阅读说明：本技术 林火蔓延速度的预测方法、装置、存储介质及电子设备 (Forest fire spreading speed prediction method and device, storage medium and electronic equipment ) 是由 郑能欢 于 2021-05-26 设计创作，主要内容包括：本申请公开了一种林火蔓延速度的预测方法、装置、存储介质及电子设备,该林火蔓延速度的预测方法通过采集目标区域的矢量边界图和目标数据；按照预设规则将所述矢量边界图划分成多个矢量网格,得到一矢量网格图；基于所述目标数据和所述矢量网格图进行空间叠加分析,分别得到每个所述矢量网格的蔓延因子数据；基于预设预测模型和所述蔓延因子数据分别对每个所述矢量网格在预设方向的林火蔓延速度进行计算,以分别得到每个所述矢量网格在预设方向上的林火蔓延速度。本方案可以提高林火蔓延速度的预测准确性。(The application discloses a forest fire spreading speed prediction method, a forest fire spreading speed prediction device, a storage medium and electronic equipment, wherein the forest fire spreading speed prediction method is implemented by acquiring a vector boundary diagram and target data of a target area; dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph; performing spatial superposition analysis based on the target data and the vector grid diagram to respectively obtain spreading factor data of each vector grid; and respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction. The scheme can improve the prediction accuracy of the forest fire spreading speed.)

1. A method for predicting forest fire spreading speed is characterized by comprising the following steps:

acquiring a vector boundary diagram and target data of a target area;

dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph;

performing spatial superposition analysis based on the target data and the vector grid diagram to respectively obtain spreading factor data of each vector grid;

and respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction.

2. A method of predicting forest fire propagation speed according to claim 1, wherein the target data includes digital elevation model data, meteorological data, combustible distribution data, water data, road data and fire zone data.

3. The method for predicting forest fire spreading speed according to claim 2, wherein the performing spatial superposition analysis based on the target data and the vector grid map to obtain spreading factor data of each vector grid respectively comprises:

extracting gradient data and slope data of the vector grid diagram from the digital elevation model data by using a geographic information system;

performing difference calculation on the meteorological data of the vector grid diagram by using a kriging interpolation method to obtain wind speed data and wind direction data of the vector grid diagram;

and carrying out spatial superposition analysis on the gradient data, the slope direction data, the wind speed data, the wind direction data, the combustible distribution data, the water body data, the road data and the fire-retardant zone data and the vector grid diagram to respectively obtain spreading factor data of each vector grid.

4. The method for predicting forest fire spreading speed according to claim 3, wherein the step of respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data to respectively obtain the forest fire spreading speed of each vector grid in the preset direction comprises the following steps:

and calculating the forest fire spreading speed of each vector grid in the preset direction based on the WangZheng non-forest fire spreading model, the ellipse model and the spreading factor data of each vector grid so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction.

5. The method for predicting forest fire spreading speed according to claim 4, wherein the step of calculating forest fire spreading speed in a preset direction of each vector grid based on the Wangzang non-forest fire spreading model, the ellipse model and spreading factor data of each vector grid to obtain forest fire spreading speed in the preset direction of each vector grid comprises the steps of:

acquiring the highest air temperature, the average wind speed and the minimum humidity in a preset time period of each vector grid on the day;

obtaining a first spreading speed of each vector grid based on the Wangzheng non-forest fire spreading model, the current highest air temperature, the average wind speed and the minimum humidity in the preset time period;

and obtaining the forest fire spreading speed of each vector grid in the preset direction based on the first spreading speed, the spreading factor and the elliptical model.

6. The method of predicting forest fire propagation speed according to claim 5, wherein the propagation factor data for each of the vector grids comprises slope data, wind speed data, wind direction data, combustible distribution data, water body data, road data, and fire zone data for each vector grid;

the obtaining of the forest fire spreading speed of each vector grid in the preset direction based on the first spreading speed, the spreading factor and the ellipse model comprises:

obtaining a second spreading speed of each vector grid based on the wind speed data, the gradient data, the combustible distribution data, the water body data, the road data, the fire zone data and the first spreading speed of each vector grid;

and obtaining the forest fire spreading speed of each vector grid in the preset direction based on the slope data, the wind direction data, the second spreading speed and the elliptical model of each vector grid.

