An integrated forest fire intelligent analysis method and system

Abstract

The present application relates to the technical field of forest fire management, and particularly relates to an integrated forest fire intelligent analysis method and system. The system comprises a background service system, a central intelligent analysis system and a mobile intelligent analysis system. The background service system is deployed on a cloud computing server cluster, is used for uniformly storing personnel information, fire scene information and geographic information data, and realizes multi-source data fusion and permission management through a standardized API. The central intelligent analysis system is deployed on a high-performance computer, integrates a forest fire risk auxiliary decision-making model, a forest fire spread trend prediction model, a power distribution dynamic display module and a collaborative mapping module, supports fire spread prediction and real-time decision instruction issuing based on multi-dimensional data such as meteorology, terrain and vegetation, and realizes fire spread trend analysis, mobile mapping and instruction receiving in an offline environment, supports offline data caching and calling in an emergency scene, and effectively improves the efficiency of forest fire emergency response and the accuracy of resource scheduling.

Description

Technical Field

[0001] This invention relates to the field of forest fire management technology, and in particular to an integrated intelligent forest fire analysis method and system. Background Technology

[0002] Forest fires not only severely threaten lives and property and damage the ecological environment, but also, due to improper command, lead to uncontrolled fires, misallocation of rescue resources, and increased risk of casualties. Currently, forest fire command systems largely rely on fixed command centers. Commanders cannot obtain real-time dynamic information about the fire while en route to the fire site, and can only make decisions based on experience or outdated information, easily leading to problems such as inaccurate firefighting route planning and inefficient resource allocation. Furthermore, multi-source data, including personnel information, meteorological data, and geographic information, are stored in a scattered manner, lacking a unified platform for integrated analysis. This makes it difficult to dynamically predict the fire's spread path, speed, and impact range using scientific models, resulting in a lack of precise quantitative basis for command decisions. Summary of the Invention

[0003] To address the aforementioned issues, this invention provides an integrated intelligent forest fire analysis method and system. It integrates multi-source data through a backend service system, utilizes the high-performance computing capabilities of a central intelligent analysis system to run complex spread models, and implements lightweight trend analysis in a mobile intelligent analysis system. This allows commanders to obtain accurate fire prediction information in real time, whether in a fixed command center or a mobile environment. This provides data support for firefighting plan formulation, force deployment, and firebreak planning, effectively improving the scientific rigor and timeliness of emergency command. Specifically, it includes the following steps:

[0004] Pre-disaster fire risk spread prediction: Based on multi-source data of the target forest area, a fire risk base map of the target forest area is generated through a fire spread early warning and judgment model;

[0005] Initial fire assessment and data collection: Receive and integrate real-time fire scene information, and combine the fire risk base map with the initial fire point location data to form a preliminary fire situation map;

[0006] Fire situation analysis and collaborative plotting: Based on the preliminary fire situation map, real-time environmental parameters are input into the forest fire spread model to generate a fire spread trend map, and collaborative plotting is performed based on the fire spread trend map to form a fire fighting plan;

[0007] Firefighting decision generation and distribution: Integrate the fire spread trend map, the real-time collected location information of forest fire prevention teams, and the firefighting plan to generate firefighting decision instructions and distribute them to each terminal;

[0008] Fire spread dynamic analysis and adjustment: Monitor changes in environmental parameters, update the input of the forest fire spread model, and accordingly revise and regenerate the fire spread trend map and firefighting decision instructions;

[0009] Task review and data archiving: Archive and store the fire fighting decision instructions, the revised fire spread trend map, and the data of the entire system operation process for subsequent review and model optimization.

[0010] Preferably, based on multi-source data of the target forest area, a fire risk base map of the target forest area is generated through a fire spread early warning and assessment model, specifically as follows:

[0011] Acquire multi-source heterogeneous data of the target forest area, and perform spatiotemporal normalization and dimensional standardization preprocessing on the multi-source heterogeneous data to form a spatiotemporally aligned fusion data pool;

[0012] Based on the fused data pool, meteorological disaster-causing factors (daily maximum temperature, minimum relative humidity, maximum wind speed, consecutive days without precipitation), vegetation characteristic factors (forest type, forest drought characteristics, forest canopy density), topographic potential factors (altitude, slope, aspect) and social exposure factors (distance from road, distance from residential area) are extracted through feature engineering to construct a multidimensional disaster-causing factor matrix.

[0013] The multidimensional disaster-causing factor matrix is ​​input into a pre-set fire spread early warning and judgment model for analysis, and outputs a fire behavior propagation vector with spatiotemporal evolution characteristics.

[0014] Based on the fire propagation vector, spatial rasterization rendering and risk level zoning are performed using a geographic information system to generate a kilometer-scale fire risk base map of the target forest area.

[0015] Preferably, the multidimensional disaster-causing factor matrix is ​​input into a pre-set fire spread early warning analysis model for analysis, and the output is a fire behavior propagation vector with spatiotemporal evolution characteristics, specifically:

[0016] The multidimensional disaster-causing factor matrix is ​​input into a pre-set fire spread early warning and judgment model, and the time-varying weight coefficients of each factor are calculated through a weight adaptive allocation mechanism to generate a time-varying weight coefficient set.

[0017] Based on the time-varying weight coefficient set, the multidimensional disaster-causing factor matrix is ​​weighted and fused to form a weighted disaster-causing factor feature field.

[0018] The weighted disaster-causing factor feature field is subjected to rule-based reasoning analysis, and a preliminary fire behavior parameter set is generated based on a pre-set fire behavior propagation rule base.

[0019] The preliminary fire behavior parameter set is subjected to nonlinear mapping analysis, and a fire behavior propagation probability field is generated by feature space transformation and probability distribution reconstruction.

[0020] The fire behavior propagation probability field is simulated for spatiotemporal evolution and reconstructed into a vector field, and the output fire behavior propagation vector with spatiotemporal continuity is obtained.

[0021] Preferably, real-time fire scene information is received and integrated, and the fire risk base map is combined with the initial fire point location data to form a preliminary fire scene situation map, specifically:

[0022] Receive real-time fire scene perception data from multiple source sensors, perform multimodal data fusion and spatiotemporal registration processing on the real-time fire scene perception data, and generate a standardized fire scene observation dataset;

[0023] Based on the standardized fire scene observation dataset, feature parameters such as initial fire location, fire intensity, fire line outline and spread direction are extracted to generate fire feature vectors.

[0024] The fire point feature vector is spatially overlaid with a pre-generated fire risk base map, and the coupling coefficient between the fire point and the risk grid is calculated by a weighted fusion algorithm to generate a fire risk-fire point coupling map.

[0025] The fire hazard-fire point coupling map is analyzed in a spatiotemporal correlation with real-time meteorological observation data to extract fire boundary features, fire intensity distribution and spread trend parameters, and generate a situation element layer.

[0026] The aforementioned situational element layers are spatially fused with basic geographic information data and then visualized to create a preliminary fire situation map.

[0027] Preferably, based on the preliminary fire situation map, real-time environmental parameters are input into the forest fire spread model to generate a fire spread trend map, and collaborative plotting is performed based on the fire spread trend map to form a firefighting plan, specifically as follows:

[0028] Based on the preliminary fire situation map, the fire boundary features, fire intensity distribution and spread trend parameters are extracted and analyzed as the initial input conditions for the forest fire spread model.

[0029] Collect real-time updated meteorological observation data, combustible material humidity parameters, and terrain feature data to construct an environmental feature parameter set;

[0030] The environmental feature parameter set and the initial input conditions are input into the forest fire spread model, and fire behavior is simulated through the cellular automata mechanism to generate a fire behavior simulation parameter set.

[0031] Based on the fire behavior simulation parameter set, and combined with the geographic information system, a fire spread trend map including the fire line location, spread speed and hazard level zoning is generated for spatiotemporal evolution visualization rendering.

[0032] The fire spread trend map is distributed to each terminal through a collaborative plotting engine. Plotting instructions generated by multiple parties based on the same trend map are received, and a firefighting plan is generated by integrating conflict detection and semantic fusion algorithms.

