Forest fire early warning method, device, equipment and storage medium

Abstract

The present invention relates to the field of geographic information technology, and in particular to a forest fire early warning method, the method comprising: obtaining multi-temporal remote sensing images of a target area at several different times within a target time period, and constructing a multi-temporal remote sensing image time series of the target area; inputting the multi-temporal remote sensing image time series into a preset forest fire risk assessment model to obtain risk assessment data for each of the multi-temporal remote sensing images; combining the risk assessment data for each of the multi-temporal remote sensing images to construct a risk assessment time series of the target area; constructing a forest fire risk change trend map for the target area based on the risk assessment time series, and performing a forest fire early warning operation based on the forest fire risk change trend map. By constructing a forest fire risk change trend map for the area to be predicted, the changing trend of the forest fire risk level in the area to be predicted is reflected, and forest fires can be predicted more clearly and directly with high accuracy.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, and in particular to a forest fire early warning method, device, equipment and storage medium. Background Art

[0002] Forest fire prevention and control is a crucial component of forestry management. Over the past few decades, advances in computer technology have significantly improved forest fire risk assessment and forecasting capabilities. Current solutions typically employ computer vision recognition methods, focusing on satellite remote sensing data from the target area. Using climate data as a basis, they calculate forest fire risk levels, identify high-risk areas within the target area, and thereby implement forest fire prevention and control measures.

[0003] However, the occurrence of forest fires is essentially a phenomenon and effect of energy accumulation and release. Many high-risk areas for forest fires have never experienced fires. Predicting forest fires based solely on forest fire risk levels is prone to misjudgment and has low accuracy. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide a forest fire early warning method, device, equipment and storage medium, which, by constructing a forest fire risk change trend map of the area to be predicted, reflects the changing trend of the forest fire risk level in the area to be predicted, and predicts forest fires more clearly and directly with high accuracy.

[0005] In a first aspect, an embodiment of the present application provides a forest fire early warning method, comprising the following steps:

[0006] Obtaining multi-temporal remote sensing images of a target area at several different times within a target time period, and constructing a time series of multi-temporal remote sensing images of the target area;

[0007] Inputting the multi-temporal remote sensing image time series into a preset forest fire risk assessment model to obtain risk assessment data for each of the multi-temporal remote sensing images, combining the risk assessment data for each of the multi-temporal remote sensing images, and constructing a risk assessment time series for the target area;

[0008] According to the risk assessment time series, a forest fire risk change trend map of the target area is constructed, and a forest fire early warning operation is performed according to the forest fire risk change trend map.

[0009] In a second aspect, an embodiment of the present application provides a forest fire warning device, comprising:

[0010] A data acquisition module is used to obtain multi-temporal remote sensing images of a target area at several different times within a target time period, and to construct a time series of multi-temporal remote sensing images of the target area;

[0011] A forest fire risk assessment module is configured to input the multi-temporal remote sensing image time series into a preset forest fire risk assessment model, obtain risk assessment data for each of the multi-temporal remote sensing images, combine the risk assessment data for each of the multi-temporal remote sensing images, and construct a risk assessment time series for the target area;

[0012] The forest fire warning module is used to construct a forest fire risk change trend map of the target area based on the risk assessment time series, and perform forest fire warning operations based on the forest fire risk change trend map.

[0013] In a third aspect, an embodiment of the present application provides a computer device comprising: a processor, a memory, and a computer program stored on the memory and executable on the processor; when the computer program is executed by the processor, the steps of the forest fire warning method described in the first aspect are implemented.

[0014] In a fourth aspect, an embodiment of the present application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the forest fire warning method as described in the first aspect.

[0015] In an embodiment of the present application, a forest fire early warning method, device, equipment and storage medium are provided. By constructing a forest fire risk change trend map of the area to be predicted, the changing trend of the forest fire risk level in the area to be predicted is reflected, and forest fires are predicted more clearly and directly with high accuracy.

[0016] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a forest fire early warning method provided in one embodiment of the present application;

[0018] Figure 2 A schematic diagram of the process of step S2 in the forest fire early warning method provided in one embodiment of the present application;

[0019] Figure 3 A schematic diagram of the process of step S3 in the forest fire early warning method provided in one embodiment of the present application;

[0020] Figure 4 A schematic diagram of the process of step S3 in the forest fire early warning method provided in one embodiment of the present application;

[0021] Figure 5 A schematic diagram of the structure of a forest fire warning device provided in one embodiment of the present application;

[0022] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0023] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0024] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0025] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words "if" / "if" as used herein may be interpreted as "at the time of" or "when" or "in response to a determination."

