1036 results about "Fire detection" patented technology

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 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 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 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 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 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 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 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.

The invention provides an intelligent fire-fighting method and system for a photovoltaic power station based on the Internet of Things, and the method comprises the steps: collecting temperature data through a distributed optical fiber temperature measurement unit, avoiding electromagnetic interference through optical signal conversion, analyzing the spectral feature difference between electro-arc light and combustion flame through a spectral feature separation unit, and carrying out the analysis of the spectral feature difference. Correlating the particulate matter concentration with the specific gas component through the multi-source coupling smoke sensing unit to obtain anti-interference data; space-time probability fusion is executed, and fire confidence is generated through alignment; a trigger response is calculated according to the combination of the fire confidence coefficient, the temperature rise trend slope and the smoke diffusion speed; running a self-adaptive parameter adjustment engine, and generating a sensing layer calibration coefficient and a decision rule optimization instruction; the parameter optimization instruction is issued to the sensing device, the calibration effect is verified, the influence of dust, electromagnetic interference and other factors is effectively avoided through the collected anti-interference data, the response is determined in combination with temperature rise and delay diffusion, the probability of false triggering is effectively reduced, and the accuracy of fire detection is improved.

The invention discloses a fire behavior detection method based on visible light and infrared image fusion, which comprises the following steps: acquiring original visible light and infrared image data, and preprocessing the original visible light and infrared image data to obtain preprocessed image data; inputting the preprocessed image data into a multi-dimensional representation collaborative fusion module to extract different modal image features, and fusing the extracted different modal image features to obtain a fused visible light and infrared feature image; extracting fire key features from the fused visible light and infrared feature images, performing optimization processing on the extracted fire key features, positioning a target boundary, and constructing a fire detection model based on visible light and infrared image fusion; and inputting the preprocessed image data into a fire behavior detection model based on visible light and infrared image fusion to generate a fire behavior detection result. The fire detection task can be effectively completed, and the detection accuracy is improved.

The invention belongs to the technical field of computer vision and fire detection, and particularly relates to a lightweight fire detection method based on bimodal image fusion, and the method comprises the steps: collecting an RGB visible light image and a Thermal thermal infrared image in the same scene, and forming a group of samples; inputting the sample into a feature extraction module to obtain an RGB exclusive feature map and a Thermal exclusive feature map; the feature extraction module comprises a backbone network, an RGB feature branch and a Thermal feature branch; inputting the RGB exclusive feature map and the Thermal exclusive feature map into a feature fusion enhancement module to obtain a fused feature map; the feature fusion enhancement module comprises an MSR-CSSA module; inputting the fused feature map into an adaptive detection output module to obtain a prediction result; the self-adaptive detection output module comprises a three-branch detection head and a dynamic weight generation module; according to the invention, the detection precision and the real-time performance can be effectively improved.

The invention relates to the technical field of fire detection, prevention and control, and provides an intelligent fire prevention and control system based on multi-information fusion and Yolo detection, and an intelligent detection device adopts a modular architecture design and is composed of a basic device and an extended function module; the basic device integrates an environmental data acquisition module and a wireless communication module, and acquires environmental data such as temperature, smoke concentration and gas content in real time. The extended function module comprises a fire extinguishing bomb launching module, an alarm module, a monitoring camera module and a 4G communication module; the fire detection module is deployed at a server side, receives data and images uploaded by the intelligent detection device, performs fire feature recognition through an improved Yolov5 algorithm, and constructs a cooperative judgment mechanism of a Yolo algorithm recognition result and multi-source environment data; the matched client receives and displays the environment data and the fire detection result, sends a remote control instruction, and has the functions of real-time monitoring, intelligent early warning and remote control.

The utility model provides an intelligent alarm for fire-fighting detection, which relates to the field of fire-fighting alarm equipment and comprises a shell, a single-chip microcomputer and a communication module are mounted in an inner cavity of the shell, the single-chip microcomputer is used for receiving signals, the communication module is used for remote transmission, a detection module is mounted on the surface of the shell, and the detection module is connected with the shell. The detection module comprises a fire-fighting camera, a smoke detector and a temperature detector, and the detection module is used for ambient environment detection; according to the utility model, the single-chip microcomputer, the detection module and the alarm assembly are arranged, so that an effect of sending alarm information when a fire disaster occurs is achieved, the fire-fighting camera and the smoke detector can detect the surrounding environment, the fire disaster can be found in time, the alarm lamp and the loudspeaker can give an alarm in time when the detection module detects the fire disaster, and the safety of the fire disaster is improved. Through cooperation of the single-chip microcomputer, the communication module, the detection module and the alarm assembly, the use function of the fire alarm is effectively improved, and thus the use effect of the fire alarm is improved.