[0033] Preferably, the fire spread trend map, the real-time collected location information of the forest fire prevention teams, and the firefighting plan are integrated to generate firefighting decision instructions and distribute them to each terminal, specifically as follows:

[0034] The plotting elements in the firefighting plan are analyzed, and the tactical target points, firefighting routes and resource deployment requirements are extracted to generate a structured tactical task set with spatiotemporal constraints.

[0035] The spatial accessibility matrix is ​​calculated by combining the structured tactical task set with the real-time collected forest fire prevention team location information. Combined with the real-time road network accessibility and terrain crossing cost factor, a team deployment optimization scheme and task allocation priority sequence are generated.

[0036] The team deployment optimization scheme is spatiotemporally coupled with the fire intensity grid, spread vector field and hazard level zoning in the fire spread trend map, and risk avoidance path set and emergency evacuation channel are generated by risk field modeling.

[0037] Based on the risk avoidance path set and the pre-set standardized instruction template library, the semantic-geographic association engine performs parameterized mapping and context adaptation to generate natural language description instructions for firefighting decision-making with geographic reference coordinates.

[0038] The firefighting decision instructions are distributed to each terminal through a multimodal communication link and an adaptive coding strategy. Based on the terminal status feedback, instruction retransmission and priority scheduling are implemented, and instruction confirmation receipts are received to complete the closed-loop instruction issuance process.

[0039] Preferably, changes in environmental parameters are monitored, the input to the forest fire spread model is updated, and the fire spread trend map and firefighting decision instructions are corrected and regenerated accordingly. Specifically:

[0040] The changes in meteorological factors, combustible material humidity and terrain feature parameters are collected in real time by multi-source heterogeneous environmental sensor data streams to generate a spatiotemporally encoded set of environmental change parameters.

[0041] The environmental change parameter set and the current input parameter set of the forest fire spread model are aligned with multidimensional features and the difference is calculated through a spatiotemporal coupler to generate a parameter difference feature matrix.

[0042] Based on the parameter difference feature matrix, the hidden layer weight allocation and cell transformation rule parameters in the wildfire spread model are adjusted through an incremental learning-weight drift compensation coupling mechanism to generate an incremental set of model parameters.

[0043] The incremental set of model parameters is injected into the parallel computing architecture of the forest fire spread model, and the modified fire behavior simulation parameter set is generated by re-initializing the fire behavior simulation operation.

[0044] Based on the modified fire behavior simulation parameter set, a modified fire spread trend map is generated by combining the real-time rendering engine of the geographic information system, and the fire fighting decision command generation logic is updated synchronously to output the modified fire fighting decision command that is synchronized in time and space.

[0045] Preferably, the firefighting decision-making instructions, the revised fire spread trend map, and the data from the entire system operation process are archived and stored for subsequent review and model optimization, specifically:

[0046] The system receives the firefighting decision instructions, the revised fire spread trend map, and data from the entire system operation process. It then performs spatiotemporal coding and feature association operations through multimodal data fusion analysis to generate a structured archived data cube.

[0047] The structured archived data cube is subjected to multi-source data archive storage analysis, and an archived data index set with multi-dimensional retrieval capabilities is generated through spatiotemporal index construction operations.

[0048] Based on the archived data index set, historical fire behavior simulation parameter set and corresponding environmental parameter sequence are extracted, and feature vector of fire behavior evolution mode is generated through feature association topology analysis.

[0049] The feature vector of the fire behavior evolution pattern is matched with the pre-set model optimization rule base for similarity and difference calculation to generate a set of model parameter optimization suggestions;

[0050] Based on the aforementioned set of model parameter optimization suggestions, the internal parameter configurations of the fire spread early warning and judgment model and the forest fire spread model are updated through parameter space mapping and weight adjustment analysis, thereby completing the model optimization iteration.

[0051] The second aspect of this invention discloses an integrated intelligent forest fire analysis system, applicable to any of the integrated intelligent forest fire analysis methods described herein, including a back-end service system, a central intelligent analysis system, and a mobile intelligent analysis system;

[0052] The back-end service system is responsible for the data storage, processing and service support center of the entire system. It adopts a cloud computing architecture to achieve elastic expansion and deploys database servers, map servers and communication servers.

[0053] The central intelligent analysis system is deployed in the command hall, supporting parallel computing of forest fire spread models; equipped with an immersive large screen for viewing multi-source information and collaborative plotting; and equipped with voice interaction devices for voice input of commands, supporting rapid generation of decision commands.

[0054] The mobile intelligent analysis system is deployed on mobile terminals and supports forest fire spread trend prediction, collaborative plotting, and rapid command generation interaction.

[0055] The central intelligent analysis system includes six areas: Area 1 displays meteorological data, including weather, wind force, wind speed, temperature, and humidity; Area 2 displays GIS geographic data, overlaying fire spread effects, collaborative plotting data, and force distribution; Area 3 is used to view camera footage and drone-transmitted footage; Area 4 displays important decision-making information; Area 5 displays real-time communication information; and Area 6 is a new page used to display the forest fire risk auxiliary decision-making module.

[0056] The mobile intelligent analysis system includes five areas: Area 1 displays meteorological data, including weather, wind force, wind speed, temperature, and humidity; Area 2 displays GIS geographic data, overlaying fire spread effects, collaborative mapping data, and force distribution; Area 3 displays decision-making information; Area 4 displays real-time communication information; and Area 5 is a new page used to display camera footage and drone-transmitted footage.

[0057] This invention addresses the technical deficiencies in the prior art and has the following beneficial effects: It achieves real-time data synchronization and cross-terminal collaborative plotting, solving the problems of insufficient mobility, data silos, and poor real-time performance in traditional command systems, and making up for the inability to grasp the development trend of fires; it adopts lightweight edge computing to ensure continuous operation in complex environments, realizes "one map" decision support for multi-department collaborative command, and improves the efficiency of forest fire emergency response and the accuracy of resource allocation. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0059] Figure 1 This is a diagram of the overall system architecture.

[0060] Figure 2 A schematic diagram of the interface of the central intelligent analysis system;

[0061] Figure 3 This is a schematic diagram of the interface of a mobile intelligent analysis system;

[0062] Figure 4 This is a flowchart of the system application process. Detailed Implementation

[0063] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0064] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0065] like Figure 1 As shown, this integrated intelligent forest fire analysis system adopts a three-tiered architecture. Through the collaborative linkage of the back-end service system, the central intelligent analysis system, and the mobile intelligent analysis system, it achieves a closed-loop process of "data acquisition - intelligent analysis - mobile command - real-time interaction". The system architecture is based on multi-source data fusion, with cross-terminal collaborative plotting as the core and multi-mode communication fault tolerance as the guarantee, making it suitable for efficient command and dispatch in complex fire scene environments.

[0066] (1) Back-end service system

[0067] The backend service system is responsible for the data storage, processing, and service support of the entire system. It adopts a cloud computing architecture for elastic scaling and deploys database servers, map servers, and communication servers. The database server stores data such as the spread of each fire scenario, decision-making instructions, and chat data for subsequent review. The map server interfaces with high-resolution imagery (DOM), high-precision digital elevation models (DEM), road networks, contour lines, water systems, and other social vector data. The communication server enables real-time, reliable, and cross-device chat functionality, facilitating collaborative control across multi-level command systems, precise instruction transmission, and efficient resource allocation.

[0068] (2) Central Intelligent Analysis System

[0069] The central intelligent analysis system is deployed in the command center, equipped with a high-performance computer to support parallel computing of forest fire spread models; it features an immersive large screen for viewing multi-source information and collaborative plotting; and it includes voice interaction devices for voice input of commands, supporting rapid generation of decision-making instructions. This includes:

[0070] The forest fire risk auxiliary decision-making module combines meteorological disaster-causing factors (daily maximum temperature, minimum relative humidity, maximum wind speed, consecutive days without precipitation), vegetation characteristic factors (forest type, forest drought characteristics, forest canopy density), topographic potential factors (altitude, slope, aspect), and social exposure factors (distance from roads, distance from residential areas) to achieve daily updates of township-level (kilometer-scale grid resolution) early warning and assessment results for fire spread susceptibility at the provincial, municipal, or large forest area level, providing a risk base map for the precise allocation of fire-fighting resources in large-scale primary forest areas before disasters.

