Method and system for predicting fire spreading trend of forest-town junction domain

Abstract

The invention, which relates to the technical field of data processing, discloses a method and system for predicting a fire spreading trend in a forest-town junction region, comprising the following steps: acquiring historical fire data, and constructing a fire spreading trend prediction model based on the historical fire data; vegetation parameters, building parameters and environment parameters of a target area are obtained, scene deployment is carried out on the fire spreading trend prediction model, and the fire spreading trend prediction model of the target area is obtained; a simulation working condition is introduced, simulation is carried out through the target area fire spreading trend prediction model, and a target area fire trend prediction result is obtained; and performing visualization processing on the fire trend prediction result of the target area. According to the method, the technical problem that quantitative prediction and dynamic display of the spreading trend of the fire in the forest-town junction region are lacked in the prior art is solved, and the technical effect of improving the accuracy, adaptability and intuition of fire spreading trend prediction is achieved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for predicting the spread trend of fires in forest-town boundary areas. Background Technology

[0002] Forest-town boundary areas, characterized by both high-density combustible vegetation and human construction activities, exhibit complex fire spread paths significantly influenced by factors such as vegetation type, building materials, wind speed, and humidity. In actual fires, fire development is rapid and directional, making comprehensive understanding through static analysis difficult. Due to the diverse and rapidly changing mechanisms of fire, traditional models often fail to accurately reflect the thermophysical behavior during spread, struggle to predict future fire trajectories in specific areas, and cannot effectively visualize the dynamic spatial progression of fires. Furthermore, they lack data-driven tools to provide technical support for fire prevention planning and disaster response. Summary of the Invention

[0003] This application provides a method and system for predicting the spread trend of fires in forest-town boundary areas, which addresses the technical problem that existing technologies lack quantitative prediction and dynamic display of the spread trend of fires in forest-town boundary areas.

[0004] In view of the above problems, this application provides a method and system for predicting the spread trend of fires in forest-town boundary areas.

[0005] The first aspect of this application provides a method for predicting the fire spread trend in a forest-town boundary area, the method comprising:

[0006] Historical fire data is acquired, and a fire spread trend prediction model is constructed based on the historical fire data; vegetation parameters, building parameters, and environmental parameters of the target area are acquired, and the fire spread trend prediction model is deployed in a scenario to obtain a fire spread trend prediction model for the target area; simulated working conditions are introduced, and the fire spread trend prediction model for the target area is used to simulate the fire spread trend prediction model for the target area to obtain the fire trend prediction result for the target area; the fire trend prediction result for the target area is visualized.

[0007] A second aspect of this application provides a fire spread trend prediction system for forest-town boundary areas, the system comprising:

[0008] The initial model building module is used to acquire historical fire data and build a fire spread trend prediction model based on the historical fire data; the scenario deployment module is used to acquire vegetation parameters, building parameters, and environmental parameters of the target area, deploy the fire spread trend prediction model in the scenario, and obtain a fire spread trend prediction model for the target area; the prediction module is used to introduce simulated working conditions, simulate the fire spread trend prediction model for the target area, and obtain the fire trend prediction result for the target area; the visualization module is used to visualize the fire trend prediction result for the target area.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application acquires historical fire data and constructs a fire spread trend prediction model based on the historical fire data; it acquires vegetation parameters, building parameters, and environmental parameters of the target area, deploys the fire spread trend prediction model in a scenario, and obtains a fire spread trend prediction model for the target area; it introduces simulated working conditions and simulates the fire spread trend prediction model for the target area to obtain the fire trend prediction result for the target area; and it visualizes the fire trend prediction result for the target area. This invention addresses the technical problem of the lack of quantitative prediction and dynamic display of fire spread trends in forest-town boundary areas in existing technologies. By constructing a trend prediction model calibrated based on historical fire data, combining it with the vegetation, building, and environmental parameters of the target area for scenario-based deployment, and introducing simulated working conditions for trend simulation and three-dimensional visualization, it achieves the technical effect of improving the accuracy, adaptability, and intuitiveness of fire spread trend prediction. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic flowchart of a method for predicting the spread trend of fires in a forest-town boundary area provided in this application embodiment;

[0013] Figure 2 This is a schematic diagram of a fire spread trend prediction system in a forest-town boundary area, provided as an embodiment of this application.