The invention provides a fire hose sealing effect detection device for fire detection, and relates to the technical field of fire detection equipment. The fire hose sealing effect detection device for fire detection comprises a base, a water tank is arranged at the upper end of the base, two first vertical rods are symmetrically and fixedly connected to the interior of the water tank, a first movable plate is slidably connected between the two first vertical rods, and a pressing block is fixedly connected to the lower end of the first movable plate. And two second vertical rods are fixedly connected to the interior of the water tank, a second movable plate is slidably connected between the two second vertical rods, and a scraping plate is fixedly connected to the lower surfaces of the two second movable plates. Through the structure, impurities on the surface of the fire hose can be treated, and the detection effect of the airtightness of the fire hose is guaranteed.

The invention discloses an HRR-based acoustic fire hazard type identification method, device and equipment and a medium, and relates to the technical field of fire hazard detection, and the method comprises the steps: obtaining an original acoustic signal through transmitting a high-frequency modulation ultrasonic wave and receiving a flame region reflection echo, obtaining channel impulse response data through channel estimation, and carrying out the channel impulse response data; flame features are extracted, a heat release rate curve is calculated and generated, finally fire type identification and fire behavior stage determination are completed based on the curve, dense smoke, shielding and illumination interference can be resisted, accurate quantification of the heat release rate is realized, fire type identification and fire behavior development stage determination can be finely carried out, and the accuracy of fire behavior determination is improved. And a stable and reliable technical support is provided for fire monitoring and emergency decision making.

The invention provides a telecontrol fire alarm system, and the system comprises a multi-mode sensing module which is used for collecting fire characteristic parameters and environment parameters; the data processing module is used for generating a fire probability index through a data fusion algorithm; the remote communication module is used for transmitting the processed data to related equipment in the form of an encrypted data packet; the fire extinguishing control module is used for starting a corresponding graded fire extinguishing strategy according to the fire probability index; the video linkage module is used for determining a video switching priority by adopting a multi-dimensional quantitative model; the self-checking module is used for regularly carrying out integrity checking; the edge computing gateway is responsible for receiving, processing and forwarding data of each module and dynamically adjusting system parameters; according to the invention, through a multi-modal sensing and dynamic weighting data fusion algorithm, high-order derivative-based gas anomaly detection, intelligent video linkage, self-adaptive channel switching and regular self-inspection technology, the accuracy and response speed of fire detection are improved, and the reliability and safety of the system are guaranteed.

The invention relates to the field of fire detection, and discloses a multivariable composite fire detection method and system, a storage medium and a device, and the method comprises the steps: obtaining first and second sensing data based on a first sensor and a second sensor, and extracting sensing target data, a sensing position and a timestamp; calculating a first timestamp difference value between the first timestamp and the second timestamp and a first position data difference value between the first sensing position and the second sensing position; calculating a first process matching value of the first timestamp difference value and the first position data difference value; if the first process matching value is smaller than a preset reference matching value, a first target difference value of the first sensing target data and the second sensing target data is calculated, and if the first target difference value is larger than or equal to a preset reference target value, sensor fault early warning is conducted and data are output; if the first process matching value is greater than or equal to a preset reference matching value, calculating first induction fusion data, and if the first induction fusion data is greater than or equal to a preset fusion reference value, outputting a fire alarm prompt, otherwise, outputting a fire early warning prompt. Fire detection accuracy and reliability are improved, and timely early warning is realized.

The invention relates to the technical field of fire safety monitoring, and discloses a linear fire detection method and system based on infrared spectrum analysis and an infrared spectrum detector, and the method comprises the steps: arranging a plurality of infrared spectrum detector nodes; the collected infrared spectrum signals are preprocessed; mapping the preprocessed spectral signal to a pre-constructed low-dimensional feature space; calculating the conditional probability density of the current infrared spectrum signal in various states; judging whether the posterior probability exceeds a set fire judgment threshold value or not; the system comprises an infrared spectrum detection module, a preprocessing module, an embedded mapping module, a discrimination module and a linkage control module. The infrared spectrum detector comprises a spectrum sensing unit, a signal processing unit, a data communication unit and a data uploading interface. According to the method, the technical effects of keeping high sensitivity and low false alarm rate under complex and changeable working conditions are achieved through a combined discrimination model of embedding mapping, kernel density estimation and Bayesian posteriori.

