1719 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 relates to a power transmission line forest fire risk assessment early warning system and method, and belongs to the technical field of power transmission line risk assessment. The system comprises an environment DNA power transmission line forest fire risk assessment and early warning system, a quantum sensing power transmission line fire remote monitoring system, a power transmission line forest fire early warning emergency training system, a digital twin power transmission line fire risk dynamic management system, and a multi-node power transmission line fire early warning system of an edge AI and a low orbit satellite. The system comprises a thermal imaging and deep learning integrated power transmission line fire risk distribution prediction system, an AI-driven power load dynamic adjustment and fire prevention system, an intelligent fire-fighting equipment linked power transmission line fire spreading path prediction system and a distributed quantum computing power transmission line fire emergency simulation and optimization response system. According to the method, the inflammable vegetation density and biocenosis change can be dynamically analyzed through a plant and microorganism gene sequencing 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 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 a forest defense method and system based on digital twin and intelligent sensors, and the method comprises the steps: collecting and monitoring environment data, and carrying out the integration processing through data fusion; a big data analysis technology is adopted to analyze a potential risk area where a fire occurs, a high-risk area is identified by evaluating a risk level, and a fire spreading trend is predicted; constructing a three-dimensional virtual model of the forest region by adopting a digital twinborn technology; the spreading conditions of the fire at different time and spatial positions are simulated; according to the predicted fire spreading trend, when a potential fire risk is detected, an early warning mechanism is started; automatic generation and screening of the emergency plans are realized, and the emergency plans are generated. The invention provides an emergency protection system integrating the functions of multi-source sensing data fusion, intelligent early warning analysis, dynamic emergency plan generation and the like. In addition, the problem that a traditional emergency plan is static and cannot be dynamically adjusted according to actual conditions is solved.

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 risk dynamic assessment and early warning method fused with a deep learning algorithm. According to the method, fire related data is collected in real time from various sources such as a sensor, data cleaning, standardization and feature extraction are carried out, and high-quality input data is generated. And based on the convolution and recurrent neural network, constructing a fire risk prediction model, and performing model training and verification by analyzing historical fire data. The system can monitor building and environment data in real time, assess fire risks and generate corresponding early warning information. And the early warning decision module compares model output with a set threshold value, and once the risk exceeds the threshold value, the system automatically pushes early warning information, so that managers and fire departments can take measures in time. The system also provides a user interface and a feedback mechanism to help a user to monitor a risk assessment result in real time and continuously optimize system performance. The method has the advantages of accurate data analysis and rapid early warning response, and the possibility of fire occurrence is reduced.

The invention discloses an energy storage power station fire safety monitoring system based on a big data Internet of Things, and relates to the technical field of fire safety monitoring. A multi-dimensional sensor network is adopted to monitor parameters in real time, a multi-dimensional parameter model under normal working conditions is established based on a multivariable Gaussian distribution model, and the real-time monitoring of the parameters is realized. In the early stage of thermal runaway, abnormity is recognized through the Mahalanobis distance, an early warning signal can be triggered immediately when the abnormity occurs, the defects that in a traditional method, the monitoring means is single, and response lags behind are overcome, fire early warning is more accurate and timely, and the concentration of harmful gas is monitored in real time in combination with a gas detection module; sensor data and gas concentration data are fused through Bayesian inference, the fire risk is evaluated more comprehensively, the comprehensiveness of fire risk evaluation is remarkably improved, a fire area is positioned through a fire source positioning algorithm, and the fire extinguishing range is adjusted in real time according to the fire risk level and the dynamic change of fire source diffusion.

The invention provides a fire early warning method and system based on smoke image joint feature analysis, which are applied to fire early warning in an indoor scene, and the method comprises the steps: collecting smoke concentration distribution data and an infrared thermogram of an indoor environment based on a smoke sensor array and an infrared sensor array, and collecting a visible light image of the indoor environment based on a camera; extracting spatio-temporal characteristics of smoke concentration distribution by using a 3D convolutional neural network, and extracting texture and shape characteristics of the visible light image and temperature distribution characteristics of the infrared thermal image by using a 2D convolutional neural network; performing weighted fusion on the three features to generate joint feature representation; expressing the fused joint features as a feature sequence based on a time sequence, and obtaining a fire risk level of a current time window based on a trained multi-layer perceptron classification model; according to the method, the accuracy, timeliness and comprehensiveness of early warning are improved, and fire risks in various complex scenes can be better dealt with.

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 relates to the technical field of fire hazard monitoring, and discloses a fire hazard remote monitoring and multistage early warning mechanism integration system which integrates a data acquisition module, a heat gathering map generation module, an image fusion module, a data calculation module, a risk prediction module, an early warning notification module and the like. Through an infrared temperature measurement unit, a smoke concentration acquisition unit and an image acquisition unit, the system can acquire temperature, smoke and image information of a key area in real time. A heat gathering map generation and image fusion technology is utilized, a fusion image is systematically generated, a fire scene and temperature distribution thereof are visually displayed, and the fire source positioning efficiency is improved. Meanwhile, the system analyzes the gradient change of the temperature, the heat accumulation area and the smoke concentration through a data calculation module, constructs a risk prediction model in combination with a machine learning algorithm, and achieves the refined prediction of the fire risk. And according to a prediction result, the early warning notification module automatically sends a corresponding early warning message to ensure rapid transmission and timely response of the early warning message. According to the invention, the intelligent, precise and efficient level of fire monitoring is improved.

