Static forest fire risk assessment method and system based on multi-source data and remote sensing technology

Abstract

The invention discloses a static forest fire risk assessment method and system based on multi-source data and a remote sensing technology. The method comprises the following steps: establishing an index system of a target area and carrying out data acquisition; the collected data are preprocessed; calculating influence factors and index weights by using the preprocessed data, determining spatial and temporal distribution of calculation results of different influence factors, and calculating a comprehensive factor result by using each single influence factor according to a set weight; and evaluating the forest fire risk based on the obtained comprehensive factor result, and realizing dynamic monitoring of the forest fire risk value. According to the method, the accuracy and the real-time performance of forest fire space-time risk prediction are improved; and theoretical support is provided for follow-up forest fire risk prevention and control of electric power companies.

Description

Background Art

[0002] Wildfires are the most influential disaster on forest ecosystems, which profoundly affects biological habitats, carbon emissions, and the balance of surrounding natural ecosystems. In the past few decades, due to global warming and changes in land use, precipitation and vegetation moisture content have changed, causing the frequency and intensity of wildfires around the world to gradually increase, which has a negative impact on social and economic development and ecological protection. Therefore, studying the static risk spatiotemporal changes of wildfire risks is of great significance for preventing wildfire disasters and reducing the losses caused by wildfires.

[0003] Wildfires not only pose a threat to the ecological environment and human health, but also have serious impacts on infrastructure, especially the power system. For example, wildfires may cause overheating and short circuits in transmission lines, or even damage transmission towers, leading to power grid paralysis. There have been many cases of power system losses caused by wildfires in history. The direct economic losses of this wildfire exceeded 10 billion US dollars, including damage to infrastructure, ecological environment, and tourism. The fire caused extensive damage to the power infrastructure, resulting in power outages in many states, directly affecting the normal power consumption of residents and businesses, and increasing the cost of post-disaster power repair and replacement.

[0004] Wildfires are extremely destructive to the economy and property, especially the threat to power infrastructure. These losses include not only direct repair and reconstruction costs, but may also trigger compensation liabilities for related companies and local governments, insurance losses, and large-scale economic activity stagnation.

[0005] Current status and shortcomings of related technologies Many studies have shown that the occurrence of wildfires is affected by a variety of inducing factors, which are generally believed to include four types of factors: vegetation type, meteorology, topography, and human factors. For example, Yu Xin et al. analyzed the influencing factors of wildfires in Fujian and found that the wildfires in Fuzhou were mainly affected by the normalized vegetation index, followed by meteorology, topography, and human interference factors; while Liu Xingxing's research found that the biggest impact on wildfires in Yunnan Province was rainfall, and the land type had the least impact. Wildfires have monthly and seasonal differences, especially for wildfire-prone seasons and months (especially dry winter and spring), the changes in the spatiotemporal pattern of wildfire risks need further study.

[0006] The main static assessment methods for wildfire risk are correlation analysis, fire-to-fire method and statistical analysis. The correlation analysis method is a simple method based on multiple factors to assess the fire risk level. The fire-to-fire method uses the relationship between field ignition experiments and meteorological elements to assess the danger of fire, establish a forest fire forecast model based on experimental data, and assess the wildfire spread speed, fire risk intensity and flying fire distance. The fire-to-fire method often requires a large number of field experiments, and it takes a long time to establish the model. The statistical analysis method is often based on static factors (factors that do not change with time in a short period of time) such as weather conditions, topography, number of times, and human activities during historical fire periods to determine the influence relationship between each factor and wildfire. The statistical analysis method is currently a method with strong applicability and wide application for comprehensive assessment of wildfire risk. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a static wildfire risk assessment method and system based on multi-source data and remote sensing technology to address the deficiencies in the above-mentioned prior art, so as to solve the technical problem of accurate spatiotemporal monitoring of wildfire risks under specific monthly and seasonal differences.

[0008] The present invention adopts the following technical solutions: The static wildfire risk assessment method based on multi-source data and remote sensing technology includes the following steps: Establish an indicator system for the target area and collect data; Preprocess the collected data; The impact factors and indicator weights are calculated using the preprocessed data, the spatial and temporal distribution of the calculation results of different impact factors is determined, and the comprehensive factor results are calculated for each single impact factor according to the set weights; The wildfire risk is evaluated based on the comprehensive factor results obtained, and dynamic monitoring of the wildfire risk value is achieved.

