1267 results about "Fire detection" patented technology

The invention provides a smoke and fire detection method and system based on deep reinforcement learning and electronic equipment, and relates to the technical field of fire fighting. The method comprises the following steps: acquiring thermal imaging data of a forest area through a thermal imager, obtaining temperature distribution conditions of different positions, and determining position coordinates, the number and specific temperature values of temperature abnormal points for an area with abnormal temperature rise; if the temperature value of the temperature abnormal point exceeds a preset temperature threshold value, judging that the temperature abnormal point is a suspected fire point, and collecting a visible light video image of the area through a visible light camera according to the position of the suspected fire point; smog detection processing is carried out on the collected visible light video image, whether smog exists in the image is judged by analyzing the color, texture, motion and other characteristics of the image, and the concentration, morphological characteristics and diffusion speed of the smog are obtained; the meteorological parameters such as the wind speed and the wind direction of a fire occurrence area are obtained through the meteorological sensor, the meteorological parameters are input into the fire spreading model, and the spreading direction and the spreading range of the fire under the current wind field condition are simulated.

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 electrical fire intelligent identification system based on multi-dimensional sensor fusion. The system comprises the following steps: constructing a reference environment model through multi-sensor scanning, data dimension reduction and intelligent node deployment; multi-sensor time sequence alignment is carried out through edge calculation denoising and dynamic time warping, and high-priority data is processed in real time through a layering mechanism; establishing a fire feature modeling system through LSTM time sequence analysis, mutual information correlation mining and self-supervised learning; through multi-level data fusion, GAN abnormal data generation and fuzzy logic reasoning; through grading alarm, an intelligent fire extinguishing strategy and remote control, full-process coverage from fire detection to emergency response is realized. A fire scene is visualized by means of a three-dimensional thermodynamic diagram, flame dynamic analysis and an augmented reality technology. The system is suitable for fire detection, alarm and response in a complex industrial scene, and can be widely applied to intelligent management of electrical fire.

The invention relates to the technical field of fire safety monitoring, in particular to a firework identification system and method based on multi-modal fusion perception, and the core of the scheme is a visible light, multispectral and temperature three-modal framework: feature extraction optimization of each modal, improved YOLOv8s for visible light branches, dynamic background modeling and flame color screening, and multi-modal fusion perception. False positive is rejected by a multispectral branch depending on a waveband ratio and an index, an error compensation algorithm is introduced into a thermopile branch, and finally, a final smoke and fire area and the confidence coefficient thereof are determined by associating a three-mode area through collaborative decision. According to the scheme, the complex environment adaptability and the recognition reliability can be improved, the false alarm risk is reduced, the extremely-early smoke and fire detection capability is enhanced, good real-time performance and deployment flexibility are achieved, and the method is suitable for various types of fire safety monitoring scenes.

The invention discloses a tunnel fire position positioning and fire source risk analysis method and system, and relates to the technical field of tunnel fire detection, and the method comprises the steps: periodically collecting image, temperature, smoke, harmful gas concentration, infrared radiation and humidity data at each monitoring point in a tunnel, and dynamically adjusting the monitoring frequency according to the humidity; feature extraction is performed on the collected data, a factor set and a fuzzy evaluation set for fire judgment are constructed, and a fuzzy algorithm is utilized to calculate the membership degree of each feature to fire evaluation; a comprehensive membership value vector is generated in combination with the feature weight and is used for judging whether a fire occurs or not and performing preliminary positioning; initial monitoring equipment and front and back adjacent equipment are set, and the fire position is further accurate through fuzzy analysis of data of the front and back adjacent equipment; performing risk assessment on the fire according to the fire position and the abnormal pixel point proportion; intelligent detection, precise positioning and risk analysis of the tunnel fire are realized, and the tunnel safety management level is effectively improved.

