CN116597602A - Forest fire early warning method, device, equipment and storage medium - Google Patents
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Abstract
The application relates to the technical field of geographic information, in particular to a forest fire early warning method, which comprises the following steps: acquiring a plurality of multi-temporal remote sensing images of a target area at different moments in a target time period, and constructing a multi-temporal remote sensing image time sequence of the target area; inputting the time sequence of the multi-temporal remote sensing images into a preset forest fire risk assessment model, obtaining risk assessment data of each multi-temporal remote sensing image, and combining the risk assessment data of each multi-temporal remote sensing image to construct a risk assessment time sequence of the target area; and constructing a forest fire risk change trend graph of the target area according to the risk assessment time sequence, and performing forest fire early warning operation according to the forest fire risk change trend graph. By constructing the forest fire risk change trend graph of the area to be predicted, the change trend of the forest fire risk level of the area to be predicted is reflected, the forest fire is predicted more clearly and directly, and the accuracy is high.
Description
Technical Field
The application relates to the technical field of geographic information, in particular to a forest fire early warning method, a forest fire early warning device, forest fire early warning equipment and a forest fire storage medium.
Background
Forest fire prevention and control is an important content of forestry management, and along with the development of computer technology, forest fire risk assessment and forecasting capability are also improved in the past decades. The prior technical scheme generally adopts a computer vision recognition method, focuses on satellite remote sensing data of an area to be predicted, calculates forest fire risk levels by taking climate data as a calculation basis, and predicts a forest fire high risk area in the area to be predicted, thereby realizing prevention and control of forest fire.
However, the occurrence of forest fires is essentially a phenomenon and effect of energy accumulation and release, and many areas with high risk of forest fires are not in fire all the time, so that the forest fires are predicted only based on the risk level of the forest fires, and the accuracy is low under the condition of misjudgment.
Disclosure of Invention
Based on the above, the application aims to provide a forest fire early warning method, a forest fire early warning device, forest fire early warning equipment and a forest fire early warning storage medium, which embody the trend of the forest fire risk level change of the area to be predicted by constructing a forest fire risk change trend graph of the area to be predicted, so that the forest fire is predicted more clearly and directly, and the accuracy is high.
In a first aspect, an embodiment of the present application provides a forest fire early warning method, including the following steps:
acquiring a plurality of multi-temporal remote sensing images of a target area at different moments in a target time period, and constructing a multi-temporal remote sensing image time sequence of the target area;
inputting the time sequence of the multi-temporal remote sensing images into a preset forest fire risk assessment model, obtaining risk assessment data of each multi-temporal remote sensing image, and combining the risk assessment data of each multi-temporal remote sensing image to construct a risk assessment time sequence of the target area;
and constructing a forest fire risk change trend graph of the target area according to the risk assessment time sequence, and performing forest fire early warning operation according to the forest fire risk change trend graph.
In a second aspect, an embodiment of the present application provides a forest fire early warning device, including:
the data acquisition module is used for acquiring a plurality of multi-temporal remote sensing images of the target area at different moments in a target time period and constructing a multi-temporal remote sensing image time sequence of the target area;
the forest fire risk assessment module is used for inputting the time sequence of the multi-temporal remote sensing images into a preset forest fire risk assessment model, obtaining risk assessment data of each multi-temporal remote sensing image, and combining the risk assessment data of each multi-temporal remote sensing image to construct a risk assessment time sequence of the target area;
and the forest fire early warning module is used for constructing a forest fire risk change trend graph of the target area according to the risk assessment time sequence and carrying out forest fire early warning operation according to the forest fire risk change trend graph.
In a third aspect, an embodiment of the present application provides a computer apparatus, including: a processor, a memory, and a computer program stored on the memory and executable on the processor; the computer program when executed by the processor implements the steps of the forest fire early warning method as described in the first aspect.
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 early warning method according to the first aspect.
In the embodiment of the application, the forest fire early warning method, the forest fire early warning device, the forest fire early warning equipment and the forest fire early warning storage medium are provided, the variation trend of the forest fire risk level of the area to be predicted is reflected by constructing the forest fire risk variation trend graph of the area to be predicted, and the forest fire is predicted more clearly and directly with high accuracy.
For a better understanding and implementation, the present application is described in detail below with reference to the drawings.
Drawings
Fig. 1 is a schematic flow chart of a forest fire early warning method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of S2 in the forest fire early warning method according to an embodiment of the present application;
fig. 3 is a schematic flow chart of S3 in the forest fire early warning method according to an embodiment of the present application;
fig. 4 is a schematic flow chart of S3 in the forest fire early warning method according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a forest fire early warning device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the application. The word "if"/"if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination", depending on the context.
