590 results about "Fire behavior" patented technology

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 a scheduling information management method and system based on fire fighting of a fire fighting truck, and relates to the technical field of intelligent fire fighting scheduling, and the method comprises the steps: receiving fire alarm information, and collecting real-time fire scene data based on a preliminary fire behavior evaluation result; combining the collected real-time fire scene data with historical fire data, and analyzing a fire spreading trend through an AI fire prediction model; according to the fire behavior spreading trend and the equipment state of the fire fighting truck, dynamically allocating fire fighting truck resources, and planning an optimal driving route; the driving state of the fire fighting truck is monitored in real time, dynamic adjustment is performed based on road conditions and equipment information, and task allocation of the fire fighting truck is updated according to the real-time fire scene situation; in the fire extinguishing process, resources are dispatched in real time for supply according to the material consumption condition of the fire fighting truck. By monitoring the material consumption condition of the fire fighting truck in real time, the supply resources are intelligently dispatched, and the continuity and high efficiency of the fire extinguishing action are ensured.

The invention discloses an aluminum-plastic composite panel with a fireproof structure, and relates to the technical field of aluminum-plastic composite panels, the aluminum-plastic composite panel comprises a fireproof layer, and a top aluminum plate mounting plate is mounted at the bottom of the fireproof layer. Then, the heat is transferred to a heat-conducting metal conduction sheet plate and then transferred to a wire fixing heat-conducting end, so that the temperature is rapidly increased, the wire fixing heat-conducting end transfers heat to a polyethylene fracture node, so that the polyethylene fracture node is melted and fractured, a polypropylene fiber connecting wire ring losing support also melts, a limiting hook is released, and a needling trigger piece rapidly punctures a flame-retardant gas storage device; the flame-retardant gas is released, and the flame-retardant gas is quickly diffused to the periphery of the flame along with the quick release of the flame-retardant gas to form a protective layer, so that the contact between the flame and surrounding combustible materials is effectively isolated, and further spreading of the fire behavior is slowed down or even stopped.

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 provides a tunnel fire behavior inversion method based on multi-modal fusion and physical constraint, and aims to solve the problem that the position and power of a fire source are difficult to invert accurately in real time in traditional fire behavior monitoring. According to the method, data, temperature distribution images and physical parameters of distributed temperature sensors in a tunnel are collected, and a spatial-temporal feature fusion network is constructed; designing an information leakage prevention training mechanism, and adopting a modal random discarding and sensor shielding technology to avoid overfitting of the model to a specific input mode; a physical constraint loss function is introduced, and the temperature gradient, the maximum temperature rise and the longitudinal attenuation law are combined to ensure the physical rationality of an inversion result; a coarse-fine two-stage position prediction framework is adopted, and meter-scale precision positioning of the position of a fire source is achieved. According to the invention, the power and position of the fire source can be inversed accurately in real time, and reliable technical support is provided for intelligent monitoring and emergency rescue of tunnel fire.

The invention relates to the technical field of fire safety, and discloses a partition controller linkage method, device and equipment and a storage medium. The method comprises the following steps: acquiring original fire sensing data acquired by a plurality of fire-fighting sensors in each independent partition, and performing preprocessing of a plurality of sensing types; extracting space-time dimension features from the preprocessed data, generating fire behavior space-time feature vectors, and performing multi-source evidence weight fusion; performing fire abnormal mode identification and evaluation on the fused fire spatio-temporal feature vector to obtain a partition preliminary fire evaluation result, transmitting the partition preliminary fire evaluation result to a main controller, and performing global fire situation fusion based on a building physical structure and a partition connection relationship to obtain an overall fire situation evaluation result; and generating a multi-partition cooperative fire extinguishing execution scheme based on the overall fire situation assessment result, and controlling fire fighting equipment in each partition to perform linkage fire fighting. According to the invention, multi-source data fusion and cross-partition collaborative linkage of building fire are realized, and the fire detection accuracy and the fire-fighting response efficiency are improved.

The invention discloses a fire alarm linkage system for fire engineering based on the Internet of Things. The operation process of the system specifically comprises the following steps: generating a dynamic risk probability distribution diagram based on a building three-dimensional point cloud model and multi-source data, and deploying a differential sensor array; environment multi-physical field parameters are collected in real time through a differential sensor array, and a multi-mode fire behavior sensing signal set is generated; building a fire situation credibility map: calling a multi-criterion decision module to calculate a linkage equipment weight distribution matrix based on the fire situation credibility map, and generating a multi-stage linkage control instruction set in combination with the building topological structure model and the personnel distribution thermodynamic diagram; and performing multi-cross-system cooperative execution and closed-loop optimization. The method has the following advantages and effects that the inherent limitation of a traditional system can be broken through by constructing an intelligent prevention and control framework combining risk quantification, situation deduction and decision closed loop, the personnel evacuation efficiency is effectively improved, and damage caused by major accidents such as detonation is reduced.