The invention discloses an intelligent fire detection method based on artificial intelligence video analysis, and particularly relates to the technical field of computer vision. The method comprises the following steps: acquiring a video monitoring stream, extracting spatial color features and morphological features of an image, constructing a multi-scale time sequence feature graph group, performing joint modeling on color disturbance and structural disturbance by using a dual-channel attention neural network, and outputting a suspected fire area probability graph; candidate fire source areas are screened in combination with boundary disturbance consistency analysis and red channel high-frequency fluctuation detection; the texture stability, the disturbance directivity and the historical smoke evolution characteristics are integrated through a multi-modal fusion judgment model, the fire state is finally judged, an alarm result is output, meanwhile, the system can mark the fire position and the development trend path, and visual tracking is achieved. The method has the advantages of high robustness, high sensitivity and low false alarm rate, and is suitable for intelligent early warning of early fire in a complex environment.

A fire suppression system includes an interface control module, and an interface module. The interface control module can activate a fire suppressant discharge system in response to receiving a fire detection signal. The interface module is connected with the interface control module and is connected with at least one of a first optical sensor and an interface expansion module. The interface expansion module is configured to connect with a second optical sensor. The first optical sensor and the second optical sensor are configured to detect a fire condition at an area of interest and provide the first detection signal to the interface control module in response to detecting the fire. The interface module includes light emitting devices corresponding to the first optical sensor and the interface expansion module, the light emitting devices configured to display different colors indicating a status of the first optical sensor and the interface expansion module.

The invention relates to the technical field of energy storage power station fire monitoring, and discloses a fire detection and early warning method and system. The method comprises the steps that real-time monitoring signals are acquired and preprocessed, and after a preliminary abnormal index is determined, changes of temperature and gas concentration are analyzed and evaluated to obtain a fire risk pre-judgment score; if the preset risk threshold value is exceeded, potential fire source area distribution is judged according to a pre-established dynamic fire feature database, fire source positioning coordinates are obtained, and consistency is verified; abnormal points of the temperature and the gas concentration are classified, a fire initial symptom set is determined, the dynamic fire feature database is updated, judgment is conducted again, and the accuracy of early warning signals is determined; and if the accuracy is higher than the preset accuracy threshold, generating a comprehensive risk assessment report, obtaining a fire identification result, extracting an optimized feature vector from the fire identification result, and generating a fire source region distribution map and an abnormal early warning signal. According to the invention, fire source positioning can be realized, the false alarm and missing alarm rate is obviously reduced, and the emergency response speed is improved.

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 an automatic fire extinguishing device for automatically sensing thermal runaway of a battery of a new energy vehicle. The automatic fire extinguishing device comprises a base, a control box, a fire blanket body, a release assembly and a fire detection probe. The whole base is of a frame-shaped structure and is fixed to a preset position of a vehicle, and a fixed joint strip is arranged on the top face of the base. The fire blanket body is rectangular and comprises a base cloth layer, a coating and a heat insulation filling layer, a positioning strip is arranged on the top face, and heavy drooping blocks are arranged on the two sides. The release assembly comprises a fixed seat, a splicing seat, an electromagnet, a melt strip and a joint block, and the electromagnet adsorbs the heavy drooping block through magnetic force so as to store the fire blanket; the fire behavior detection probe comprises a spectrum sensor, a smoke sensor and an infrared temperature sensor and is used for monitoring fire behavior in real time. When a fire occurs, the control box receives a detection signal to cut off a power supply of the electromagnet, or the melt strip melts at a high temperature to actively cut off power, so that the weight drop block drops to pull the fire blanket body to be completely unfolded and cover a fire area, and the fire is inhibited by blocking oxygen.