[0071] Forest fire spread analysis module: Combining GIS geographic data (3D terrain, altitude, slope, aspect), meteorological parameters (temperature, atmospheric humidity, wind speed, wind direction), combustible material type, combustible material humidity, combustible material load, initial fire point location, and land use type, it outputs a fire spread trend map for a preset future time period (e.g., within 4 hours) (the prediction time can be adjusted according to actual needs).

[0072] Real-time video transmission module: Supports access to real camera footage and drone-transmitted footage, allowing real-time viewing of the surrounding area of ​​the fire and drone flight path information.

[0073] Multi-terminal collaborative plotting module: Supports synchronous plotting across multiple terminals (such as isolation zone planning and water source point marking). Plotting data is synchronized to portable terminals in real time via WebSocket with a latency of ≤200ms.

[0074] Force distribution display module: Displays the real-time location of forest fire prevention teams, obtains the location of individual soldier terminals through the Beidou / GPS positioning module, and marks them with icons on the GIS map. It supports filtering teams by region (such as the east side of the fire line, the north side of the isolation zone).

[0075] Decision instruction generation module: Built-in standardized instruction templates, supporting one-click generation of decision instructions.

[0076] The central intelligent analysis system interface includes six important areas, such as... Figure 2 As shown in the diagram. Area 1 displays meteorological data, including weather, wind force, wind speed, temperature, and humidity; Area 2 displays GIS geographic data, overlaying fire spread effects, collaborative plotting data, and force distribution; Area 3 is used to view camera footage and drone-transmitted footage; Area 4 displays important decision-making information, such as officially released firefighting plans; Area 5 displays real-time communication information, such as real-time communication information on the situation near the fire, the movement of fire brigades, and the firefighting situation; Area 6 is a new page used to display the forest fire risk auxiliary decision-making module.

[0077] (3) Mobile intelligent analysis system

[0078] The mobile intelligent analysis system is deployed on mobile terminals and supports forest fire spread trend prediction, collaborative plotting, and rapid command generation interaction. Unlike the centralized intelligent analysis system, due to performance limitations, the mobile intelligent analysis system deploys lightweight forest fire spread analysis algorithms and does not access camera data or drone-transmitted images.

[0079] Lightweight forest fire spread analysis module: It incorporates a rule-based fast prediction algorithm that combines GIS geographic data (3D terrain, altitude, slope, aspect), meteorological parameters (temperature, humidity, wind speed, wind direction), combustible material type, combustible material humidity, combustible material load, initial fire location, and land use type to quickly generate a fire spread trend map for a preset future time period (the prediction time can be adjusted according to actual needs). Offline operation is supported. It automatically caches the GIS map of the current task area and team location data, allowing users to view updates within the last 30 minutes even during network interruptions.

[0080] Real-time video transmission module: Supports access to real camera footage and drone-transmitted footage, allowing real-time viewing of the surrounding area of ​​the fire and drone flight path information.

[0081] Mobile plotting module: Features a touch-screen interface that supports gesture-based map zooming and quick marking of fire-line locations. Plotting operations are uploaded to the backend in real-time (when the network is normal) or temporarily stored locally (when the network is offline). Plotting operations are also uploaded to the backend service system in real-time and synchronized to the central intelligent analysis system.

[0082] Force distribution display module: Displays the real-time location of forest fire prevention teams, obtains the location of individual soldier terminals through the Beidou / GPS positioning module, and marks them with icons on the GIS map. It supports filtering teams by region (such as the east side of the fire line, the north side of the isolation zone).

[0083] Decision instruction generation module: Built-in standardized instruction templates, supporting one-click generation of decision instructions.

[0084] The mobile intelligent analytics system interface includes five key areas, such as... Figure 3 As shown. Area 1 displays meteorological data, including weather, wind force, wind speed, temperature, and humidity; Area 2 displays GIS geographic data, overlaying fire spread effects, collaborative mapping data, and force distribution; Area 3 displays important decision-making information, such as officially released firefighting plans; Area 4 displays real-time communication information, such as real-time communication information on the situation near the fire, the movement of teams, and the firefighting situation; Area 5 is a new page used to display camera footage and drone-transmitted footage.

[0085] like Figure 4 As shown, the integrated intelligent forest fire analysis method includes the following steps:

[0086] S1. Pre-disaster fire risk spread prediction: Based on multi-source data of the target forest area, a fire risk base map of the target forest area is generated through a fire spread early warning and judgment model.

[0087] It should be noted that before a fire occurs, the forest fire risk auxiliary decision-making module can be used to view the early warning and assessment results of the fire's potential for spread, providing a risk baseline for the precise allocation of fire-fighting resources in large-scale primary forest areas before a disaster.

[0088] S2. Initial fire assessment and data collection: Receive and integrate real-time fire scene information, and combine the fire risk base map with the initial fire point location data to form a preliminary fire scene situation map;

[0089] It should be noted that when a fire occurs, the system reviews footage from nearby cameras and drones, as well as meteorological information and GIS data to assess the fire situation. Commanders mark fire points on the central intelligent analysis system's large screen, and the plotted data (coordinates, timestamps) is synchronized in real time to all mobile intelligent analysis systems via WebSocket. Commanders heading to the fire site can view the basic fire situation in real time on their tablets.

[0090] S3. Fire situation analysis and collaborative plotting: Based on the preliminary fire situation map, input real-time environmental parameters into the forest fire spread model to generate a fire spread trend map, and perform collaborative plotting based on the fire spread trend map to form a fire fighting plan;

[0091] It should be noted that by inputting meteorological data, terrain slope, type of combustible material, and prediction duration into the central intelligent analysis system, a fire spread trend map for the specified prediction duration is generated within 15 seconds. Video transmitted from drones is used by computer vision algorithms to identify the fire line location, automatically correcting the model's prediction results, and the trend map is updated in real time on a large screen (red indicates high-risk areas, orange indicates medium-risk areas). The fire situation map and team firefighting plans are then plotted on the central intelligent analysis system or a mobile intelligent analysis system.

[0092] S4. Firefighting Decision Generation and Issuance: Integrate the fire spread trend map, the real-time collected location information of the forest fire prevention teams, and the firefighting plan to generate firefighting decision instructions and issue them to each terminal;

[0093] It should be noted that the current location of the forest fire prevention team can be viewed in the central intelligent analysis system or the mobile intelligent analysis system. Combining information such as fire situation, weather, terrain and team status, the system can call the command library to generate firefighting commands, such as "Send team XX to the eastern front of the fire, select a suitable location to open a breakthrough point, and adopt the tactic of breaking through one point and advancing on both flanks". After confirming that there are no errors, the firefighting decision command is issued.

[0094] S5. Dynamic analysis and adjustment of fire spread: Monitor changes in environmental parameters, update the input of the forest fire spread model, and accordingly revise and regenerate the fire spread trend map and firefighting decision instructions;

[0095] It should be noted that if the meteorological information changes, the backend service will automatically update the input parameters of the fire spread prediction model based on the collected information and re-predict the fire spread trend map.

[0096] S6. Task Review and Data Archiving: Archive and store the fire fighting decision instructions, the revised fire spread trend map, and the data of the entire system operation process for subsequent review and model optimization.

[0097] It should be noted that the back-end service system stores all the data of this fire (plotting records, model calculation logs, terminal status, etc.) in the database to support subsequent review and analysis.