The invention discloses a virtual reality fire drill and training method and system, and the method comprises the steps: building a three-dimensional fire scene based on a BIM model and fluid dynamics, resolving the fire spreading rate and the smoke concentration in real time, and generating a dynamic disaster evolution model in combination with explosive parameters; collecting the heart rate variability and the action track of the user through the wearable device, and calculating a physiological stress index and a device operation deviation degree; smoke suppression, explosion delay and fire behavior correction instructions are triggered based on fire behavior and physiological data association; a cooperative avoidance strategy is generated in combination with the path overlap ratio and the fire-fighting role and is fed back to the fire behavior correction module; aligning the spatio-temporal data to evaluate the contribution degree and the cooperative influence weight of the fire extinguishing operation on fire behavior inhibition, and outputting a multi-dimensional evaluation matrix; generating a three-dimensional defect thermodynamic diagram by using a space-time convolutional network, marking a high-risk area and feeding back the high-risk area to a disaster model, and realizing adaptive difficulty iteration; according to the invention, through multi-source data fusion and dynamic regulation and control, the drill authenticity and the emergency disposal efficiency are improved.

The invention discloses a concrete filled steel tube structure fire early warning method and device based on multi-agent cooperative detection, and belongs to the technical field of concrete filled steel tube structure fire early warning. The invention discloses a concrete filled steel tube structure fire early warning method and device based on multi-agent cooperative detection. The method comprises the following steps that S1, a fire early warning threshold value is set; s2, collecting real-time physical state data of the concrete-filled steel tube structure; s3, performing cross validation on the fire signal; s4, comparing the composite fire behavior index with a set fire early warning threshold value; and S5, sending out corresponding fire early warning information. According to the invention, the problem that a fire detection system for detecting a single parameter in the prior art cannot accurately distinguish a real fire source from environmental interference so as to cause false alarm is solved, compared with single-point monitoring, early fire characteristics such as local temperature abnormity and a smoke diffusion path can be accurately captured, the false alarm rate is obviously reduced, and the system is suitable for popularization and application. And the accuracy of fire early warning is ensured.

The invention provides an unmanned aerial vehicle inspection method, system and device and a storage medium, and relates to the technical field of unmanned aerial vehicle inspection, and the method comprises the steps: dividing a to-be-inspected fire region into a plurality of sub-regions, and determining the number of unmanned aerial vehicles required by each sub-region and a first inspection route according to the region area and the region feature of each sub-region; according to the fire behavior data of each sub-region, predicting a fire behavior spreading range of each sub-region in a preset duration, and determining a newly added to-be-inspected sub-region of each sub-region; determining a second inspection route of each unmanned aerial vehicle by combining the target region area and the target region feature of the newly added sub-region to be inspected and the residual electric quantity and the target position of each unmanned aerial vehicle; and sending the first inspection route and the second inspection route to each unmanned aerial vehicle, so that each unmanned aerial vehicle performs inspection according to the first inspection route and the second inspection route. The technical effect of the invention is that the monitoring requirement of dynamic spreading of forest fire can be effectively met.

The invention relates to the technical field of urban fire safety rescue, in particular to a low-altitude fire fighting system and method based on urban CIM and Beidou positioning. According to the system and the method, full space-time accurate positioning is realized through the Beidou differential base station and the UWB indoor positioning equipment; integrating the urban information model system and unmanned aerial vehicle cluster data, and constructing a multi-source heterogeneous data set; a coordinate system is unified based on a space-time reference calibration module, and fire behavior prediction, path planning and resource scheduling are realized through AI computing nodes; the unmanned aerial vehicle task distribution module is used for realizing multi-vehicle cooperative rescue; and iteratively optimizing system parameters through a closed-loop adaptive optimization module. The method is suitable for fire emergency rescue in an urban complex building environment, and the rescue response speed, the decision-making precision and the cooperative combat capability are remarkably improved.

