795 results about "Fire hazard" patented technology

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 discloses a forest fire hidden danger identification method and device for a power transmission line based on a multi-modal large model, and the method comprises the steps: constructing a forest fire hidden danger identification model for a smog or flame-containing image outputted by a conventional target detection model, and enabling the model to guide a dialogue set through the combination with forest fire hidden danger identification, thereby achieving the recognition of the forest fire hidden danger. The dialogue context and the image visual features are fused, so that the deep fusion of the text semantics and the firework image features is realized; and finally, inputting the deeply fused features into a large language model, so that a forest fire hidden danger output dialogue of the power transmission line can be obtained by utilizing the deep semantic understanding capability of the large language model, and then a forest fire hidden danger recognition result of the power transmission line is obtained. Therefore, according to the invention, the judgment of whether the smoke and fire can damage the power transmission line is realized, the technology upgrade from smoke and fire existence detection to equipment threat research and judgment is completed, and on the basis, the problems of resource waste and untimely crisis response caused by reporting all smoke and fire information in the traditional technology can be avoided.

The invention relates to the technical field of fire safety, in particular to an intelligent fire-fighting electrical fire monitoring method, device and equipment and a medium, and the method comprises the steps: obtaining electrical parameters; according to the electrical parameters, a fire hazard identification model is established by using an LSTM algorithm and a CNN algorithm, the operation state of the electrical equipment is predicted according to the fire hazard identification model, an operation state trend prediction result is obtained, and a potential fire hazard type is identified; according to a fire hazard type identification result, in combination with a use environment of electrical equipment and a threshold determination algorithm, performing quantitative evaluation on a fire hazard risk to obtain a corresponding risk level, and when the risk level exceeds a set multi-layer threshold, automatically starting a linkage control mechanism; therefore, the problems of response lagging, high labor cost, high intermittent fault omission ratio and the like in the prior art are solved.

The invention relates to the technical field of electric bicycles, and discloses a data-driven electric bicycle battery fire hazard detection and protection method, which comprises the following steps: S1, multi-modal data acquisition and fusion, S2, data preprocessing and feature extraction, S3, data-driven intelligent detection: classifying normal and abnormal states through a supervised learning model, S4, hierarchical protection strategy, and S5, data-driven intelligent detection. S5, cloud data optimization and model continuous learning; S6, model data updating; S7, intelligent linkage and long-term optimization; S8, cloud risk data acquisition; and low, middle and high three-level protection mechanisms are adopted. Through dynamic risk assessment, charging and discharging power is limited in a medium risk, a charger is automatically disconnected, a user is prompted to stop operation, active protection is triggered in a high risk, and sound-light alarm and remote notification are triggered at the same time, so that accidents are prevented from further spreading.

The invention relates to the technical field of fire protection and security, in particular to a security and disinfection integrated multi-modal fusion embedded edge calculation method and system, and provides a security and disinfection integrated multi-modal fusion embedded edge calculation method which comprises the following steps: constructing a data set; the AI visual recognition model uses an improved YOLOv8 model to train a data set and execute target detection, meanwhile, an optical flow method is adopted to extract motion features of flames or smog in continuous key frame images in a monitoring video, and a spreading prediction model is established to send out an early warning signal in advance; and the fusion processing module establishes a dynamic threshold adjustment model based on the environmental parameters to optimize an early warning trigger threshold, and transmits the early warning trigger threshold to the fire-fighting linkage control module to verify and trigger fire-fighting linkage operation. AI intelligent analysis and fire-fighting linkage disposal of fire hazards are realized, false alarm of early warning information is effectively prevented, the discovery and disposal efficiency of the fire hazards is improved, and the fire hazards are eliminated in the bud.

The invention relates to the technical field of fire alarm, in particular to a fire ground-air integrated monitoring and early warning system and method, and the system comprises a flame and smoke recognition module which collects image data from a ground monitoring camera and an air unmanned aerial vehicle, carries out the feature extraction of the image data, generates a feature data set, and carries out the early warning based on the feature data set. And judging existence of flames and smog by using image analysis, and generating a fire identification result. According to the invention, multi-source heterogeneous data obtained by the ground monitoring camera, the aerial unmanned aerial vehicle and the infrared thermal imaging equipment are integrated for collaborative analysis, so that high-precision feature extraction of fire hazards and judgment of fire behaviors are realized, and the accuracy and response efficiency of fire recognition are improved; the unmanned aerial vehicle is used for dynamically collecting air thermal imaging data in real time, and an alarm is quickly formed based on temperature abnormal points, so that an abnormal temperature area can be timely positioned and pre-warned in the early stage of a disaster.