7. The method for predicting forest fire spreading speed according to claim 6, wherein the step of obtaining the forest fire spreading speed of each vector grid in a preset direction based on the slope data, the wind direction data, the second spreading speed and the elliptical model of each vector grid comprises the following steps:

obtaining the forest fire spreading direction of each vector grid based on the slope data, the wind direction data and the ellipse model of the grid;

and decomposing the second spreading speed based on the forest fire spreading direction and the elliptical model to obtain the forest fire spreading speed of each vector grid in the preset direction.

8. A forest fire spreading speed prediction device, comprising:

the data acquisition unit is used for acquiring a vector boundary diagram of a target area and target data;

the grid dividing unit is used for dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph;

the factor analysis unit is used for carrying out spatial superposition analysis based on the target data and the vector grid graph to respectively obtain spreading factor data of each vector grid;

and the speed prediction unit is used for respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction.

9. A storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the method of any of claims 1 to 7.

10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 7 when executing the computer program.

Technical Field

The embodiment of the application relates to the technical field of forest fire spreading, in particular to a forest fire spreading speed prediction method and device, a storage medium and electronic equipment.

Background

When a forest fire event occurs, scientific and effective prediction analysis on the forest fire spreading speed is significant for fighting the forest fire and reducing economic loss caused by the forest fire. Forest fire spreading is a complex and multi-phase process, and environmental factors have great influence on the forest fire spreading speed.

However, at present, the forest fire spreading speed is mostly predicted in an ideal environment, so that the accuracy of prediction of the forest fire spreading speed is low.

Disclosure of Invention

The embodiment of the application provides a forest fire spreading speed prediction method and device, a storage medium and electronic equipment, and prediction accuracy of forest fire spreading speed can be improved.

In a first aspect, an embodiment of the present application provides a method for predicting forest fire spreading speed, including:

acquiring a vector boundary diagram and target data of a target area;

dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph;

performing spatial superposition analysis based on the target data and the vector grid diagram to respectively obtain spreading factor data of each vector grid;

and respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction.

In the forest fire spreading speed prediction method provided by the embodiment of the application, the target data comprises digital elevation model data, meteorological data, combustible distribution data, water body data, road data and fire-protection belt data.

In the method for predicting forest fire spreading speed provided by the embodiment of the present application, the performing spatial superposition analysis based on the target data and the vector grid map to obtain spreading factor data of each vector grid respectively includes:

extracting gradient data and slope data of the vector grid diagram from the digital elevation model data by using a geographic information system;

performing difference calculation on the meteorological data of the vector grid diagram by using a kriging interpolation method to obtain wind speed data and wind direction data of the vector grid diagram;

and carrying out spatial superposition analysis on the gradient data, the slope direction data, the wind speed data, the wind direction data, the combustible distribution data, the water body data, the road data and the fire-retardant zone data and the vector grid diagram to respectively obtain spreading factor data of each vector grid.

In the method for predicting forest fire spreading speed provided in the embodiment of the present application, the calculating, based on the preset prediction model and the spreading factor data, the forest fire spreading speed of each vector grid in the preset direction respectively to obtain the forest fire spreading speed of each vector grid in the preset direction respectively includes:

and calculating the forest fire spreading speed of each vector grid in the preset direction based on the WangZheng non-forest fire spreading model, the ellipse model and the spreading factor data of each vector grid so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction.

In the forest fire spreading speed prediction method provided in the embodiment of the present application, the forest fire spreading speed in the preset direction of each vector grid is calculated based on the wangzang non-forest fire spreading model, the elliptical model and the spreading factor data of each vector grid, so as to obtain the forest fire spreading speed in the preset direction of each vector grid, and the method includes:

acquiring the highest air temperature, the average wind speed and the minimum humidity in a preset time period of each vector grid on the day;

obtaining a first spreading speed of each vector grid based on the Wangzheng non-forest fire spreading model, the current highest air temperature, the average wind speed and the minimum humidity in the preset time period;

and obtaining the forest fire spreading speed of each vector grid in the preset direction based on the first spreading speed, the spreading factor and the elliptical model.

In the method for predicting forest fire spreading speed provided by the embodiment of the application, the spreading factor data of each vector grid comprises gradient data, slope data, wind speed data, wind direction data, combustible distribution data, water body data, road data and fire-protecting strip data of each vector grid;

the obtaining of the forest fire spreading speed of each vector grid in the preset direction based on the first spreading speed, the spreading factor and the ellipse model comprises:

obtaining a second spreading speed of each vector grid based on the wind speed data, the gradient data, the combustible distribution data, the water body data, the road data, the fire zone data and the first spreading speed of each vector grid;

and obtaining the forest fire spreading speed of each vector grid in the preset direction based on the slope data, the wind direction data, the second spreading speed and the elliptical model of each vector grid.

