1100 results about "Fire risk" patented technology

The embodiment of the invention discloses a fire monitoring and early warning method and device based on big data analysis, and belongs to the technical field of data analysis. According to the embodiment of the invention, multi-source data such as satellite remote sensing heat source images, meteorological environment parameters, historical fire event records and geographic information raster data are integrated, a space-time associated dynamic monitoring system is constructed, and environmental anomaly symptoms before fire occurrence can be comprehensively captured. Wherein the space-time reference difference of different source data is eliminated through the space-time fusion processing, so that the heat source distribution, the meteorological change and the historical fire mode form multi-dimensional association under a unified geographic framework, the sensitivity and the reliability of early fire point identification are remarkably improved, and the subsequent accurate fire risk prediction is realized.

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 an urban underground passage fire risk dynamic monitoring and early warning method and system, and solves the problems that single index evaluation is difficult to reflect real risks and a fixed threshold cannot adapt to complex working conditions, and the method comprises the steps: building a four-level threshold library according to risk values, and dynamically adjusting a threshold boundary in combination with a scene; when the risk value is lower than a preset threshold value, the monitoring state is continued; when the risk value exceeds a threshold value in the current scene, triggering a corresponding early warning and pushing the early warning to a command center, coupling historical fire data and dynamic attribute matrix data, and calculating the fire probability of each grid; the smoke diffusion path and the structural fire endurance are simulated by calculating fluid mechanics, the fire consequence severity is quantified, the fire probability of each grid and the fire consequence severity are fused by adopting a fuzzy comprehensive evaluation method to generate a risk matrix, and a high-level risk area is marked. The method has the following technical effects that dynamic monitoring and early warning of the urban underground passage fire risk are realized, and the evaluation accuracy and the early warning reliability are improved.

The invention discloses a forest fire risk assessment method based on a composite chain type disaster evolution mechanism, and relates to the field of forest fire risk assessment, and the method comprises the steps: carrying out the chain type feature field construction of multi-source heterogeneous data through the collection of the multi-source heterogeneous data, including remote sensing data, meteorological data, geographic information data and disaster loss data, obtaining a disaster chain core driving factor tensor, and carrying out the feature field construction of the multi-source heterogeneous data; performing composite disaster chain coupling analysis based on the disaster chain core driving factor tensor to obtain a disaster chain space-time cascade trajectory, performing dynamic risk assessment and grading based on the disaster chain space-time cascade trajectory to obtain a five-level risk zoning map, and performing intelligent early warning analysis based on the five-level risk zoning map to obtain a forest fire dynamic early warning map. According to the method, the chain type driving relation among drought, high temperature, vegetation dryness and fire danger is synthesized, the dynamic threshold value is adopted for risk grading, the dynamic influence of climatic variation and vegetation phenology on the fire danger can be captured, and the accuracy of forest fire risk assessment is improved.

The invention discloses an energy management and safety protection cooperation method for a liquid cooling industrial and commercial energy storage system, and particularly relates to the technical field of energy storage system management. A battery electrochemical model, a heat distribution diagram, temperature gradient data and electrical parameters are used as input, and a battery temperature change trend curve is output; a liquid cooling control strategy is set according to the prediction result; fusing the temperature gradient abnormal parameters, the temperature trend risk and the multi-modal environment data abnormal parameters, starting a fire risk assessment model, predicting the fire probability and position, calculating a fire risk coefficient, generating a fire risk report and setting safety protection measures; a multi-objective optimization mathematical model is constructed based on the energy efficiency ratio, the full life cycle income and the battery health degree, energy storage operation data and power grid requirements are combined, a Pareto optimal solution set is generated by adopting a non-dominated sorting genetic algorithm, and a charging and discharging strategy and liquid cooling parameters are optimized; the liquid cooling pipeline layout is optimized through reinforcement learning, and the problem that the battery temperature cannot be effectively managed is solved.

