing power limitation, also realizes the full-process automation from fire behavior recognition to fire extinguishing treatment, and significantly improves the response efficiency and treatment safety of forest fire prevention.

The invention relates to the technical field of bridge safety, and discloses a cable system bridge fire risk assessment method, system, equipment and medium, which considers the influence of weather factors and road linetype factors on the increase probability of fire accidents, is based on collision accident fire and vehicle spontaneous combustion fire, and fuses the risk probability and risk consequence analysis to obtain a risk risk assessment result. Obtaining the probability that each part of the cable system bridge to be evaluated is influenced by various vehicle fire disasters, and obtaining the fire risk consequence of the parts of the cable system bridge to be evaluated under the vulnerability of the bridge structure caused by different types of vehicle fire disasters when the parts of the cable system bridge to be evaluated have / do not have fire prevention measures in combination with the structural damage and the social economic influence; therefore, the fire risk level of each component of the cable system bridge to be evaluated is obtained, and the highest risk of the most unfavorable component is taken as the fire risk level of the cable system bridge to be evaluated. The method has important guiding significance for cable system bridge fire risk assessment, fire prevention measure design and protection countermeasure formulation.

The invention relates to the technical field of data analysis, in particular to a fire monitoring and recognition system based on multi-sensor fusion. Comprising a multi-modal sensor data fusion processing module, a fire occurrence probability intelligent evaluation module, a fire occurrence probability real-time prediction module, a multi-level detection and feedback module and an automatic control and emergency response module. Extracting multi-modal fire data fusion features through a data fusion algorithm to capture fire signs; a three-dimensional fire monitoring model is created, and model parameters and performance evaluation are adjusted, so that the fire risk is quickly judged; the probability of fire occurrence in the current monitoring area state is predicted, so that emergency measures can be planned in advance subsequently; fire identification is carried out through a multi-level verification mechanism, the reliability of the system is improved, and the response speed is improved by dividing fire grades and constructing an emergency response strategy library. The invention is used for solving the technical problems of incomplete monitoring and insufficient management and control in current fire prevention and management.