The utility model discloses a control cabinet with an isolation structure, and relates to the technical field of control cabinets, the control cabinet comprises a control cabinet body, the front surface of the control cabinet body is rotatably connected with a cabinet door, and the top end of the inner wall of the control cabinet body is provided with an isolation assembly. A pair of fire extinguishing tanks extending into the control cabinet body is mounted on the rear surface of the control cabinet body; the isolation assembly comprises a storage frame fixed to the inner wall of the top end of the control cabinet body. The control cabinet on fire is blocked through the isolation assembly, fire is prevented from spreading towards the two sides to burn other control cabinets or articles, the operation space on the front side of the control cabinet is guaranteed, internal components can be disassembled and assembled manually in the follow-up process, meanwhile, people do not need to get close to operate the blocking assembly to open the control cabinet, and operation is convenient. And after blocking, the fire extinguishing tank is controlled to spray dry powder into the cabinet body, so that expansion of the fire behavior is inhibited, and sufficient time is provided for subsequent fire extinguishing of firefighters.

The invention relates to the technical field of image recognition, in particular to a fire behavior recognition method and system based on video monitoring. The technical problem that the early warning reliability of a video monitoring system on fire smoke is insufficient is solved. The method comprises the following steps: acquiring a monitoring video stream of a monitoring area and time sequence data acquired by a sensor in the monitoring area; determining an evaluation value of the to-be-detected area based on the image feature and the morphological change of the to-be-detected area; determining a confidence factor based on the form change trend of the to-be-detected region and the change trend of the time series data; determining a smoke authenticity value based on the evaluation value and the confidence factor; and adjusting an alarm threshold value of the smoke alarm based on the smoke authenticity value. The method is used for fire smoke early warning and monitoring scenes.

The invention provides an intelligent fire extinguishing strategy control method for an autonomous flight fire extinguisher, and belongs to the technical field of intelligent navigation fire extinguishment. The method comprises the following steps: firstly, acquiring fire scene data through a multi-source sensor, and constructing a fire behavior positive and negative development matrix; then, route planning is carried out based on a pre-trained deep learning model, the model adopts a special convolution kernel structure to extract fire scene features, and a decision is optimized through a mathematical calculation module; and the system updates a fire matrix in real time, dynamically adjusts a fire extinguishing strategy, and finally completes a precise fire extinguishing task through a time sequence aligned control instruction. According to the method, efficient control over the autonomous flight fire extinguisher in the complex environment is achieved through data driving and intelligent decision making, and the technical problem that in the prior art, an autonomous flight fire extinguisher is difficult to achieve precise fire extinguishing path planning and fire extinguishing strategy control in the complex fire environment is solved.

The invention relates to the technical field of fire safety, in particular to a multi-dimensional fire safety management system which comprises an environment monitoring module, a data analysis module, a real-time video monitoring module, an intelligent decision module and a self-adaptive evacuation navigation module. The data analysis module processes and analyzes the collected environmental data, the real-time video monitoring module monitors the internal situation of the building by adopting a high-resolution camera, the intelligent decision-making module generates a fire coping strategy, and the self-adaptive evacuation navigation module dynamically generates and updates a safe evacuation route in the building. The self-adaptive evacuation navigation module is introduced, so that a safe evacuation route in the building can be dynamically generated and updated in real time. The system can adjust the evacuation route according to the current environmental condition by analyzing the fire behavior development and the personnel distribution condition in the building in real time, thereby effectively preventing the personnel from entering a high-risk area by mistake, and ensuring that the personnel can be quickly and orderly guided to a safe area in an emergency.

The invention belongs to the field of fire safety, relates to a data analysis technology, is used for solving the problem that a fire safety monitoring system in the prior art cannot automatically screen a fire behavior control processing decision according to a field environment, and particularly relates to an intelligent building fire safety monitoring system based on artificial intelligence, which comprises a safety monitoring platform, the safety monitoring platform is in communication connection with a safety monitoring module, a risk analysis module, a decision processing module and a database, and the risk analysis module is also in communication connection with an instant control module; the safety monitoring module is used for carrying out fire safety monitoring analysis on the intelligent building; according to the method, the spreading coefficient can be obtained through calculation in combination with the multiple risk parameters about the fire point in the risk data set, the fire spreading trend of the floor where the fire point is located is evaluated according to the spreading coefficient, then the most appropriate control means is adopted for fire control according to the evaluation result, and the fire handling efficiency is improved.

