565 results about "Fire risk" patented technology

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 relates to the technical field of fire safety management, and discloses a fire safety management supervision platform system. The system comprises a building information acquisition module for acquiring building structure data and environment monitoring data of a target building in real time; the risk area division module is used for dividing a plurality of independent fire risk areas according to the building structure data and distributing area identifiers; the historical data indexing module is used for indexing a historical fire accident data set and a fire-fighting facility maintenance record set of each region according to the region identifier; the dynamic risk analysis module fuses the environment monitoring data and the historical fire accident data set to generate a real-time fire risk coefficient sequence; the fire-fighting resource scheduling module is used for generating a fire-fighting resource allocation scheme based on the real-time fire risk coefficient sequence and the facility maintenance record set; and the emergency response decision module generates a multi-stage fire emergency response strategy according to the resource allocation scheme. The system improves the timeliness and pertinence of fire safety management.

The invention discloses a transformer substation fire risk dynamic assessment method, which comprises the following steps: constructing a transformer substation digital twin model according to the live information of a transformer substation; constructing a transformer substation fire risk deduction model according to a fire propagation mechanism and the live information; and generating substation fire risk assessment information according to fire deduction information output by the substation fire risk deduction model. By adopting the transformer substation fire risk dynamic assessment method, the current data of the transformer substation can be collected and analyzed in real time to dynamically assess and predict the fire risk, the fire risk management and prevention and control level of the transformer substation is remarkably improved, and the operation requirements of the transformer substation for high reliability and high safety are met.

The invention relates to the technical field of environment monitoring and early warning, in particular to a forest fire monitoring and early warning system based on big data, which performs multi-period data superposition calculation in combination with a temperature change difference value, remarkably improves the recognition precision of a suspected point of a fire source, enhances the capture capability of dynamic change of forest environment temperature, and improves the accuracy of forest fire monitoring and early warning. According to the method, the identification rate of potential fire behavior is improved, difference analysis is performed by using a time period highly overlapped with humidity in a temperature trend curve, a high-risk section is accurately marked in combination with the temperature rise rate and the wind direction continuity, the judgment capability of a fire risk range is improved, and the judgment is matched with the change rate of a risk point through path overlapping number judgment. Quantitative control of trigger conditions is realized, so that a risk control decision has multiple response capabilities of spatial dimension, time dimension and dynamic evolution characteristics, and timeliness, spatial accuracy and risk response perspectiveness of forest fire prediction are improved.

The invention discloses an energy corridor forest fire risk prediction method and system based on multi-source data fusion, and the method comprises the steps: constructing a fusion data set, and determining risk evolution parameters; if the risk evolution parameter exceeds a preset threshold value, triggering a vegetation difference analysis function to obtain a section heterogeneity label; after section heterogeneity labels are obtained, time-space non-uniformity compensation correction is conducted on historical data through a time sequence change tracking method, the risk transition probability of each section is calculated, and potential critical points are judged; if the potential critical point is judged to be high in probability, activating a difference early warning mechanism, generating a targeted risk level map according to a section heterogeneity label and a risk transition probability, and obtaining a dynamic early warning signal; and carrying out iterative verification on the dynamic early warning signal by adopting a real-time update flow in the fusion data set, adjusting a risk level in combination with prediction output of the long and short-term memory network, and determining a final risk prediction result. According to the invention, the timeliness and accuracy of fire risk prediction are improved.

The invention discloses a forest fire risk prediction method based on multi-source data, and belongs to the technical field of forest fire prevention. Aiming at the problem of low prediction precision caused by one-sided information of a single data source and insufficient multi-source data fusion in the prior art, the method is realized by the following steps: acquiring micrometeorological data including temperature and humidity, wind speed and air pressure, and image data including an infrared image and a visible light image; the data of the mountain fire-prone area comprises historical fire frequency, vegetation type and topographic information; uTC + 8 time synchronization and WGS84 coordinate system space calibration are carried out on the data, and missing values and abnormal values are processed; carrying out feature layer fusion by adopting an attention mechanism, and extracting core features such as a temperature and humidity coupling index and vegetation dryness; spatial correlation features are captured through CNN, a time sequence trend is captured through LSTM, a mountain fire occurrence probability is output by using a Sigmoid function after decision-making layer fusion, and a result is calibrated in combination with sub-region features. Through multi-source data deep fusion and spatial-temporal feature collaborative analysis, the accuracy and timeliness of forest fire risk prediction are improved, a new data source can be expanded and accessed, and the method is suitable for a complex forest fire prevention scene.