[0014] Figure labeling: Initial model building module 11, scene deployment module 12, prediction module 13, visualization module 14. Detailed Implementation

[0015] This application provides a method and system for predicting the spread trend of fires in forest-town boundary areas. It addresses the technical problem that existing technologies lack quantitative prediction and dynamic display of the spread trend of fires in forest-town boundary areas. By constructing a trend prediction model based on historical fire data calibration, and combining it with vegetation, building and environmental parameters of the target area for scenario-based deployment, and introducing simulated working conditions for trend simulation and three-dimensional visualization processing, the technical effect of improving the accuracy, adaptability and intuitiveness of fire spread trend prediction is achieved.

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0018] Example 1, as Figure 1 As shown, this application provides a method for predicting the fire spread trend in forest-town boundary areas, the method comprising:

[0019] Step S100: Obtain historical fire data and construct a fire spread trend prediction model based on the historical fire data.

[0020] In this embodiment of the application, pre-stored historical fire data is first obtained, and key fire feature information is extracted, such as the terrain of the fire area, the speed of flame spread, the distribution of heat radiation intensity, and the damage status of buildings.

[0021] Then, a vegetation combustion simulation model with fire behavior simulation capabilities is introduced. The fire characteristics extracted above are used as a reference. By comparing and analyzing the flame development process and heat propagation characteristics of the model, the relevant parameters of the model are continuously adjusted to make the simulation results as close as possible to the historical fire performance. Finally, a fire spread trend prediction model that can reflect the actual spread characteristics is formed.

[0022] Furthermore, the method provided in the application embodiments, which involves acquiring historical fire data and constructing a fire spread trend prediction model based on the historical fire data, further includes:

[0023] Fire features are extracted from the historical fire data, including historical fire scenarios, flame spread rate, heat radiation intensity distribution, and building damage characteristics. A preset vegetation-building combustion simulation model is obtained, and the preset vegetation-building combustion simulation model is calibrated based on the fire features to obtain the fire spread trend prediction model.

[0024] In this embodiment, historical fire data is first processed to extract key feature information related to fire behavior, including historical fire scenes, flame spread rate, heat radiation intensity distribution, and building damage characteristic data. Historical fire scenes record the spatial environmental characteristics at the time of the fire, such as terrain undulations, vegetation distribution, and building density, used to reconstruct the fire's development background. Flame spread rate represents the distance the fire spreads per unit time, reflecting the fire's intensity and spread efficiency; heat radiation intensity distribution describes the amount and range of heat energy released from the burning area to the surrounding space during the fire; building damage characteristic data records the damage forms, damaged areas, and degrees of damage to buildings of different materials during the fire, used to reflect the building's response under different thermal environments.

[0025] After completing the above fire feature extraction, a pre-defined vegetation-building combustion simulation model is obtained. This model is constructed based on a comprehensive analysis of forest fire spread experimental results and building thermal response laws. It integrates the combustible material definition method and building heat flux response module in existing fire dynamics simulation platforms (such as FDS), and pre-sets the combustion characteristics and thermal feedback behavior of vegetation types and common building materials under different thermal environments. It can simulate flame development and heat release during vegetation combustion, and can also describe the thermal impact and possible ignition response of flames approaching buildings on structures such as walls and roofs.

[0026] Subsequently, the pre-defined vegetation-building combustion simulation model was calibrated using fire characteristics. In this process, historical fire scenarios were first reproduced using the pre-defined vegetation-building combustion simulation model to obtain initial simulation results. Then, the simulation results were compared and analyzed with the extracted fire characteristics to identify the differences between the two. Finally, the model was adjusted and optimized based on these differences to more accurately reflect the spread behavior of historical fires, thus forming a fire spread trend prediction model for subsequent trend prediction.