The invention provides a fire early warning and intelligent fire extinguishing method based on image recognition. The fire early warning and intelligent fire extinguishing method comprises the steps that a camera network is used for covering a target monitoring area, flame and smoke characteristic parameters are input, noise is removed through image preprocessing, and image data are standardized. A heat source point is selected as a camera installation position in combination with a fire propagation mode, and layout is optimized. And calling image data, performing real-time analysis based on a dynamic flame sequence, extracting abnormal response, calculating a fire risk value by using a mode recognition algorithm, and generating peak fire probability data. And collecting real-time fire characteristic data, comparing the data with a risk value after filtering and noise reduction, and setting multi-level threshold values to generate an alarm result. And analyzing a false alarm reason, optimizing the position of the camera and the starting condition of the fire extinguishing device, and generating a fire risk and fire extinguishing efficiency report. According to the invention, the accuracy and timeliness of fire early warning can be improved, the false alarm rate is reduced, and the fire extinguishing efficiency is enhanced.

The invention discloses an electric vehicle parking lot fire prevention and control system based on multi-source information fusion. A sensing layer captures the environment state of an electric vehicle parking lot and the operation parameters of a vehicle battery in real time through a multi-mode sensor network; the network layer adopts an intelligent fusion network architecture to realize communication between the sensing layer and the data processing layer; the data processing layer is used for performing fusion analysis on the environment state and the operation parameters, judging a fire risk level and generating a dynamic control instruction; the data processing layer is composed of a multi-dimensional analysis engine, and a hierarchical progressive intelligent analysis architecture is adopted to realize multi-level research and judgment of fire risks; and the application layer receives the dynamic control instruction generated by the data processing layer, and realizes full-process closed-loop management of fire prevention and control through a multi-mode terminal device. The method not only improves the fire prevention and control precision of the electric vehicle parking lot, but also has good interpretability, and can be directly applied to a fire prevention and control system of the electric vehicle parking lot.

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 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 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 provides a fire early warning method and system based on three-dimensional modeling, and the method comprises the steps: determining the transaction indexes of fire danger at different space units through the spatial structure characteristics of a three-dimensional digital twin model in combination with the state evolution mode of a physical field, and determining a risk cloud picture through all transaction indexes; extracting a risk sub-domain of the fire according to the risk cloud picture, and further generating a heat flux density field matrix among different space units; determining risk fitting degrees of different space units by combining the heat flow diffusion information with the monitoring information of the fire behavior in the target building, and performing time sequence aggregation on the fire risk in the target building according to all the risk fitting degrees and the local temperature field of each space unit to obtain fire danger propagation potential energy; and performing fire diffusion situation monitoring on the target building according to the fire danger propagation potential energy, and outputting a fire diffusion early warning signal. By adopting the scheme of the invention, the diffusion track of the fire heat flow can be accurately identified in the three-dimensional model.

The invention discloses a cable aging test system and method based on the Internet of Things, and relates to the technical field of cable testing, and the method comprises the steps: collecting cable operation parameters in real time through a circuit breaker node disposed in a cable line, and synchronously monitoring the environment temperature and humidity; wherein the cable operation parameters comprise conductor temperature, leakage current, voltage fluctuation value and load power; the collected cable operation parameters are transmitted to a cloud platform through an Internet of Things communication module; analyzing the aging degree value of the cable insulation layer based on an electrothermal two-factor synergistic aging model; dynamically calculating an ignition risk value of the cable through a fire risk assessment algorithm in combination with the aging degree value; and when the aging degree value or the ignition risk value exceeds a threshold value, generating an early warning report, pushing the early warning report to the terminal equipment, and marking the position of the hidden danger at the same time.

The invention relates to a fire prevention and control smart network method and system based on Beidou satellite navigation. The method comprises the following steps: firstly, acquiring real-time operation data of fire-fighting equipment in a building, and scanning a building structure through a three-dimensional imaging device to generate a three-dimensional digital model; secondly, fusing the real-time operation data, the three-dimensional digital model and preset environment parameters to form a fire prevention and control reference data set; thirdly, carrying out multi-dimensional data comparison on the reference data set based on a preset fire-fighting standard program, and generating an alarm signal with spatial positioning information; then, determining a fire-fighting facility failure area according to the alarm signal, and calculating a fire risk diffusion parameter; and finally, generating a grading early warning instruction according to the risk diffusion parameters, and constructing an electronic combat map containing the optimal rescue path. The method provides global and accurate basic data support for fire prevention and control, improves the timeliness and accuracy of early warning, optimizes the rescue path, and improves the fire disposal efficiency.

