4562 results about "Smoke Emission" patented technology

The invention relates to the technical field of communication machine room safety protection, and discloses a communication machine room intelligent fire monitoring and fire extinguishing system based on edge computing, which comprises a fire parameter acquisition module, a temperature characteristic analysis module, a smoke dynamic detection module, a multi-source fusion judgment module, a regulation and control parameter generation module and the like. The fire parameter acquisition module acquires parameters such as temperature gradient, smoke concentration and equipment current, judges fire risk and triggers cooperative monitoring; the temperature characteristic analysis module extracts temperature distribution characteristics and quantitatively evaluates hot spot abnormity; the smoke dynamic detection module analyzes the smoke diffusion trend; the multi-source fusion judgment module is combined with multiple parameters to generate a fire extinguishing or optimizing signal; and the regulation and control parameter generation module is matched with the strategy to generate injection and ventilation parameters. The system also corrects a risk index through an equipment coupling analysis module, and forms closed-loop control by using a dynamic feedback execution module. According to the system, multi-source data fusion monitoring and intelligent fire extinguishing are realized, and the fire prevention and control accuracy of the communication machine room is improved.

The invention relates to the technical field of fire early-stage recognition, in particular to a multi-source information-based conveying belt fire early-stage recognition and early-warning system, which comprises a sudden change detection module, a synchronous analysis module, a spatial trend recognition module, a coupling fluctuation screening module and a probability evaluation module. According to the invention, through multi-source information linkage acquisition, collaborative monitoring of parameters such as along-line temperature, smoke, gas, images, load and heat source temperature difference, combined characteristic analysis among parameters and synchronous response trend determination are driven, and active revelation of early risk hidden dangers and dynamic discrimination of spatial distribution consistency and fluctuation continuity are realized. Potential abnormal focusing locking under a high-interference complex working condition is promoted, collaborative fluctuation between a load and a heat source temperature difference further eliminates environmental noise influence, risk weight dynamic adjustment strengthens classification sensitivity of probability identification, fire risk clustering division promotes accurate mastering of distribution of tiny initial hidden dangers, and the probability identification accuracy is improved. And the reliability of fire early warning in a coal mine area conveying belt scene is obviously improved.

The invention discloses a distribution box fire early warning method and system based on multi-source information fusion, and particularly relates to the technical field of distribution box fire early warning. Temperature, smoke, acoustic vibration and current harmonic signals are synchronously acquired, time sequence alignment and amplitude normalization are performed, and a dynamic baseline is generated in real time; extracting multi-scale statistics and morphological characteristics according to an adaptive window, and calibrating an operation mode through unsupervised clustering; then retrieving a matched baseline signature in a historical feature library, and calculating a multi-modal standardization deviation of a current window; mapping the deviation index into a graph node, combining a covariance edge weight, a phase synchronization index and a smoke discrete index, obtaining a coupling risk coefficient through a fusion model, and outputting a comprehensive risk index; and finally, carrying out multi-scale rate and acceleration analysis on the comprehensive risk index, triggering three-level early warning of attention, warning and danger by adopting a dynamic threshold, and correcting the threshold by utilizing operation and maintenance feedback self-learning, thereby effectively solving the problems of early warning response lag and unknown early warning level.

Disclosed are a method, system, and device for removing smoke from laparoscope images based on a conditional diffusion model. The method includes: segmenting a video of a laparoscopic surgery according to the number of frames to form a data set; performing smoke rendering on the obtained laparoscope smokeless images, and synthesizing paired smoky images to obtain a synthetic data set containing the smokeless images and the smoky images; inputting the smokeless images into the conditional diffusion model for forward noise addition, and continuously adding noise until the smokeless images are completely noised; inputting the smoky images into a smoke sensing module to obtain smoke concentration and position information, then training a neural network, and continuously performing reverse denoising on the completely noised images using the trained neural network until clear smokeless images are outputted; and optimizing a smoke removal model through a multi-loss function fusion strategy.

