Forest fire prediction and forest fire driving factor analysis method and equipment

Abstract

The embodiment of the invention discloses a forest fire prediction and forest fire driving factor analysis method and device and a computer readable medium. The specific implementation mode of the method comprises the steps that 15 static variables and 10 dynamic variables are used for generating corresponding static variables and dynamic variables through a LOAN with DACSA model, and then splicing and normalization are carried out to obtain a prediction result; carrying out weight extraction on the variables through a DACSA attention mechanism so as to carry out forest fire driving factor analysis; according to the embodiment, the prediction accuracy of the forest fire is effectively improved, and the driving factors of the forest fire are better analyzed and quantified by the DACSA attention mechanism.

Description

Technical Field

[0001] The present invention relates to the field of forest fire risk prediction, and in particular to a method for prediction and analysis using a deep learning attention mechanism. Background Art

[0002] The advent of industrialization has led to a significant increase in the frequency of extreme weather events, which has a huge impact on human production and life. Traditional forest fire risk prediction uses empirical models or machine learning methods, but these methods are not accurate because they cannot integrate large amounts of data for prediction. Summary of the invention

[0003] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0004] Some embodiments of the present disclosure propose a forest fire risk prediction method, device and computer-readable medium to solve the technical problems mentioned in the above background technology section.

[0005] In a first aspect, some embodiments of the present disclosure provide a forest fire risk prediction method, which includes: first, performing position-aware adaptive normalization on dynamic features in pre-acquired satellite data to obtain a dynamic vector; inputting static features in the satellite data into a deformable average channel and spatial attention module (DACSA) to obtain a static vector; concatenating the dynamic vector and the static vector to obtain a feature vector; and normalizing the feature vector to obtain a forest fire prediction result.

[0006] In a second aspect, some embodiments of the present disclosure provide an attention method, namely, a deformable average channel and spatial attention module (DACSA) inputs the static features in the satellite data to obtain a static vector, including: inputting the static features into a first deformable convolution module to obtain a first intermediate static feature; performing batch normalization and maximum pooling processing on the first intermediate static feature to obtain a second intermediate static feature; inputting the second intermediate static feature into a spatial and channel attention module to obtain a third intermediate static feature; inputting the third intermediate static feature into a second deformable convolution module to obtain a fourth intermediate static feature; performing global average pooling processing on the fourth intermediate static feature to obtain the static vector.

[0007] On the third aspect, some embodiments of the present disclosure extract weight values ​​of different features through a deformable average channel and spatial attention module (DACSA) to analyze the driving factors of forest fires.

[0008] In a fourth aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0009] In a fifth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner in the first aspect is implemented.

[0010] One of the above-mentioned embodiments of the present disclosure has the following beneficial effects: the accuracy of forest fire risk prediction and forest fire driving factor analysis methods of some embodiments of the present disclosure can be improved, and the specific weight values ​​of forest fire driving factors can be given through a large amount of feature data. This makes the analysis of forest fire driving factors more accurate. The DACSA attention method proposed in the present invention is better than other mainstream attention methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is the overall flow chart of the present invention;

[0012] Figure 2 This is a flow chart of the DACSA attention module in the present invention;

[0013] Figure 3 A flow chart of the model LOAN with DACSA for forest fire prediction according to the present invention;

[0014] Figure 4 A forest fire prediction map drawn for the present invention;

[0015] Figure 5 It is a schematic diagram of the structure of an electronic device suitable for implementing the present invention. DETAILED DESCRIPTION

[0016] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings.

[0017] exist Figure 1 The overall flow chart of the present invention is given in FIG. First, 15 static variables and 10 dynamic variables in the satellite data are input into the LOAN with DACSA model, and the dynamic variables are processed by LOAN and the static variables are processed by DACSA. Then, DACSA is used to extract weights to analyze the driving factors of forest fires. LOAN with DACSA is used to predict forest fire risks and draw risk prediction maps. At this point, forest fire risk prediction and driving factor analysis are completed.

[0018] Figure 2 This is the structural diagram of the DACSA model proposed in this invention. First, the input variable is convolved with variability and then batch normalized and max pooled, and then enters the spatial and channel attention extraction branch. Both spatial and channel attention are operated using average pooling. Then we can extract the weight value M in the channel attention. c , in order to analyze the driving factors of forest fires.