The invention relates to the technical field of fire safety, and provides a smart campus multi-dimensional fire safety monitoring method and system. The method comprises the following steps: collecting multi-source security data of a campus building; extracting features based on a preset risk dimension, and generating a fire risk feature vector; constructing a risk model according to historical fire cases, and scoring the feature vectors to obtain a real-time risk score; when the score exceeds a dynamic threshold value, triggering a grading early warning instruction; an emergency scheme is generated according to the early warning, and fire fighting equipment and a personnel evacuation system are linked to execute operation. The technical problem that an existing campus fire safety monitoring system lacks a multi-dimensional, real-time and comprehensive risk assessment means and is difficult to accurately judge different fire risks and deal with the fire risks in real time is solved, and accurate fire risk early warning and efficient emergency response are achieved through multi-source data fusion and intelligent analysis. And the campus fire safety management level is improved.

The invention discloses a fire prejudgment detection adjustment optimization method and system, and relates to the technical field of adjustment prediction, and the method comprises the following steps: obtaining fire feature data of a to-be-detected region, carrying out the feature extraction and splicing, and constructing a fire feature vector; performing initial node division on a to-be-detected area, dynamically adjusting node division density according to fire features and geographic positions of nodes, extracting spatio-temporal features, and constructing a multi-scale adjacent matrix; obtaining an included angle and a propagation time difference of propagation directions between nodes, establishing a mapping model, obtaining direction sensitivity, and correcting an adjacent matrix; inputting the corrected adjacency matrix and the fire feature vector into a space-time diagram convolutional network model, and outputting a fire risk level; according to the method, the number and density of the nodes are dynamically adjusted, and the multi-scale adjacent matrix and the space-time diagram convolutional network model are combined, so that the accuracy and the real-time performance of fire risk prediction are improved, and the problems of inaccurate node division and insufficient space-time feature processing in fire pre-judgment are solved.

The invention relates to the technical field of regional fire-fighting analysis, in particular to a regional fire-fighting risk analysis method and system based on multi-modal AI analysis. The method comprises the following steps: collecting a multi-modal fire-fighting related sensing source in an area, carrying out cross-modal fusion processing and abnormal event detection, and identifying a fire-fighting risk mutation point; carrying out risk propagation trend evolution on the fire risk mutation points, and carrying out fire source high-dimensional traceability analysis to obtain regional fire risk traceability points; performing risk evolution time estimation on the regional fire-fighting risk tracing point, and then performing fire risk situation evolution prediction to obtain a fire-fighting simulation situation map; and performing dynamic risk level change tracking according to the fire-fighting simulation situation map, and performing comprehensive emergency demand adjustment to obtain comprehensive risk intervention decision parameters. Through refined resource matching, the utilization rate of fire-fighting resources is improved, and the regional fire prevention and control quality and safety efficiency are improved.

The invention discloses a forest steppe fire risk assessment method and system, and belongs to the technical field of forest steppe fire prevention. The problem of high false alarm and missing report rate caused by lagging fire risk identification, weak multi-source data fusion and insufficient dynamic response in the prior art is solved. According to the technical scheme, the method comprises the following steps: deploying a ground sensing node array to obtain real-time environment data of a grassland region, fusing high-resolution satellite remote sensing and regional weather forecast data, and constructing a space-time aligned risk assessment data cube; establishing a grassland fire risk factor dynamic coupling model, and dynamically allocating factor weights in combination with the adaptive weight decision tree; generating a comprehensive risk index, carrying out nonlinear mapping to five risk levels, and linking a visual engine to generate a dynamic thermodynamic diagram and push the dynamic thermodynamic diagram to a command terminal; the system supports online updating of the model, and weight parameters are automatically optimized based on a new fire event. According to the invention, high-precision, real-time and spatialized evaluation is realized, the early warning capability and prevention and control decision efficiency are improved, and ecological and economic losses are reduced.