The invention relates to the technical field of evacuation intelligent simulation, in particular to a fire-fighting evacuation path dynamic planning method and system based on digital twinning. In the fire-fighting evacuation path dynamic planning method and system based on digital twinning, a digital twinning environment containing multi-dimensional real-time sensing data such as temperature, smoke and heat radiation intensity is constructed; the method can dynamically analyze space-time evolution characteristics such as fire spreading trend and heat accumulation, further carries out accurate quantitative evaluation on the danger level of each segment of an evacuation path, and meanwhile, integrates video monitoring and wireless positioning information to grasp the specific positions and moving tracks of people in a building in real time, thereby improving the safety and reliability of the evacuation path. The actual flow bottleneck is identified based on the flow direction consistency, the distribution density and the congestion condition of the crowd, and finally, the dynamic danger of the fire environment and the macroscopic flow trend of the crowd are combined, and the passage cost value of each path section is subjected to dual dynamic correction, so that the flow rate of the crowd is improved. And an evacuation path planning result giving consideration to fire hazard avoidance and crowd congestion evacuation is generated, so that the real-time performance and safety of evacuation decision making are effectively improved.

The invention relates to the technical field of mine fire-fighting early warning, and particularly provides a mine intelligent fire-fighting control method based on the Internet of Things and artificial intelligence, which comprises the following steps: collecting multidimensional parameters related to mine fire-fighting in real time, synchronously obtaining mine fire-fighting historical data, extracting the multidimensional parameters collected in real time according to a preset time interval, and sending the extracted multidimensional parameters to a server; performing parameter fusion on the extracted multi-dimensional parameters and the obtained mine fire-fighting historical data to obtain a fire-fighting associated feature set containing time-space attributes; inputting the fire-fighting associated feature set into a pre-trained fire-fighting risk prediction decision model, and outputting a fire-fighting disposal strategy through the fire-fighting risk prediction decision model; and starting graded early warning based on the fire risk evolution trend and the fire-fighting disposal strategy, and executing fire-fighting control actions in a linkage manner. According to the method, mine fire-fighting full-dimension monitoring, accurate prediction and dynamic closed-loop control are realized, the fire-fighting intelligent level and the emergency disposal efficiency are improved, and a reliable guarantee is provided for mine safety production.

The invention discloses a multi-terminal linkage control method and system for a fire alarm scene, and relates to the technical field of energy storage system fire safety, and the method comprises the steps: configuring a composite cluster-level detector for each cluster of batteries of a target energy storage system, and collecting cluster-level external environment data; the method comprises the following steps: acquiring internal state data of a battery pack through a BMS, and uploading the two types of data to an edge calculation unit; the edge calculation unit performs fire risk assessment on the data and determines fire risk assessment parameters of the battery cluster; and determining a battery cluster fire extinguishing decision result, and finally performing multi-end linkage fire extinguishing control on the fire extinguishing device through the result. The technical problems that a traditional fire detection and fire extinguishing control scheme is slow in response, high in false alarm and missing alarm rate and incapable of giving consideration to internal and external states when dealing with thermal runaway of the energy storage system are solved, and the technical effects of early-stage accurate early warning and rapid linkage fire extinguishing and effective improvement of the fire safety level of the energy storage system are achieved.

The invention discloses a quantitative decision-making method for an electric vehicle fire safety three-level evaluation system. The quantitative decision-making method comprises the following steps: step 1, constructing a three-level evaluation index system; 2, performing quantitative scoring on the three-level indexes by the plurality of large language models to obtain an expert comprehensive scoring vector of each large language model; 3, calculating a subjective weight vector W (x) of each level of index according to the expert comprehensive scoring vector, and if the consistency ratio CR does not meet the consistency requirement, iteratively correcting W (x) by adopting a power method to obtain an initial weight vector system W; 4, introducing a fire news collection agent to generate a vehicle type subtraction item delta G and an accident cause amplification coefficient (1 + delta), and obtaining a subjective weight vector system W'after dynamic correction; and step 5, calculating a comprehensive score Sfinal, dividing fire risk grades, and generating a visual report. According to the invention, a three-level index system is constructed and a fire news collection agent is introduced, so that the timeliness and adaptability of the system are significantly improved.