A control method for fire-fighting false alarm of an energy storage system comprises the following steps of: calculating a fluctuation period, a derivative and a probability density function by using fast Fourier transform and the like by acquiring data streams such as current, voltage, power and heat energy of the energy storage system, determining a system state and predicting a related trend. And predicting power by using an autoregressive moving average algorithm and a support vector machine, and setting a threshold value according to a probability density function to judge abnormity. For heat energy data, through derivative calculation and signal analysis, states are identified and early warning is carried out in combination with models such as a deep convolutional adversarial neural network and a support vector machine. The normal range of monitoring data is determined by means of the long-short-term memory neural network, the change trend and physical quantity relation of abnormal data are analyzed and predicted through a cubic exponential smoothing method and Gaussian process regression, potential risks are judged through weighted fusion and a self-adaptive Simpson algorithm, fire-fighting false alarms are effectively reduced, fire risks are accurately recognized, and the safety of an energy storage system is guaranteed.

The invention discloses a remote Internet of Things control management system and method based on fire fighting, and belongs to the technical field of Internet of Things. The system comprises an image recognition module which obtains a video stream of a fire scene through a monitoring camera, and carries out the real-time analysis and recognition of an image through image processing and a deep convolutional neural network; the gas recognition module is used for monitoring the gas concentration of a fire scene in real time and recognizing gas components by using a gas recognition algorithm; the data fusion analysis module is used for carrying out feature fusion on the data acquired by the image recognition module and the gas recognition module, and carrying out analysis and modeling on the fused data; the decision support module is used for performing fire risk assessment according to an analysis result of the data fusion analysis module; based on historical data and the scene model, performing simulation and prediction of a fire development trend; and the remote internet-of-things control module is used for providing data for checking fire scene conditions in real time, so that a user can remotely control all equipment and devices of the fire extinguishing system.

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 present invention discloses an intelligent control system for fire safety management, which relates to the field of safety management technology, including a multimodal sensor module and a digital twin modeling module, wherein the multimodal sensor module and the digital twin modeling module are connected and communicated through a data transmission network, and the digital twin modeling module includes an edge computing node in the region and a central data processing server, and constructs a three-dimensional digital twin model of a building and its environment by receiving and fusing data from the multimodal sensor module and utilizing real-time data processing and model updating algorithms. The present invention realizes the function of constructing and continuously updating a digital twin model of a building and its environment by designing a multimodal sensor module and a digital twin modeling module, and reflects the internal structure of the building, environmental parameters and personnel flow status in real time, solves the problems of data dispersion, lack of real-time performance and static model in traditional systems, and ensures the accuracy and timeliness of fire risk assessment.

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 provides a power transmission corridor forest fire spreading prediction and risk assessment method based on a satellite internet and a related device, and relates to the technical field of disaster prevention and early warning of a power system. Building a fire risk factor database based on multi-modal data of a satellite-ground fusion network mode; applying a random forest algorithm to screen out key factors and determining fire risk contribution degrees corresponding to the key factors; based on the power forest fire hidden danger factors, similar features of a power transmission corridor neighborhood are extracted, and sub-regions are divided by adopting a clustering algorithm; constructing a fire risk weighted scoring model to identify high, medium and low risk areas; a mountain fire spreading model is constructed based on meteorological observation data driving, a fire risk state transition matrix is established, and a fire spreading path is predicted in real time in combination with a dynamic Bayesian network; evaluating the damage risk of the forest fire to the power transmission line, and generating a damage risk grading graph of the power transmission corridor. The technical effects of dynamic assessment of the forest fire risk and accurate prediction of the spreading trend are achieved.

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 provides an intelligent forest fire prediction and early warning system which integrates satellite remote sensing, unmanned aerial vehicle patrol, ground monitoring and other technologies and intelligently identifies ecological behaviors by using a deep learning algorithm. The system is composed of four core modules: a multi-source data acquisition module, an ecological behavior identification module, a machine learning prediction model module and an intelligent early warning and emergency response module. The forest environment is comprehensively monitored, ecological data is analyzed, the fire risk is accurately predicted, early warning information is automatically generated, and the system is closely linked with an emergency system. The system effectively solves the problems of single traditional early warning means, poor timeliness and insufficient precision, significantly improves the accuracy and response speed of forest fire early warning, and provides powerful technical support for forest resource protection and ecological environment safety.

The invention discloses a community fire early warning method combined with a knowledge graph, and relates to the technical field of fire early warning, and the method comprises the steps: carrying out the traversing of community region information, carrying out the data collection, and constructing a community fire knowledge graph according to a data collection result; performing feature extraction based on the community fire knowledge graph to obtain a plurality of fire features; constructing a fire risk assessment model to perform fire risk assessment on the community, and generating a plurality of risk assessment results; monitoring the community in real time, and generating a trigger instruction; and starting a fire early warning mechanism according to the trigger instruction, and generating a fire response decision. The technical problems that in the prior art, a traditional fire early warning method is inaccurate in evaluation and cannot effectively prevent fire in advance are solved, and the technical effects of improving the accuracy of fire risk evaluation, improving the accuracy of fire prevention and effectively reducing fire hazards are achieved by combining the knowledge graph.

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.