[0098] Preferably, based on multi-source data of the target forest area, a fire risk base map of the target forest area is generated through a fire spread early warning and assessment model, specifically as follows:

[0099] Acquire multi-source heterogeneous data of the target forest area, and perform spatiotemporal normalization and dimensional standardization preprocessing on the multi-source heterogeneous data to form a spatiotemporally aligned fusion data pool;

[0100] It should be noted that the multi-source heterogeneous data includes meteorological data (daily maximum temperature, minimum relative humidity, maximum wind speed, consecutive days without precipitation), vegetation data (forest type, forest drought characteristics, forest canopy density), topographic data (elevation, slope, aspect), and social data (distance from roads, distance from residential areas). These data come from various heterogeneous data sources such as meteorological stations, remote sensing satellites, digital elevation models, and geographic information system databases, and have different spatiotemporal resolutions, data formats, and coordinate reference systems.

[0101] Based on the fused data pool, meteorological disaster-causing factors, vegetation characteristic factors, topographic potential factors and social exposure factors are extracted through feature engineering to construct a multidimensional disaster-causing factor matrix.

[0102] It should be noted that, from the fused data pool, the above four types of factors are extracted for each 1-kilometer grid to construct a feature vector representing the fire risk of that grid. The feature vectors of all grids together form a multi-dimensional disaster factor matrix of size [N, M], where N is the total number of grids and M is the total number of factors (10 in this example: daily maximum temperature, minimum relative humidity, maximum wind speed, consecutive days without precipitation, forest type, forest drought characteristics, forest canopy density, altitude, slope, and aspect).

[0103] The multidimensional disaster-causing factor matrix is ​​input into a pre-set fire spread early warning and judgment model for analysis, and outputs a fire behavior propagation vector with spatiotemporal evolution characteristics.

[0104] This embodiment uses an integrated model as an example to illustrate the internal judgment process of a fire spread early warning judgment model:

[0105] (1) Calculation of time-varying weight coefficients: The model's built-in adaptive weight allocation mechanism dynamically adjusts the importance of each factor based on the input data. For example, after a long period of no precipitation, the weights of consecutive days without precipitation and minimum relative humidity will automatically increase; while in windy weather, the weight of maximum wind speed will become dominant. This mechanism can be implemented through a weight rule base based on an attention mechanism, ultimately outputting a set of time-varying weight coefficients W={w1,w2,...,wM} that is closely related to the current meteorological conditions.

[0106] (2) Generate a weighted disaster-causing factor feature field: The weight coefficient set W is weighted and summed with the multidimensional disaster-causing factor matrix, i.e., weighted feature = w1*factor1 + w2*factor2 + ... + wM*factorM, and a comprehensive weighted risk value is generated for each grid to form a weighted disaster-causing factor feature field covering the entire province (which can be regarded as a preliminary risk surface layer).

[0107] (3) Rule-based reasoning analysis: The weighted hazard factor feature field is input into a fire behavior propagation rule base. This rule base contains predefined rules, such as: "IF weighted risk value > threshold A AND wind speed > level 4 THEN fire spread rate = fast". Through rule matching, preliminary fire behavior parameters, such as spread rate and fire line intensity, are output for each grid, forming a preliminary fire behavior parameter set.

[0108] (4) Nonlinear mapping and probability field generation: The initial fire behavior parameter set is input into a neural network (such as a multilayer perceptron MLP) for nonlinear mapping. The network is trained on a large amount of historical fire data to learn the mapping relationship from basic parameters to complex fire behavior patterns, and outputs the probability value of each grid being affected by fire in the next 24 hours, generating a continuous fire behavior propagation probability field.

[0109] (5) Vector field reconstruction: Using the fire behavior propagation probability field, combined with topography (slope aspect), wind direction and other guiding factors, the spatiotemporal evolution is simulated by algorithms such as Dijkstra's algorithm or particle swarm optimization algorithm to simulate the most likely spread path and direction of the fire, and these paths and directions are converted into fire behavior propagation vectors with magnitude and direction (e.g., arrows to indicate direction, and arrow length to indicate spread speed or probability).

[0110] Based on the fire propagation vector, spatial rasterization rendering and risk level zoning are performed using geographic information systems (such as ArcGIS and SuperMap) to generate a kilometer-scale fire risk base map of the target forest area.

[0111] It should be noted that the direction and magnitude information of the vector are converted back into raster data at a 1-kilometer grid scale, with each grid value representing a risk value (such as the probability value mentioned above). Continuous risk values ​​are then divided into different risk levels. For example, risk values ​​are divided into 5 levels: [low risk, lower risk, medium risk, higher risk, high risk]. Each risk level is then assigned a different color (e.g., green for low risk, red for high risk), generating an intuitive, province-wide kilometer-scale fire risk base map. This base map can be used as a daily fire risk warning product, pushed to fire prevention command departments at all levels to guide fire prevention work such as patrol force deployment and material reserves.

[0112] Preferably, real-time fire scene information is received and integrated, and the fire risk base map is combined with the initial fire point location data to form a preliminary fire scene situation map, specifically:

[0113] Receive real-time fire scene perception data from multiple source sensors, perform multimodal data fusion and spatiotemporal registration processing on the real-time fire scene perception data, and generate a standardized fire scene observation dataset;

[0114] The real-time fire scene perception data includes visible light images, infrared heat source data, fire line outline features, and real-time geographic coordinate information collected by visible light cameras, infrared thermal imagers, drone aerial photography equipment, and satellite remote sensors.

[0115] Based on the standardized fire scene observation dataset, feature parameters such as initial fire location, fire intensity, fire line outline and spread direction are extracted to generate fire feature vectors.

[0116] It should be noted that threshold segmentation and clustering algorithms (such as DBSCAN) are used to identify pixel clusters with abnormal temperatures from infrared heat source data. The geographic coordinates of their centroids are used as the initial fire point locations. At the same time, the fire point intensity is calculated based on the average radiation temperature value of the pixels within the cluster. Then, the fire field boundary is extracted from the visible light and thermal imaging data to generate a vectorized fire line profile. Finally, by combining multi-temporal image data, the displacement vector field of the fire line edge is analyzed using optical flow or feature point tracking algorithms to determine the dominant direction of fire spread. These parameters are finally integrated into a structured fire point feature vector [initial fire point coordinates (X,Y), fire point intensity (megawatts), fire line profile polygon sequence, spread direction (angle)].

[0117] The fire point feature vector is spatially overlaid with a pre-generated fire risk base map, and the coupling coefficient between the fire point and the risk grid is calculated by a weighted fusion algorithm to generate a fire risk-fire point coupling map.

[0118] The fire hazard-fire point coupling map is analyzed in a spatiotemporal correlation with real-time meteorological observation data to extract fire boundary features, fire intensity distribution and spread trend parameters, and generate a situation element layer.

[0119] The aforementioned situational element layers are spatially fused with basic geographic information data and then visualized to create a preliminary fire situation map.

[0120] It should be noted that the spatial proximity matrix between fire points and each risk grid is generated by calculating Euclidean distance. Then, a weighted linear combination method is used, with fire point intensity as the core weight factor, and dynamic weighted fusion is performed by combining the inherent risk values ​​in the risk base map to calculate the coupling coefficient of each grid. Then, based on the coupling coefficient, the natural breakpoint method is used for risk reclassification to generate a fire risk-fire point coupling map that integrates real-time fire conditions and static risks. Subsequently, this coupling map is subjected to spatiotemporal correlation analysis with spatiotemporally interpolated real-time meteorological observation data (including wind speed, wind direction, temperature, and humidity). The dynamic expansion process of the fire boundary under different wind fields and terrain conditions is simulated using a cellular automata model to extract key parameters such as the fire boundary contour, the fire intensity distribution hot zone, and the spread trend vector field. Finally, a situation element layer containing spatial distribution characteristics and dynamic evolution trends is generated.

[0121] Preferably, based on the preliminary fire situation map, real-time environmental parameters are input into the forest fire spread model to generate a fire spread trend map, and collaborative plotting is performed based on the fire spread trend map to form a firefighting plan, specifically as follows:

[0122] Based on the preliminary fire situation map, the fire boundary features, fire intensity distribution and spread trend parameters are extracted and analyzed as the initial input conditions for the forest fire spread model.