The invention relates to the technical field of fire safety monitoring and early warning, in particular to a fire risk dynamic assessment and early warning method based on deep reinforcement learning, comprising the following steps: acquiring multi-source sensing data of a target area; a dynamically updated fire risk assessment state space is constructed based on multi-source sensing data, and a real-time state in the state space is processed and a risk assessment decision is generated through a deep reinforcement learning agent optimized based on an accumulated award function weighted by an accuracy award, a false alarm punishment and a missing alarm punishment; the deep reinforcement learning agent adopts a deep deterministic strategy gradient network structure; mapping the continuous output of the risk assessment decision into a discrete dynamic fire risk level; and triggering an early warning signal matched with the dynamic fire risk level according to the dynamic fire risk level. Through multi-source data standardization, dynamic state space, reinforcement learning optimization and grading early warning binding, accurate dynamic evaluation, reliable early warning and efficient emergency response are realized.

The invention discloses a community fire risk dynamic assessment method and system based on AI prediction, and belongs to the technical field of fire prevention and control, and the method comprises the steps: S1, collecting and preprocessing multi-source data of a community, and constructing a comprehensive risk factor database; s2, constructing a risk prediction model, taking the preprocessed multi-source data as input, identifying a key risk factor, and outputting a fire risk probability; s3, calculating the dynamic weight of each index by fusing the static expert experience and the real-time risk contribution degree; s4, based on the dynamic weight and the risk probability, calculating a comprehensive risk value and dividing risk levels; s5, constructing a prevention and control strategy library based on the risk level and the key risk factor; according to the community fire risk dynamic assessment method and system based on AI prediction, the change rule of the fire risk factors in the community can be captured in real time, potential fire hazards can be predicted in advance, and a targeted prevention and control strategy can be automatically generated.

The invention discloses a multi-source data fusion energy storage system fire risk real-time early warning method and system, and the method comprises the steps: collecting energy storage system multi-source fire risk correlation parameters, and screening a key parameter set through a data correlation mapping mechanism; calculating a preliminary thermal runaway probability value by adopting a random forest integration model for thermal runaway probability prediction; outputting a time sequence parameter prediction sequence by using the variational auto-encoder-gating cycle unit time sequence prediction model; inputting the two into a self-adaptive gradient lifting tree algorithm model to obtain a comprehensive fire risk value; and comparing a preset threshold interval to generate a corresponding grade early warning signal. The system comprises six units which are sequentially associated and cooperatively work. According to the method and the system, the problems of redundant parameter processing and single algorithm in the prior art are solved, the early warning accuracy and real-time performance are improved, the operation safety of the energy storage system is ensured, the complex association of multi-source parameters is fully processed through multi-algorithm cooperation, and the prediction accuracy and the evaluation comprehensiveness are greatly improved.

The invention discloses a fire-fighting early warning method and device and a medium, and the method comprises the steps: collecting the operation data of fire-fighting equipment, and determining the operation state of the fire-fighting equipment through signal analysis and parameter comparison; preprocessing environmental sensor monitoring data to obtain standard monitoring data; determining fusion weight of each sensor according to scene fire risk characteristics, and constructing a space-time diagram model; inputting the two into a space-time diagram neural network, optimizing the weight through reinforcement learning, and extracting dynamic fusion features; inputting the features into a time sequence prediction model, and outputting a risk prediction sequence in a future preset time period; inputting the dynamic characteristics and the prediction sequence into a digital twinborn model for simulation verification, and outputting an early warning result; and according to the early warning and the space-time diagram, causal inference is used for screening the collaborative agent to generate a disposal instruction. Through a cognitive closed loop of perception, fusion, prediction and verification, the false alarm rate is greatly reduced, and the early warning efficiency is improved.

The invention provides a power transmission line fire risk grade zoning prediction method and system, and relates to the technical field of intelligent power grid environment perception and risk early warning, and the method comprises the steps: data collection and multi-source space-time fusion: collecting the regional characteristics of the historical fire occurrence space-time, real-time meteorological parameters, vegetation types and combustible rates of a power transmission line to form a multi-source data set, the multi-source data set realizes alignment coding through a unified geographic grid and a timestamp, and a fire risk sample set is generated. Through a multi-source data deep fusion and grid space-time alignment mechanism, unified coding and coupling modeling of regional features such as historical fire occurrence space-time, real-time meteorological parameters, vegetation types and combustible rates are realized; risk zoning precision and spatial resolution are remarkably improved, zoning granularity can be refined in practical application, the resolution is far better than that of a traditional method, and the overlapping rate of a risk zoning result and actual fire distribution is improved.