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

The invention discloses a non-key unit fire-fighting early-warning management method based on dynamic risk grading, and the method comprises the steps: collecting multi-dimensional parameters, such as operation of fire-fighting facilities and activity of personnel, in real time through a multi-dimensional fire-fighting abnormal parameter perception model, carrying out the structural storage, dividing time windows through a dynamic risk time sequence management model, and extracting features; and fire risk values are calculated and graded by combining the two models. And determining an early warning trigger condition according to a risk grading result and fire-fighting early warning parameters, continuously monitoring and updating risk values and grades after early warning is started, and dynamically adjusting early warning information. And establishing a feedback mechanism, and feeding back a response measure effect to the model to optimize parameters and calculation rules. According to the invention, dynamic and precise early warning management of fire risks of non-key units is realized.

The invention discloses an energy storage power station fire early warning method and system based on multi-parameter fusion, and the method comprises the following steps: collecting the temperature, characteristic gas concentration, cell expansion force, voltage fluctuation and environment temperature and humidity data of a lithium battery of an energy storage power station in real time through a distributed sensor, and carrying out the cleaning, denoising and standardization processing of the collected parameters, temperature and gas concentration monitoring values are corrected through an environment temperature and humidity compensation algorithm, abnormal data caused by environment interference are eliminated, the preprocessed data are input into a preset intelligent early warning module, and the model is based on a random forest algorithm. Through multi-parameter fusion and intelligent algorithm deep analysis, in combination with environment compensation, interference elimination, early warning accuracy improvement, graded early warning and linkage response, full-stage accurate disposal is achieved, timeliness is enhanced, sensor redundancy, multi-cabin cooperation and other mechanisms guarantee reliability, a closed loop from monitoring to disposal is formed, and the fire risk and loss are effectively reduced.

The invention discloses a fire-fighting early warning system and method based on artificial intelligence, and relates to the technical field of intelligent fire-fighting early warning, and the system comprises an environment sensing module which comprises a multispectral sensor, a temperature sensor, a smoke sensor and a high-definition camera, and is used for collecting fire characteristic spectral data and transmitting the data to a data analysis module; the data analysis module comprises an edge calculation unit and a data fusion unit, and is used for performing feature extraction and anomaly detection on the fire feature spectrum data through a deep learning model to generate a fire risk assessment result; the early warning decision module comprises a risk assessment unit, an early warning grading unit and a communication unit, and is used for determining an early warning grade according to a fire risk assessment result and triggering equipment of the execution feedback module; and the execution feedback module comprises a spraying system, an emergency lighting system and an alarm and is used for executing fire emergency measures according to the instruction of the early warning decision module. According to the invention, comprehensive improvement of fire prevention and control efficiency is realized through cooperative work of multiple modules.

The invention relates to the technical field of data processing, in particular to an Internet of Things data early warning method based on multi-sensor cooperative monitoring. The method comprises the following steps: acquiring data in a warehouse by using each sensor, the data comprising temperature, CO concentration and smoke concentration, and respectively constructing various data acquired every day into a group of sequences; and calculating the sum of the difference values between the monitoring values of the sensors at the current monitoring moment and the alarm threshold value. According to the method, temperature, CO concentration and smoke concentration are constructed into a sequence through multi-sensor data fusion, data difference value weighting is carried out by combining sensor credibility (based on historical data fluctuation variance) and monitoring time importance degree (historical alarm frequency normalization), and an early warning time period is dynamically delimited based on data curve inflection points. Therefore, potential signals of the fire risk can be found earlier, the accuracy of calculating the danger degree at the current moment is improved, and the false report and missing report rate is reduced.

The invention discloses a forest-town junction region fire dynamic early warning method based on multi-source data assimilation, and the method comprises the following steps: inputting collected multi-source observation data into a fire risk prediction model for training, and outputting a fire risk grid map covering the whole forest-town junction region for monitoring a high-risk building group; constructing a fire spreading numerical model covering multi-material, multi-scale and multi-physical mechanism coupling; a fire propagation characteristic quantitative model is constructed, and the building heat influence is evaluated; and constructing a data assimilation and fire dynamic prediction model, and dynamically predicting and outputting fire spreading parameters and a spreading trend chart. According to the method, a forest-building-oriented complex scene is constructed, and propagation behaviors of flames under multiple mechanisms of convection, radiation and conduction are effectively described; through the modes of risk identification, dynamic simulation, data assimilation, visual early warning and the like, the precision, response speed and engineering applicability of forest-town boundary region fire prediction are remarkably improved.