[0009] Preferably, for the research target area, geospatial data, meteorological station observation data, remote sensing land use data and vegetation data are collected, wherein the meteorological data includes temperature, specific humidity, wind speed and wind direction.

[0010] Preferably, the preprocessing includes resampling, interpolation and clipping, and normalizing positive indicators and negative indicators of all data according to the constructed evaluation index system.

[0011] Preferably, the positive indicator is normalized:

[0012] Negative indicator normalization:

[0013] in, is the original value of the indicator; is the maximum value of the original value of the indicator; The minimum value of the original indicator value.

[0014] Preferably, the positive indicators include distance from water body, slope, average daily temperature, maximum daily temperature and minimum daily temperature, and the rest are negative indicators.

[0015] Preferably, the calculation result of the comprehensive factor is specifically: The entropy weight method and hierarchical analysis method are combined to confirm the indicator weights and obtain the weights of each influencing factor; the influencing factors and weights are calculated to obtain the spatiotemporal distribution of the calculation results of different influencing factors, and the comprehensive factor results are calculated for each single influencing factor according to the set weights.

[0016] Preferably, the wildfire risk is assessed based on the obtained comprehensive factor results as follows: The human factor evaluation index, terrain factor evaluation index, climate factor evaluation index and vegetation factor evaluation index are calculated separately; after obtaining each individual evaluation index, a weighted summary is performed to obtain a comprehensive risk assessment result.

[0017] Preferably, the comprehensive risk assessment results , as follows:

[0018] in, L(X), B(X), S(X), P(X) are the evaluation index of human factors, terrain factors, climate factors and vegetation factors respectively, and W l , W b , W s , W p , which respectively represent the weight of the human factor evaluation index, the weight of the terrain factor evaluation index, the weight of the climate factor evaluation index, and the weight of the vegetation factor evaluation index.

[0019] Preferably, the human factor evaluation index L(X) :

[0020] Topographic factor evaluation index B(X) :

[0021] Climate Factor Evaluation Index S(X) :

[0022] Vegetation factor evaluation index P(X) :

[0023] in, is the weight of each indicator; L in (X), B in (X), S in (X) and P in (X) is the normalized value of each index.

[0024] In a second aspect, an embodiment of the present invention provides a static wildfire risk assessment system based on multi-source data and remote sensing technology, including: Build modules, establish indicator systems for target areas and collect data; A processing module pre-processes the collected data; The calculation module uses the obtained data to calculate the impact factors and indicator weights, obtains the temporal and spatial distribution of the calculation results of different impact factors, and calculates the comprehensive factor results of each single impact factor according to the set weight; The evaluation module evaluates wildfire risk based on the comprehensive factor results obtained, and realizes dynamic monitoring of wildfire risk values.

[0025] Preferably, in the calculation module, the result of calculating the comprehensive factor is specifically: The index weights are confirmed by combining the entropy weight method and the hierarchical analysis method to obtain the weight of each influencing factor; the influencing factors and weights are calculated to obtain the spatiotemporal distribution of the calculation results of different influencing factors, and the comprehensive factor results are calculated for each single influencing factor according to the set weights; The wildfire risk is assessed based on the comprehensive factor results obtained: The human factor evaluation index, terrain factor evaluation index, climate factor evaluation index and vegetation factor evaluation index are calculated separately; after obtaining each individual evaluation index, a weighted summary is performed to obtain a comprehensive risk assessment result.

[0026] Preferably, the comprehensive risk assessment results , as follows:

[0027] in, L(X), B(X), S(X), P(X) are the evaluation index of human factors, terrain factors, climate factors and vegetation factors respectively, and W l , W b , W s , W p , which respectively represent the weight of the human factor evaluation index, the weight of the terrain factor evaluation index, the weight of the climate factor evaluation index, and the weight of the vegetation factor evaluation index.

[0028] Preferably, the human factor evaluation index L(X) :

[0029] Topographic factor evaluation index B(X) :

[0030] Climate Factor Evaluation Index S(X) :

[0031] Vegetation factor evaluation index P(X) :

[0032] in, is the weight of each indicator; L in (X), B in (X), S in (X) and P in (X) is the normalized value of each index.

[0033] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned static wildfire risk assessment method based on multi-source data and remote sensing technology when executing the computer program.

[0034] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned static wildfire risk assessment method based on multi-source data and remote sensing technology.

[0035] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned static wildfire risk assessment method based on multi-source data and remote sensing technology when executing the computer program.

[0036] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned static wildfire risk assessment method based on multi-source data and remote sensing technology.

