1775results about "Fire alarms" patented technology

The invention provides a charging pile line fire-fighting early warning method based on big data and a storage medium, and the method comprises the steps: collecting a line operation data set of a target charging pile cluster, the line operation data set comprises multi-source time sequence monitoring data, carrying out the cross-modal feature alignment of the multi-source time sequence monitoring data, and generating a time-space correlation feature matrix; inputting the space-time correlation feature matrix into a pre-trained fire risk prediction model, generating a line abnormal risk probability distribution diagram, generating a layered early warning signal set according to risk levels corresponding to space nodes in the line abnormal risk probability distribution diagram, and triggering a dynamic protection mechanism based on the layered early warning signal set. The dynamic protection mechanism includes performing a current cut-off operation on a high-risk line segment, and performing a power attenuation operation on an adjacent line segment. According to the invention, the reliability and the intelligent level of the charging pile line fire-fighting early warning system can be comprehensively improved.

The invention relates to the technical field of environment monitoring, in particular to a forest fire monitoring method based on edge intelligence, which comprises the following steps: S1, deploying edge nodes and sensing equipment; s2, collecting data acquired by the sensing equipment in real time, and performing primary processing on the data; s3, fire risk intelligent identification and decision making are carried out through the edge nodes; s4, emergency response and event management; and S5, user interaction and feedback. The edge intelligence technology is introduced, the architecture of the forest fire monitoring system is optimized, and local processing and real-time analysis of data are realized, so that the response speed and the detection precision of fire monitoring are improved, and the method is suitable for further popularization and application.

The invention provides a new energy automobile fire dynamic risk assessment method and system based on multi-modal spatial-temporal feature fusion. The new energy automobile safety monitoring and fault early warning method comprises the steps that a multi-source data acquisition layer obtains multi-source heterogeneous data, and space-time marking is carried out; the feature fusion preprocessing layer generates a space-time consistency data stream through a space-time alignment module and missing value filling; the deep learning evaluation layer extracts multi-modal features by using a dedicated network, and outputs feature scores through time-space dependence modeling fusion; and the dynamic early warning decision-making layer generates a dynamic risk score in combination with the thermal runaway probability, the risk gradient and the aging factor, constructs a five-level risk map and executes vehicle-cloud collaborative graded early warning. According to the new energy automobile fire dynamic risk assessment method and system based on multi-modal spatial-temporal feature fusion, the limitation of traditional single data static assessment is broken through, accurate dynamic early warning is achieved, the early warning time is longer than or equal to 15 min, the false alarm rate is reduced to 4.7%, and the thermal runaway prediction accuracy rate reaches 98.3%.

The invention provides a thermal power plant environment monitoring and early warning method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: forming a preprocessing data stream for collected environment monitoring data through an edge calculation node; and carrying out real-time data stream processing by utilizing a distributed message queue, constructing a hierarchical distributed data storage structure, and carrying out homomorphic encryption and desensitization processing on data to form an encrypted data set. Based on an encrypted data set, a long-short term memory network, a random forest algorithm and a limit gradient boosting tree are adopted to construct a layered anomaly detection engine, environmental parameter spatial-temporal characteristics are extracted through a multi-head attention mechanism and residual connection, an early warning threshold value is iteratively calculated based on a Bayesian optimization algorithm, and an early warning level judgment standard is dynamically adjusted. And generating early warning information. And finally, through a three-dimensional visualization module and a knowledge inference engine based on a graph neural network and a deep reinforcement learning algorithm, visualization of early warning information and generation of a self-adaptive emergency processing strategy are realized respectively.

The invention relates to the technical field of public safety and intelligent fire protection, in particular to a real-time processing method and system based on fire alarm data, and the method comprises the steps: environment steady state reconstruction: accessing non-fire environment dynamic parameters; presetting thermotechnical static parameters of the building space; constructing a space thermal inertia differential equation; generating an ideal reference baseline; knowledge-driven simulation: receiving the ideal reference baseline; calling a synthesis operator in a preset disaster interference knowledge base; executing dynamic superposition injection; generating a virtual sensor state flow which has physical characteristics and contains environmental background characteristics; homomorphic judgment: executing double difference calculation; obtaining a real residual feature vector; geometric homomorphism verification is executed; outputting fire alarm triggering, interference filtering or fault prompting instructions; according to the method, the problem of false alarm caused by non-stable fluctuation of the environment background in the background technology is solved, and dynamic fusion and scene adaptation of the standard signal model and the real environment background are realized.