The invention discloses a fire risk accurate prediction method and system based on a multi-source data fusion knowledge graph, and belongs to the technical field of machine learning, and the method comprises the following steps: accessing a multi-source heterogeneous data set, integrating sensor data, geographic information, historical data and external data, and obtaining a multi-source heterogeneous data set; a full-size forest three-dimensional model is constructed by means of FDS and SolidWorks, combustion simulation is carried out, environmental parameters are set carefully to ensure that the result is accurate, and a foundation is laid for construction of a multi-source heterogeneous data set; the method comprises the steps of data acquisition, data preprocessing, noise data cleaning, space-time alignment and feature engineering, multi-source heterogeneous data acquisition and preprocessing are carried out, and forest fire related data covering structured, semi-structured and non-structured types are acquired from a multi-source heterogeneous data set. The accuracy and timeliness of fire early warning are remarkably improved, effective fusion of forest fire multi-source heterogeneous data can be achieved, and transparency and traceability in the data fusion process are ensured.

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 relates to the technical field of intelligent fire fighting, in particular to a building electrical fire identification method and system based on multi-source information fusion, and the method comprises the steps: firstly collecting the multi-source monitoring data of an electrical system in real time, then carrying out the preprocessing of the multi-source data, including noise filtering, time alignment and feature extraction, and constructing a dynamic weight fusion model; the weight of each data source is adaptively allocated based on the feature entropy, then fusion features are input to the fire risk grading model, a fire probability index and an early warning level are output, and when the early warning level exceeds a threshold value, a linkage control instruction is triggered and an alarm is given; according to the method, through dynamic fusion of multi-source information, the electrical fire identification precision is remarkably improved, multi-dimensional parameters such as current, temperature, smoke and arc are adaptively integrated by adopting an entropy weight assignment mechanism, the problems of false alarm and missing alarm of traditional single-threshold detection are solved, the missing alarm rate and the false alarm rate are remarkably reduced, and the reliability of fire early warning is ensured.

The invention discloses a preparation method of flame-retardant silicone rubber resistant to long-term thermal aging, and relates to the technical field of high-molecular compound composition anti-aging rubber. The flame-retardant silicone rubber is prepared from the following components in parts by mass: 75 to 100 parts of methyl vinyl silicone rubber raw rubber, 18 to 28 parts of fumed silica, 20 to 35 parts of aluminum oxide trihydrate, 0.5 to 3 parts of platinum flame-retardant synergist, 2 to 5 parts of heat-resistant additive, 1.5 to 2.5 parts of structured control agent, 1 to 2 parts of cross-linking agent, 2 to 10 parts of anti-aging agent and 8 to 13 parts of fluorinated silicone rubber. Through a synergistic flame-retardant system, the silicone rubber disclosed by the invention shows obvious advantages in flame-retardant capability, can reach a higher flame-retardant grade, effectively delays flame propagation, and reduces the risk of fire disasters. The weather resistance of the silicone rubber is remarkably improved due to the ultraviolet absorption function and the anti-oxidation function of the anti-aging agent, so that the silicone rubber can effectively resist ultraviolet rays and oxidation under the long-term outdoor solarization condition, and the performance reduction and the appearance deterioration are reduced.

The invention discloses a forest fire dynamic monitoring method and system based on multi-source data fusion, and relates to the technical field of fire dynamic monitoring, and the method comprises the steps: carrying out the calculation of the fire occurrence probability of a forest region, and outputting a dynamic risk thermodynamic diagram; non-uniform deployment of integrated sensing units is carried out to form a ground fire sensing network; large-range abnormal hot spot distribution is obtained; performing abnormal hot spot cooperative verification, and outputting a real-time fire risk alarm; matching a high-credibility unmanned aerial vehicle according to the fire risk alarm to execute real-time fire image acquisition; fire spreading modeling is carried out in combination with real-time meteorological data, and a fire spreading trend prediction result is obtained; and executing high-response fire extinguishing management and control of the forest area. According to the invention, the technical problem of low fire extinguishing management and control efficiency caused by inaccurate forest fire monitoring risk in the prior art is solved, and the technical effects of realizing dynamic monitoring and efficient management and control of forest fire and improving the efficiency and timeliness of fire extinguishing management and control are achieved.

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 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.