[0019] Figure 3 FIG. 1 is a structural diagram of the LOAN with DACSA model for forest fire prediction in the present invention. Figure 2 The DACSA attention model in the paper is combined with the Location-Aware Adaptive Normalization (LOAN) model. LOAN processes dynamic variables, and DACSA processes static variables. LOAN and DACSA are modified by conditional maps. Then the dynamic variables and static variables are processed into dynamic vectors and static vectors, and finally the two vectors are spliced ​​to obtain the forest fire prediction results.

[0020] Figure 4 The forest fire risk prediction map drawn for the present invention selects two seasons, summer and winter. The area studied is the Greek region. The probability of forest fire is quantified from 0 to 1, corresponding to the depth of color on the map. The diamonds in the figure represent the locations where real forest fires occur.

[0021] Figure 5 An electronic device (eg Figure 1 The electronic devices in some embodiments of the present disclosure may include but are not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0022] like Figure 5As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0023] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 5 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 5 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0024] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0025] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0026] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0027] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: processes satellite data sets through the LOAN with DACSA model to predict forest fires, analyzes forest fire driving factors through the DACSA model, and draws a prediction map of forest fire occurrence through the LOAN with DACSA model.

[0028] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, Python, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). The units described in some embodiments of the present disclosure may be implemented in software or in hardware.

[0029] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0030] According to one or more embodiments of the present disclosure, a method for forest fire prediction and driving factor analysis is provided, comprising: performing location-aware adaptive normalization on dynamic features in pre-acquired satellite data to obtain a dynamic vector. Inputting static features in the satellite data into a deformable average channel and spatial attention module (DACSA) to obtain a static vector. Concatenating the dynamic vector and the static vector to obtain a feature vector. Normalizing the feature vector to obtain a forest fire prediction result.

[0031] According to one or more embodiments of the present disclosure, static features in satellite data are input into a deformable average channel and spatial attention module (DACSA) to obtain a static vector, including: inputting the static features into a first deformable convolution module to obtain a first intermediate static feature. Performing batch normalization and maximum pooling processing on the first intermediate static feature to obtain a second intermediate static feature. Inputting the second intermediate static feature into a spatial and channel attention module to obtain a third intermediate static feature. Inputting the third intermediate static feature into a second deformable convolution module to obtain a fourth intermediate static feature. Performing global average pooling processing on the fourth intermediate static feature to obtain the static vector.

[0032] According to one or more embodiments of the present disclosure, the weight values ​​of different features are extracted through a deformable average channel and spatial attention (DACSA) module (DACSA) to analyze the driving factors of forest fires.

[0033] According to one or more embodiments of the present disclosure, there is provided an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any of the above methods.

[0034] According to one or more embodiments of the present disclosure, a computer-readable medium is provided, on which a computer program is stored, wherein when the program is executed by a processor, any of the above methods is implemented.

[0035] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for forest fire prediction and forest fire driving factor analysis, comprising: Performing position-aware adaptive normalization on the dynamic features in the pre-acquired satellite data to obtain a dynamic vector; Inputting the static features in the satellite data into a deformable average channel and spatial attention module (DACSA) to obtain a static vector; Concatenate the dynamic vector and the static vector to obtain a feature vector; The characteristic vector is normalized to obtain a forest fire prediction result.

2. The method according to claim 1, wherein the step of inputting the static features in the satellite data into a deformable average channel and spatial attention module (DACSA) to obtain a static vector comprises: Inputting the static feature into a first deformable convolution module to obtain a first intermediate static feature; Performing batch normalization and maximum pooling processing on the first intermediate static features to obtain second intermediate static features; Input the second intermediate static feature into the spatial and channel attention module to obtain a third intermediate static feature; input the third intermediate static feature into the second deformable convolution module to obtain a fourth intermediate static feature; Perform global average pooling processing on the fourth intermediate static feature to obtain the static vector.

3. The method according to claim 1, wherein the weight values ​​of different features are extracted through a deformable average channel and spatial attention module (DACSA) to analyze the driving factors of forest fires.

4. An electronic device comprising: one or more processors; A storage device having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1-2.

5. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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