The invention discloses a building intelligent electrical fire monitoring method and system, and relates to the field of fire monitoring, and the method comprises the steps: obtaining distribution box parameters of different floors in a target monitoring area in a preset time period, and the distribution box parameters comprise a leakage current value and a temperature value; performing multi-scale decomposition on the parameters of the distribution box to obtain a plurality of intrinsic mode functions and residual trends; performing feature extraction on each intrinsic mode function and the residual trend to obtain an electrical fire feature vector; constructing a fire risk assessment model according to the electrical fire feature vector; and predicting a fire risk level according to the fire risk assessment model. Aiming at neglecting a nonlinear relation between leakage current and temperature of a distribution box in the prior art, the building electrical fire monitoring precision is improved based on multivariable time-delay analysis, ensemble empirical mode decomposition and the like.

The invention discloses a metro fire detection method based on YOLOv8. The metro fire detection method is used for improving the accuracy and response speed of an alarm system. According to the invention, a YOLOv8 target detection algorithm is adopted, a network structure is optimized in combination with deep learning, and a weighted focusing loss function and multi-modal data fusion method is designed, so that the detection precision is improved. A data set covers images and sensor data of different subway environments, an attention mechanism and multi-scale detection are introduced, and the flame and smoke recognition capability is improved. And by combining temperature sensor data, false alarms are reduced. The optimization strategy aims at the carriage and platform environment, the robustness of the model under the conditions of low illumination, reflection and shielding is enhanced, and real-time detection is realized by means of TensorRT accelerated reasoning. The system can operate independently, does not need a network, can be linked with a subway FAS system, and improves the early warning efficiency. Experiments show that the detection performance of the method is better than that of a traditional method, the reasoning speed exceeds 50 frames per second, and the method has wide application prospects.

The invention discloses a hazardous chemical substance storage risk artificial intelligence identification method and device and a medium, and relates to the technical field of hazardous chemical substance management. The method comprises the following steps: acquiring product information, operation time and access frequency of hazardous chemicals, and respectively acquiring three-dimensional images of a warehouse and a preset distance outside the warehouse; stacking the hazardous chemical substances according to the product information, the operation time and the access frequency to obtain a stack of the hazardous chemical substances, and obtaining a three-dimensional image of the stack; environment data of a warehouse and a preset distance outside the warehouse are collected, three-dimensional modeling is carried out according to the three-dimensional image, product information is integrated, and a digital twinborn model of hazardous chemical substance storage is obtained to reflect dynamic changes of the warehouse environment; and according to the dynamic change, identifying whether the stacking height of the hazardous chemical substances, the adjacent stacking distance and the environmental data exceed preset thresholds or not. According to the method, artificial intelligence, three-dimensional modeling and digital twinning are comprehensively applied, and intelligent identification and management of hazardous chemical substance storage risks are realized.

The invention discloses a power transmission channel forest fire risk early warning method and system based on spatial-temporal feature fusion, and relates to the technical field of risk early warning. The method comprises the following steps: constructing a power transmission channel risk grid and fusing historical multi-source data to generate a static flammability background map; the method comprises the following steps: collecting real-time visual and micrometeorological data, and after space-time alignment processing, respectively extracting visual risk features and environmental flammability trend features by using a double-branch space-time feature extraction network; inputting the static background and the dynamic characteristics into a space-time fusion module to obtain a comprehensive forest fire risk index; when the index exceeds a threshold, triggering a forest fire spreading deduction model corrected by combining a power transmission corridor effect, predicting fire spreading, evaluating a line tripping probability, and issuing a graded early warning and emergency strategy; according to the method, the defects of serious data islands and inaccurate early warning are overcome, the crossing from pure fire point monitoring to risk situation dynamic prediction is realized, and the accuracy of forest fire early warning and the intelligent level of power grid prevention and control are remarkably improved.

The invention discloses a forest fire risk prediction method and system based on machine learning, and the method comprises the steps: taking data of a monitoring data source of a preset forest in a specified time period as to-be-analyzed data; extracting space-time distribution characteristics of the to-be-analyzed data and the reference data source, performing area division on the space-time distribution characteristics according to similarity, taking an area where the to-be-analyzed data with the similarity higher than a similarity threshold value is located as a candidate area, and otherwise, taking the area as a fuzzy area; performing autocorrelation analysis on the fuzzy region based on the undetermined data source, adding the fuzzy region of which the autocorrelation is higher than an autocorrelation threshold to the candidate region, and performing time-varying influence analysis on the environmental data and the human activity data to obtain a fire driving coefficient; and constructing a forest fire risk prediction model according to the fire driving coefficient and the candidate region, optimizing the forest fire risk prediction model according to a fire spread rule, and outputting a prediction result.