In the method for predicting forest fire spreading speed provided by the embodiment of the application, the obtaining of the forest fire spreading speed of each vector grid in the preset direction based on the slope data, the wind direction data, the second spreading speed and the ellipse model of each vector grid includes:

obtaining the forest fire spreading direction of each vector grid based on the slope data, the wind direction data and the ellipse model of the grid;

and decomposing the second spreading speed based on the forest fire spreading direction and the elliptical model to obtain the forest fire spreading speed of each vector grid in the preset direction.

In a second aspect, an embodiment of the present application provides an apparatus for predicting forest fire spreading speed, including:

the data acquisition unit is used for acquiring a vector boundary diagram of a target area and target data;

the grid dividing unit is used for dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph;

the factor analysis unit is used for carrying out spatial superposition analysis based on the target data and the vector grid graph to respectively obtain spreading factor data of each vector grid;

and the speed prediction unit is used for respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction.

In a third aspect, an embodiment of the present application provides a storage medium, where a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor to perform the above-mentioned method.

In a fourth aspect, the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described above.

The method comprises the steps of acquiring a vector boundary diagram and target data of a target area; dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph; performing spatial superposition analysis based on the target data and the vector grid diagram to respectively obtain spreading factor data of each vector grid; and respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction. According to the scheme, the target area is divided into the vector grids to obtain a vector grid map, and then spread factor data of each vector grid in the vector grid map is obtained by combining target data of the target area, so that the forest fire spreading speed of each vector grid in the preset direction is obtained, and the prediction accuracy of the forest fire spreading speed can be improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic flow chart of a method for predicting forest fire spreading speed according to an embodiment of the present application.

Fig. 2 is a schematic diagram of a forest fire spreading direction according to an embodiment of the present application.

Fig. 3 is an exploded schematic view of a forest fire spreading direction according to an embodiment of the present application.

Fig. 4 is a schematic structural diagram of a forest fire spreading speed predicting device according to an embodiment of the present application.

Fig. 5 is a schematic structural diagram of a server according to an embodiment of the present application.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The terms "first" and "second", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to the listed steps or modules but may alternatively include other steps or modules not listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

The embodiment of the application provides a forest fire spreading speed prediction method and device, a storage medium and electronic equipment. It should be noted that the electronic device may be a mobile phone, a tablet computer, a notebook computer, or the like.

The following detailed description will be made separately, and the description sequence of each embodiment below does not limit the specific implementation sequence.

Referring to fig. 1, fig. 1 is a schematic flow chart of a forest fire spreading speed prediction method provided by the present application. The method for predicting forest fire spreading speed of the embodiment can be implemented by using the electronic device. The specific flow of the forest fire spreading speed prediction method can be as follows:

101. and acquiring a vector boundary diagram of the target area and target data.

Wherein the target data may comprise digital elevation model data, said meteorological data, combustible distribution data, water body data, road data, fire zone data, and the like.

It can be understood that the vector boundary map and the target data can be retrieved through an existing database, or can be collected through a satellite or a drone.

102. And dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph.

Specifically, the vector boundary map may be divided into a plurality of vector grids by using Geographic Information System (GIS) software, so as to obtain a vector grid map. For example, the vector boundary graph may be divided into a 10 × 10 vector grid graph, a 15 × 15 vector grid graph, or a 20 × 20 vector grid graph according to a preset ratio.

The preset ratio may be set according to actual conditions.

103. And performing spatial superposition analysis based on the target data and the vector grid graph to respectively obtain spreading factor data of each vector grid.

Specifically, a geographic information system can be used for extracting gradient data and slope direction data of a vector grid map from Digital Elevation Model (DEM), then a kriging interpolation method is used for carrying out difference value calculation on meteorological data of the vector grid map to obtain wind speed data and wind direction data of the vector grid map, and finally spatial superposition analysis is carried out on the gradient data, the slope direction data, the wind speed data, the wind direction data, combustible distribution data, water body data, road data, fireproof belt data and the vector grid map to respectively obtain spreading factor data of each vector grid.