[0037] Compared with the prior art, the present invention has at least the following beneficial effects: A static wildfire risk assessment method based on multi-source data and remote sensing technology comprehensively considers natural conditions such as regional topography, vegetation, climate, and the potential impact of human activities, clarifies the key influencing factors of wildfire risk assessment, and constructs a scientific and reasonable indicator system to ensure the comprehensiveness and accuracy of the assessment, providing theoretical support for subsequent analysis.

[0038] Furthermore, the occurrence of wildfire risk is closely related to a variety of natural and human factors, such as the higher probability of vegetation burning in high-slope areas and the greater likelihood of fires being triggered by dry climates. Comprehensive collection of multi-source data can provide comprehensive input data for multi-factor analysis. Remote sensing technology is particularly suitable for the rapid collection of large-scale data, such as topography, vegetation cover index (NDVI), surface temperature, etc.

[0039] Furthermore, to improve the consistency and availability of data, different data sources may have differences in spatial resolution, temporal resolution or coordinate system. Preprocessing solves these problems through resampling, interpolation and other methods to make the data have a unified standard. The preprocessing step can effectively integrate these heterogeneous data, improve data compatibility, and ensure that the data can be accurately aligned and applied to the same spatial analysis model.

[0040] Furthermore, by standardizing and formatting the data, we can ensure that data from different sources can be analyzed on the same platform. Standardization makes the influencing factors of different scales and units comparable, laying the foundation for multi-factor weighted analysis.

[0041] Furthermore, the relative importance of each indicator to wildfire risk is determined, and the contribution of different factors to the comprehensive risk is quantified to ensure the scientific nature of weight allocation. When using methods such as the analytic hierarchy process (AHP) to determine weights, expert knowledge is relied upon to construct a pairwise comparison matrix to reflect the relative importance of factors; while data-driven methods such as the entropy method calculate weights based on the distribution characteristics of factor data to reduce subjectivity.

[0042] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0043] In summary, the present invention achieves the comprehensiveness, accuracy and dynamism of wildfire risk assessment by combining multi-source data and remote sensing technology; through scientific preprocessing and standardization, reasonable weight distribution, and accurate factor comprehensive calculation, the reliability and practicality of the assessment results are ensured. The introduction of these innovative steps makes the present invention more adaptable and more accurate in wildfire risk assessment, especially suitable for large-scale, multi-factor complex environment analysis, and can provide important support for wildfire management and disaster prevention and mitigation.

[0044] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 is a flow chart of the method of the present invention; Figure 2 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 3 The block diagram is a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0049] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0050] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0051] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0052] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0053] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0054] The present invention provides a static wildfire risk assessment method based on multi-source data and remote sensing technology. First, for the research target area, key geospatial data, meteorological station observation data, remote sensing land use data, vegetation, etc. are collected, wherein the meteorological data include data including but not limited to temperature, specific humidity, wind speed, wind direction, etc. Secondly, the collected data are processed, including resampling, interpolation, clipping, etc. Since the units of different data are quite different, all data are normalized for positive indicators and negative indicators according to the evaluation index system. At the same time, the entropy weight method and the hierarchical analysis method are combined to confirm the indicator weights, and the weights of each influencing factor are obtained. Next, the influencing factors and weights are calculated according to the formula to obtain the spatiotemporal distribution of the calculation results of different influencing factors, and the comprehensive factor results are calculated for each single influencing factor according to a certain weight.

[0055] Finally, the meteorological factors, land use and vegetation index of different years and months are input to calculate the spatiotemporal change assessment of wildfire risk in the study area. The goal of the entire technical process is to obtain multi-source data, analyze the comprehensive results of single factors and multiple factors affecting wildfire risk through data processing, and finally realize dynamic monitoring of wildfire risk value.

[0056] The present invention provides a static wildfire risk assessment method based on multi-source data and remote sensing technology, comprising the following steps: S1. Establish an indicator system and collect data; The influencing factors of wildfire disasters mainly include four aspects: human, topography, climate and vegetation, with a total of 14 indicator layers, as shown in Table 1: Table 1 Wildfire disaster risk assessment index table

[0057] Among them, the land use data comes from the MCD12Q1 land use product of the IGBP classification standard with a resolution of 250m; The distance to roads, settlements and water bodies among the human factors were calculated based on the Euclidean distance function of ArcGIS spatial distribution; The terrain data comes from the geospatial data cloud platform and is clipped in ArcGIS software to obtain DEM data. Then, the slope and aspect indicators are calculated using spatial analysis tools. Daily meteorological factors were interpolated from meteorological station data using the spline interpolation method, and monthly averages were calculated for subsequent evaluation; The LAI data comes from the 8-day MCD15A2H data with a resolution of 250m, and the average is used to synthesize the monthly LAI data set.