The invention relates to the technical field of tunnel fire detection, and provides a rapid tunnel moving fire detection method, which comprises the following steps of: calculating signals received by sensors in parallel by an intelligent algorithm, extracting undistorted characteristic signals in time, and combining data from a plurality of sensors through a data fusion technology; false alarms possibly generated by a single sensor are reduced, so that the fire detection accuracy is improved, and the time from fire occurrence to alarm giving is shortened; by fusing data of a plurality of sensors, the system is more robust to faults or abnormities of a single sensor, and the stability and reliability of the system are improved; the intelligent algorithm can be combined with the time domain oscillogram of a plurality of detectors, space and time information can be extracted in time, the specific position of the fire can be positioned more accurately, and fire extinguishing measures can be taken quickly.

The invention discloses a graded detection method and system for an air-breathing smoke-sensing fire in a complex environment, and relates to the technical field of fire-fighting early warning, and the method comprises the steps: recognizing a ventilation opening of a monitoring region in the complex environment, and arranging air-breathing smoke-sensing equipment on the ventilation opening; state monitoring is conducted on the ventilation opening, and working mode parameters of the ventilation opening are obtained and comprise the ventilation starting and stopping state, the wind speed and the ventilation direction; and analyzing to obtain a self-adaptive strategy matched with the working mode parameters to control the sucking pump to drive the air-breathing smoke sensing equipment to collect smoke gas samples, performing multi-time sequence grading detection, and outputting a fire early warning signal. According to the invention, the technical problem that the existing fire detection technology is difficult to accurately collect smoke samples and give out early warning in time in places with complex ventilation structures and obvious air disturbance is solved, and the air-breathing smoke sensing equipment is arranged at the ventilation opening and the sampling strategy is dynamically adjusted; and the accuracy of smoke collection and the real-time performance of fire early warning are improved.

The invention belongs to the technical field of fire detection, and particularly discloses a smoke sensor labyrinth optimization design and dust pollution resistance method, which comprises the following steps: detecting corresponding smoke and dust fusion particles and water vapor particles in an air detection area through a signal transmitting device; water vapor particles are screened to weaken smoke alarm triggered by mistake caused by the water vapor particles, a differential signal processing technology is adopted to distinguish and identify fusion particles, meanwhile, the fusion particles are introduced into a labyrinth measuring cavity, the concentration of smoke and dust is measured in the labyrinth measuring cavity, and the detection stability is enhanced; the width of a labyrinth channel in a labyrinth measuring cavity is automatically adjusted according to the smoke concentration to carry out space division of smoke particles and dust particles, so that the adaptability of the detector to a complex environment is improved; the smoke concentration in the labyrinth measuring cavity is subjected to fire early warning judgment, the cleaning device additionally arranged in the labyrinth measuring cavity is started according to the smoke concentration and the dust concentration for cleaning treatment, and the detection continuity is improved.

The invention provides a forest disaster detection method and device, and belongs to the field of forest disaster monitoring, and the method comprises the steps: collecting an infrared image, a stereoscopic vision image and positioning data of a target forest region in real time; an ASSA module is introduced in the feature extraction stage of the YOLOv11, and an infrared image is input into the improved YOLOv11 for flame target recognition; recognizing the stereoscopic vision image through a smoke and fire recognition algorithm, and detecting flame and smoke targets; determining a fire detection result by combining the identification results of the flame target and the smoke target; under the condition that the fire disaster is determined to occur, an HITNet algorithm is adopted to carry out ignition point positioning through a stereoscopic vision image, and the geographic coordinates of a fire disaster point are determined based on positioning data; and sending alarm information based on the fire point geographic coordinates. Therefore, the capability of extracting the flame heat radiation characteristics of the tiny fire points is effectively enhanced, and the detection result of the forest fire is improved.