[0123] Collect real-time updated meteorological observation data, combustible material humidity parameters, and terrain feature data to construct an environmental feature parameter set;

[0124] The environmental feature parameter set and the initial input conditions are input into the forest fire spread model, and fire behavior is simulated through the cellular automata mechanism to generate a fire behavior simulation parameter set.

[0125] Based on the fire behavior simulation parameter set, and combined with the geographic information system, a fire spread trend map including the fire line location, spread speed and hazard level zoning is generated for spatiotemporal evolution visualization rendering.

[0126] The fire spread trend map is distributed to each terminal through a collaborative plotting engine. Plotting instructions generated by multiple parties based on the same trend map are received, and a firefighting plan is generated by integrating conflict detection and semantic fusion algorithms.

[0127] It should be noted that, firstly, from the preliminary fire situation map, the coordinate set of the fire boundary polygon, the thermal radiation intensity values ​​of different areas, and the spread direction and speed calculated from historical displacement are automatically extracted through the GIS parsing interface. These parameters are then structured and used as the initial input conditions for the forest fire spread model to ensure that the starting point of the model simulation is consistent with the actual situation.

[0128] Subsequently, through the data interface cluster, real-time updated meteorological observation data (such as wind speed, wind direction, temperature and humidity measured by automatic weather stations), combustible humidity parameters retrieved from satellites, and terrain feature data (slope and aspect) extracted from digital elevation models (DEM) are collected to construct a unified spatiotemporal benchmark set of environmental feature parameters.

[0129] Next, the environmental feature parameter set and initial input conditions are fed into a forest fire spread model based on cellular automata. This model divides the fire area into a regular cellular grid. Each cell, based on its current state (whether it is burning), the states of surrounding cells, and environmental parameters (such as wind speed and direction determining the dominant spread direction, and slope and aspect correcting the spread rate), iterates through predefined state transition rules to simulate the fire's spread in the next time step. After multiple iterations, the model finally outputs the ignition time, fire line intensity, and spread rate of each cell over a future period, forming a set of fire behavior simulation parameters.

[0130] Then, based on this parameter set, the spatiotemporal rendering engine of the geographic information system is invoked to visualize the simulation results of each cell. For example, different colors are used to render areas that may be burned by fire at different times (e.g., red represents high-risk areas in 0-1 hours, and orange represents medium-risk areas in 1-2 hours), and the predicted fire line locations and spread speed vector arrows are overlaid on the map to ultimately generate an intuitive fire spread trend map.

[0131] Finally, the trend map is distributed in real time to various terminals such as the command center and mobile tablets via a collaborative plotting engine. Commanders in different locations can plot the same trend map using devices such as touch screens and mice, for example, drawing planned firebreaks, marking troop deployment points, and planning firefighting routes. All plotting instructions are uploaded to the server in real time, and the engine integrates them using conflict detection algorithms (such as checking if two labels completely overlap) and semantic fusion algorithms (such as automatically merging adjacent labels of the same type), ultimately generating a unified firefighting plan map that incorporates the wisdom of multiple parties.

[0132] Preferably, the fire spread trend map, the real-time collected location information of the forest fire prevention teams, and the firefighting plan are integrated to generate firefighting decision instructions and distribute them to each terminal, specifically as follows:

[0133] The plotting elements in the firefighting plan are analyzed, and the tactical target points, firefighting routes and resource deployment requirements are extracted to generate a structured tactical task set with spatiotemporal constraints.

[0134] For example, when a commander marks a "water pump placement point" icon and a "firefighting route" on an electronic map, the system will identify the geometric features and attribute information of these layers, automatically extract the "tactical target points" (such as the coordinates of the water pump point), the "firefighting operation route" (such as the sequence of path nodes), and the "resource deployment requirements" (such as the requirement for 3 team members to carry one water pump), and structure them into a set of clear tactical tasks with time and space constraints (such as the requirement to arrive within 30 minutes).

[0135] The spatial accessibility matrix is ​​calculated by combining the structured tactical task set with the real-time collected forest fire prevention team location information. Combined with the real-time road network accessibility and terrain crossing cost factor, a team deployment optimization scheme and task allocation priority sequence are generated.

[0136] It should be noted that the above tactical tasks are overlaid with the real-time location information of forest fire prevention teams obtained through BeiDou / GPS terminals. Using the spatial analysis function of GIS, a spatial accessibility matrix from each team's current location to each task point is calculated. The calculation process comprehensively considers real-time road network accessibility (e.g., whether roads are blocked by fire) and terrain crossing cost factors (e.g., the steeper the slope, the slower the travel speed and the higher the cost). Based on this, appropriate teams are assigned to each task, and a task allocation priority sequence considering efficiency and feasibility is generated, forming an optimized team deployment scheme.

[0137] The team deployment optimization scheme is spatiotemporally coupled with the fire intensity grid, spread vector field and hazard level zoning in the fire spread trend map, and risk avoidance path set and emergency evacuation channel are generated by risk field modeling.

[0138] It should be noted that, to ensure personnel safety, the deployment plan is spatiotemporally coupled with the fire spread trend map for analysis. For example, the fire spread scenario is simulated over the next two hours, and risk field modeling techniques (such as converting fire intensity, spread speed, and direction into spatial risk probability distributions) are used to automatically plan risk avoidance paths for each team performing the mission, avoiding high-risk areas, and pre-set emergency evacuation routes in case of sudden changes in the fire situation.

[0139] Based on the risk avoidance path set and the pre-set standardized instruction template library, the semantic-geographic association engine performs parameterized mapping and context adaptation to generate natural language description instructions for firefighting decision-making with geographic reference coordinates.

[0140] It should be noted that the pre-built standardized instruction template library is invoked (e.g., the template: "[Team Name], please depart immediately from [Current Location], proceed along [Path Name], and arrive at [Target Point Coordinates] before [Time] to execute [Task Content]. Please be aware of avoiding [High-Risk Areas], and evacuate along [Evacuation Route] in emergencies."). The semantic-geographic association engine will automatically populate the template with the deployment plan, path coordinates, time nodes, and other parameters generated in the previous steps, generating precise, easy-to-understand natural language description instructions that include specific geographic reference coordinates.

[0141] The firefighting decision instructions are distributed to each terminal through a multimodal communication link and an adaptive coding strategy. Based on the terminal status feedback, instruction retransmission and priority scheduling are implemented, and instruction confirmation receipts are received to complete the closed-loop instruction issuance process.

[0142] It should be noted that instructions are sent to each team member's terminal via a multimodal communication link (e.g., prioritizing the use of 5G networks to send detailed instructions with images and text, and automatically switching to BeiDou short messages to send only the core text and coordinates if the signal is weak) and an adaptive encoding strategy. The system monitors the terminal's reception status feedback, retransmits instructions that are not successfully received, and prioritizes high-priority instructions (such as evacuation instructions). After receiving an instruction, a team member must click to confirm the receipt, and the status of the instruction on the command center's large screen will change from undelivered to confirmed, thus forming a reliable closed-loop instruction issuance process.

[0143] Preferably, changes in environmental parameters are monitored, the input to the forest fire spread model is updated, and the fire spread trend map and firefighting decision instructions are corrected and regenerated accordingly. Specifically:

[0144] The changes in meteorological factors, combustible material humidity and terrain feature parameters are collected in real time by multi-source heterogeneous environmental sensor data streams to generate a spatiotemporally encoded set of environmental change parameters.

[0145] For example, automatic weather stations deployed around the fire detected a sudden increase in wind speed and a shift in wind direction from westerly to southwesterly; data transmitted by multispectral sensors on drones showed that the humidity of combustibles on the east side of the fire had dropped sharply due to the high temperature. These data changes were assigned timestamps and geographic coordinates to generate a spatiotemporally coded set of environmental change parameters.

[0146] The environmental change parameter set and the current input parameter set of the forest fire spread model are aligned with multidimensional features and the difference is calculated through a spatiotemporal coupler to generate a parameter difference feature matrix.