A service inputs building characteristics for a building into a machine learning model configured to output a probability that a given building will be lost should a fire reach the building, and receives as output from the model a building loss factor for the building. The service determines determining an exposure measurement for the building by performing simulations, over a plurality of candidate environmental parameters, of whether a simulated fire would encroach on the building. The service determines a building damage potential measurement based on the building loss factor, the exposure measurement, and an intensity measurement, and generates for display a graphical user interface showing fire risk for the building based on the building damage potential measurement.

The invention relates to the technical field of fire prevention, and discloses a high-and-low-voltage power distribution room fire prevention early warning system based on potassium ion aerosol fire extinguishing, which comprises a smoke fire detection module used for detecting fire related data inside and outside a high-and-low-voltage power distribution room; the fire related data comprises smoke data and temperature data; the comprehensive evaluation module is used for judging whether an adopted evaluation model is a temperature evaluation model or a smoke evaluation model according to the fire related data; the fireproof grade classification module is used for grading fireproof grades in the high and low voltage power distribution room according to the screened evaluation model; the fire prevention measure implementation module is used for starting the potassium ion aerial fog fire extinguishing equipment in the corresponding fire prevention subareas to extinguish fire according to different grades; the electrical characteristic monitoring module is used for monitoring the load current and arc signal intensity of electrical equipment in the high-low voltage power distribution room and the temperature change rate of key nodes in real time; according to the invention, multi-dimensional accurate monitoring and graded prevention and control of the fire risk of the high and low voltage power distribution room are realized.

The invention belongs to the technical field of fire safety management, and particularly relates to an intelligent early warning method and system for fire safety management, and the method comprises the steps: obtaining multi-source fire sensor data; training a basic multi-modal fusion model according to the multi-source fire-fighting sensor data to obtain fire risk probability data, and meanwhile, generating sequential aligned continuous video frames through basic inter-frame feature matching; according to continuous video frames, space-time joint features are extracted by using a basic space-time convolutional network, local feature maps of flames and smog are generated in combination with a lightweight backbone network, a fire source key area is positioned, and a fire spreading direction and a fire spreading speed are analyzed to obtain a risk change trend; and based on the risk change trend, identifying a fire risk level through a lightweight classification model, carrying out risk scoring on the safety degree, and generating a thermodynamic diagram to mark a high-risk area. Therefore, the problems of insufficient multi-source data fusion depth, weak video analysis time sequence coherence, lack of visualization ability and the like in the prior art are solved.

The invention provides an electrical fire risk early warning method and system based on the Internet of Things, and relates to the technical field of electrical fire risk early warning, and the method comprises the steps: collecting the real-time and historical state data, including operation parameters, line state parameters and environment data, of electrical equipment, constructing a training set containing a historical state structured data set and a risk level; preprocessing real-time data by using edge computing nodes, and eliminating abnormal values to generate a structured data set; and constructing and training a risk assessment model based on a deep learning algorithm, inputting a real-time data sample to obtain a risk level, feeding back the risk level to a user terminal, and sending an early warning signal to operation and maintenance personnel when the risk level does not conform to expectation. The device state can be monitored in real time, the risk level can be accurately evaluated, feedback can be timely sent to the user and the operation and maintenance personnel, the safety and reliability of the device can be improved, and the operation and maintenance cost can be reduced.

The invention is applicable to the technical field of conveying belts, and provides a macromolecular flame-retardant conveying belt which comprises a mounting frame, a transmission roller and a guide roller, and the transmission roller and the guide roller are rotationally mounted on the mounting frame; the conveying belt is arranged on the transmission roller in a sleeving mode and used for conveying materials, the conveying belt makes contact with the guide roller, the conveying belt is composed of upper covering rubber, a bonding layer, a belt core framework and lower covering rubber, and the belt core framework is located between the upper covering rubber and the lower covering rubber and firmly bonded through the bonding layer. According to the high-molecular flame-retardant conveying belt, lump coal with high value and pulverized coal with low value are separated in real time at the conveying front end, value loss caused by mixed transportation is avoided, graded utilization of resources is achieved, and meanwhile through the arrangement of the conveying belt, atomization dust falling, a dust cover, spontaneous combustion monitoring, fixed-point fire extinguishing and the like, the conveying efficiency is improved. And dust pollution and fire risks are effectively reduced, and the working environment is improved.