The invention relates to an intelligent partitioned fire extinguishing electrical cabinet, and effectively solves the problems that a fire extinguishing agent cannot cover an ignition point and the fire behavior is easy to spread. According to the technical scheme, the fire extinguishing device comprises an outer box body, and a support assembly and a fire extinguishing assembly are arranged in the outer box body; a cabinet door is arranged on the front side of the outer box body, supporting plates are arranged on the left side and the right side of the support assembly, a plurality of ventilation openings are formed in the supporting plates, a plurality of blocking pieces are arranged in each ventilation opening, and the blocking pieces can block the ventilation openings after rotating synchronously. A plurality of partition plates and cross beams are arranged between the two supporting plates, and the partition plates divide the interior of the support into a plurality of cavities. A plurality of threading holes are formed in each partition plate; the fire extinguishing assembly comprises a fire extinguisher, an outlet of the fire extinguisher is connected with a pipeline, a branch pipe located in each cavity is arranged on the pipeline, and an electromagnetic valve is arranged at the end of each branch pipe; according to the intelligent fire extinguishing device, the heat dissipation design is considered, meanwhile, intelligent partition fire extinguishing can be achieved, and the ignition point is completely covered.

When the intelligent fire-fighting unmanned aerial vehicle cluster system works, the method comprises the following steps that 1, an intelligent fire-fighting unmanned aerial vehicle cluster detects a fire scene and searches a water source near the fire scene, and an optimal route is searched between the water source and the fire scene to lay a pressurizing water pump and a fire hose; 2, the intelligent fire-fighting unmanned aerial vehicle cluster controls a fire-fighting nozzle to conduct fire extinguishing operation, and the positions of a pressurizing water pump and a fire hose are adjusted according to fire behavior changes of a fire scene; and 3, the intelligent fire-fighting unmanned aerial vehicle cluster performs handover control on a fire-fighting nozzle, a pressurizing water pump and a fire-fighting hose in turn, and fuel oil is supplemented to the pressurizing water pump until fire extinguishing operation is finished. A fire-fighting water supply channel can be laid between a water source and a fire scene at an ultra-fast speed, the fire extinguishing operation speed is high, manual intervention is not needed, and nearly infinite fire-fighting water can be continuously provided.

An indoor fire source positioning method based on an infrared thermogram of the outer surface of a glass curtain wall comprises the following steps: database construction, which mainly comprises construction of a fire data set, temperature distribution of the outer surface of the glass curtain wall, and image extraction and processing; a Resnet deep learning model is constructed and trained; and carrying out fire source positioning by using a Resnet deep learning model. In the early stage of fire disaster development, when fire behavior reconnaissance of a super high-rise building fire disaster is carried out, a reconnaissance unmanned aerial vehicle carrying an infrared thermal imager is controlled to fly out of a glass curtain wall of a fire floor, and the position of the reconnaissance unmanned aerial vehicle is adjusted to obtain a complete glass curtain wall outer surface infrared thermal image of the fire floor; an infrared thermal image of the outer surface of the glass curtain wall acquired by the thermal infrared imager is input into the trained Resnet-50 deep learning model, the model analyzes the infrared thermal image to give a position number of a fire source, the position of the fire source can be accurately recognized in the early stage of fire development, the real-time performance and reliability are high, and the fire early warning capacity and rescue efficiency of the super high-rise building are improved.

The invention discloses a subway tunnel fire behavior spreading prediction method, which comprises the steps of 1, fire source type digital modeling, 2, fire scene initialization, 3, dynamic fire source loading and ignition triggering, 4, fire behavior spreading multi-physics field coupling calculation, 5, ventilation system dynamic response, 6, visual risk thermodynamic diagram generation, and 7, fire behavior spreading prediction. Step 7, carrying out real-time early warning on the safety margin; the method has the beneficial effects that a total factor modeling capability is realized, multi-dimensional parameter input from fire source characteristics to a tunnel environment is supported, a scene range covering a typical subway fire disaster is expanded, solving is realized through a multi-physics field simultaneous equation, a fire spreading error is reduced, smoke diffusion simulation precision is improved, and a dynamic ventilation adjustment and safety early warning mechanism is realized. And more buffer time is provided for emergency response.