[0026] See also Figure 1 , Figure 1 A flowchart of a forest fire early warning method provided in one embodiment of the present application is provided, wherein the method comprises the following steps:

[0027] S1: Obtain multi-temporal remote sensing images of a target area at several different times within a target time period, and construct a time series of multi-temporal remote sensing images of the target area.

[0028] The executor of the forest fire warning method is the warning device of the forest fire warning method (hereinafter referred to as the warning device). In an optional embodiment, the warning device can be a computer device, a server, or a server cluster composed of multiple computer devices.

[0029] The early warning equipment can obtain multi-temporal remote sensing images of the target area at several different times within the target time period through satellites, or establish a data connection with a preset network database, extract multi-temporal remote sensing images of the target area at several different times within the target time period from the network database, and construct a time series of multi-temporal remote sensing images of the target area.

[0030] S2: Input the multi-temporal remote sensing image time series into a preset forest fire risk assessment model to obtain risk assessment data of each of the multi-temporal remote sensing images, combine the risk assessment data of each of the multi-temporal remote sensing images, and construct a risk assessment time series for the target area.

[0031] The forest fire risk assessment model adopts the Fire Potential Index model FPI (Fire Potential Index) model. In this embodiment, the early warning device inputs the multi-temporal remote sensing image time series into a preset forest fire risk assessment model, obtains the risk assessment data of each of the multi-temporal remote sensing images, combines the risk assessment data of each of the multi-temporal remote sensing images in chronological order, and constructs the risk assessment time series of the target area.

[0032] See also Figure 2 , Figure 2 The flowchart of S2 in the forest fire early warning method provided in one embodiment of the present application includes steps S21 to S22, which are specifically as follows:

[0033] S21: Obtaining time-lagged dead combustible material humidity to water loss ratio data and vegetation coverage data corresponding to each pixel in each of the multi-temporal remote sensing images.

[0034] Since the moisture content in vegetation is an important parameter for determining whether a forest can burn and measuring the spread speed of a forest fire, it has the greatest impact on the moisture content of combustible materials. In this embodiment, the early warning equipment can obtain MODIS (moderate-resolution imaging spectroradiometer) data corresponding to each of the multi-temporal remote sensing images, and extract the time-lagged dead combustible material humidity and moisture disappearance ratio data and vegetation coverage data corresponding to each pixel.

[0035] S22: Based on the time-lagged dead combustible material humidity and water disappearance ratio data, vegetation coverage data and a preset fire risk potential index calculation algorithm, the fire risk potential index corresponding to each pixel is obtained, and the fire risk potential indices corresponding to the pixels of the same multi-temporal remote sensing image are combined to obtain risk assessment data for each of the multi-temporal remote sensing images.

[0036] The calculation algorithm of the fire hazard potential index is:

[0037] FPI=100*(1–FMC10HR_frac)*(1-Vc)

[0038] Wherein, FPI is the fire hazard potential index, FMC10HR_frac is the time-lagged dead combustible material humidity to water loss ratio data, and Vc is the vegetation coverage data.

[0039] In this embodiment, the early warning device obtains the fire risk potential index corresponding to each pixel based on the time-lagged dead combustible material humidity and water disappearance ratio data, vegetation coverage data and a preset fire risk potential index calculation algorithm, and combines the fire risk potential indexes corresponding to the pixels of the same multi-temporal remote sensing image to obtain risk assessment data for each of the multi-temporal remote sensing images, so as to reflect a more detailed forest fire risk situation of the multi-temporal remote sensing images at each moment.

[0040] S3: Constructing a forest fire risk change trend map for the target area based on the risk assessment time series, and performing a forest fire early warning operation based on the forest fire risk change trend map.

[0041] In this embodiment, the early warning device constructs a forest fire risk trend map for the target area based on the risk assessment time series and performs a forest fire early warning based on this map. This map reflects the changing trend of the forest fire risk level in the predicted area, making forest fire predictions more clear and direct, with high accuracy.

[0042] See also Figure 3 , Figure 3 The flowchart of S3 in the forest fire early warning method provided in one embodiment of the present application includes steps S31 to S32, which are specifically as follows:

[0043] S31: Using the least squares linear regression method, according to the fire risk potential index corresponding to the same pixel in the risk assessment data of each multi-temporal remote sensing image in the risk assessment time series, obtain the linear change trend calculation value corresponding to each pixel in the target area.

[0044] In this embodiment, the early warning device uses the least squares linear regression method to obtain the linear change trend calculation value corresponding to each pixel in the target area based on the fire risk potential index corresponding to the same pixel in the risk assessment data of each multi-temporal remote sensing image in the risk assessment time series, as follows:

[0045] Y i =ki+b

[0046] Where i is the time sequence mark, which means the i-th moment, Y iis the fire risk potential index corresponding to the same pixel at the i-th moment; k is the slope, that is, the calculated value of the linear change trend; b is the preset intercept parameter.