The invention relates to the technical field of computer aided design, and particularly discloses an intelligent fire-fighting design simulation evaluation system based on intelligent building engineering. The system comprises a building information model analysis module, a fire risk quantitative evaluation module, a multi-objective optimization decision module, a dynamic disaster evolution simulation module and an evacuation path real-time planning module, and through quantitative analysis of building fire risk, multi-objective optimization of equipment configuration, dynamic simulation of disaster evolution and real-time planning of an evacuation path, the real-time planning of the evacuation path is realized. The accuracy, balance and reliability of fire-fighting design are achieved, and the overall efficiency and compliance of an intelligent building fire-fighting system are improved.

The invention discloses a method and a system for realizing intelligent prevention and control of an electrical fire in an enclosed space, relates to the technical field of intelligent monitoring and prevention and control of fire, and aims to solve the problems of insufficient environmental protection property, hysteresis quality and intelligence in the prevention and control of the electrical fire in the enclosed space. The method comprises the following steps: acquiring multi-modal sensing data and preprocessing the multi-modal sensing data to generate a feature vector; based on the vector, early warning of the fire hazard is realized through a warning model; when early warning is triggered, the positioning model is used for accurately positioning a fire source and evaluating the risk; according to an evaluation result, determining an optimal fire extinguishing strategy through a decision model; and the fire-extinguishing execution module is driven to release fire-extinguishing agents. The system comprises a multi-mode sensing module, a data processing and early warning module, a positioning and evaluating module, a fire extinguishing decision module and a fire extinguishing execution module. According to the technical scheme, full-chain intelligent and automatic prevention and control can be achieved, the intelligent, precise and environment-friendly efficiency of fire prevention and control is improved, and the fire risk and loss are reduced.

The invention provides a lightning fire occurrence probability prediction method fusing extreme climate factors, and relates to the technical field of forest fire prediction and risk assessment. The method comprises the following steps: collecting multi-source data such as thunder and lightning, extreme climate, weather, vegetation, soil and terrain, and constructing a balanced sample set; training an ignition probability model by adopting a random forest, XGBoost, a convolutional neural network and other models, and evaluating the performance difference before and after the extreme climate index is included; explaining variable contribution by using an SHAP method, and identifying a key driving factor; a risk grading grid map is generated according to the prediction probability, and the fire danger grade is automatically improved under the composite condition of heat waves and drought. The method has high precision and strong interpretability, and is suitable for fine prediction and prevention and control decision support of the occurrence probability of the lightning fire under the extreme climate background.

The invention provides a multi-sensor fire early warning method and device, computer equipment and a medium, and belongs to the field of fire early warning, and the method comprises the steps: collecting environmental parameters in real time through a plurality of heterogeneous sensors, the environmental parameters at least comprising temperature, smoke concentration and humidity; preprocessing and normalizing the acquired environmental parameters to unify dimensions and compensate environmental interference, and outputting standardized data; organizing the standardized data into time series data, inputting the time series data into a convolutional neural network for deep feature extraction to capture a space-time evolution mode related to the fire, and outputting a fusion feature vector; and inputting the fusion feature vector into a decision model based on fuzzy logic and Bayesian reasoning, calculating a comprehensive fire risk value, and outputting a corresponding alarm grade according to a preset grading threshold. Through multi-source data fusion and deep feature extraction, the data fusion capability is improved, computing resource consumption is reduced, and accurate recognition and quick response of fire risks are realized.

The invention provides an electrical equipment fire risk assessment system and method for a power distribution network, and relates to the technical field of fire risk assessment, and the method comprises the steps: extracting fault arc features and odd harmonic features of target electrical equipment from a current signal and a voltage signal of the target electrical equipment in the power distribution network; carrying out gradient analysis on the coupling effect of the target electrical equipment due to harmonic heating and load fluctuation thermal shock to obtain a sensitive gradient of thermal runaway generated by the target electrical equipment, and further determining a thermal stability margin of the target electrical equipment under a harmonic distortion working condition; according to the transient current impact value of the target electrical equipment in the nonlinear load fluctuation process, determining an accumulative effect coefficient of thermal aging generated by the target electrical equipment; and then performing fire risk assessment on the target electrical equipment to obtain a risk assessment index of the fire of the target electrical equipment. According to the invention, the method can achieve the evaluation of the fire risk of the electrical equipment under the complex working condition of current and voltage signal distortion caused by a nonlinear load in a power distribution network.