The invention is suitable for the technical field of mountain fire disaster monitoring and early warning, and provides a power distribution network mountain fire disaster state monitoring and early warning system, which comprises a multi-source data processing module, a space-time coupling monitoring module, an intelligent response module and a remote management platform, the multi-source data processing module is used for integrating and processing satellite remote sensing data, meteorological data, topographic data and power distribution network topology data; the space-time coupling monitoring module is connected with the multi-source data processing module and is used for dynamically analyzing the space-time characteristics of the fire behavior; the intelligent response module is connected with the space-time coupling monitoring module and is used for executing hierarchical prevention and control operation according to a fire behavior space-time characteristic analysis result; the remote management platform is connected with the space-time coupling monitoring module and the intelligent response module and used for outputting a forest fire disaster early warning scheme according to the fire behavior space-time characteristic analysis result and the graded prevention and control operation process, the emergency evacuation efficiency can be improved, and power grid facility damage and economic loss caused by fire behavior diffusion are reduced.

The embodiment of the invention provides a fire behavior detection method, device and equipment, a medium and a computer program product. The fire behavior detection method comprises the following steps: acquiring an environment temperature value of a target area in each unit monitoring time; generating a plurality of groups of temperature difference values of the target area according to each acquired environment temperature value; judging whether at least one group of temperature difference values in the plurality of groups of temperature difference values is greater than a preset temperature difference value threshold value in the fire monitoring parameters or not; the fire monitoring parameter is used for indicating that the target area has a fire; and determining whether a fire occurs in the target area or not according to a judgment result. According to the embodiment of the invention, the false alarm generated when the fire behavior is detected only through a temperature sensor or other sensors is avoided, and the fire behavior detection accuracy is improved.

The utility model relates to the technical field of fire fighting equipment, in particular to a fire extinguishing device for an unmanned mine car, which comprises a fire extinguisher with a self-starting function, a temperature detection device, a pipeline and a plurality of sprayers, the spray head is connected with the fire extinguisher with the self-starting function through the pipeline; the plurality of nozzles are respectively aligned with a wiring harness, an oil pipe and an engine exhaust pipe area of the unmanned mine car; the temperature detection device is mounted in the areas of a wiring harness, an oil pipe and an engine exhaust pipe of the unmanned mine car and is electrically connected with the fire extinguisher with the self-starting function; when the temperature detection device detects that the temperature exceeds a preset value, a signal is fed back to the fire extinguisher with the self-starting function, the fire extinguisher with the self-starting function starts the fire extinguishing function and sprays a fire extinguishing agent outwards through the spray head, and the sprayed fire extinguishing agent immediately extinguishes the ignition part in the spray head installation area. Therefore, the purposes of extinguishing flames and controlling fire behaviors are achieved.

The invention discloses an automatic patrol intelligent fire extinguishing method and control system based on data analysis, and relates to the technical field of intelligent fire extinguishing control, and the method comprises the steps: obtaining multi-dimensional data through a multi-dimensional data integration platform, and carrying out the preprocessing; calculating a multi-dimensional risk assessment index based on the preprocessed data, performing nonlinear standardization, constructing a multi-dimensional risk assessment feature vector, and performing clustering analysis on all the to-be-patrolled areas based on the multi-dimensional risk assessment feature vector according to a distance metric function; according to the clustering analysis result, comprehensive risk score calculation is carried out on all the to-be-patrolled areas, a priority to-be-patrolled list is generated, automatic patrolling is carried out on the to-be-patrolled areas, and when a fire behavior is detected, fire extinguishing equipment in the areas is started to extinguish fire. Key elements of fire occurrence and spreading are comprehensively described through multi-dimensional risk assessment, and risk scoring and grading are performed on all to-be-patrolled areas after clustering analysis, so that dynamic priority patrolling of high-risk areas is realized, and the patrolling efficiency is effectively improved.

The invention relates to the technical field of comprehensive pipe galleries, and provides a comprehensive pipe gallery cable fire intelligent identification and prevention and control linkage system. In the system, fire simulation is carried out on a comprehensive pipe gallery cable through a cable fire simulation device; the fire behavior of the cable in the pipe gallery is monitored in real time through the image acquisition unit, and the acquired simulated fire image information is transmitted to the deployed fire identification model in real time for fire identification, so that the fire identification result of the fire identification model is judged through the deployed fire early warning model. A fire extinguishing instruction or an early warning instruction is sent out according to the judgment result; further, the fire extinguishing linkage device responds after receiving the fire extinguishing instruction, and a self-made fire resisting shutter with a flexible steel brush is firstly started to block a fire point area; and then a telescopic end of an electric push rod mounted at the top of the pipe rack quickly extends out to crush a temperature sensing glass ball of the suspension type fire extinguisher, so that quick linkage prevention and control of the pipe rack cable fire are realized.