[0147] It should be noted that the aforementioned set of changed parameters is compared with the input parameter set currently being used in the wildfire spread model. A multi-dimensional feature alignment algorithm is used to identify which parameters changed significantly in time and place (e.g., "the wind speed in grid 15 increased from 3 m / s to 8 m / s"), and their degree of difference is calculated. Finally, a clear parameter difference feature matrix is ​​generated, quantifying the degree and spatial distribution of environmental changes.

[0148] Based on the parameter difference feature matrix, the hidden layer weight allocation and cell transformation rule parameters in the wildfire spread model are adjusted through an incremental learning-weight drift compensation coupling mechanism to generate an incremental set of model parameters.

[0149] For example, a significant increase in wind speed will increase the weight of the "wind speed" factor in the fire spread calculation formula (weight allocation adjustment). At the same time, for areas where the moisture content of combustibles decreases, the model will adaptively modify the cell state transition rules to make the cells in that area easier to "ignite" (cell transition rule parameter adjustment). All these fine-tunings are aggregated into a set of model parameter increments, rather than retraining the entire model.

[0150] The incremental set of model parameters is injected into the parallel computing architecture of the forest fire spread model, and the modified fire behavior simulation parameter set is generated by re-initializing the fire behavior simulation operation.

[0151] It should be noted that the incremental set is injected into the wildfire spread model based on a high-performance parallel computing architecture. The model is not calculated from scratch, but rather based on the current simulation state, and a new set of parameters is applied for re-initialization calculations to quickly simulate the spread of the fire under new environmental conditions, generating an updated set of corrected fire behavior simulation parameters.

[0152] Based on the modified fire behavior simulation parameter set, a modified fire spread trend map is generated by combining the real-time rendering engine of the geographic information system, and the fire fighting decision command generation logic is updated synchronously to output the modified fire fighting decision command that is synchronized in time and space.

[0153] For example, the system will automatically retrieve previously issued but not yet executed instructions. If the original course of action is now in a new high-risk area, a corrected firefighting decision instruction will be generated immediately (such as "order team XX to cancel the original route and immediately change course to route B") and urgently issued through the communication system to ensure the time and space synchronization of the instructions.

[0154] Preferably, the firefighting decision-making instructions, the revised fire spread trend map, and the data from the entire system operation process are archived and stored for subsequent review and model optimization, specifically:

[0155] The system receives the firefighting decision instructions, the revised fire spread trend map, and data from the entire system operation process. It then performs spatiotemporal coding and feature association operations through multimodal data fusion analysis to generate a structured archived data cube.

[0156] The structured archived data cube is subjected to multi-source data archive storage analysis, and an archived data index set with multi-dimensional retrieval capabilities is generated through spatiotemporal index construction operations.

[0157] Based on the archived data index set, historical fire behavior simulation parameter set and corresponding environmental parameter sequence are extracted, and feature vector of fire behavior evolution mode is generated through feature association topology analysis.

[0158] The feature vector of the fire behavior evolution pattern is matched with the pre-set model optimization rule base for similarity and difference calculation to generate a set of model parameter optimization suggestions;

[0159] Based on the aforementioned set of model parameter optimization suggestions, the internal parameter configurations of the fire spread early warning and judgment model and the forest fire spread model are updated through parameter space mapping and weight adjustment analysis, thereby completing the model optimization iteration.

[0160] It should be noted that, firstly, during and after the firefighting operation, all relevant data is automatically received and aggregated, including: every final firefighting decision command and its timestamp, all revised versions of the fire spread trend map, time-series data on changes in the fire scene environment, intermediate parameters of model calculations, and feedback logs from terminal devices. This multimodal data is integrated into a well-structured, interconnected, structured archived data cube through spatiotemporal encoding (marking time and geographical location for each data point) and feature association operations (such as associating a command with the corresponding version of the fire trend map at the time of its issuance). Its dimensions include time, space, and data type.

[0161] Subsequently, the data cube is archived, stored, and analyzed. Efficient storage is achieved using big data storage technologies (such as the distributed database HBase or a spatiotemporal database), and a spatiotemporal joint index is constructed (e.g., a "time-geographic grid" secondary index) to enable multi-dimensional retrieval capabilities. Commanders or the system can quickly query complex questions such as "all fire intensity data located within the grid range of X degrees east longitude and Y degrees north latitude between 14:00 and 15:00 on July 25th."

[0162] Furthermore, based on the index, historical fire behavior simulation parameter sets and their corresponding real environmental parameter sequences are extracted from the archive. Through feature-related topology analysis (e.g., comparing the fire spread rate predicted by the model with the actual spread rate retrieved from UAV imagery at the same time and location), the deviation between the predicted and actual values ​​is calculated, and these deviation patterns are quantified and represented as a fire behavior evolution pattern feature vector. This vector essentially characterizes the prediction error features of the model under specific environmental conditions.

[0163] Then, these feature vectors are matched against a pre-built model optimization rule base. The rule base contains machine learning-generated optimization strategies (e.g., "When the actual wind speed continuously exceeds the predicted wind speed and the humidity is lower than the predicted value, the weight of the wind speed factor in the spread formula should be appropriately increased"). Through similarity matching and difference calculation, a targeted, quantified set of model parameter optimization suggestions is automatically generated (e.g., suggesting increasing the wind speed weight coefficient from 0.32 to 0.38).

[0164] Finally, model iteration is performed based on the optimized suggestion set. Through parameter space mapping and weight adjustment analysis, the internal parameter configurations of the fire spread early warning judgment model and the forest fire spread model are automatically fine-tuned (such as adjusting the weights of the neural network model or the state transition threshold of the cellular automaton).

[0165] According to an embodiment of the present invention, it further includes:

[0166] Real-time collection of isoprene and monoterpene concentration data released by specific dominant tree species generates a spatiotemporal sequence dataset of BVOC concentration. Adaptive Kalman filtering and spatiotemporal co-kriging interpolation are then performed on the BVOC concentration spatiotemporal sequence dataset to generate a standardized BVOC flux grid field.

[0167] The standardized BVOC flux grid field was subjected to a physiological drought response analysis based on partial least squares regression with the synchronously monitored saturated vapor pressure difference and soil volumetric water content meteorological stress factors. The nonlinear correlation between the analysis and the vegetation drought stress state was calculated to generate a standardized biological stress index.

[0168] The standardized biological stress index, along with the synchronously acquired meteorological drought index and vegetation spectral reflectance index, are input into an adaptive extreme learning machine (AELM) network. Through adaptive matching and regularization optimization analysis of hidden layer nodes, a nonlinear mapping relationship between BVOC release characteristics and vegetation water status is established, and the BVOC stress response coefficient is calculated.

[0169] Based on the BVOC stress response coefficient, a quantitative analysis of dynamic tissue water deficit was performed using a radial basis function neural network (RBFNN) to generate a physiologically significant quantitative factor for tissue water deficit.

[0170] The tissue water deficit quantification factor and the original multidimensional disaster-causing factor matrix of the fire risk base map are fused by Bayesian network analysis to reconstruct the weight allocation strategy of meteorological disaster-causing factors and vegetation characteristic factors, and generate a biochemical-environment coupled risk field that integrates the perception of vegetation physiological state.

[0171] Specifically, in key forest areas (such as fire-prone coniferous forests), a sensor network is deployed in the canopy layer to collect real-time concentration data of BVOCs such as isoprene and monoterpenes released by specific dominant tree species (such as Scots pine and Masson pine). This concentration data, along with its collection time and geographical coordinates, forms an original spatiotemporal sequence dataset of BVOC concentration. This dataset is first processed using an adaptive Kalman filter algorithm to remove environmental interference signals. Then, a spatiotemporal co-kriging interpolation method is used to interpolate the concentration data at discrete points into a spatially continuous standardized BVOC flux grid field covering the entire forest area, thus visually displaying the spatial distribution pattern of BVOC release.