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

The invention relates to the technical field of data processing, in particular to a data acquisition method and system based on internet-of-things equipment, and the method comprises the steps: installing a smoke concentration sensor and a temperature sensor in a cargo area of a warehouse, and calculating the smoke concentration and temperature of the cargo area according to the deviation degrees and change rates of the smoke concentration and temperature at the current moment; determining a smoke concentration risk index and a temperature risk index, and determining a comprehensive risk index by combining the mutual information of the smoke concentration and the temperature; and determining a comprehensive value index according to the respective uncertainty of the historical smoke concentration sequence and the historical temperature sequence and the similarity between the historical smoke concentration sequence and the historical temperature sequence and the corresponding standard sequence, and adjusting the acquisition frequency of the sensor by using the comprehensive risk index and the comprehensive value index to realize a self-adaptive data acquisition strategy. According to the method, environmental changes can be responded in time, potential risks can be recognized in time, and generation of excessive redundant data can be avoided.

Cooking devices, cooking systems, and smoke assemblies for use therewith are provided. In one embodiment, a cooking system is provided and includes a housing and a smoke assembly. The housing can have a lower base portion and an upper lid portion that together define a hollow cooking chamber. The smoke assembly can be coupled to the upper lid portion, and can be in fluid communication with the hollow cooking chamber. The smoke assembly can generate smoke during cooking operations in order to impart smoke flavor onto a food product disposed within the hollow cooking chamber.

The invention relates to the technical field of fire-fighting engineering, and discloses a real-time fireproof monitoring data transmission system for fire-fighting engineering construction, which comprises a fire parameter analysis module, an environmental data analysis module, a fire source positioning module, a model construction module and a safety database. The fire parameter analysis module generates a temperature change record table through gridding temperature measurement nodes, estimates the smoke diffusion rate, and evaluates the thermal stress and deformation risk of the building structure; the environment data analysis module associates the air velocity with the smoke diffusion rate and corrects the parameter weight of the evaluation model; the fire source positioning module generates a fire source three-dimensional positioning map by using multispectral imaging equipment; the model construction module constructs a dynamic fire assessment model according to multi-dimensional data and outputs a comprehensive threat level; the security database stores building material parameters, historical case characteristics, and dynamic threshold rules. According to the system, through multi-module cooperation and multi-data fusion, the accuracy and timeliness of fire-fighting monitoring are improved, and the system is suitable for safety management of fire-fighting engineering.

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

The invention relates to the technical field of fire alarm systems, in particular to a distributed intelligent smoke monitoring and alarm management system, which comprises a data acquisition and trend analysis module for analyzing a smoke concentration change trend and generating a preliminary early warning signal based on real-time data acquired by a smoke sensor; the method specifically comprises the steps that the smoke concentration value Cs (t) of a smoke sensor in the time sequence is obtained, the average value Cmean and the standard deviation Std of the smoke concentration values in the current period are calculated, the upper limit threshold value and the lower limit threshold value of the smoke concentration are determined according to the formulas that Threshold High = Cmean + K1 * Std and Threshold Low = Cmean-K2 * Std, K1 and K2 are adjustment coefficients and are used for weighing sensitivity, when Cs (t) exceeds Threshold High or is lower than Threshold Low, preliminary early warning is triggered, and when Cs (t) is larger than Threshold Low, the smoke concentration is not subjected to early warning. The system analyzes the smoke concentration trend in real time, filters interference, links environment variables to adjust threshold values, performs multi-stage filtering, performs self-learning optimization, performs intelligent hierarchical scheduling, and efficiently prevents and controls fire disasters.

A device, and method for its use, which may be worn by a firefighter for detecting the source of a fire in a smoke-filled or noisy environment, the device having directional infrared sensors for detecting the direction of maximum or minimum intensity of infrared radiation in two or more directions relative to the wearer and reporting at least the direction of maximum or minimum intensity to the wearer by a tactile indicator contacting the skin of the wearer.