The invention discloses a cable tunnel fire risk feature identification system and identification method, and belongs to the technical field of cable tunnel safety monitoring. The whole stage of a fire is covered through multi-class cooperative detection, the target identification precision and scene adaptability are greatly improved, and the safety of the cable tunnel is improved. According to the whole system, a flame and smoke detection unit and a flame / smoke special detection unit are innovatively added to a detection engine module, an original heat source detection unit and an original human body detection unit are combined, a heat source-flame-smoke-human body four-category collaborative detection framework is formed, high-precision recognition is achieved on the basis of a YOLO model framework, and the detection efficiency is improved. Compared with the problems that a traditional system is high in single target detection omission ratio and cannot cover the whole stage of smoldering-initial open fire-violent combustion of a fire, the system has the advantages that the recognition rate of early flame and weak smoke is increased, the false alarm rate is greatly reduced, meanwhile, a heat source of operation and maintenance personnel is prevented from being misjudged as a fire hazard through human body detection, and the safety of the fire hazard is improved. And cable monitoring and personnel safety protection are both considered.

The embodiment of the invention relates to the technical field of fire spreading prediction, and particularly discloses a fire spreading trend AI prediction method and system based on a high-rise building. According to the embodiment of the invention, fire alarm information is received; acquiring smoke monitoring data of the target fire building, and performing smoke spreading prediction on the smoke monitoring data; selecting an auxiliary monitoring building; flame monitoring data transmitted by the auxiliary monitoring building are acquired, and flame spreading prediction is carried out; aI prediction of fire hazards and visual display of the fire spreading trend are carried out. Smoke monitoring data of a target fire building can be acquired, auxiliary monitoring analysis is performed, an auxiliary monitoring building is selected, flame monitoring data transmitted by the auxiliary monitoring building is acquired, and AI prediction of fire hazards and visual display of a fire spreading trend are performed by integrating a smoke prediction result and a flame prediction result. The defect of single data acquisition is overcome, and the accuracy of fire spreading trend prediction is effectively ensured in combination with flame monitoring of an auxiliary monitoring building.

The invention belongs to the technical field of fire-fighting supervision, and particularly relates to an intelligent fire-fighting alarm system and method based on regionalized monitoring, and the system comprises a regionalized monitoring network module, an intelligent data analysis module, an early warning information issuing module, an emergency evacuation plan module, a fire-fighting facility linkage module, and a regional fire-fighting supervision terminal. Environmental parameters in a target area are comprehensively collected in real time through the regionalized monitoring network module, the intelligent data analysis module identifies a fire disaster based on the collected data, quick response and emergency evacuation are performed after the fire disaster is identified, loss caused by the fire disaster is effectively reduced, and the safety of the fire disaster is improved. After fire early warning is removed, fire hazards are accurately analyzed and fed back in time, regional fire control management personnel can master the severity degree of each fire in detail, and regional fire control safety supervision is enhanced in time by comprehensively evaluating and analyzing the regional fire control potential safety hazard degree; the regional fire-fighting hidden danger is obviously reduced; and the regional fire-fighting management difficulty is reduced.

The invention discloses a building outer wall fire fighting system based on an unmanned aerial vehicle and a fire extinguishing method thereof, and belongs to the technical field of fire fighting, the building outer wall fire fighting system comprises fixed fire fighting equipment and mobile fire fighting equipment, the fixed fire fighting equipment is arranged on a building body, and the mobile fire fighting equipment is in butt joint with the fixed fire fighting equipment according to fire conditions; and the mobile fire-fighting equipment and the fixed fire-fighting equipment are electrically connected with the fire-fighting control center. The fire early warning cameras and the smoke alarm are arranged on the periphery of the roof parapet wall and used for discovering a fire in the early stage; the outer wall fire-fighting vertical pipe and the fire-fighting power supply are connected with a fire-fighting system of the whole building, and the unmanned aerial vehicle obtains a water source and the power supply through an adapter on the outer wall fire-fighting vertical pipe, so that the unmanned aerial vehicle can continuously work until a building fire is successfully extinguished; the building builds a three-dimensional fire-fighting network by means of indoor and outer wall fire-fighting systems, and the internal and external fire-fighting systems cooperate with each other to accelerate fire extinguishing of the building, reduce fire hazards and guarantee personal and property safety.