The invention discloses an old-age care indoor safety monitoring and positioning system based on multi-sensor fusion, which belongs to the technical field of intelligent old-age care, and comprises a data acquisition module, a data processing and analysis module, a positioning module, an alarm and notification module, a storage module and a power supply module, the indoor safety monitoring and positioning system based on multi-sensor fusion is designed by comprehensively using technologies of indoor safety monitoring, FMCW radar positioning, YOLOv8 dangerous scene detection, multi-sensor fusion and the like. The system is composed of a data acquisition module, a data processing and analysis module, a positioning module, an alarm and notification module, a storage module and a power supply module, a series of system optimization schemes are designed, the false alarm rate of the system is reduced by adopting modes of optimizing a detection threshold value, analyzing historical data by means of machine learning and the like, and the data transmission efficiency is improved. Secondly, fall detection and fire detection are optimized by using an AI algorithm, so that the system can identify potential dangers more accurately and timely and take corresponding measures timely.

The invention discloses a fire detection and early warning method and system based on improved YOLOv8, and belongs to the technical field of image recognition, and the method comprises the steps: building a fire data set, marking the fire data set, and dividing the fire data set into a training set, a verification set and a test set; the YOLOv8 network is improved; the original YOLOv8 network and the improved YOLOv8 network are trained, evaluated, adjusted and tested, and an optimal model is determined; deploying the optimal model to an edge AI development board of a ground station; a real-time video stream of the unmanned aerial vehicle is obtained through the unmanned aerial vehicle camera and is transmitted to the edge AI development board for fire detection; a fire signal is detected by using the edge AI development board, and early warning is carried out through the fire alarm module; the system comprises a fire data set establishment and processing module, a YOLOv8 network improvement module, a YOLOv8 network training module, an optimal model deployment module, a fire detection module and a fire early warning module. The method has the technical advantages of high precision and light weight.

The invention discloses an unmanned aerial vehicle-mounted GNSSINS visual combination fire detection method, system and device and a medium, belongs to the technical field of unmanned aerial vehicles, and aims to solve the technical problem of how to improve the positioning precision and fire recognition capability of an unmanned aerial vehicle in a complex environment. Setting a dynamic temperature threshold value, and calibrating the threshold value in real time in combination with the environment temperature; identifying a fire source diffusion trend by adopting a temperature gradient analysis method, and excluding isolated high-temperature points; an image is collected through an RGB camera and converted into an HSV color space, flame features are extracted, and the flame shape is detected in real time through a YOLOv5 model; detecting a smoke area based on a U-Net segmentation network, outputting a binary mask, carrying out space-time alignment on the binary mask and the wind speed and wind direction of the meteorological data, and predicting the fire spreading direction; thermal imaging, visible light and smoke detection results are fused through the D-S evidence theory, and when the confidence coefficients of the thermal imaging, visible light and smoke detection results are all larger than 80%, it is judged that a fire exists.

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.

This disclosure introduces an automated computer system designed to detect fire and smoke in video frames. Within this system, an object detection model is trained to recognize fire and smoke patterns. In a post-training evaluation phase, the system utilizes the model to determine an instance-level detection threshold from a validation set, which dictates the minimum confidence score required for a fire or smoke designation. In addition, a frame-level detection rule is defined, considering both the cumulative count of detected fire or smoke instances and their associated confidence scores. During inference, the system processes video frames through the model, designates detected patterns with bounding boxes, and subsequently applies the instance-level threshold and frame-level rule. When a sequence of frames fulfills a predetermined criterion, the system activates an alarm.

A fire detection and warning system includes a kitchen exhaust hood connected to an exhaust fan configured to ventilate convective heat and cooking fumes released by one or more cooking appliances installed under the kitchen exhaust hood, the kitchen exhaust hood having a plenum. A fire suppression system is operatively connected to the hood and positioned above the one or more cooking appliances and configured to be triggered by one or more fusible links with a predefined melting temperature. At least one temperature sensor is installed inside the plenum of the kitchen exhaust hood and provides temperature measurements to a control module. The control module is configured to generate a fire warning signal in response to a fume temperature reaching a predefined temperature threshold Tset for a predefined period of time Tdur, where Tset and the time Tdur are selected based on a melting temperature rating the fusible links.