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

The invention relates to an emergency evacuation path generation method and system based on STDE, and the method comprises the following steps: extracting a topological network structure of a target building, and collecting smoke detection data and crowd density; building a building topology layer; obtaining a room grid layer according to the building topology layer; constructing a smoke risk value; constructing a congestion risk value; performing dynamic risk fusion on the smoke risk value and the congestion risk value to obtain a total risk; constructing a cost function from each node to an edge of an adjacent node in the building topology layer, and obtaining an optimal path of the building topology layer through a space-time joint reverse search algorithm; performing path planning on each room in the optimal path of the building topology layer in the corresponding room grid layer to obtain the optimal path of the room grid layer; and integrating the optimal paths of the building topology layer and the room grid layer to obtain a final emergency evacuation path. Compared with the prior art, the accuracy of evacuation risk assessment is improved, and different evacuation strategy requirements can be met.

The smoke detection method based on multi-sensor fusion analysis provided by the invention comprises the following steps: when a smoke sensor detects that the smoke concentration in a detection range reaches a first threshold value, acquiring a temperature rising speed in the detection range detected by a temperature sensor, acquiring the environmental heat radiation uniformity in the detection range detected by the human body sensor; generating a corresponding correction coefficient to correct the smoke concentration and the first threshold value to obtain a corresponding corrected smoke concentration and a corrected threshold value; if the corrected smoke concentration reaches a corrected threshold value, a comprehensive risk value is calculated based on the corrected smoke concentration and the temperature rising speed; and if the comprehensive risk value reaches a risk threshold, determining that the fire is a real fire. According to the invention, through the fusion processing of the smoke sensor, the temperature sensor and the human body sensor, the defect that fire detection through smoke is easily interfered by the environment and misinformation is generated at present is overcome.

The invention relates to the field of intelligent early warning, and particularly discloses a composite home fire early warning system combined with smoke and gas monitoring, which is characterized in that firstly, multi-parameter acquisition is started by taking temperature abnormity as a trigger condition, and gas concentration, smoke diffusion and electrical load data are synchronously acquired; local feature extraction is carried out on the time sequence of each parameter; and then establishing an association relationship among the parameters through a cross-time-sequence dynamic message passing fusion mechanism. And finally, fusing the multi-dimensional time sequence characteristics, inputting the fused multi-dimensional time sequence characteristics into a risk grading model, and judging the current risk grade in combination with a historical fire evolution rule. According to the scheme, through time-space correlation analysis of multi-dimensional parameters, daily environment fluctuation (such as cooking fume) and real fire initial characteristics can be effectively distinguished, the method can adapt to risk characteristics of different house type structures, and upgrading from passive alarm to intelligent pre-judgment is achieved.

The invention provides a tear-resistant, wear-resistant, low-smoke and non-dripping sheath material as well as a preparation method and application thereof. The tear-resistant wear-resistant low-smoke non-dripping sheath material is prepared from the following components in parts by weight: 25 to 35 parts of metallocene linear low-density polyethylene, 10 to 15 parts of ethylene-vinyl acetate copolymer, 6 to 8 parts of compatilizer, 50 to 60 parts of aluminum hydroxide, 10 to 15 parts of magnesium hydroxide, 3 to 4 parts of microencapsulated red phosphorus, 5 to 8 parts of lithium oxide synergist, 4 to 6 parts of silicone master batch, 0.3 to 0.7 part of carbon black master batch and 1.5 to 2.5 parts of coupling agent. The tear-resistant wear-resistant low-smoke non-dripping sheath material provided by the invention has the characteristics of being halogen-free, flame-retardant, tear-resistant and wear-resistant, and a low-smoke halogen-free flame-retardant power cable prepared from the sheath material has the characteristics of high smoke light transmittance during combustion, no dripping and cracking resistance.