The invention relates to the technical field of data processing, and discloses a fire scene situation awareness and early warning method and system. The method comprises the following steps: performing gridding segmentation through infrared thermal imaging and visible light fusion, identifying similar electrical equipment and calculating a temperature baseline deviation value and an abnormal propagation coefficient, constructing a heat conduction coupling matrix to predict an abnormal diffusion probability, determining a fire development stage and generating a graded coloring situation visualization graph. According to the invention, the problem that a traditional infrared thermal imaging technology cannot realize intelligent identification of similar electrical equipment and prediction of a temperature anomaly propagation path is solved, and the accuracy of electrical fire hazard monitoring is improved.

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, which relates to the technical field of fire safety, discloses a multi-modal intelligent identification method for fire-fighting hidden troubles based on a large model, comprising the following steps: S1, acquiring multi-source heterogeneous feature data through a sensor network deployed in a fire-fighting water pump room to obtain an initial feature set; s2, performing unified scale conversion on the initial feature set, calculating heterogeneous bridging parameters according to the environmental parameters and the equipment operation data, constructing a fusion feature vector, inputting the fusion feature vector into a preset neural network for feature weight adaptive adjustment, and outputting an adjusted weight vector; according to the fire-fighting hidden danger intelligent identification method based on the large-model multi-mode, intelligent closed loop from hidden danger identification to prevention and control decision making is realized, and the intelligent, dynamic and precise management level of a fire-fighting safety system is comprehensively improved.

The invention discloses a fire positioning method based on heat source gradient identification, and belongs to the technical field of fire positioning, and the method specifically comprises the steps: obtaining a two-dimensional temperature distribution image of a fire scene; drawing an isotherm, and marking a potential fire source area based on a preset temperature threshold value; calculating the area of each potential fire source region, screening out an undetermined region from the potential fire source regions, calculating the temperature change rate of a central isotherm of the undetermined region in unit time, and screening out a candidate region from the undetermined region; marking a plurality of points on the peripheral isotherm of the candidate area at equal intervals, generating a temperature gradient path in the reverse direction of the temperature gradient based on each point, calculating the temperature gradient of each path, obtaining the candidate area with the temperature gradient mean value larger than a threshold value, and marking the candidate area as an initial fire source area; continuously drawing vertical lines into the central isotherm from a tangent line of the intersection point on the central isotherm, and marking the intersection point of the vertical lines as an initial fire source center point; according to the invention, rapid positioning of the initial fire source center in the fire scene is realized.

The invention relates to the technical field of fire early warning, and particularly discloses a fire-fighting electrical fire early warning method and system based on the Internet of Things, and the method comprises the steps: configuring an edge computing gateway, and collecting a current waveform signal and a high-frequency electromagnetic radiation signal of an electrical loop based on the edge computing gateway; extracting harmonic characteristics and electromagnetic radiation characteristics; constructing a risk value prediction and propagation model based on the graph neural network model according to the power grid topological structure, generating a data packet when the coupling relationship between the harmonic characteristics and the electromagnetic radiation characteristics meets a joint triggering condition, inputting the data packet into the risk value prediction and propagation model, and transmitting the data packet to the power grid. A dynamic risk value and a fault source position are output based on a risk value prediction and propagation model, through bimodal acquisition, electrical fire hazards can be found in an early stage, real-time feature extraction is performed by using an edge computing gateway, the data transmission amount is reduced, modeling is performed on power grid topology through a graph neural network model, a fault propagation path can be accurately predicted, and the fault propagation efficiency is improved. And the early warning precision is improved.

The invention belongs to the technical field of fire safety, and particularly relates to a control method of a fire detection alarm. Firstly, a distributed sensor network is deployed to collect temperature and smoke data, self-calibration is carried out, and abnormal data is eliminated. Then, through three-level progressive anomaly screening, namely, first-level preliminary screening monitoring mutation features and second-level feature verification, a space-time matrix is constructed, CNN analysis is used for the space-time matrix, third-level scene adaptation is combined with historical data to calculate the probability, and an anomaly screening signal is output. A visual camera is used for collecting images to generate a visual evidence matrix, a sensor data vector and an abnormal screening signal are combined to calculate a comprehensive confidence coefficient, and an alarm signal is triggered if the comprehensive confidence coefficient is larger than a threshold value; and finally, through time window sliding verification taking one minute as a period, determining an alarm when an alarm signal is greater than or equal to three periods. According to the method, the data accuracy is improved, fire hazards are accurately identified, the false alarm rate is reduced, and the alarm timeliness and accuracy are guaranteed.