[0027] Furthermore, in the method provided in the application embodiments, calibrating the preset vegetation-building combustion simulation model based on the fire characteristics to obtain the fire spread trend prediction model further includes:

[0028] Based on the preset vegetation-building combustion simulation model, historical fire scenarios are reproduced and simulated to obtain initial simulation results; the initial simulation results are compared with the fire characteristics to obtain simulation deviation; the preset vegetation-building combustion simulation model is calibrated according to the simulation deviation to obtain the fire spread trend prediction model.

[0029] In this embodiment, a historical fire scenario is first simulated based on a pre-defined vegetation-building combustion simulation model. During this process, a simulation environment consistent with historical fires is constructed by setting the location of the fire source, vegetation distribution, building location, and meteorological conditions. The historical fire scenario includes real terrain data, combustion characteristic parameters of vegetation types (such as Yunnan pine), thermal response parameters of building materials (such as the specific heat capacity, thermal conductivity, and density of wooden buildings), and environmental conditions such as wind speed, wind direction, temperature, and humidity recorded at the time. During the simulation, an ignition source is set, and a fixed intensity of heat energy is released within a specific time period to ignite the vegetation, thereby observing the spread of the flames and their impact on the buildings.

[0030] Initial simulation results are obtained through the simulation process, including key data such as the flame spread rate, the spatial distribution of heat radiation, the change of building surface temperature over time, and whether the building was ignited and the time required for ignition. These simulation results serve as a benchmark, and are analyzed in correspondence with fire characteristics extracted from historical fire data. Fire characteristics include the measured value of the fire's advance speed at the time, the radiation intensity distribution at the heated locations of the building, the time of structural ignition, and the extent of building damage. By comparing the simulated data with historical records one by one, differences in numerical values ​​and trends are identified, forming a simulation bias that includes differences in flame spread rate, radiation intensity deviation, and surface temperature response differences.

[0031] After identifying simulation biases, the simulation model is corrected item by item based on the specific biases. For example, if the trend of building surface temperature change lags significantly behind historical records, the heat conduction parameters of building materials are adjusted appropriately; if the flame spread rate is too slow, the heat release characteristics or density settings of vegetation are reassessed; if the simulated thermal radiation value is lower than the actual observed value, the proportion of radiated energy during vegetation combustion is corrected. After each adjustment, the same fire scenario is run again, the simulation results are regenerated, and the results are continuously compared with fire characteristics for repeated optimization until the model results achieve good consistency with historical data.

[0032] Through the above process of reproduction simulation, result comparison and gradual correction, the preset vegetation-building combustion simulation model is finally adjusted into a fire spread trend prediction model that truly reflects the development process of historical fires.

[0033] Step S200: Obtain vegetation parameters, building parameters, and environmental parameters of the target area, deploy the fire spread trend prediction model in the scenario, and obtain the fire spread trend prediction model of the target area.

[0034] In this embodiment, vegetation parameters of the target area are first obtained, including vegetation type (such as coniferous forest, shrubs, and herbaceous plants) and its spatial distribution density. Secondly, building parameters are collected, including building material characteristics (such as wood structure and concrete structure), spatial layout (i.e., the planar distribution relationship of buildings within the area), and the distance between buildings and surrounding vegetation. Finally, environmental parameters are obtained, including real-time wind speed, air temperature, and humidity data of the target area.

[0035] After completing the above data collection, all parameters are input into the fire spread trend prediction model, and matched and spatially located in conjunction with the geographic information system (GIS) spatial data of the target area. By connecting vegetation, building, and environmental elements with coordinate points, grid structure, and elevation information in the GIS map, the fire prediction model is spatially deployed in the target area, thereby obtaining a fire spread trend prediction model of the target area that reflects the actual terrain, combustible material layout, and meteorological conditions.