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 discloses a hydropower station unit fire alarm auxiliary decision-making management system, which relates to the technical field of power equipment safety and comprises a fire data acquisition module, a fire data analysis module, a fire alarm module, a linkage control module and a system management module. And the fire data acquisition module is used for acquiring fire related parameter data in a hydropower station unit area in real time, wherein the fire related parameter data comprises smoke concentration, temperature change rate, flame characteristics and carbon monoxide concentration. By adopting multiple types of sensor units, a smoke sensor, a temperature sensor, a flame sensor, a carbon monoxide sensor and the like are covered for combined acquisition, compared with the condition that the types of sensors of an existing system are limited, multi-dimensional fire related data can be comprehensively and accurately acquired, deep analysis is performed on the acquired data by means of an intelligent algorithm, and the accuracy of the system is improved. The historical data and the real-time data are compared, the false alarm probability is reduced, and the alarm accuracy is improved.

The invention discloses a flame smoke detection method and system based on dynamic convolution and multi-scale feature fusion, and belongs to the technical field of computer vision target detection, and the method comprises the steps: 1, obtaining a fire data set, carrying out the preprocessing of the fire data set, and dividing the fire data set into a training set and a test set; 2, constructing an improved YOLOv5s model: replacing a C3 module with C3-ODConv, introducing a GCB module into a backbone network, and adopting BiFPN on a neck; 3, training the improved YOLOv5s model by using the training set, and testing the improved YOLOv5s model by using the test set; and step 4, performing fire data detection by using the improved YOLOv5s model after training and testing. According to the invention, the learning and identification capability of flame and smoke characteristics can be enhanced, and the detection precision of the method in a complex scene is improved.

The invention discloses an automatic suction removal method, device and equipment for welding smoke and a medium, and relates to the technical field of machine learning. Linear interpolation and capacity expansion are conducted on a temperature matrix of a welding area; inputting the interpolated and expanded temperature matrix into a decision tree model, outputting predicted position information of the smoke source, and calculating spatial position coordinates; converting the point cloud data of the welding area into a grey-scale map, fusing the grey-scale map and the interpolated and expanded temperature matrix data to obtain a temperature space distribution matrix, inputting the temperature space distribution matrix into a first convolutional neural network, and outputting obstacle avoidance point cloud data; calculating parameters of the obstacle avoidance area, inputting the parameters, the spatial position coordinates and the initial joint rotation angle of the actuator into a second convolutional neural network, and outputting a target joint rotation angle; therefore, the welding smoke in the welding area can be automatically sucked by the actuator, the sucking efficiency is improved, the smoke concentration in the welding working area is reduced, the conflict condition of the smoke sucking disc working area is avoided, and the interference to the welding work and the sucking leakage rate are reduced.

The invention discloses a rotor punching and stacking welding defect identification system based on edge calculation, and relates to the technical field of rotor welding identification, and the rotor punching and stacking welding defect identification system comprises a data acquisition module which is used for carrying out real-time acquisition on smoke dust data generated in the welding process, and the smoke dust data comprises smoke concentration, suspended particulate matter concentration and smoke infrared thermal imaging temperature; the model matching module is used for comparing the smoke concentration, the suspended particulate matter concentration and the smoke infrared thermal imaging temperature with corresponding preset reference models respectively to obtain abnormal data with an abnormal trend; and the judgment module is used for performing time sequence analysis on the abnormal data, calculating a standard deviation and a mean deviation rate based on a sliding window, and generating a corresponding early warning growth value. According to the method, a dynamic alarm value generation and attenuation mechanism is constructed, so that false alarm caused by continuous influence of historical data is avoided, and the real-time response accuracy of the integrated value is improved. And through a pre-judgment mechanism, it is ensured that alarm value attenuation is executed only on the premise that data are stable or no early warning growth trend exists.