It will be appreciated that the spread factor data for each vector grid includes grade data, slope data, wind speed data, wind direction data, combustible distribution data, water data, road data, fire zone data, etc. for each vector grid.

104. And respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction.

In some embodiments, the forest fire spreading speed of each vector grid in the preset direction may be calculated based on the Wangzang non-forest fire spreading model, the ellipse model and the spreading factor data of each vector grid, so as to obtain the forest fire spreading speed of each vector grid in the preset direction respectively.

Specifically, the daily maximum air temperature, the average wind speed and the minimum humidity within the preset time period of each vector grid can be obtained, then the first spreading speed of each vector grid is obtained based on the Wangzhen non-forest fire spreading model, the daily maximum air temperature, the average wind speed and the minimum humidity within the preset time period, and finally the forest fire spreading speed of each vector grid in the preset direction is obtained based on the first spreading speed, the spreading factor and the elliptical model.

The highest temperature, average wind speed and minimum humidity of each vector grid in the day in a preset time period can be extracted and calculated from meteorological data and water body data of target data, and real-time acquisition can also be carried out.

It should be noted that the first spreading speed is the maximum spreading speed of the forest fire in the windless environment in the one-dimensional space.

In the embodiment of the present application, the first propagation speed R may be specifically calculated as R ═ fT + gV + jH-D. Where T is the highest temperature on the day, V is the average wind speed, H is the minimum humidity within a preset time period, and f, g, j, and D are constants (f is 0.03, g is 0.05, j is 0.01, and D is 0.3). The preset time period may be set according to actual conditions, such as 100 days, 30 days, 130 days, 200 days, and the like.

It is understood that the rate of forest fire spread is related to environmental factors. Therefore, after the first rate of spread is obtained, the effect of environmental factors on the rate of forest fire spread needs to be considered.

It will be appreciated that in one-dimensional space, the factors that affect the forest fire spread rate may include wind speed data, grade data, combustible distribution data, water data, road data, fire zone data, etc. for each vector grid.

In some embodiments, the step of "obtaining the forest fire spreading rate of each vector grid in the preset direction based on the first spreading rate, the spreading factor and the elliptical model" may include:

obtaining a second spreading speed of each vector grid based on the wind speed data, the gradient data, the combustible distribution data, the water body data, the road data, the fire zone data and the first spreading speed of each vector grid;

and obtaining the forest fire spreading speed of each vector grid in the preset direction based on the slope data, the wind direction data, the second spreading speed and the elliptical model of each vector grid.

It can be understood that the second spreading speed is the maximum spreading speed of the forest fire in the one-dimensional space after the forest fire is influenced by the environmental factors.

In the embodiment of the present application, the specific calculation method of the second propagation speed R1 may be Where Ka represents the wind speed data for each vector gridThe combustible distribution data, the water body data, the road data, the fire-belt data and the like, Kr represents the wind speed correction coefficient,the surface average slope correction factor is represented.

In some embodiments, the step of "obtaining the forest fire spreading speed of each vector grid in the preset direction based on the slope data, the wind direction data, the second spreading speed and the elliptical model of each vector grid" may include:

obtaining the forest fire spreading direction of each vector grid based on the slope data, the wind direction data and the ellipse model of the grid;

and decomposing the second spreading speed based on the forest fire spreading direction and the elliptical model to obtain the forest fire spreading speed of each vector grid in the preset direction.

Specifically, as shown in fig. 2, one focus of the ellipse may be used as the ignition point, and the second propagation speed R1 obtained in the above embodiment may be represented by the length of b + c. Because the forest fire can be influenced by the wind direction and the gradient in the spreading process, the spreading direction of the forest fire can be known according to the parallelogram rule.

According to the ratio LB of the long axis and the short axis proposed by Anderson, LB is 0.936e0.2566U +0.461 e-0.1548U-0.397. Where U is the effective mid-flame wind speed in m/s, where U is R1. Ratio of front and rear flame peaksFurther, the short and long semi-axis lengths of the ellipse and the distances from the focal point to the center of the ellipse, i.e., the values of a, b, and c in FIG. 2 can be obtained. The method comprises the following specific steps: a is 0.5 × (R)₁+R₁/HB)/LB，b＝(R₁+R₁/HB)/2，c＝b-R/HB。

As shown in fig. 3, in some embodiments, the forest fire propagation direction may be decomposed into eight directions based on an elliptical model: and decomposing the second spreading speed based on the decomposition of the forest fire spreading direction so as to obtain the forest fire spreading speed of each vector grid in the preset direction.