The invention relates to the technical field of fire safety inspection, in particular to a fire safety inspection method and system based on machine vision recognition. The method comprises the following steps: carrying out inspection equipment deployment on an inspection area to obtain a camera deployment scheme and a sensor network deployment scheme; performing multispectral dynamic sensing according to the camera deployment scheme to obtain a time sequence spectral flow; performing multispectral channel motion estimation on the time sequence spectral flow, and performing motion vector noise suppression to obtain a filtered motion vector diagram; performing spectral characteristic-based motion enhancement on the filtered motion vector diagram to obtain a spectral enhanced motion vector; and performing motion amplification algorithm application on the time sequence spectrum flow according to the spectrum enhanced motion vector, and constructing an enhanced motion vector field to obtain the enhanced motion vector field. According to the invention, through multi-modal information fusion, context awareness and capturing of early fine features, early and accurate fire hazard identification and early warning are realized.

The invention discloses a new energy automobile fire monitoring, early warning and emergency method and device based on the Internet of Things, and relates to the technical field of fire early warning and emergency. In the data acquisition stage, the charging state and environmental data of a charging area are monitored in real time through a multi-mode sensor, and data fusion processing is achieved through an Internet of Things platform; then, in a risk assessment stage, a deep learning model is introduced to dynamically generate a risk score in combination with historical data and real-time data, grading early warning conditions are further defined, and when data such as the temperature of the charging pile and the gas concentration are abnormal but do not reach a fire hazard standard, secondary early warning is triggered. Suspending power supply and ventilation equipment, and recording detailed abnormal data; when the smoke sensor and the temperature sensor jointly confirm the fire risk, primary early warning is triggered, linkage response is started, and the accuracy and response speed of early warning are greatly improved through intelligent evaluation and a linkage mechanism.

The invention discloses a fire early warning method and device based on sensing parameter fusion and a medium, belongs to the technical field of fire prediction, and is used for solving the technical problems that existing fire early prediction is relatively difficult, relevance among sensing data is easily ignored, and fire early warning and recognition of fire early features are not facilitated. The method comprises the following steps: monitoring an environment state in an indoor building in real time to obtain initial environment parameter data; performing environmental parameter numerical value change calculation on the initial environmental parameter data in the previous time node and the current environmental parameter data in the current time node; performing dynamic analysis on a plurality of environmental parameters in the environmental parameter change rate under related time sequence relevance to determine abnormal environmental parameter information; performing positioning processing on the abnormal environment parameter information in a fire hazard local area to obtain an abnormal environment subarea; and performing real-time smoke detection processing related to smoke sensing characteristics on the abnormal environment partition to obtain a smoke detection result.

The invention belongs to the technical field of artificial intelligence, and discloses a fire spreading path dynamic prediction method and system based on artificial intelligence. The method comprises the following steps: acquiring image data, sensor data, meteorological data, fire spreading rate and burnt area of a fire scene; performing feature extraction on the image data to obtain image features; performing feature extraction on the sensor data to obtain environment features; performing feature extraction on the meteorological data to obtain meteorological features; calculating correlation weights among the different modal features, and performing weighted fusion on the different modal features based on the correlation weights to obtain aligned feature vectors; dynamically adjusting a model prediction time step length based on a reinforcement learning mechanism to obtain a dynamic time step length; performing fire hazard path dynamic prediction based on a path prediction model according to the dynamic time step length; according to the method, the change of the fire spreading path can be carefully captured, so that the prediction accuracy is improved.

The invention discloses a scene preprocessing method for fire simulation, and relates to the technical field of building fire simulation, and the method comprises the steps: calculating a three-dimensional thermal weight through employing a fire propagation dynamics model, and generating a fire dangerous region thermal diagram; through a tensor field generation algorithm, fire behavior main diffusion direction and each axial anisotropic parameter are extracted, and fire behavior grid parameters are generated; through a hybrid grid generator, performing hexahedron dominant grid construction and increasing the grid density of a fire behavior dangerous area to obtain a fire behavior optimization hybrid grid; performing Reynolds number verification and boundary layer matching on the fire behavior optimization hybrid grid through a fire behavior feature adaptive algorithm, and performing iterative correction to generate a fire simulation correction grid; according to the invention, through the hybrid grid generator, the self-adaptive distribution of the grid density of the fire hazard area and the smooth connection of the transition area are realized, so that the grid distribution of the fire hazard area is accurately matched with the fire spreading characteristics.