[0047] S32: According to the linear change trend calculation value and the preset linear change trend threshold, the linear change trend result corresponding to each pixel is obtained, and according to the linear change trend result corresponding to each pixel, a forest fire risk change trend map of the target area is constructed.

[0048] In this embodiment, the early warning device uses a single threshold method to obtain a linear trend result corresponding to each pixel based on the calculated linear trend value and a preset linear trend threshold, thereby performing a significance test on the calculated linear trend value corresponding to each pixel. Specifically, based on a preset linear trend threshold, which can be set to 0.67, the early warning device compares the calculated linear trend value corresponding to each pixel with the preset linear trend threshold. When the calculated linear trend value is greater than the linear trend threshold, a positive trend result corresponding to the pixel is obtained, indicating that the forest fire risk in the pixel area is showing a significantly increasing trend, and the probability of a forest fire occurring in the pixel area in the future is high. When the calculated linear trend value is less than or equal to the linear trend threshold, a negative trend result is obtained, indicating that the forest fire risk in the pixel area is showing a significantly stable or decreasing trend, and the probability of a forest fire occurring in the pixel area in the future is low. This allows for a clearer and more direct prediction of forest fires, with higher accuracy.

[0049] See also Figure 4 , Figure 4 The flowchart of S3 in the forest fire early warning method provided in one embodiment of the present application includes step S33, which is specifically as follows:

[0050] S33: Obtain an electronic map of the target area, obtain an identifier corresponding to the positive change trend result corresponding to each pixel according to the forest fire risk change trend map, and display the electronic map and the identifier on a preset display interface.

[0051] In this embodiment, the early warning device obtains an electronic map of the target area, obtains an identifier corresponding to the positive change trend result corresponding to each pixel based on the forest fire risk change trend map, and displays the electronic map and identifier on a preset display interface.

[0052] Please refer to Figure 5 , Figure 5This is a schematic diagram of the structure of a forest fire warning device provided in one embodiment of the present application. The device can implement all or part of the forest fire warning device through software, hardware, or a combination of both. The device 5 includes:

[0053] The data acquisition module 51 is used to obtain multi-temporal remote sensing images of a target area at several different times within a target time period, and to construct a time series of multi-temporal remote sensing images of the target area;

[0054] The forest fire risk assessment module 52 is configured to input the multi-temporal remote sensing image time series into a preset forest fire risk assessment model, obtain risk assessment data for each of the multi-temporal remote sensing images, combine the risk assessment data for each of the multi-temporal remote sensing images, and construct a risk assessment time series for the target area;

[0055] The forest fire warning module 53 is used to construct a forest fire risk change trend map of the target area according to the risk assessment time series, and perform forest fire warning operations according to the forest fire risk change trend map.

[0056] In an embodiment of the present application, a data acquisition module is used to obtain multi-temporal remote sensing images of a target area at several different times within a target time period, and a multi-temporal remote sensing image time series of the target area is constructed; a forest fire risk assessment module is used to input the multi-temporal remote sensing image time series into a preset forest fire risk assessment model to obtain risk assessment data for each of the multi-temporal remote sensing images, and the risk assessment data for each of the multi-temporal remote sensing images are combined to construct a risk assessment time series of the target area; a forest fire early warning module is used to construct a forest fire risk change trend map of the target area based on the risk assessment time series, and a forest fire early warning operation is performed based on the forest fire risk change trend map. By constructing a forest fire risk change trend map of the area to be predicted, the changing trend of the forest fire risk level of the area to be predicted is reflected, and forest fires are predicted more clearly and directly with high accuracy.

[0057] Please refer to Figure 6 , Figure 6 The computer device 6 is a schematic diagram of a structure of a computer device provided in an embodiment of the present application. The computer device 6 includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61; the computer device may store multiple instructions, which are suitable for being loaded and executed by the processor 61. Figures 1 to 4 The method steps of the embodiment shown, the specific execution process can be found in Figures 1 to 4 The detailed description of the illustrated embodiment will not be repeated here.

[0058] The processor 61 may include one or more processing cores. The processor 61 utilizes various interfaces and circuits to connect to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 62, as well as accesses data within the memory 62, to perform various functions and process data in the forest fire warning device 5. Optionally, the processor 61 may be implemented in the form of at least one of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 61 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content displayed on the touchscreen display; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 61 and may be implemented as a separate chip.

[0059] Among them, the memory 62 may include a random access memory 62 (Random Access Memory, RAM), and may also include a read-only memory 62 (Read-Only Memory). Optionally, the memory 62 includes a non-transitory computer-readable storage medium. The memory 62 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 62 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 62 may also be optionally at least one storage device located away from the aforementioned processor 61.