Referring to fig. 1, fig. 1 is a flow chart of a forest fire early warning method according to an embodiment of the application, the method includes the following steps:
s1: and obtaining a plurality of multi-temporal remote sensing images of the target area at different moments in a target time period, and constructing a multi-temporal remote sensing image time sequence of the target area.
The main implementation body of the forest fire early warning method is early warning equipment (hereinafter referred to as early warning equipment) of the forest fire early warning method, and in an optional embodiment, the early warning equipment may be a computer device, a server, or a server cluster formed by combining multiple computer devices.
The early warning device can acquire a plurality of multi-time-phase remote sensing images of the target area at different moments in a target time period through satellites, and can also establish data connection with a preset network database, extract the multi-time-phase remote sensing images of the target area at the different moments in the target time period from the network database, and construct a multi-time-phase remote sensing image time sequence of the target area.
S2: and inputting the time sequence of the multi-temporal remote sensing images into a preset forest fire risk assessment model, obtaining risk assessment data of each multi-temporal remote sensing image, and combining the risk assessment data of each multi-temporal remote sensing image to construct a risk assessment time sequence of the target area.
The forest fire risk assessment model adopts a fire risk potential index model FPI (Fire Potential Index) model, in this embodiment, the early warning device inputs the time sequence of the multi-temporal remote sensing images to a preset forest fire risk assessment model, obtains risk assessment data of each multi-temporal remote sensing image, combines the risk assessment data of each multi-temporal remote sensing image according to a time sequence, and constructs a risk assessment time sequence of the target area.
Referring to fig. 2, fig. 2 is a schematic flow chart of step S2 in the forest fire early warning method according to an embodiment of the present application, including steps S21 to S22, specifically as follows:
s21: and obtaining time-delay combustible humidity and moisture disappearance ratio data corresponding to each pixel in each multi-time-phase remote sensing image and vegetation coverage data.
Because the moisture content in the vegetation is an important parameter for enabling the forest to burn and measuring the forest fire spreading speed, the influence on the moisture content of the combustible is the greatest, in the embodiment, the early warning device can obtain MODIS (model-resolution imaging spectroradiometer) data corresponding to each multi-time-phase remote sensing image, and extract the time-delay combustible humidity and moisture disappearance ratio data and vegetation coverage data corresponding to each pixel.
S22: and according to the time-lag combustible humidity and moisture disappearance ratio data, vegetation coverage data and a preset fire risk potential index calculation algorithm, obtaining fire risk potential indexes corresponding to all pixels, and combining the fire risk potential indexes corresponding to the pixels of the same multi-temporal remote sensing image to obtain risk evaluation data of all 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 risk potential index, FMC10HR_frac is the time-lapse combustible humidity and moisture disappearance ratio data, and Vc is the vegetation coverage data.
In this embodiment, the early warning device obtains the fire risk potential indexes corresponding to the pixels according to the time-lag combustible humidity and moisture disappearance ratio data, the vegetation coverage data and the preset fire risk potential index calculation algorithm, combines the fire risk potential indexes corresponding to the pixels of the same multi-time remote sensing image, and obtains the risk assessment data of each multi-time remote sensing image so as to embody finer forest fire risk conditions of the multi-time remote sensing image at each moment.
S3: and constructing a forest fire risk change trend graph of the target area according to the risk assessment time sequence, and performing forest fire early warning operation according to the forest fire risk change trend graph.
In this embodiment, the early warning device constructs a forest fire risk variation trend graph of the target area according to the risk assessment time sequence, and performs forest fire early warning operation according to the forest fire risk variation trend graph. The variation trend of the forest fire risk level of the area to be predicted is reflected, the forest fire is predicted more clearly and directly, and the accuracy is high.
Referring to fig. 3, fig. 3 is a schematic flow chart of step S3 in the forest fire early warning method according to an embodiment of the present application, including steps S31 to S32, specifically as follows:
s31: and obtaining a linear change trend calculation value corresponding to each pixel of the target area according to a 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 sequence by adopting a least square linear regression method.
In this embodiment, the early warning device adopts a least square linear regression method, and obtains a linear variation trend calculation value corresponding to each pixel of the target area according to a fire risk potential index corresponding to the same pixel in risk assessment data of each multi-temporal remote sensing image in the risk assessment time sequence, where the linear variation trend calculation value is specifically as follows:
Y i ＝ki+b
wherein i is a time sequence mark, which is expressed as the ith moment, Y i The corresponding fire risk potential index of the same pixel at the ith moment; k is a slope, namely a linear change trend calculated value; b is a preset intercept parameter.
S32: obtaining a linear change trend result corresponding to each pixel according to the linear change trend calculated value and a preset linear change trend threshold value, and constructing a forest fire risk change trend graph of the target area according to the linear change trend result corresponding to each pixel.