[0036] Furthermore, the method provided in the application embodiment, which involves obtaining vegetation parameters, building parameters, and environmental parameters of the target area, deploying the fire spread trend prediction model in a scenario, and obtaining a fire spread trend prediction model for the target area, further includes:

[0037] The vegetation parameters of the target area are obtained, including vegetation type and its spatial distribution density; the building parameters of the target area are collected, including building material characteristics, spatial layout and distance from vegetation; the environmental parameters of the target area are measured, including real-time wind speed, temperature and humidity data; the vegetation parameters, building parameters and environmental parameters are input into the fire spread trend prediction model, and spatial deployment is carried out in combination with the GIS data of the target area to obtain the fire spread trend prediction model of the target area.

[0038] In this embodiment of the application, vegetation parameters of the target area are first obtained. By interpreting remote sensing images, taking aerial photos of drones or forest survey data, the vegetation types (such as coniferous forests, broad-leaved forests, shrubs, grasslands, etc.) and their spatial distribution density in the target area are identified, that is, the coverage of different vegetation types per unit area.

[0039] Next, we collect building parameters for the target area. Through building information modeling (such as BIM) or on-site measurement, we obtain the building material characteristics (such as wood structure, concrete structure), spatial layout (i.e., the relative positional relationship of buildings in two-dimensional or three-dimensional space), and distance from surrounding vegetation of various buildings in the area.

[0040] Subsequently, environmental parameters of the target area are measured, and current meteorological conditions, including real-time wind speed, wind direction, air temperature, and relative humidity, are obtained through automatic weather stations or regional meteorological data platforms.

[0041] After acquiring the above three types of parameters, the collected vegetation, building, and environmental parameters are integrated with the GIS spatial data of the target area to complete the spatial mapping of the parameters. Subsequently, based on the mapping results, the fire spread trend prediction model is adjusted and updated to reflect the actual terrain, combustible material distribution, and building layout of the target area, ultimately forming a target area fire spread trend prediction model suitable for the region.

[0042] Furthermore, in the method provided in the application embodiments, the vegetation parameters, building parameters, and environmental parameters are input into the fire spread trend prediction model, and spatially deployed in conjunction with GIS data of the target area to obtain the fire spread trend prediction model of the target area, which further includes:

[0043] The vegetation parameters, building parameters, and environmental parameters are integrated with the GIS data to complete spatial mapping; based on the spatial mapping results, the fire spread trend prediction model is updated to obtain the fire spread trend prediction model for the target area.

[0044] In this embodiment, vegetation parameters, building parameters, and environmental parameters are first matched with the geospatial data of the target area. This process involves locating each type of parameter information to its actual spatial position using geographic coordinates within a geographic information system. Vegetation type and distribution density from the vegetation parameters are mapped onto a topographic map to identify the spatial extent of combustible materials; building materials, plan outlines, and distances from vegetation from the building parameters are marked in a building layer to represent the distribution of buildings within the area; and wind speed, wind direction, temperature, and humidity from the environmental parameters are interpolated to form a spatial distribution map covering the entire area, reflecting differences in meteorological conditions at different locations.

[0045] After completing the spatial mapping, the fire spread trend prediction model is structurally adjusted based on the mapping results. By inputting the mapped parameters into the fire spread trend prediction model and replacing the original general scenario settings, the model acquires terrain, vegetation, and building distribution characteristics corresponding to the target area. Simultaneously, based on the coordinate and height information from the spatial mapping, the calculation area, fire source location, and heat propagation range in the model are redefined to ensure that the fire spread process is expressed in the simulation according to actual geographical relationships.

[0046] After the above parameter docking and model update operations, a target area fire spread trend prediction model that can truly reflect the actual spatial pattern and fire spread conditions of the target area is obtained.

[0047] Step S300: Introduce simulated working conditions and simulate them using the target area fire spread trend prediction model to obtain the target area fire trend prediction results.