That is, the preset direction in the embodiment of the present application may be one or more of eight directions, i.e., east, south, west, north, southeast, southwest, northeast, northwest, etc. The forest fire spreading speed of each vector grid in the preset direction can be the spreading speed of the forest fire in one or more of eight directions, namely east, south, west, north, southeast, southwest, northeast, northwest, and the like.

At this time: southern forest fire spreading velocity v_sB + c. Forest fire spreading speed v in north_nB-c. Velocity of forest fire spread in both the west and eastForest fire spreading speed in northeast and northwest directionsForest fire spreading speed in southeast and southwest directions

Wherein A ═ a²+b²，B＝-2cb²，C＝b²*(c²-a²)，delta＝B²-4AC。

Note that when delta>0, and x1>At the time of x2, the speed of the motor is higher,y₁＝x₁-c，y₂＝x₂-c。

when delta>0, and x1<At the time of x2, the speed of the motor is higher,y₁＝x₁-c，y₂＝x₂-c. When delta>0, and x1 is x2,y₁＝y₂＝x₁-c＝x₂-c. When delta<And when 0, the value is null.

It will be appreciated that the second propagation velocity R1 can be decomposed into eight directions of forest fire propagation velocity by the above embodiments, thereby achieving a spatial transformation from one dimension to two dimensions.

All the above technical solutions can be combined arbitrarily to form the optional embodiments of the present application, and are not described herein again.

In summary, the embodiment of the application collects the vector boundary diagram and the target data of the target area; dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph; performing spatial superposition analysis based on the target data and the vector grid diagram to respectively obtain spreading factor data of each vector grid; and respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction. According to the scheme, the target area is divided into the vector grids to obtain a vector grid map, and then spread factor data of each vector grid in the vector grid map is obtained by combining target data of the target area, so that the forest fire spreading speed of each vector grid in the preset direction is obtained, and the prediction accuracy of the forest fire spreading speed can be improved.

In order to better implement the forest fire spreading speed prediction method, correspondingly, the embodiment of the application also provides a forest fire spreading speed prediction device, wherein the forest fire spreading speed prediction device can be integrated in electronic equipment. The terms are the same as those in the above-mentioned forest fire spread rate prediction method, and specific implementation details can refer to the description in the method embodiment.

Fig. 4 is a schematic structural diagram of a forest fire propagation speed prediction device according to an embodiment of the present application, as shown in fig. 4. The forest fire spreading speed prediction apparatus 200 may include a data acquisition unit 201, a grid division unit 202, a factor analysis unit 203, and a speed prediction unit 204. Wherein the content of the first and second substances,

and the data acquisition unit 201 is used for acquiring the vector boundary diagram of the target area and the target data.

The mesh dividing unit 202 is configured to divide the vector boundary map into a plurality of vector meshes according to a preset rule, so as to obtain a vector mesh map.

And the factor analysis unit 203 is configured to perform spatial superposition analysis based on the target data and the vector grid map, and obtain propagation factor data of each vector grid respectively.

And the speed prediction unit 204 is configured to calculate, based on a preset prediction model and the propagation factor data, a forest fire propagation speed of each vector grid in a preset direction, so as to obtain the forest fire propagation speed of each vector grid in the preset direction.

The forest fire spreading speed prediction device 200 provided by the embodiment of the application acquires a vector boundary diagram and target data of a target area through the data acquisition unit 201. The grid dividing unit 202 divides the vector boundary graph into a plurality of vector grids according to a preset rule, so as to obtain a vector grid graph. And performing spatial superposition analysis by the factor analysis unit 203 based on the target data and the vector grid map to respectively obtain spreading factor data of each vector grid. The speed prediction unit 204 calculates the forest fire spreading speed of each vector grid in the preset direction respectively based on a preset prediction model and the spreading factor data, so as to obtain the forest fire spreading speed of each vector grid in the preset direction respectively. According to the scheme, the target area is divided into a plurality of vector grids to obtain a vector grid map, then spread factor data of each vector grid in the vector grid map is obtained by combining target data of the target area, so that the forest fire spreading speed of each vector grid in the preset direction is obtained, and the prediction accuracy of the forest fire spreading speed can be improved

The embodiment of the present application further provides a server, as shown in fig. 5, which shows a schematic structural diagram of the server according to the embodiment of the present application, specifically:

the server may include components such as a processor 301 of one or more processing cores, memory 302 of one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will appreciate that the server architecture shown in FIG. 5 is not meant to be limiting, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components. Wherein:

the processor 301 is a control center of the server, connects various parts of the entire server using various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory 302 and calling data stored in the memory 302, thereby performing overall monitoring of the server. Optionally, processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 301.