In this embodiment, the early warning device adopts a single threshold method, and obtains the linear variation trend result corresponding to each pixel according to the linear variation trend calculated value and the preset linear variation trend threshold value, so as to implement the significance test of the linear variation trend calculated value corresponding to each pixel. Specifically, the early warning device may set the linear change trend threshold to 0.67 according to a preset linear change trend threshold, compare the linear change trend calculated value corresponding to each pixel with the preset linear change trend threshold, and obtain the forward change trend result corresponding to the pixel when the linear change trend calculated value is greater than the linear change trend threshold, so as to indicate that the forest fire risk of the pixel area presents a significantly increased trend, and the possibility of forest fire occurrence of the pixel area in future time is high. When the linear change trend calculated value is smaller than or equal to the linear change trend threshold value, a negative change trend result corresponding to the pixel is obtained to indicate that the forest fire risk of the pixel area shows a remarkably stable or reduced trend, and the possibility of occurrence of the forest fire of the pixel area in future time is low. The forest fire prediction method is more definite and can be used for directly predicting forest fires, and the accuracy is high.
Referring to fig. 4, fig. 4 is a schematic flow chart of step S3 in the forest fire early warning method according to an embodiment of the present application, including step S33, specifically including the following steps:
s33: and obtaining an electronic map of the target area, obtaining marks corresponding to forward change trend results corresponding to the pixels according to the forest fire risk change trend graph, and displaying the electronic map and the marks on a preset display interface.
In this embodiment, the early warning device obtains an electronic map of the target area, obtains, according to the forest fire risk variation trend graph, a mark corresponding to a forward variation trend result corresponding to each pixel, and displays the electronic map and the mark on a preset display interface.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a forest fire early warning device according to an embodiment of the present application, where the device may implement all or a part of the forest fire early warning device by software, hardware or a combination of the two, and the device 5 includes:
the data acquisition module 51 is configured to obtain multi-temporal remote sensing images of a target area at a plurality of different moments in a target time period, and construct a multi-temporal remote sensing image time sequence of the target area;
the forest fire risk assessment module 52 is configured to input the time sequence of the multi-temporal remote sensing images to a preset forest fire risk assessment model, obtain risk assessment data of each multi-temporal remote sensing image, and combine the risk assessment data of each multi-temporal remote sensing image to construct a risk assessment time sequence of the target area;
the forest fire early warning module 53 is configured to construct a forest fire risk variation trend graph of the target area according to the risk assessment time sequence, and perform forest fire early warning operation according to the forest fire risk variation trend graph.
In the embodiment of the application, a data acquisition module is used for acquiring a plurality of multi-temporal remote sensing images of a target area at different moments in a target time period, and a multi-temporal remote sensing image time sequence of the target area is constructed; inputting the time sequence of the multi-temporal remote sensing images into a preset forest fire risk assessment model through a forest fire risk assessment module, obtaining risk assessment data of each multi-temporal remote sensing image, and combining the risk assessment data of each multi-temporal remote sensing image to construct a risk assessment time sequence of the target area; and constructing a forest fire risk change trend graph of the target area according to the risk assessment time sequence by a forest fire early warning module, and carrying out forest fire early warning operation according to the forest fire risk change trend graph. By constructing the forest fire risk change trend graph of the area to be predicted, the change trend of the forest fire risk level of the area to be predicted is reflected, the forest fire is predicted more clearly and directly, and the accuracy is high.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present application, where the computer device 6 includes: a processor 61, a memory 62, and a computer program 63 stored on the memory 62 and executable on the processor 61; the computer device may store a plurality of instructions adapted to be loaded by the processor 61 and to execute the steps of the method according to the embodiment shown in fig. 1 to 4, and the specific execution process may be referred to in the specific description of the embodiment shown in fig. 1 to 4, which is not repeated here.
Wherein processor 61 may comprise one or more processing cores. The processor 61 performs various functions of the forest fire early warning device 5 and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 62 and calling data in the memory 62 using various interfaces and various parts within the wired connection server, alternatively the processor 61 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field-programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programble Logic Array, PLA). The processor 61 may integrate one or a combination of several of a central processor 61 (Central Processing Unit, CPU), an image processor 61 (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the touch display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 61 and may be implemented by a single chip.
The Memory 62 may include a random access Memory 62 (Random Access Memory, RAM) or a Read-Only Memory 62 (Read-Only Memory). Optionally, the memory 62 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 62 may be used to store instructions, programs, code sets, or instruction sets. The memory 62 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the various method embodiments described above, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 62 may alternatively be at least one memory device located remotely from the aforementioned processor 61.