[0048] In this embodiment, simulated operating conditions, including wind speed and buffer distance, are first set up and input into the fire spread trend prediction model for simulation calculation. Then, through simulation operation, key data such as the fire spread rate under different conditions, the building's thermal radiation intensity, and the building's surface temperature are obtained, thereby forming the fire spread trend prediction result.

[0049] Furthermore, the method provided in the application embodiment introduces simulated working conditions, simulates the fire spread trend prediction model of the target area, and obtains the fire trend prediction result of the target area, and also includes:

[0050] The wind speed and buffer distance parameters are set as the simulated conditions; the simulated conditions are input into the fire spread trend prediction model of the target area for simulation calculation to obtain the fire spread speed, building receptor thermal radiation intensity and surface temperature, which constitute the fire spread trend prediction data.

[0051] In this embodiment, wind speed conditions and buffer distance parameters are first set as simulation conditions. Based on prediction requirements, wind speed conditions are set to 4.8 m / s and 17.2 m / s, representing wind field intensity under different meteorological conditions. Buffer distance parameters are set to 10 m, 15 m, 20 m, and 25 m, representing the spatial interval between buildings and vegetation. After this step, a complete set of simulation condition parameters is formed.

[0052] Next, the simulated conditions are input into the fire spread trend prediction model for the target area for simulation calculation. The aforementioned wind speed and buffer distance parameters are imported into the deployed model as input conditions, triggering the fire source. Continuous calculations are performed within a given time range to simulate the fire spread process within the target area and its thermal impact on buildings. After this step, preliminary simulation results data containing the spread path, heat transfer, and building response are obtained. Then, the fire spread velocity is extracted. Based on the change in the flame front position over time in the simulation output, the velocity (unit: m / s) of the flame spreading outward from the ignition point is calculated to reflect the fire propagation efficiency. After this step, a set of fire spread velocity data is obtained. Subsequently, the thermal radiation intensity of the building's receptors is extracted. The thermal radiation flux received by the building's outer surface throughout the simulation process is recorded through measurement points in the model (unit: kW / m²). 2This step is used to assess the heat load on a building. After completing this step, data on the building's thermal radiation intensity is obtained. Simultaneously, data on changes in building surface temperature are extracted. The surface temperature changes of parts such as the building's exterior walls or roof are tracked over time to determine if the ignition temperature range of the building materials has been reached. After completing this step, building surface temperature data is obtained for analyzing whether the building has been ignited.

[0053] Through the above steps, the final fire spread trend prediction data, including the fire spread rate, the building's heat radiation intensity, and the building's surface temperature, is generated.

[0054] Step S400: Visualize the fire trend prediction results for the target area.

[0055] In this embodiment of the application, when visualizing the fire trend prediction results of the target area, the fire spread trend prediction data is first converted into a three-dimensional fire spread map. Then, the fire spread path and heat radiation intensity distribution information are marked on the map and dynamically demonstrated. By continuously updating the fire spread situation at different time stages, the fire spread trend is visualized.

[0056] Furthermore, the method provided in the application embodiment, which visualizes the fire trend prediction results of the target area, further includes:

[0057] The fire spread trend prediction data is converted into a three-dimensional fire spread map; the fire spread path and heat radiation intensity distribution are marked on the three-dimensional fire spread map, and the fire dynamics are demonstrated. The fire spread situation is dynamically updated according to different time stages of fire spread, and the fire spread trend is visualized.

[0058] In this embodiment of the application, when visualizing the fire spread trend prediction data of the target area, the predicted results such as fire spread speed, thermal radiation intensity, and building surface temperature in the fire spread trend prediction data are first mapped to corresponding spatial coordinates, and a three-dimensional fire spread map is generated using a three-dimensional modeling tool. This map uses the terrain and building base data provided by the Geographic Information System (GIS) as the base map, and overlays a thermal radiation distribution layer and a fire spread path layer, using different colors and transparency to represent heat intensity and the progress of fire line expansion.