The invention relates to the technical field of pipe gallery fire analysis, in particular to an underground pipe gallery fire feature correlation analysis system based on an improved Apriori algorithm, and aims to comprehensively collect underground pipe gallery data through multi-modal sensing equipment and pre-process the underground pipe gallery data into a standardized three-dimensional time sequence data set to provide a high-quality data basis for subsequent analysis; a multi-dimensional feature association matrix is constructed based on the three-dimensional time sequence data set, feature association strength is quantified, and a multi-dimensional feature association basis is provided for fire risk mining; a fire risk rule knowledge base is constructed through a space-time weighted support degree optimization Apriori algorithm, effective association rules are accurately extracted, and the accuracy and reliability of fire risk early warning are improved; based on the fire risk rule knowledge base, a decision tree model is established, association rule weights of branches of the decision tree are analyzed, fire risk grades are divided, a composite early warning instruction set is generated, different coping strategies can be adopted for risks of different grades, and the pertinence and effectiveness of emergency response are improved.

The invention discloses a fire fighting system intelligent management method and device based on intelligent property management, and relates to the technical field of data management. The intelligent management method and device for the fire fighting system based on the intelligent property management comprises the following steps: S1, dynamically triggering and collecting a multi-source fire fighting data set through multiple protocols, and temporarily storing the multi-source fire fighting data set at the edge in a normalized manner; s2, constructing an event full-period window and a ternary association index of the event, the time sequence data and the original message; s3, performing double-track storage of the semantic analysis data and the original message, and constructing multi-dimensional index association; s4, reconstructing a visual playback alarm process through edge cloud increment synchronization and a multi-dimensional time sequence chain; and S5, performing transient analysis, protocol upgrading and responsibility evidence obtaining based on the cloud data collaborative link and the original message data set. According to the method, the complete acquisition of the fire-fighting data and the reproducibility of the event process are effectively improved, and the problem that in the prior art, only polling recording of single-point alarm is carried out, and an original message is discarded, so that the transient process and message-level evidence is lost is solved.

The invention discloses a fire-fighting fireproof door alarm information transmission device and method based on the Internet of Things, and relates to the technical field of fire-fighting alarm, and the method comprises the following steps: carrying out the multi-band continuous scanning of a wireless communication environment of a fireproof door alarm device deployment region after a fire emergency mode is started, identifying a target communication protocol frequency band used by the fireproof door alarm device, and obtaining spectrum parameter information of each frequency band to form a to-be-analyzed communication frequency band set; and extracting a working frequency band used by the peripheral wireless equipment based on a pre-established environment wireless equipment frequency band database. Frequency spectrum mapping is constructed through frequency spectrum scanning and an environment database, intelligent interference prediction is realized in combination with a support vector machine, coding parameters are dynamically adjusted based on interference intensity, the transmission reliability of key alarm information in a high-interference environment is improved, feedforward recognition and closed-loop regulation and control capabilities are achieved, and the method is suitable for popularization and application. And the problems of slow response and unstable transmission of a traditional system are effectively solved.

The invention relates to the technical field of fire early warning, in particular to an electrical fire monitoring and early warning method and system, and the method comprises the steps: collecting the current electrical parameter data of a monitored object, and obtaining the electrical parameter data before multiple historical fire early warning and in multiple normal states; analyzing the first necessity of each historical fire early warning according to the characteristics of abnormal data in the data collected before each historical fire early warning and the correlation between the abnormal data and the fire early warning; analyzing the second necessity of each historical fire early warning according to the difference between any two kinds of data in each historical normal state and the correlation between the abnormal condition and the fire early warning; and combining the first necessity and the second necessity to calculate the disaster early warning necessity of each moment in the current and previous preset time periods so as to judge the current disaster early warning necessity. According to the invention, the historical correlation change among various electrical parameters is analyzed, the fire risk is evaluated more comprehensively, and the accuracy of early warning is improved.