[0172] To reveal the intrinsic link between BVOC release and water stress, the aforementioned flux grid field was coupled with two key meteorological stress factors monitored simultaneously: saturated vapor pressure gradient (VPD) (characterizing atmospheric drought) and soil volumetric water content (SVWC) (characterizing soil drought). Partial least squares regression was used to analyze the nonlinear relationship between BVOC flux and these stress factors, and a standardized biological stress index was calculated. This index quantifies the physiological response of vegetation to drought stress.

[0173] To more accurately establish a predictive model from BVOC release characteristics to vegetation moisture status, the aforementioned biological stress indicators, along with traditional meteorological drought indices (such as SPEI) and vegetation spectral reflectance indices (such as NDVI and NDWI), are input into an improved Adaptive Extreme Learning Machine (AELM) network. This network can automatically optimize its structure through an adaptive matching mechanism of hidden layer nodes and prevent overfitting through regularization, thereby robustly establishing a nonlinear mapping relationship between BVOC characteristics and vegetation moisture status, ultimately outputting an accurate BVOC stress response coefficient.

[0174] The stress response coefficient was inverted and quantified using a radial basis function neural network (RBFNN). The powerful nonlinear fitting capability of RBFNN allows the coefficient to be mapped to a more physiologically meaningful quantitative description of the dynamic degree of tissue water deficit, thus generating a tissue water deficit quantification factor. This factor directly reflects the "thirst" level of vegetation cells.

[0175] Finally, this physiological factor is fused with the multidimensional disaster-causing factor matrix (including meteorological, vegetation, topographic, and social factors) upon which traditional fire risk base maps rely. A Bayesian network is used for probabilistic fusion analysis. The Bayesian network can dynamically reconstruct the weight allocation strategy of each factor. For example, when the "tissue water deficit quantification factor" significantly increases, its weight in risk assessment is automatically increased, thereby dynamically and more accurately adjusting the final risk field. This generates a biochemical-environment coupled risk field that includes both external environmental disaster-causing factors and internal vegetation physiological response signals, improving the real-time and accurate assessment capability of vegetation's true flammability, making risk warnings more forward-looking and precise, and providing support for forest fire prevention and resource allocation.

[0176] According to an embodiment of the present invention, it further includes:

[0177] The system receives natural language instructions from the commander, performs semantic role labeling and dependency parsing, extracts directional action words, geographical entities and spatial relationship elements, and generates a structured instruction semantic element set.

[0178] The structured instruction semantic element set is matched and mapped with a pre-set semantic-geographic mapping rule base, wherein the rule base contains the conversion relationship between directional words and geographic coordinate system and the correspondence between fire scene professional terms and geographic entities, generating preliminary geographic semantic parsing results.

[0179] The preliminary geographic semantic parsing results are spatially correlated with the fire line vector, hazard level zoning and basic geographic information data in the real-time fire spread trend map, and the target geographic coordinate set is parsed out through the geocoding library.

[0180] Based on the target geographic coordinate set, combined with the preset safety buffer distance parameters and terrain accessibility grid data, the spatial buffer analysis and path cost function are used to perform dynamic geofence generation calculations, and output accurate geofences that are adapted to the current fire situation.

[0181] The precise geofence is combined with real-time road network data and forest fire prevention team location information to perform multi-factor weighted path planning analysis, generating an optimal action path set that meets the emergency response time constraint;

[0182] The precise geofence and the optimal action path set are overlaid on the fire situation map to complete the automatic generation of precise spatial decision-making elements from natural language commands.

[0183] To address the problem that in existing forest fire emergency command processes, commanders must manually operate geographic information systems for complex spatial mapping and route planning, which is cumbersome, time-consuming, and prone to errors, making it difficult to meet the real-time decision-making needs under rapidly changing fire conditions, this embodiment proposes an intelligent spatial decision element generation process based on natural language processing:

[0184] First, the system receives natural language instructions input by the commander via voice or text, such as "deploy forces to the firehead in the southeast direction." Using semantic role labeling and dependency parsing models in natural language processing, the instructions undergo deep semantic parsing, extracting directional action words (e.g., "deploy"), geographic entities (e.g., "firehead"), and spatial relationship elements (e.g., "southeast direction") to generate a structured set of instruction semantic elements. Next, this structured set of instruction semantic elements is matched against a pre-built semantic-geographic mapping rule base. This rule base, constructed under the guidance of domain knowledge, includes precise conversion relationships between directional words and geographic coordinate systems (e.g., "southeast direction" corresponds to a 135-degree azimuth angle), as well as correspondences between fire-related technical terms (e.g., "firehead," "fire line," "flank") and geographic entity types in GIS. Through rule mapping, the language elements are transformed into preliminary geographic semantic parsing results, including the type of target action and the expected spatial range of its effect. Then, the preliminary geographic semantic parsing results are spatially correlated with the real-time updated fire spread trend map, fire line vector data, hazard level zoning layer, and basic geographic information data (such as elevation, water system, and residential areas). A geocoding library is invoked to match the parsed semantic targets (such as "fire front") with specific geographic coordinates in the real-time fire scene, thereby resolving a precise set of target geographic coordinates. This allows the abstract descriptions in the instructions to be anchored to specific, dynamically changing geographical locations. Based on the obtained precise target geographic coordinate set, combined with preset safety buffer distance parameters (dynamically adjusted according to fire intensity level) and terrain accessibility raster data (representing the difficulty of traversing different terrains), spatial buffer analysis and path cost functions are used to perform dynamic geofence generation calculations. This process calculates a geographic area boundary that ensures both personnel safety and effective task execution, outputting a precise geofence adapted to the current fire situation. Subsequently, precise geofencing is integrated with real-time road network data and the real-time location information of forest fire prevention teams. Multi-factor weighted path planning analysis is employed to comprehensively consider multiple weighted factors such as shortest distance, least travel time, and avoidance of high-risk areas, ultimately generating one or more optimal action paths that meet emergency response time constraints. Finally, the generated precise geofencing and optimal action path sets are automatically overlaid onto the fire situation map on the command platform and visualized. This achieves fully automated generation of precise and actionable spatial decision-making elements (geofencing, action paths) from simple natural language commands from commanders. This enables rapid, accurate, and automatic conversion from command intent to spatial decision-making, improving the intelligence level and timeliness of emergency command.

[0185] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. An integrated intelligent forest fire analysis method, characterized in that, Includes the following steps: Pre-disaster fire risk and spread prediction: Multi-source heterogeneous data of the target forest area is acquired. Meteorological disaster-causing factors, vegetation characteristic factors, topographic potential factors, and social exposure factors are extracted through feature engineering to construct a multi-dimensional disaster-causing factor matrix. This matrix is ​​then input into a pre-set fire spread early warning and judgment model. A time-varying weight coefficient for each factor is calculated using an adaptive weight allocation mechanism. Based on this time-varying weight coefficient set, the multi-dimensional disaster-causing factor matrix is ​​weighted and fused to form a weighted disaster-causing factor feature field. The weighted disaster-causing factor feature field is then subjected to rule-based reasoning analysis to generate a preliminary fire behavior parameter set based on a pre-set fire behavior propagation rule library. This preliminary fire behavior parameter set is then subjected to nonlinear mapping analysis, and a fire behavior propagation probability field is generated through feature space transformation and probability distribution reconstruction. The fire behavior propagation probability field is then subjected to spatiotemporal evolution simulation and vector field reconstruction to output a fire behavior propagation vector with spatiotemporal continuity. Finally, a kilometer-scale fire risk base map of the target forest area is generated based on the fire behavior propagation vector. Initial fire assessment and data collection: Receive and integrate real-time fire scene information, and combine the fire risk base map with the initial fire point location data to form a preliminary fire situation map; Fire situation analysis and collaborative plotting: Based on the preliminary fire situation map, real-time environmental parameters are input into the forest fire spread model to generate a fire spread trend map, and collaborative plotting is performed based on the fire spread trend map to form a fire fighting plan; Firefighting decision generation and issuance: Analyze the plotted elements in the firefighting plan to generate a set of structured tactical tasks with spatiotemporal constraints; The structured tactical task set and the real-time collected forest fire prevention team location information are used to calculate the spatial accessibility matrix. Combined with the real-time road network accessibility and terrain crossing cost factor, a team deployment optimization scheme and task allocation priority sequence are generated. The team deployment optimization scheme is then subjected to spatiotemporal coupling analysis with the fire line intensity grid, spread vector field and hazard level zoning in the fire spread trend map. Risk field modeling is used to generate a risk avoidance path set and emergency evacuation channel. Based on the risk avoidance path set and the pre-set standardized instruction template library, the semantic-geographic association engine performs parameterized mapping and context adaptation to generate natural language description instructions for firefighting decision-making with geographic reference coordinates, and distributes them to each terminal. Fire spread dynamic analysis and adjustment: Monitor changes in environmental parameters, adjust the input parameters of the forest fire spread model through an incremental learning-weight drift compensation coupling mechanism, and thereby correct and regenerate the fire spread trend map and firefighting decision instructions; Task review and data archiving: The fire fighting decision instructions, the revised fire spread trend map, and the data of the entire system operation process are archived and stored. Through spatiotemporal index construction and feature association topology analysis, a feature vector of fire behavior evolution mode is generated. The feature vector of fire behavior evolution mode is matched with a pre-set model optimization rule library to generate a set of model parameter optimization suggestions to update the internal parameter configuration of the fire easy spread early warning judgment model and the forest fire spread model, and complete the model optimization iteration.