[0059] Subsequently, fire evolution simulation technology is used to dynamically render the fire spread process, enabling a continuous display of fire intensity changes across time frames. For example, the simulation time is divided into several stages, with each stage updating the spread path and thermal impact area, thus achieving a continuous and visual representation of the fire's development.

[0060] In summary, the embodiments of this application have at least the following technical effects:

[0061] This application acquires historical fire data and constructs a fire spread trend prediction model based on the historical fire data; it acquires vegetation parameters, building parameters, and environmental parameters of the target area, deploys the fire spread trend prediction model in a scenario, and obtains a fire spread trend prediction model for the target area; it introduces simulated working conditions and simulates the fire spread trend prediction model for the target area to obtain the fire trend prediction result for the target area; and it visualizes the fire trend prediction result for the target area. This invention addresses the technical problem of the lack of quantitative prediction and dynamic display of fire spread trends in forest-town boundary areas in existing technologies. By constructing a trend prediction model calibrated based on historical fire data, combining it with the vegetation, building, and environmental parameters of the target area for scenario-based deployment, and introducing simulated working conditions for trend simulation and three-dimensional visualization, it achieves the technical effect of improving the accuracy, adaptability, and intuitiveness of fire spread trend prediction.

[0062] Example 2, based on the same inventive concept as the method for predicting the fire spread trend in a forest-town boundary area described in the previous examples, such as... Figure 2 As shown, this application provides a fire spread trend prediction system for forest-town boundary areas. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0063] The initial model building module 11 is used to acquire historical fire data and build a fire spread trend prediction model based on the historical fire data; the scenario deployment module 12 is used to acquire vegetation parameters, building parameters and environmental parameters of the target area, deploy the fire spread trend prediction model in the scenario, and obtain the fire spread trend prediction model of the target area; the prediction module 13 is used to introduce simulated working conditions, simulate the fire spread trend prediction model of the target area, and obtain the fire trend prediction result of the target area; the visualization module 14 is used to visualize the fire trend prediction result of the target area.

[0064] Furthermore, the system is also used to implement the following functions:

[0065] Fire features are extracted from the historical fire data, including historical fire scenarios, flame spread rate, heat radiation intensity distribution, and building damage characteristics. A preset vegetation-building combustion simulation model is obtained, and the preset vegetation-building combustion simulation model is calibrated based on the fire features to obtain the fire spread trend prediction model.

[0066] Furthermore, the system is also used to implement the following functions:

[0067] Based on the preset vegetation-building combustion simulation model, historical fire scenarios are reproduced and simulated to obtain initial simulation results; the initial simulation results are compared with the fire characteristics to obtain simulation deviation; the preset vegetation-building combustion simulation model is calibrated according to the simulation deviation to obtain the fire spread trend prediction model.

[0068] Furthermore, the system is also used to implement the following functions:

[0069] The vegetation parameters of the target area are obtained, including vegetation type and its spatial distribution density; the building parameters of the target area are collected, including building material characteristics, spatial layout and distance from vegetation; the environmental parameters of the target area are measured, including real-time wind speed, temperature and humidity data; the vegetation parameters, building parameters and environmental parameters are input into the fire spread trend prediction model, and spatial deployment is carried out in combination with the GIS data of the target area to obtain the fire spread trend prediction model of the target area.

[0070] Furthermore, the system is also used to implement the following functions:

[0071] The vegetation parameters, building parameters, and environmental parameters are integrated with the GIS data to complete spatial mapping; based on the spatial mapping results, the fire spread trend prediction model is updated to obtain the fire spread trend prediction model for the target area.

[0072] Furthermore, the system is also used to implement the following functions:

[0073] The wind speed and buffer distance parameters are set as the simulated conditions; the simulated conditions are input into the fire spread trend prediction model of the target area for simulation calculation to obtain the fire spread speed, building receptor thermal radiation intensity and surface temperature, which constitute the fire spread trend prediction data.