The invention discloses a multi-modal data fusion-based household aging transformation demand evaluation and scheme generation system. According to the system, technologies such as attitude sensing and image acquisition are comprehensively applied, acceleration and angular velocity data of a user are acquired by means of an attitude sensing module, and behavior image data are acquired by an image acquisition module. Fusion analysis is carried out on the multi-mode signals through edge AI processing equipment, accurate recognition of the behavior state of the old people is achieved, and alarm is triggered when an abnormal posture is detected. Meanwhile, the system utilizes the environment sensing module to assist in behavior judgment and path planning, has the advantages of non-intrusive type, high real-time performance, high accuracy and the like, can effectively improve the life safety and behavior monitoring intelligence level of the old people under an unattended condition, is suitable for a safety monitoring scene in a family old people nursing environment, and has a wide application prospect. Good popularization and application values are realized.

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 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 coal mine goaf fire monitoring method and system based on multi-source data fusion, and relates to the technical field of coal mine goaf fire monitoring, and the method comprises the steps: collecting the concentration, concentration and air pressure of each wireless sensing node in a coal mine goaf in real time; according to the concentration of each node, a dual-parameter dynamic coupling gradient field traceability algorithm of fusion / gas expansion and chemical reaction mechanism is adopted to calculate a fire source coordinate; performing anomaly detection according to the concentration of each node, and positioning an air leakage point by adopting a three-dimensional oxygen concentration gradient field positioning algorithm and combining air pressure compensation for an oxygen concentration anomaly point; based on goaf point cloud data, a goaf three-dimensional grid model is constructed, and a fire source spreading path, an air leakage channel and gas concentration distribution are dynamically and visually displayed in combination with multi-source monitoring data and fire source and air leakage point positioning results. The goaf fire prevention and control system can achieve all-around prevention and control of goaf fire.

The invention discloses a fire-fighting early warning management system and method, and the system comprises a monitoring module which is used for obtaining and analyzing real-time feature monitoring data of preset monitoring equipment, determining abnormal equipment, abnormal feature monitoring data and corresponding abnormal change features, and generating an early warning work order; the determination module is used for determining historical abnormal feature monitoring data, fire behavior change features, historical false abnormal feature monitoring data and hidden danger change features based on the historical fire behavior logs and the historical hidden danger logs; the analysis module is used for performing similarity analysis to obtain a first similarity and a second similarity; and the notification module is used for setting the credibility of the early warning work order according to the first similarity and the second similarity and generating a corresponding notification instruction, and by improving the judgment accuracy of the early warning work order, whether the early warning work order is a false alarm or a real fire behavior is accurately judged, and the accuracy and the early warning efficiency of fire fighting early warning are further improved.

Methods, systems, and devices for determination and notification of a location of a building safety system event are described herein. One embodiment includes a method for notifying a user of a fire system event, comprising detecting an event in a building through an event detection device and transmitting information about at least one of the event or the event detection device to a remote server and to a first computing device. The first computing device may include a memory wherein floorplans of each floor of the building are stored. The method may further comprise determining, based on the information and through the first computing device, a floor of the building on which the first event was detected; transmitting a particular floorplan of that floor to the remote server; determining a specific location within the floor at which the first event was detected; generating a visual floor representation, including an indication of the specific location, based on the particular floorplan and the specific location; and transmitting the visual floor representation to a software application comprised of executable instructions stored in the memory of a second computing device to present the visual floor representation to a user.

A premises system may select a sensor of a fire protection subsystem. A premises system may select one or more elements of the one or more other subsystems. A premises system may collect data across the selected sensors and selected elements for a period of time. A premises system may analyze the collected data for correlation among the selected sensors and selected elements.

In an apparatus including a personal monitoring device which includes a microphone, speaker, camera, memory or database, global positioning device, controller, and transmitter, the improvement including a wearable device configured to include at least one of more components of the personal monitoring device on, with, or within, the wearable device, and a smoke detector, the fire detector, or the carbon monoxide (CO) detector. The microphone, speaker, or camera is located on an exterior of the wearable device. The smoke detector, fire detector, or carbon monoxide (CO) detector, is located on an exterior of the wearable device. The smoke detector, fire detector, or carbon monoxide (CO) detector, detects a presence of smoke, a fire, or a presence of carbon monoxide, and the speaker emits an alarm, a siren blare, or an audible and verbal message. The transmitter transmits a message to a computer or communication device.