The invention relates to the technical field of medical instruments, in particular to a bendable minimally invasive surgery electric hook with a smoke suction function. Comprising a smoke suction pipe, a handle assembly, an electrode pipe, a multi-stage bending assembly and an electric hook. The smoke suction pipe is fixedly connected to an outer smoke suction pipe of the handle assembly, an adjusting part is movably connected to the end of the handle assembly, the electrode pipe is arranged on the adjusting part in a penetrating mode and fixedly arranged in the handle assembly, and a control module is arranged in the handle assembly and electrically connected with the electrode pipe; the multi-stage bending assembly is fixedly arranged at the end of the electrode tube, a movable part is further arranged in the electrode tube and fixedly connected with the steel wire set, and the electric hook is fixedly arranged at the end of the extending end of the electrode tube and extends out. The adjusting piece is controlled to drive the movable piece to move in the electrode tube, and then the steel wire set is driven to pull the multi-stage bending assembly to conduct bending movement. The bending electric hook can integrate a real-time smoke suction function in a flexible bending scene, and the cooperation problem of bending operation and smoke removal in minimally invasive surgery is solved.

A system and apparatus for automatic smoke evacuation using an RF sensor. The system and apparatus utilizes an RF sensor for automatic activation of an evacuator that evacuates smoke and / or debris during the use of any electrosurgical instrument or surgical instrument that is capable of generating smoke and / or debris and capable of producing a radiofrequency during its operation. Exemplary embodiments of the system and apparatus for automatic smoke evacuation include both wired and wireless embodiments.

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

An outdoor cooking station configured to simultaneously and independently cook food with separate cooking modes. The cooking station includes a main body extending to define a smoker chamber and a separate air fryer chamber. The smoker chamber is heated with smoke from igniting and burning pellets and the air fryer chamber is heated with an electrical heating element and a fan for mixing the air therein. In one embodiment, the cooking station includes a motor fan that draws smoke from the smoker chamber to the air fryer chamber with ducting therebetween. With this arrangement, the cooking station provides different cooking chambers dedicated for cooking food independent of each other as well as a secondary chamber or the air fryer chamber being employed for cold smoking food items or the option to provide smoke to the air fryer chamber while air frying the food therein.

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 discloses a logistics center flame smoke detection method, apparatus and device, and a storage medium. The method comprises the steps of obtaining a training data set; an initial YOLOv12 model is constructed, the model comprises a multi-scale large kernel attention mechanism module embedded in front of the detection head, and the multi-scale large kernel attention mechanism module is used for performing convolution processing on the fusion feature map through a multi-scale convolution kernel to obtain an enhanced feature map highlighting key features; performing optimization training on the initial YOLOv12 model based on the training data set to obtain a target YOLOv12 model, and in the optimization training process, adopting a Focaler-IoU loss function as a loss function; and inputting the target to-be-detected image into the target YOLOv12 model for detection, and determining the flame smoke type of the target to-be-detected image based on the output type of the target YOLOv12 model. According to the scheme, the recognition accuracy of the small target is improved.

The invention relates to the technical field of fire-fighting simulation, and discloses a fire-fighting fire dynamic simulation system based on multi-physics field coupling. The system collects temperature field distribution data, smoke concentration gradient data, airflow velocity field data and building structure heat conduction parameters of a fire scene; establishing a bidirectional coupling model of a temperature field and a smoke diffusion field, realizing dynamic correction of a smoke convection effect by a temperature gradient through real-time data exchange, constructing a correlation model of an airflow velocity field and a heat conduction parameter of a building structure, and adjusting a velocity field vortex coefficient according to heat flow intensity; integrating the bidirectional coupling model and the correlation model to form a multi-physical field joint calculation framework, and propelling a simulation process by adopting a discrete time slicing mode; and outputting a three-dimensional dynamic simulation map including a temperature field evolution track, a smoke diffusion path and an airflow disturbance range. The system can accurately restore the fire scene multi-physics field synergistic effect process, and provides effective support for fire rescue decision and building fire protection design.

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.