[0074] Furthermore, the system is also used to implement the following functions:

[0075] The fire spread trend prediction data is converted into a three-dimensional fire spread map; the fire spread path and heat radiation intensity distribution are marked on the three-dimensional fire spread map, and the fire dynamics are demonstrated. The fire spread situation is dynamically updated according to different time stages of fire spread, and the fire spread trend is visualized.

[0076] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0077] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0078] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method of predicting a fire spread tendency in a forest-urban interface, characterized by, The method comprises the following steps: acquiring historical fire data and constructing a fire spread trend prediction model based on the historical fire data; acquiring vegetation parameters, building parameters and environmental parameters of a target area, and performing scene deployment on the fire spread trend prediction model to obtain a target area fire spread trend prediction model; introducing a simulation working condition, simulating through the target area fire spread trend prediction model to obtain a target area fire trend prediction result; visualizing the target area fire trend prediction result.

2. The method of claim 1, wherein the forest-town interface fire spread tendency prediction method is characterized by, The method comprises the following steps: extracting fire features from the historical fire data, including historical fire scene, flame spread speed, heat radiation intensity distribution and building damage feature data; acquiring a preset vegetation-building combustion simulation model, calibrating the preset vegetation-building combustion simulation model based on the fire features to obtain the fire spread trend prediction model.

3. The method of claim 2, wherein the forest-town interface fire spread tendency prediction method is characterized by, The method comprises the following steps: reproducing simulation of the historical fire scene based on the preset vegetation-building combustion simulation model to obtain an initial simulation result; comparing the initial simulation result with the fire features to obtain simulation deviation; calibrating the preset vegetation-building combustion simulation model according to the simulation deviation to obtain the fire spread trend prediction model.

4. The method of claim 1, wherein the forest-town interface fire spread tendency prediction method is characterized by, The method comprises the following steps: acquiring vegetation parameters of a target area, including vegetation type and spatial distribution density; collecting building parameters of a target area, including building material characteristics, spatial layout and distance from vegetation; measuring environmental parameters of a target area, including real-time wind speed, temperature and humidity data; inputting the vegetation parameters, building parameters and environmental parameters into the fire spread trend prediction model, and combining target area GIS data for spatial deployment to obtain the target area fire spread trend prediction model.

5. The method of claim 4, wherein the forest-town interface fire spread tendency prediction method is characterized by, The method comprises the following steps: connecting the vegetation parameters, building parameters and environmental parameters with the GIS data to complete spatial mapping; updating the fire spread trend prediction model based on the spatial mapping result to obtain the target area fire spread trend prediction model.

6. The method of claim 1, wherein the forest-town interface fire spread tendency prediction method is characterized by, The method comprises the following steps: setting wind speed conditions and buffer distance parameters as the simulation working condition; inputting the simulation working condition into the target area fire spread trend prediction model for simulation calculation to obtain fire spread speed, building receptor heat radiation intensity and surface temperature, which constitute the fire spread trend prediction data.

7. The method of claim 1, wherein the forest-town interface fire spread tendency prediction method is characterized by, The target region fire trend prediction result is visualized, and the visualization processing includes: The fire spread trend prediction data is converted into a three-dimensional fire spread map; The fire spread path and the heat radiation intensity distribution are marked on the three-dimensional fire spread map, the fire dynamic demonstration is performed, the fire spread situation is dynamically updated according to different time stages of the fire spread, and the visualization processing of the fire spread trend is completed.

8. A fire spread trend prediction system for forest-town boundary areas, characterized in that, The system is used for executing the forest-town interface fire spread trend prediction method in any one of claims 1-7, and the system includes: An initial model construction module is configured to acquire historical fire data and construct a fire spread trend prediction model based on the historical fire data; A scene deployment module is configured to acquire vegetation parameters, building parameters and environment parameters of a target region, deploy the fire spread trend prediction model in a scene, and obtain a target region fire spread trend prediction model; A prediction module is configured to introduce a simulation working condition, simulate through the target region fire spread trend prediction model, and obtain a target region fire trend prediction result; A visualization module is configured to visualize the target region fire trend prediction result.

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