The invention relates to a gas-water curtain partition type tunnel smoke control system and method, and belongs to the field of tunnel fire prevention and control, the gas-water curtain partition type tunnel smoke control system comprises a tunnel, smoke control partitions, a tunnel fire extinguishing system, a gas curtain generation system, a water curtain generation system and a fire detection alarm, the gas curtain generation system comprises a gas curtain generation tail end, and the water curtain generation system comprises a water curtain generation tail end. The tunnel is divided into a plurality of smoke control subareas by the air curtain generation tail end and / or the water curtain generation tail end. A plurality of air curtains and water curtains can be formed at the boundaries of the two sides of the smoke control subareas, at the moment, smoke generated by a fire source can be isolated into the smoke control subareas, the smoke can be continuously eliminated under the internal circulation purification effect of the tunnel fire extinguishing system, meanwhile, the tunnel fire extinguishing system can automatically restrain fire spreading, and therefore the smoke spreading path is thoroughly blocked.

The invention relates to the technical field of forest fire recognition, in particular to a forest fire recognition method based on improved YOLOv11. The method comprises the steps of establishing a forest fire image data set, improving a YOLOv11 model, training the model, accelerating and optimizing hardware and detecting forest fire in sequence. The recognition precision of a small target is improved by improving a convolutional layer and a pooling layer of the YOLOv11 model, and the detection precision and the positioning completeness are ensured under the conditions of long distance, low smoke concentration or small flame area; by improving a loss function in a YOLOv11 model, regression samples are widely concerned, and higher accuracy is achieved in a complex scene in which smoke and flames exist at the same time; the calculation amount is reduced by improving a C3k2 module in a YOLOv11 model backbone network, the model response speed is improved, hardware acceleration is carried out through TensorRT, and the forest fire detection precision and detection efficiency are improved.

The invention discloses a multi-modal lightweight forest fire detection method suitable for a vertical take-off and landing fixed-wing unmanned aerial vehicle platform, and the method comprises the steps: carrying out the feature alignment and weight fusion of a visible light image and an infrared image, which are synchronously collected by an unmanned aerial vehicle, through a multi-modal early fusion method, and obtaining a fusion feature; carrying out lightweight down-sampling and multi-scale feature extraction on the fusion features to obtain a multi-scale feature set; performing cross-scale feature fusion on the multi-scale feature set by adopting a bidirectional feature pyramid network, and extracting dynamic change information to obtain time sequence features; performing multi-scale context enhancement on the time sequence features by using a spatial pyramid method based on cavity convolution, and performing global context and local texture modeling by using a lightweight Transform module to obtain high-level semantic features; and based on the high-level semantic features, a target detection frame, fire level classification, prediction uncertainty estimation and a pixel-level fire probability graph are generated in parallel through a multi-task output header.

The invention belongs to the technical field of fire safety early warning, and discloses a fire detection alarm method and system. The method comprises the following steps: monitoring the environmental smoke concentration in real time through a smoke detector arranged in a battery compartment to obtain smoke parameters; synchronously monitoring the environment temperature change through a temperature sensing detector to obtain a temperature parameter; comparing the smoke parameter with a preset smoke threshold value, and comparing the temperature parameter with a preset temperature threshold value; determining an alarm signal grade according to a comparison result, and triggering a corresponding alarm signal based on the alarm signal grade; and after the alarm signal is triggered, corresponding alarm measures and corresponding fire extinguishing measures are executed according to the grade of the triggered alarm signal. Through the above mode, composite grading alarm can be carried out, the accuracy of fire alarm is improved, targeted fire extinguishing measures are taken for different alarms, and the fire extinguishing efficiency is also improved.