The memory 302 may be used to store software programs and modules, and the processor 301 executes various functional applications and data processing by operating the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to the use of the server, and the like. Further, the memory 302 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

The server further includes a power supply 303 for supplying power to the various components, and preferably, the power supply 303 may be logically connected to the processor 301 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system. The power supply 303 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.

The server may also include an input unit 304, the input unit 304 being operable to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

Although not shown, the server may further include a display unit and the like, which will not be described in detail herein. Specifically, in this embodiment, the processor 301 in the server loads the executable file corresponding to the process of one or more application programs into the memory 302 according to the following instructions, and the processor 301 runs the application programs stored in the memory 302, thereby implementing various functions as follows:

acquiring a vector boundary diagram and target data of a target area;

dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph;

performing spatial superposition analysis based on the target data and the vector grid diagram to respectively obtain spreading factor data of each vector grid;

and respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction.

The above operations can be specifically referred to the previous embodiments, and are not described herein.

As can be seen from the above, the server provided in this embodiment collects the vector boundary map of the target area and the target data; dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph; performing spatial superposition analysis based on the target data and the vector grid diagram to respectively obtain spreading factor data of each vector grid; and respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction. According to the scheme, the target area is divided into the vector grids to obtain a vector grid map, and then spread factor data of each vector grid in the vector grid map is obtained by combining target data of the target area, so that the forest fire spreading speed of each vector grid in the preset direction is obtained, and the prediction accuracy of the forest fire spreading speed can be improved.

Accordingly, an electronic device according to an embodiment of the present disclosure may include, as shown in fig. 6, a Radio Frequency (RF) circuit 401, a memory 402 including one or more computer-readable storage media, an input unit 403, a display unit 404, a sensor 405, an audio circuit 406, a Wireless Fidelity (WiFi) module 407, a processor 408 including one or more processing cores, and a power supply 409. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 6 does not constitute a limitation of the electronic device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components. Wherein:

the RF circuit 401 may be used for receiving and transmitting signals during a message transmission or communication process, and in particular, for receiving downlink information of a base station and then sending the received downlink information to the one or more processors 408 for processing; in addition, data relating to uplink is transmitted to the base station. In general, the RF circuitry 401 includes, but is not limited to, an antenna, at least one Amplifier, a tuner, one or more oscillators, a Subscriber Identity Module (SIM) card, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the RF circuitry 401 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to Global System for Mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Message Service (SMS), and the like.

The memory 402 may be used to store software programs and modules, and the processor 408 executes various functional applications and data processing by operating the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the electronic device, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 408 and the input unit 403 access to the memory 402.

The input unit 403 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, in a particular embodiment, the input unit 403 may include a touch-sensitive surface as well as other input devices. The touch-sensitive surface, also referred to as a touch display screen or a touch pad, may collect touch operations by a user (e.g., operations by a user on or near the touch-sensitive surface using a finger, a stylus, or any other suitable object or attachment) thereon or nearby, and drive the corresponding connection device according to a predetermined program. Alternatively, the touch sensitive surface may comprise two parts, a touch detection means and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts it to touch point coordinates, and sends the touch point coordinates to the processor 408, and can receive and execute commands from the processor 408. In addition, touch sensitive surfaces may be implemented using various types of resistive, capacitive, infrared, and surface acoustic waves. The input unit 403 may include other input devices in addition to the touch-sensitive surface. In particular, other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.

The display unit 404 may be used to display information input by or provided to a user and various graphical user interfaces of the electronic device, which may be made up of graphics, text, icons, video, and any combination thereof. The Display unit 404 may include a Display panel, and optionally, the Display panel may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch-sensitive surface may overlay the display panel, and when a touch operation is detected on or near the touch-sensitive surface, the touch operation is transmitted to the processor 408 to determine the type of touch event, and then the processor 408 provides a corresponding visual output on the display panel according to the type of touch event. Although in FIG. 6 the touch-sensitive surface and the display panel are two separate components to implement input and output functions, in some embodiments the touch-sensitive surface may be integrated with the display panel to implement input and output functions.