The invention discloses an emergency linkage control system for oil depot fire hazard real-time monitoring, and relates to the technical field of safety monitoring, the emergency linkage control system comprises an emergency linkage control center, the emergency linkage control center is in communication connection with the following modules: a multi-source data fusion sensing module used for constructing a multi-mode sensor network, and collecting multi-source heterogeneous data in real time, carrying out fusion analysis, and generating a hidden danger dynamic distribution diagram. Real-time high-frequency acquisition of oil depot environment, equipment and disaster evolution data is realized by constructing a multi-mode sensor network and integrating various sensors, data noise is eliminated and a hidden danger dynamic distribution diagram is generated in combination with a spatio-temporal data fusion engine, so that the monitoring data covers the whole area of the oil depot and the time resolution reaches a second level; compared with a traditional threshold triggering mode, the system can dynamically capture multi-source heterogeneous data of the key area of the oil depot, discover the hidden danger evolution trend in advance, shorten the disaster early warning time and remarkably improve the active suppression capability.

The invention discloses a continuous mining and continuous filling recovery section coal pillar mining method and system, and the method comprises the steps: determining the position of a coal pillar working face crossheading, and carrying out gob-side entry driving along the positions, close to goaf, of the two sides of a coal pillar; grouting and plugging the goaf before tunneling, and injecting a flame retardant; side filling roadway forming is conducted on the goaf through coal gangue, and a supporting system of a coal pillar working face crossheading is constructed; supporting a coal pillar working face crossheading; the section coal pillars are recovered by adopting a continuous mining and continuous filling process, and coal bodies are replaced by filling bodies, so that the whole coal pillar recovery-filling process is ensured to be smoothly carried out until all coal resources are recovered. According to the method, the coal recovery rate can be increased, the problems of spontaneous combustion danger and toxic and harmful gas over-limit caused by goaf air leakage are solved, the fire hazard caused by sparks generated when a fully-mechanized coal winning machine touches old roadway anchor rods in the roadway tunneling process is solved by injecting the flame retardant, waste coal gangue can be effectively utilized, the problems of environmental pollution and the like are avoided, green mining is achieved, and the method is suitable for large-scale popularization and application. The mine service life is prolonged.

The invention discloses a fire spreading prediction method, a server and a storage medium, and the method comprises the following steps: 1, collecting fire related data from a sensor layer and a block chain layer, and carrying out the preprocessing of the fire related data; and 2, inputting the preprocessed fire related data into a fire spreading prediction model for prediction to obtain a fire spreading trend. According to the method, the change of the fire along with the time is accurately grasped through the LSTM, the GCN accurately predicts the spreading path of the fire in the space, the spatial-temporal characteristics can be integrated through the fusion of the LSTM and the GCN, and the fire spreading probability of each room is given. The method is suitable for fire-fighting early warning of various buildings such as shopping malls, office buildings and residences, effectively assists fire-fighting decision making, plans rescue paths and resource allocation in advance, and reduces fire hazard and loss.

The invention provides a vehicle-mounted power battery fire prevention and emergency disposal system, and aims to improve the safety of an electric vehicle and reduce casualties and property loss caused by fire of a power battery. The system monitors key parameters such as temperature inside and outside the battery pack, carbon monoxide concentration, cavity temperature and the like in real time through multiple sensors, performs data analysis and trend prediction in combination with data of a battery management system, and can identify fire hazards in time and provide early warning. According to different stages of the fire risk, the system automatically selects and executes optimal emergency disposal schemes, such as sound-light alarm, standby circuit switching, battery pack ejection, water-based fire extinguishing agent injection, automatic driving for searching an open parking area and the like. In addition, through deep integration of the system and a vehicle master control system, data sharing and cooperative operation are achieved, and cooperative work of all functional modules is achieved. And the system further adopts a redundancy design and an environment-friendly fire extinguishing agent, so that the reliability and the environmental friendliness are further improved.