[0060] The embodiment of the present application also provides a storage medium, which can store multiple instructions, which are suitable for the processor to load and execute the above Figures 1 to 4 The method steps of the embodiment shown, the specific execution process can be found in Figures 1 to 4 The detailed description of the illustrated embodiment will not be repeated here.

[0061] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0062] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0063] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraint algorithm of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0064] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0065] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0066] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0067] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form.

[0068] The present invention is not limited to the above-mentioned embodiments. If various changes or modifications of the present invention do not depart from the spirit and scope of the present invention, and if these changes and modifications fall within the scope of the claims of the present invention and equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A forest fire early warning method, characterized in that: The following steps are involved: Obtaining multi-temporal remote sensing images of a target area at several different times within a target time period, and constructing a time series of multi-temporal remote sensing images of the target area; Obtaining time-lagged dead combustible material humidity to water loss ratio data and vegetation coverage data corresponding to each pixel in each of the multi-temporal remote sensing images; The fire risk potential index corresponding to each pixel is obtained based on the time-lagged dead combustible material humidity to water loss ratio data, vegetation coverage data, and a preset fire risk potential index calculation algorithm. The fire risk potential index corresponding to the pixels of the same multi-temporal remote sensing image is combined to obtain risk assessment data for each of the multi-temporal remote sensing images. The fire risk potential index calculation algorithm is as follows: FPI=100*(1–FMC10HR_frac)*(1-Vc) Wherein, FPI is the fire hazard potential index, FMC10HR_frac is the time-lagged dead combustible material humidity and water loss ratio data, and Vc is the vegetation coverage data; Obtaining risk assessment data of each of the multi-temporal remote sensing images, combining the risk assessment data of each of the multi-temporal remote sensing images, and constructing a risk assessment time series for the target area; Using a least squares linear regression method, based on the fire risk potential index corresponding to the same pixel in the risk assessment data of each multi-temporal remote sensing image in the risk assessment time series, a linear change trend calculation value corresponding to each pixel in the target area is obtained; According to the linear change trend calculation value and the preset linear change trend threshold, the linear change trend results corresponding to each pixel are obtained. According to the linear change trend results corresponding to each pixel, a forest fire risk change trend map of the target area is constructed. According to the forest fire risk change trend map, a forest fire warning operation is performed.

2. The forest fire early warning method according to claim 1, characterized in that: in, The linear change trend results include positive change trend results and negative change trend results; The forest fire early warning operation is performed according to the forest fire risk change trend map, including the steps of: An electronic map of the target area is obtained, and based on the forest fire risk change trend map, an identifier corresponding to the positive change trend result corresponding to each pixel is obtained, and the electronic map and the identifier are displayed on a preset display interface.

3. A forest fire early warning device, characterized in that: include: A data acquisition module is used to obtain multi-temporal remote sensing images of a target area at several different times within a target time period, and to construct a time series of multi-temporal remote sensing images of the target area; A forest fire risk assessment module is used to obtain the time-lagged dead combustible material humidity to water loss ratio data and vegetation coverage data corresponding to each pixel in each of the multi-temporal remote sensing images; The fire risk potential index corresponding to each pixel is obtained based on the time-lagged dead combustible material humidity to water loss ratio data, vegetation coverage data, and a preset fire risk potential index calculation algorithm. The fire risk potential index corresponding to the pixels of the same multi-temporal remote sensing image is combined to obtain risk assessment data for each of the multi-temporal remote sensing images. The fire risk potential index calculation algorithm is as follows: FPI=100*(1–FMC10HR_frac)*(1-Vc) Wherein, FPI is the fire hazard potential index, FMC10HR_frac is the time-lagged dead combustible material humidity and water loss ratio data, and Vc is the vegetation coverage data; Combining the risk assessment data of each of the multi-temporal remote sensing images to construct a risk assessment time series for the target area; A forest fire warning module is configured to obtain a linear change trend calculation value corresponding to each pixel in the target area based on the fire risk potential index corresponding to the same pixel in the risk assessment data of each multi-temporal remote sensing image in the risk assessment time series using a least squares linear regression method; According to the linear change trend calculation value and the preset linear change trend threshold, the linear change trend results corresponding to each pixel are obtained. According to the linear change trend results corresponding to each pixel, a forest fire risk change trend map of the target area is constructed. According to the forest fire risk change trend map, a forest fire warning operation is performed.

4. A computer device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, the steps of the forest fire early warning method as claimed in claim 1 or 2 are implemented.

5. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the forest fire early warning method according to claim 1 or 2 are implemented.

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