2. The integrated intelligent forest fire analysis method according to claim 1, characterized in that, The pre-disaster fire risk spread prediction specifically includes: Acquire multi-source heterogeneous data of the target forest area, and perform spatiotemporal normalization and dimensional standardization preprocessing on the multi-source heterogeneous data to form a spatiotemporally aligned fusion data pool.

3. The integrated intelligent forest fire analysis method according to claim 1, characterized in that, The system receives and integrates real-time fire scene information, combines the fire risk base map with the initial fire point location data, and forms a preliminary fire situation map, specifically as follows: Receive real-time fire scene perception data from multiple source sensors, perform multimodal data fusion and spatiotemporal registration processing on the real-time fire scene perception data, and generate a standardized fire scene observation dataset; Based on the standardized fire scene observation dataset, feature parameters such as initial fire location, fire intensity, fire line outline and spread direction are extracted to generate fire feature vectors. The fire point feature vector is spatially overlaid with a pre-generated fire risk base map, and the coupling coefficient between the fire point and the risk grid is calculated by a weighted fusion algorithm to generate a fire risk-fire point coupling map. The fire hazard-fire point coupling map is analyzed in a spatiotemporal correlation with real-time meteorological observation data to extract fire boundary features, fire intensity distribution and spread trend parameters, and generate a situation element layer. The aforementioned situational element layers are spatially fused with basic geographic information data and then visualized to create a preliminary fire situation map.

4. The integrated intelligent forest fire analysis method according to claim 1, characterized in that, Based on the preliminary fire situation map, real-time environmental parameters are input into the forest fire spread model to generate a fire spread trend map. Then, based on this fire spread trend map, collaborative plotting is performed to formulate a firefighting plan, specifically as follows: Based on the preliminary fire situation map, the fire boundary features, fire intensity distribution and spread trend parameters are extracted and analyzed as the initial input conditions for the forest fire spread model. Collect real-time updated meteorological observation data, combustible material humidity parameters, and terrain feature data to construct an environmental feature parameter set; The environmental feature parameter set and the initial input conditions are input into the forest fire spread model, and fire behavior is simulated through the cellular automata mechanism to generate a fire behavior simulation parameter set. Based on the fire behavior simulation parameter set, and combined with the geographic information system, a fire spread trend map including the fire line location, spread speed and hazard level zoning is generated for spatiotemporal evolution visualization rendering. The fire spread trend map is distributed to each terminal through a collaborative plotting engine. Plotting instructions generated by multiple parties based on the same trend map are received, and a firefighting plan is generated by integrating conflict detection and semantic fusion algorithms.

5. The integrated intelligent forest fire analysis method according to claim 1, characterized in that, By monitoring changes in environmental parameters and adjusting the input parameters of the forest fire spread model through an incremental learning-weight drift compensation coupling mechanism, the fire spread trend map and firefighting decision instructions are corrected and regenerated accordingly. Specifically: The changes in meteorological factors, combustible material humidity and terrain feature parameters are collected in real time by multi-source heterogeneous environmental sensor data streams to generate a spatiotemporally encoded set of environmental change parameters. The environmental change parameter set and the current input parameter set of the forest fire spread model are aligned with multidimensional features and the difference is calculated through a spatiotemporal coupler to generate a parameter difference feature matrix. Based on the parameter difference feature matrix, the hidden layer weight allocation and cell transformation rule parameters in the wildfire spread model are adjusted through an incremental learning-weight drift compensation coupling mechanism to generate an incremental set of model parameters. The incremental set of model parameters is injected into the parallel computing architecture of the forest fire spread model, and the modified fire behavior simulation parameter set is generated by re-initializing the fire behavior simulation operation. Based on the modified fire behavior simulation parameter set, a modified fire spread trend map is generated by combining the real-time rendering engine of the geographic information system, and the fire fighting decision command generation logic is updated synchronously to output the modified fire fighting decision command that is synchronized in time and space.

6. The integrated intelligent forest fire analysis method according to claim 1, characterized in that, The firefighting decision-making instructions, the revised fire spread trend map, and the data from the entire system operation process will be archived and stored for subsequent review and model optimization. Specifically: The system receives the firefighting decision instructions, the revised fire spread trend map, and data from the entire system operation process. It then performs spatiotemporal coding and feature association operations through multimodal data fusion analysis to generate a structured archived data cube. The structured archived data cube is subjected to multi-source data archive storage analysis, and an archived data index set with multi-dimensional retrieval capabilities is generated through spatiotemporal index construction operations. Based on the archived data index set, historical fire behavior simulation parameter set and corresponding environmental parameter sequence are extracted, and feature vector of fire behavior evolution mode is generated through feature association topology analysis. The feature vector of the fire behavior evolution pattern is matched with the pre-set model optimization rule base for similarity and difference calculation to generate a set of model parameter optimization suggestions; Based on the aforementioned set of model parameter optimization suggestions, the internal parameter configurations of the fire spread early warning and judgment model and the forest fire spread model are updated through parameter space mapping and weight adjustment analysis, thereby completing the model optimization iteration.

7. An integrated intelligent forest fire analysis system, applied to the integrated intelligent forest fire analysis method according to any one of claims 1 to 6, characterized in that, This includes a back-end service system, a central intelligent analysis system, and a mobile intelligent analysis system; The back-end service system is responsible for the data storage, processing and service support center of the entire system. It adopts a cloud computing architecture to achieve elastic expansion and deploys database servers, map servers and communication servers. The central intelligent analysis system is deployed in the command hall, supporting parallel computing of forest fire spread models; equipped with an immersive large screen for viewing multi-source information and collaborative plotting; and equipped with voice interaction devices for voice input of commands, supporting rapid generation of decision commands. The mobile intelligent analysis system is deployed on mobile terminals and supports forest fire spread trend prediction, collaborative plotting, and rapid command generation interaction.

8. The integrated intelligent forest fire analysis system according to claim 7, characterized in that: The central intelligent analysis system includes six areas: Area 1 displays meteorological data, including weather, wind force, wind speed, temperature, and humidity; Area 2 displays GIS geographic data, overlaying fire spread effects, collaborative plotting data, and force distribution; Area 3 is used to view camera footage and drone-transmitted footage; Area 4 displays decision-making information; Area 5 displays real-time communication information; and Area 6 is a new page used to display the forest fire risk auxiliary decision-making module. The mobile intelligent analysis system includes five areas: Area 1 displays meteorological data, including weather, wind force, wind speed, temperature, and humidity; Area 2 displays GIS geographic data, overlaying fire spread effects, collaborative mapping data, and force distribution; Area 3 displays decision-making information; Area 4 displays real-time communication information; and Area 5 is a new page used to display camera footage and drone-transmitted footage.

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