The invention relates to the technical field of fire fighting, in particular to an intelligent fire fighting remote management system which comprises a server side and a user side. The server side comprises a multi-dimensional data acquisition module, a judgment module, a processing module, an early warning grade determination module, an alarm pushing module and a fire extinguishing control module, and the processing module is used for sending an alarm when a judgment result is that a fire occurs in a detected area at the current moment; if yes, determining a fire hazard degree corresponding to the detected area at the current moment according to temperature data, smoke data and flame data which are acquired in real time and correspond to the detected area; the early warning level determination module is used for determining a fire early warning level corresponding to the detected area at the current moment; and the alarm pushing module is used for calling a fire alarm pushing strategy corresponding to the fire early warning level from the database according to the determined fire early warning level corresponding to the detected area at the current moment, and executing the corresponding fire alarm pushing strategy to push an alarm signal to the corresponding user side.

The invention relates to a fire monitoring and early warning system for a coal mine belt conveyor, and belongs to the technical field of coal mine safety. The system comprises a fire hazard grading early warning system, an acquisition subsystem and a watering subsystem. The acquisition subsystem comprises an AI video subsystem, a voiceprint recognition subsystem, an inspection robot subsystem and a distributed optical fiber temperature measurement subsystem which are respectively used for acquiring the video image characteristics of the belt conveyor, the voiceprint characteristics of the belt conveyor, the concentration of symbolic gas and smoke in a mine and the temperature of the belt conveyor; the fire hazard grading early warning system carries out fusion analysis on symbolic gas concentration, smoke concentration, temperature, video image features and voiceprint features acquired by the acquisition subsystem through a multi-mode information fusion analysis model, and realizes fire hazard and fire hazard grading early warning in a coal mine belt conveyor area. Comprising equipment abnormity monitoring alarm, single index early warning and multi-parameter fusion early warning.

The invention provides a chamber fire flashover prediction method, device and system and a training method of a chamber fire flashover prediction model. The chamber fire flashover prediction method comprises the following steps: acquiring a temperature data sequence acquired by using a wearable temperature sensor and a radiation heat flow data sequence acquired by using a wearable radiation heat flow sensor; respectively inputting the temperature data sequence and the radiation heat flow data sequence into a chamber fire hazard flashover prediction model, and outputting predicted temperature data and predicted radiation heat flow data at a future moment; and when the predicted temperature data is greater than or equal to the flashover temperature critical value or the predicted radiation heat flow data is greater than or equal to the flashover radiation heat flow critical value, sending an alarm signal.

The invention relates to the technical field of fire fighting, and discloses an intelligent control system for a fireproof electronic display screen, and the system comprises a temperature monitoring module which collects the temperature data of each peripheral region in real time through a sensor array which is uniformly distributed in a region where the number of through holes at the back of a screen body exceeds a preset number; the smoke monitoring module collects the environment smoke concentration in real time through sensors installed at the positions of a plurality of through holes in the back of the screen body. The probe monitoring module acquires monitoring videos outside the screen body and at the perforation by means of a high-definition probe at the edge of the screen body or at the perforation position; the multi-source data analysis module establishes a three-dimensional temperature field model, a smoke diffusion track and fire source characteristic data based on temperature, smoke concentration and internal and external monitoring videos; and the intelligent control module triggers the fire-fighting linkage system according to the data and the multi-stage early warning rule, and intelligently controls the display screen at the same time. The intelligent level of fire prevention of the electronic display screen is improved, fire hazards can be automatically and efficiently dealt with without close attention of workers, and the management efficiency is improved.

The invention discloses a community fire risk dynamic assessment method and system based on AI prediction, and belongs to the technical field of fire prevention and control, and the method comprises the steps: S1, collecting and preprocessing multi-source data of a community, and constructing a comprehensive risk factor database; s2, constructing a risk prediction model, taking the preprocessed multi-source data as input, identifying a key risk factor, and outputting a fire risk probability; s3, calculating the dynamic weight of each index by fusing the static expert experience and the real-time risk contribution degree; s4, based on the dynamic weight and the risk probability, calculating a comprehensive risk value and dividing risk levels; s5, constructing a prevention and control strategy library based on the risk level and the key risk factor; according to the community fire risk dynamic assessment method and system based on AI prediction, the change rule of the fire risk factors in the community can be captured in real time, potential fire hazards can be predicted in advance, and a targeted prevention and control strategy can be automatically generated.