The electronic device may also include at least one sensor 405, such as a light sensor, motion sensor, and other sensors. In particular, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel according to the brightness of ambient light, and a proximity sensor that may turn off the display panel and/or the backlight when the electronic device is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when the mobile phone is stationary, and can be used for applications of recognizing the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which may be further configured to the electronic device, detailed descriptions thereof are omitted.

Audio circuitry 406, a speaker, and a microphone may provide an audio interface between the user and the electronic device. The audio circuit 406 may transmit the electrical signal converted from the received audio data to a speaker, and convert the electrical signal into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 406 and converted into audio data, which is then processed by the audio data output processor 408, and then passed through the RF circuit 401 to be sent to, for example, another electronic device, or output to the memory 402 for further processing. The audio circuitry 406 may also include an earbud jack to provide communication of a peripheral headset with the electronic device.

WiFi belongs to short distance wireless transmission technology, and the electronic device can help the user send and receive e-mail, browse web page and access streaming media, etc. through the WiFi module 407, which provides wireless broadband internet access for the user. Although fig. 6 shows the WiFi module 407, it is understood that it does not belong to the essential constitution of the electronic device, and may be omitted entirely as needed within the scope not changing the essence of the invention.

The processor 408 is a control center of the electronic device, connects various parts of the entire mobile phone by using various interfaces and lines, and performs various functions of the electronic device and processes data by operating or executing software programs and/or modules stored in the memory 402 and calling data stored in the memory 402, thereby monitoring the mobile phone as a whole. Optionally, processor 408 may include one or more processing cores; preferably, the processor 408 may integrate an application processor, which handles primarily the operating system, user interface, applications, etc., and a modem processor, which handles primarily the wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 408.

The electronic device also includes a power source 409 (e.g., a battery) for powering the various components, which may preferably be logically coupled to the processor 408 via a power management system to manage charging, discharging, and power consumption via the power management system. The power supply 409 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.

Although not shown, the electronic device may further include a camera, a bluetooth module, and the like, which are not described in detail herein. Specifically, in this embodiment, the processor 408 in the electronic device loads the executable file corresponding to the process of one or more application programs into the memory 402 according to the following instructions, and the processor 408 runs the application programs stored in the memory 402, thereby implementing various functions:

acquiring a vector boundary diagram and target data of a target area;

dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph;

performing spatial superposition analysis based on the target data and the vector grid diagram to respectively obtain spreading factor data of each vector grid;

and respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction.

The above operations can be specifically referred to the previous embodiments, and are not described herein.

As can be seen from the above, in the electronic device provided in this embodiment, the electronic device collects the vector boundary map of the target area and the target data; dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph; performing spatial superposition analysis based on the target data and the vector grid diagram to respectively obtain spreading factor data of each vector grid; and respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction. According to the scheme, the target area is divided into the vector grids to obtain a vector grid map, and then spread factor data of each vector grid in the vector grid map is obtained by combining target data of the target area, so that the forest fire spreading speed of each vector grid in the preset direction is obtained, and the prediction accuracy of the forest fire spreading speed can be improved.

It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.

To this end, the present application provides a storage medium, in which a plurality of instructions are stored, where the instructions can be loaded by a processor to execute the steps in any one of the methods for predicting forest fire spreading speed provided by the present application. For example, the instructions may perform the steps of:

acquiring a vector boundary diagram and target data of a target area;

dividing the vector boundary graph into a plurality of vector grids according to a preset rule to obtain a vector grid graph;

performing spatial superposition analysis based on the target data and the vector grid diagram to respectively obtain spreading factor data of each vector grid;

and respectively calculating the forest fire spreading speed of each vector grid in the preset direction based on a preset prediction model and the spreading factor data so as to respectively obtain the forest fire spreading speed of each vector grid in the preset direction.

The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.

Wherein the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.

The instructions stored in the storage medium may execute the steps in the method for predicting forest fire spreading speed provided in the embodiment of the present application, so that the beneficial effects that can be achieved by any method for predicting forest fire spreading speed provided in the embodiment of the present application may be achieved, which are detailed in the foregoing embodiments and will not be described herein again.

The method, the apparatus, the storage medium, and the electronic device for predicting forest fire spreading speed provided by the embodiments of the present application are described in detail above, and a specific example is applied in the present application to explain the principle and the implementation of the present application, and the description of the above embodiments is only used to help understand the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.