The embodiment of the present application further provides a storage medium, where the storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executed by the processor, and the specific execution process may refer to the specific description of the embodiment shown in fig. 1 to 4, and details are not repeated herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc.
The present application is not limited to the above-described embodiments, but, if various modifications or variations of the present application are not departing from the spirit and scope of the present application, the present application is intended to include such modifications and variations as fall within the scope of the claims and the equivalents thereof.
Claims (7)
1. The forest fire early warning method is characterized by comprising the following steps of:
acquiring a plurality of multi-temporal remote sensing images of a target area at different moments in a target time period, and constructing a multi-temporal remote sensing image time sequence of the target area;
inputting the time sequence of the multi-temporal remote sensing images into a preset forest fire risk assessment model, obtaining risk assessment data of each multi-temporal remote sensing image, and combining the risk assessment data of each multi-temporal remote sensing image to construct a risk assessment time sequence of the target area;
and constructing a forest fire risk change trend graph of the target area according to the risk assessment time sequence, and performing forest fire early warning operation according to the forest fire risk change trend graph.
2. The forest fire early warning method according to claim 1, wherein the step of inputting the time series of multi-temporal remote sensing images to a preset forest fire risk assessment model to obtain risk assessment data of each multi-temporal remote sensing image comprises the steps of:
acquiring time-delay combustible humidity and moisture disappearance ratio data and vegetation coverage data corresponding to each pixel in each multi-time-phase remote sensing image;
according to the time-lag combustible humidity and moisture disappearance ratio data, vegetation coverage data and a preset fire risk potential index calculation algorithm, a fire risk potential index corresponding to each pixel is obtained, the fire risk potential indexes corresponding to the pixels of the same multi-temporal remote sensing image are combined, and risk assessment data of each multi-temporal remote sensing image are obtained, wherein the fire risk potential index calculation algorithm is as follows:
FPI＝100*(1–FMC10HR_frac)*(1-Vc)
wherein FPI is the fire risk potential index, FMC10HR_frac is the time-lapse combustible humidity and moisture disappearance ratio data, and Vc is the vegetation coverage data.
3. The forest fire early warning method according to claim 2, wherein the step of inputting the risk assessment time series to a preset forest fire risk variation trend calculation model to obtain a forest fire risk variation trend graph of the target area includes the steps of:
obtaining a linear variation trend calculation value corresponding to each pixel of the target area according to a least square linear regression method in the risk assessment data of each multi-temporal remote sensing image in the risk assessment time sequence and a fire risk potential index corresponding to the same pixel;
obtaining a linear change trend result corresponding to each pixel according to the linear change trend calculated value and a preset linear change trend threshold value, and constructing a forest fire risk change trend graph of the target area according to the linear change trend result corresponding to each pixel.
4. A forest fire warning method according to claim 3, characterised in that: the linear change trend result comprises a positive change trend result and a negative change trend result;
according to the forest fire risk change trend graph, forest fire early warning operation is carried out, and the method comprises the following steps:
and obtaining an electronic map of the target area, obtaining marks corresponding to forward change trend results corresponding to the pixels according to the forest fire risk change trend graph, and displaying the electronic map and the marks on a preset display interface.
5. A forest fire early warning device, characterized by comprising:
the data acquisition module is used for acquiring a plurality of multi-temporal remote sensing images of the target area at different moments in a target time period and constructing a multi-temporal remote sensing image time sequence of the target area;
the forest fire risk assessment module is used for inputting the time sequence of the multi-temporal remote sensing images into a preset forest fire risk assessment model, obtaining risk assessment data of each multi-temporal remote sensing image, and combining the risk assessment data of each multi-temporal remote sensing image to construct a risk assessment time sequence of the target area;
and the forest fire early warning module is used for constructing a forest fire risk change trend graph of the target area according to the risk assessment time sequence and carrying out forest fire early warning operation according to the forest fire risk change trend graph.
6. A computer device, comprising: a processor, a memory, and a computer program stored on the memory and executable on the processor; the computer program when executed by the processor implements the steps of the forest fire warning method as defined in any one of claims 1 to 4.
7. A storage medium, characterized by: the storage medium stores a computer program which, when executed by a processor, implements the steps of the forest fire warning method as claimed in any one of claims 1 to 4.
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| CN117746602A (en) * | 2024-02-19 | 2024-03-22 | 及安盾(海南)科技有限公司 | Fire risk intelligent early warning method and system based on multi-source data fusion |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117746602A (en) * | 2024-02-19 | 2024-03-22 | 及安盾(海南)科技有限公司 | Fire risk intelligent early warning method and system based on multi-source data fusion |
| CN117746602B (en) * | 2024-02-19 | 2024-05-28 | 及安盾(海南)科技有限公司 | Fire risk intelligent early warning method and system based on multi-source data fusion |
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