The invention relates to the technical field of fire hose strength detection, in particular to a fire hose tensile strength detection device for fire detection and a method thereof.The fire hose tensile strength detection device comprises a base, a partition plate is integrally formed in the middle of the inner side of the base, a blocking groove is formed in one side of the partition plate, and the interior of the base is divided into a water storage cavity and a dry cavity by the partition plate; and a driving assembly is arranged in the dry cavity, and the top end of the driving assembly is movably connected with an eccentric adjusting assembly. According to the improved device and method for detecting the tensile strength of the fire hose for fire detection, through mutual cooperation of the driving assembly, the eccentric adjusting assembly and the water injection assembly, the tensile property of the fire hose under the conditions of no water injection and water injection can be detected; meanwhile, the detection comprehensiveness can be improved by changing the direction of the tensile force applied to the water hose in the detection process, and the radial extrusion assembly can simulate the situation that the water hose is externally treaded or impacted in actual use, so that the diversity of detection items is improved.

The invention discloses a safety and disinfection integrated system based on AI analysis linkage control electromagnetic valves, and relates to the technical field of intelligent fire fighting, and the safety and disinfection integrated system comprises a flame detection camera, a network input and output module, a safety and disinfection integrated machine, an execution module and an acousto-optic alarm bell. According to the security and disinfection integrated system for linkage control of the electromagnetic valve based on AI analysis, the advanced AI visual recognition technology and an electromagnetic valve linkage control mechanism are deeply fused, the efficiency and the precision degree of fire safety management are greatly improved, flame characteristics are precisely recognized by means of a deep learning model, and the safety and disinfection effect is improved. The limitation that traditional fire behavior detection is easily influenced by environmental factors is broken through, the accuracy and reliability of fire behavior recognition are greatly enhanced, the system supports multi-modal data fusion, the confidence coefficient of fire behavior judgment is further improved, adjacent electromagnetic valves can be automatically started according to the position of a fire behavior in the linkage control link, an effective fire extinguishing isolation belt is constructed, and the fire extinguishing efficiency is improved. And fire spreading is effectively restrained.

The invention discloses a flame smoke recognition method and device, and belongs to the technical field of fire detection, and the method comprises the steps: extracting flame smoke features of a received target image based on a preliminary feature extraction module, and obtaining initial image features; performing multi-dimensional and multi-scale multi-time local feature extraction on the initial image features based on a local attention module to obtain local image features; global image features are obtained by aggregating global feature information of the initial image features and the local image features based on a self-attention aggregation module; performing feature fusion on the initial image feature, the local image feature and the global image feature based on a neck module to obtain a fused image feature; and detecting flame smoke recognition results of different scales in the target image according to the fused image features based on the plurality of lightweight detection heads. According to the invention, the method achieves the precise recognition of small target flames and rarefied smog through the optimization and reinforcement of local features and global features and a lightweight detection head.

The invention discloses a multi-scene fire early warning and identification method and system. The method comprises the following steps: acquiring a fire monitoring video stream, and decoding the video stream into a continuous frame sequence; performing multi-scale preprocessing on the frame sequence; according to the preprocessed frame sequence, fire feature time sequence enhancement processing is carried out, and a standardized feature set D is obtained; performing multi-level feature coding and aggregation on the standardized feature set D to obtain a fire feature vector set; performing fire detection of different scenes according to the fire feature vector set V; wherein the different scenes comprise a closed space, an open space, a semi-open space, a strong light environment, a weak light environment and a normal light environment; in order to solve the problem that early warning cannot be realized due to the fact that existing fire detection is difficult to effectively recognize weak features such as spark splashing and rarefied smoke in the early stage of a fire, the method and the device specially recognize and enhance the features of flames and rarefied smoke in the early stage, fully utilize time sequence information to enhance weak signals and the like, and improve the detection precision of small targets in the early stage of the fire.