The invention discloses an intelligent park fire-fighting management method and system based on a multi-dimensional Internet of Things, and relates to the technical field of intelligent fire fighting, and the method comprises the steps: sensing a node network through the multi-dimensional Internet of Things deployed in a park, collecting environment data and equipment state data in real time, and carrying out the real-time monitoring of the environment data and the equipment state data based on a fire risk assessment model; and performing fusion analysis on the environment data and the equipment state data, determining a fire risk level in the park, when the fire risk level is greater than a preset threshold value, generating a linkage early warning instruction at least including a risk position and a risk type, generating fire-fighting scheduling information according to the linkage early warning instruction, and sending the fire-fighting scheduling information to the park. And performing park fire-fighting management based on the fire-fighting scheduling information. By means of the mode, accurate, early-stage and automatic sensing and linkage response of the park fire risk are achieved, and the problems that in a traditional fire fighting system, early warning lags behind, response is isolated, and decision depends on artificial experience are solved.

The invention discloses a mine fire grading early warning method, system, device, medium and product, and the method comprises the steps: constructing an underground global map of a mine, and obtaining the multi-modal data of all local areas of the mine according to the underground global map; performing preprocessing and three-level data fusion on the multi-modal data, and determining a preliminary fire early warning result of the corresponding local area by adopting an edge hybrid neural network according to the fused multi-modal data; and according to the global multi-modal data of the mine, the initial fire early warning result and the real-time dynamic early warning threshold, performing fire risk prediction on the mine by adopting a central hybrid neural network to obtain a fire graded early warning result of the mine. The fire risk can be predicted more accurately based on multi-modal data fusion and a hybrid neural network; the early warning threshold value and strategy are automatically adjusted according to the real-time change of the mine environment by utilizing the technologies of online incremental learning, meta learning, reinforcement learning and the like, the parameters do not need to be manually adjusted, real-time calibration is realized, and the timeliness of early warning is improved.

The invention provides a forest fire risk area division and prevention and control method, electronic equipment and a storage medium, and the method comprises the steps: obtaining environment data of each area of a target forest area, and carrying out the timestamp marking of the environment data, and forming an environment data set; based on the environment data set, identifying a region in which the environment data is remarkably changed, and creating risk nodes according to region attributes; according to the method, multi-source data such as meteorology, vegetation, landform and human activities are integrated, environmental data are preprocessed through a sliding window algorithm, data mutation is captured, a dynamic risk data graph is constructed in combination with space-time weight, and meanwhile, real-time environmental parameters are introduced as dynamic correction factors, so that the dynamic risk data graph is obtained. The current situation that a traditional static assessment mode cannot respond to environmental changes in time is changed, the risk assessment precision and the high-risk area identification accuracy are improved, and accurate dynamic assessment of forest fire risks is achieved.

The invention relates to the technical field of fire risk assessment, in particular to a fire risk assessment method and device for an energy storage power station, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining the structure information and equipment information of the energy storage power station, and constructing a three-dimensional simulation model; a three-dimensional simulation model is used for conducting simulation analysis through multiple physical fields, and disaster diffusion trends under various emergencies are obtained; the method comprises the following steps: simulating an emergency response plan in a multi-physics field simulation process based on disaster diffusion trends under various emergencies, fire-fighting equipment deployed by an energy storage power station and the corresponding emergency response plan, and performing fire-fighting risk assessment on a simulation result to obtain an assessment result. Modeling is carried out by utilizing physical characteristics and fire dynamics, so that the fire extinguishing design efficiency and accuracy are improved, the design cost and time investment are remarkably reduced, and a powerful guarantee is provided for the fire safety of the energy storage power station.

The invention relates to a forest fire sensing and predicting method based on a deep space-time hyperspectral fusion network, and belongs to the field of forestry artificial intelligence. The method comprises the following steps: obtaining a high-resolution and hyperspectral forest image, constructing a data set, and dividing a training set and a test set; data registration and preprocessing are carried out on the training set images, a forest fire perception and prediction model based on a deep space-time hyperspectral fusion network is constructed, and the model covers an image feature extraction module, a time sequence attention guiding module, a time sequence feature extraction module, a hyperspectral feature extraction module, a thermal anomaly enhancement module, a variable receptive field fusion module and a fire risk prediction module; preprocessing the data training model by using the training set, constructing a loss function based on the difference between the predicted fire risk level and the real fire risk level, and iteratively updating the parameters by using an optimizer until the training is completed; and finally, inputting a test set preprocessing image into the trained model to obtain a predicted fire risk level. According to the invention, the timeliness and accuracy of early fire prediction can be improved.