The invention discloses a superfine dry powder automatic fire extinguishing method and system, and the method comprises the following steps: collecting multi-source parameters of a monitoring area according to a preset time interval; based on the multi-source parameters, whether an ignition point exists in the monitoring area or not is judged, and if yes, the first environment temperature rising rate, the smoke concentration change gradient and the first flame energy ratio are calculated; according to the temperature rising rate, the smoke concentration change gradient and the flame energy ratio, the fire behavior grade of the ignition point is judged; based on the fire behavior grade, a superfine dry powder spraying device is controlled to conduct graded spraying fire extinguishing; after graded jet fire extinguishing is conducted, the second temperature rising rate and the second flame energy ratio of the ignition point are calculated according to the preset time interval; and whether the ignition point has a reburning risk or not is judged based on the second temperature rising rate and the second flame energy ratio, and if yes, the superfine dry powder jetting device is controlled to conduct supplementary jetting.

The invention is suitable for the technical field of smoke alarm, and particularly relates to a smoke alarm method and system based on cloud management, and the method comprises the steps: building a smoke early warning model based on the installation position of a smoke alarm; loading a smoke early warning model of the corresponding area according to the position of the smoke alarm; acquiring smoke concentration data of the smoke area, dividing the smoke area into a plurality of detection grid units, and performing fire behavior simulation to obtain fire behavior simulation data; and acquiring the real-time concentration data of each smoke alarm, calculating the matching relationship between the fire behavior simulation data and the real-time concentration data, determining the position of the fire behavior, and sending out fire behavior early warning through the cloud server. According to the invention, when the smoke concentration is increased preliminarily, the smoke early warning model is used for fire behavior simulation, and the occurrence area of the fire behavior is determined according to the simulation result and the actually detected smoke concentration change, so that the purpose of fire behavior early warning is achieved, the source of the fire behavior is disposed in advance, and the safety is improved.

The invention relates to the technical field of intelligent fire fighting, and discloses an intelligent fire fighting system with an automatic fire behavior monitoring function, a target area is divided into a plurality of sub-areas, and remote sensing images of each sub-area are collected according to a fixed time interval; mapping each sub-region into a topological node, and constructing a topological edge between the topological nodes corresponding to the adjacent sub-regions to obtain a topological structure; analyzing the remote sensing image through a preset image analysis model to obtain a fire score of each sub-region, and mapping the fire score into a feature value of a corresponding topological node; and obtaining a characteristic value of a topological node of the time sequence based on the remote sensing image of the time sequence, combining the environment information of the target area to obtain an updating model of a topological structure, obtaining a sub-area corresponding to the rescue task, and obtaining a rescue scheme of the rescue task through the updating model of the topological structure.

The invention relates to the technical field of fireproof safety, in particular to a cable type linear independent temperature sensing terminal which comprises a cable type temperature sensing cable module, a data processing module, a self-adaptive fire behavior detection module, a fire behavior analysis and judgment module, a fire extinguishing device control module and a power failure guarantee module. Wherein the cable type temperature sensing cable module is used for detecting temperature data of different positions; the data processing module is used for formatting the temperature data; the self-adaptive fire detection module is used for dynamically adjusting a fire detection threshold value according to environmental conditions; the fire analysis and judgment module is used for judging whether a fire exists or not; the fire extinguishing device control module is used for starting a fire extinguishing device to execute fire extinguishing operation; and the power-off guarantee module is used for providing a standby power supply. According to the invention, through self-adaptive fire behavior detection, accurate fire behavior analysis and judgment, and starting and power-off guarantee of the automatic fire extinguishing device, the accuracy and response speed of fire monitoring of the electrical cabinet are improved, and the loss caused by the fire is effectively reduced.

The invention discloses a forest fire remote sensing intelligent monitoring integrated system. The system comprises a multi-dimensional sensing subsystem which is responsible for collecting data related to a fire; the unified data fusion platform module is responsible for performing data cleaning, verification, integration and analysis on the data related to the fire to generate fire monitoring information; the intelligent identification and early warning module is responsible for carrying out intelligent analysis on the fire monitoring information and identifying a potential fire behavior; the fire danger grade evaluation module is responsible for evaluating the fire danger grade in real time; the real-time data quality control module is responsible for carrying out real-time monitoring and quality control on data input into the system; the user interaction module is used for providing a user interaction interface; according to the invention, a richer and more comprehensive flame and smoke sample library is established to cover fire behavior data under different time and different environment conditions, and the sample library is periodically updated and expanded to adapt to continuously changing monitoring requirements.