The invention relates to the technical field of industrial safety, in particular to an intelligent machine tool fire extinguishing system which comprises a fire detection module, a data processing module, a fire type recognition module, a fire extinguishing control module, a man-machine interaction module and a power failure guarantee module. Wherein the fire detection module is used for monitoring multi-dimensional data of a working environment of a machine tool in real time; the data processing module is used for identifying different types of fire features; the fire type identification module is used for identifying the specific type of the fire; the fire extinguishing control module automatically selects a corresponding fire extinguishing medium; the man-machine interaction module is used for sending an alarm signal to an operator when a fire disaster occurs; and the power-off guarantee module is used for automatically cutting off a machine tool power supply when a fire occurs. According to the invention, through the technologies of intelligent fire identification, accurate fire source positioning and timely power cut-off, the fire extinguishing efficiency is effectively improved, early fire control is ensured, and secondary disasters are prevented.

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 environment early warning, and discloses a holographic sensing multi-mode environment early warning control method and system, and the method comprises the steps: obtaining an original multi-mode signal, and constructing a multi-mode fire signal matrix; performing matrix decomposition operation of combustion dynamics constraint and flue gas diffusion constraint on the multi-modal fire signal matrix to obtain a fire basic characteristic matrix and a spatio-temporal evolution coefficient matrix; performing feature fusion on the fire basic feature matrix and the spatio-temporal evolution coefficient matrix to obtain a comprehensive fire feature vector; according to the method, multi-dimensional fire feature information such as temperature, smoke, gas, acoustics and infrared can be captured at the same time, the comprehensiveness and reliability of fire detection are improved, and the method is suitable for popularization and application. And the three-dimensional space coordinates of the fire source can be accurately determined.

The invention discloses an efficient fire smoke detection method based on lightweight convolution and cross channel feature fusion. The method belongs to the field of deep learning in computer vision, and comprises the following steps: capturing a video signal by using a camera, segmenting the video signal into independent images, and carrying out image preprocessing; and detecting the images by using a trained fire detection model, and displaying a detection result. According to the method, through lightweight convolution fitting and cross channel feature fusion, the detection precision and the real-time performance of the model are improved; on the basis of a YOLOv11 model, a lightweight convolution structure is adopted to reduce the calculation overhead, and meanwhile, the distinguishing capability of fire smoke features is enhanced through a cross channel feature fusion mechanism, so that the detection robustness and precision of the model in a complex scene are further improved. The improvement provides a new technical path for a fire early warning system.

The invention discloses a fire monitoring system and method. The system comprises a terminal layer, an edge layer and a cloud layer, the terminal layer is used for monitoring a target area in real time and transmitting monitoring data acquired by each sensor to the edge layer; the edge layer is used for performing feature fusion of a time dimension and a space dimension on the monitoring data to obtain a space-time fusion feature, and extracting a visual feature from the monitoring data; fire point and smoke recognition is carried out by fusing the space-time fusion features and the visual features, when a fire point or smoke is recognized, a fire alarm is triggered, and monitoring data is transmitted to the cloud layer; and the cloud layer is used for constructing a hierarchical risk assessment system based on the monitoring data, performing fire risk evolution path assessment based on the hierarchical risk assessment system, and generating a fire processing decision according to an assessment result. According to the invention, the fire detection precision is improved, the missing report rate and the false alarm rate are reduced, and the practicability of the system is improved.

The invention discloses a fire detection method and device based on multi-modal perception and D-S evidence theory fusion. The method comprises the following steps: collecting multi-modal perception big data at least comprising video image data and temperature sensing data in a monitoring area; performing fire visual feature analysis on the video image data to generate first basic probability distribution; performing fire temperature characteristic analysis on the temperature sensing data to generate second basic probability distribution; taking the first basic probability distribution and the second basic probability distribution as two independent evidence sources, and fusing by adopting a D-S evidence theory to obtain a fused third basic probability distribution; converting the third basic probability distribution into a fire occurrence probability for decision making; and when the fire occurrence probability exceeds a preset alarm threshold, determining that a fire occurs and triggering an alarm. According to the fire detection method, multi-modal sensing big data are synchronously collected and fused, multi-source uncertain information is processed and decided under the framework of the D-S evidence theory, and the early stage of fire detection is remarkably improved.