The invention discloses a forest fire prevention and monitoring system for forestry engineering, and relates to the technical field of forest fire prevention and alarm. A preliminary early warning updating and integrating module is used for integrating and updating risk intervals of preliminary early warning and judgment; the data acquisition module is used for monitoring and acquiring forest environment data in a forest area M in a current monitoring stage in real time, and the risk processing and feedback module is used for sequentially calculating and outputting a fire risk index HZF, an alarm threshold BHZ and a risk adjustment coefficient FT. And the alarm and adjustment module is used for carrying out preliminary early warning and triggering an alarm according to comparative analysis of the fire risk index HZF and the alarm threshold BHZ, and carrying out early warning and adjustment on the risk adjustment coefficient FT on the basis of comparative analysis of the fire risk index HZF and the alarm threshold BHZ and under the condition that the alarm is not triggered. The fire risk can be evaluated more comprehensively, and the alarm threshold value and the monitoring strategy can be adjusted dynamically, so that the self-adaptive capability and the early warning accuracy of the system are improved.

A method and system of providing guidance and information to an end-user during an emergency situation. A network of beacon devices is arranged throughout different rooms of a physical structure, such as a house. When an emergency situation such as a fire is detected, for example via data collected from one or more smart sensors installed in the house, a navigation server can activate the beacon device network. Each beacon device will begin to provide navigation cues such as audio content and / or light display that guide the end-user to the nearest designated exit.

The invention relates to the technical field of radar echo denoising, in particular to a radar echo signal filtering noise self-adaptive suppression method and a radar echo signal filtering noise self-adaptive suppression system. Acquiring radar station position and beam pointing data, and synchronously acquiring forest vegetation data and environment interference data of a beam pointing target area to form radar environment data; the radar environment data are identified through the multi-band decision model, and multi-modal waveform parameters are obtained; based on the multi-modal waveform parameters, radar equipment is controlled to perform periodic detection on the target area, and radar echo data are obtained; the method comprises the steps of identifying radar echo data and radar environment data, extracting dynamic spectrum characteristics in the radar echo data, performing clutter suppression processing on the dynamic spectrum characteristics by using the radar environment data, calculating the confidence probability that an early fire exists in a target area, and executing space-time consistency joint verification; according to the invention, fire early warning is carried out through the confidence probability of the fire.

The invention relates to a fire hazard degree comprehensive assessment method integrating forest lightning stroke parameters, and belongs to the technical field of forest fire early warning. The method comprises the following steps: acquiring a meteorological factor, a vegetation factor, a terrain factor, a lightning stroke factor, an exposure factor and a resistance factor in a determined spatial scale resolution; processing the meteorological factors through a meteorological index model to obtain a meteorological fire danger index; inputting the vegetation factor, the terrain factor and the lightning stroke factor into an ignition probability index model to obtain a lightning stroke ignition probability; based on the vegetation factor, the terrain factor and the meteorological fire danger index, obtaining a combustible flammability index through a fire danger index model; performing dimensionless processing on the exposure degree factor and the resistance degree factor, and calculating the exposure degree and the resistance degree; inputting the exposure degree E and the resistance degree R into a fire hazard degree comprehensive model to obtain a comprehensive fire hazard index; and performing automatic grading according to the threshold value and issuing the lightning fire early warning information in real time. According to the invention, the accuracy of comprehensive assessment of the fire hazard degree can be improved.

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 provides an emergency broadcast playing method and system for disasters in an outdoor scene, and relates to the technical field of emergency broadcasts, cameras in a region are adopted to monitor the evacuation state of personnel in the outdoor scene in real time, and an emergency broadcast system and a rescue system are triggered through the evacuation state to perform linkage, so that bidirectional information transmission is realized; and the priority of the broadcast content played by the emergency broadcast is adjusted according to different instruction information in bidirectional information transmission, so that the broadcast content has pertinence, and rescue workers can receive key instructions.

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 discloses a fire-fighting monitoring method and system based on multi-sensor fusion, a medium and equipment. The method comprises the following steps: acquiring equipment fire-fighting data acquired by multiple sensors in real time; performing preprocessing and abnormal data processing on the equipment fire-fighting data to obtain target fire-fighting data; performing fire prediction on the target fire-fighting data based on a space-time fusion model to obtain a fire probability; performing fire-fighting early warning judgment on the target fire-fighting data based on a risk level triggering condition; and generating a fire-fighting control instruction according to the fire probability and the fire-fighting early warning judgment result, and feeding back the fire-fighting control instruction to the equipment end. Therefore, the problem of hidden fire source recognition delay of an existing system is solved, and millisecond-level fire early warning and graded response closed-loop treatment are achieved.