[0058] S2, processing the data collected in step S1; Since the data resolutions obtained are different, the data sources need to be resampled to the same resolution (30m). The resampling method uses the raster resampling function of ArcGIS.

[0059] In addition, in order to eliminate the influence of different dimensions and variable types on wildfire risk, the present invention distinguishes the above wildfire impact factors into positive and negative indicators and performs normalization during the calculation process, and the positive indicators are normalized: (1) Negative indicator normalization: (2) in, is the original value of the indicator; is the maximum value of the original value of the indicator; The minimum value of the original indicator value.

[0060] Among the indicators, distance from water bodies, slope, daily average temperature, daily maximum temperature and daily minimum temperature are positive indicators. Areas far from water bodies have low soil moisture, which is prone to wildfires. The slope describes the inclination of the ground. Affected by factors such as gravity, vegetation has low water retention capacity in places with large slopes, soil erosion is more serious, and the risk of wildfires is higher. The higher the daily average temperature, daily maximum temperature and daily minimum temperature, the higher the risk of wildfires. Other values ​​are negative indicators.

[0061] S3, using the data obtained in step S2 to calculate the indicator weight; The indicator weight calculation mainly adopts the weight of the combination of entropy weight method and hierarchical analysis method; The entropy weight method is an objective weighting method that determines the weight of indicators based on the amount of information contained in each indicator value. The smaller the entropy of an indicator, the greater the degree of variation of the indicator value, the more information it provides, the greater the role it plays in the comprehensive evaluation, and the greater the weight of the indicator should be.

[0062] AHP is a systematic analysis method that combines qualitative and quantitative methods to determine the subjective weights of each indicator. After the subjective weights are obtained by AHP method and the objective weights are obtained by entropy weight method, the combined weights are obtained by the mean method, considering that AHP and AHP method have the same importance.

[0063] S4. Evaluate wildfire risk based on the indicator weights obtained in step S3.

[0064] (1) Factor evaluation Calculating each individual evaluation index is the basis for comprehensive evaluation. The individual evaluation index can provide a clear quantitative basis for complex multi-indicator evaluation and clearly display the performance of each indicator.

[0065] Human factors influence: (3) Influence of terrain factors: (4) Impact of climate factors: (5) Vegetation factors: (6) Among them, L(X), B(X), S(X) and P(X) are the individual evaluation indexes (human factors, terrain factors, climate factors and vegetation factors); is the weight of each indicator; L in (X), B in (X), S in (X) and P in (X) is the normalized value of each index.

[0066] By calculating the indicators and weights through the above formula, each individual evaluation indicator can be obtained. By quantitatively displaying each indicator, the risk assessment results of each individual indicator can be obtained.

[0067] (2) Comprehensive evaluation By weighting and summarizing each individual index, a comprehensive evaluation result can be obtained, thereby making an accurate judgment on the overall situation of the object. After obtaining each individual evaluation index, it is brought into the following formula for comprehensive evaluation: (7) Where: R(X) represents comprehensive risk assessment, L(X), B(X), S(X), P(X) are the evaluation indexes of human factors, terrain factors, climate factors and vegetation factors respectively, and W l , W b , W s , W p , respectively represent the weights of the corresponding factors, and the result of risk assessment is obtained by calculation.

[0068] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0069] In another embodiment of the present invention, a static wildfire risk assessment system based on multi-source data and remote sensing technology is provided. The system can be used to implement the above-mentioned static wildfire risk assessment method based on multi-source data and remote sensing technology. Specifically, the static wildfire risk assessment system based on multi-source data and remote sensing technology includes a construction module, a processing module, a calculation module and an evaluation module.

[0070] Among them, the construction module establishes the indicator system of the target area and collects data; A processing module pre-processes the collected data; The calculation module uses the obtained data to calculate the impact factors and indicator weights, obtains the temporal and spatial distribution of the calculation results of different impact factors, and calculates the comprehensive factor results of each single impact factor according to the set weight; The calculation results of the comprehensive factors are as follows: The index weights are confirmed by combining the entropy weight method and the hierarchical analysis method to obtain the weight of each influencing factor; the influencing factors and weights are calculated to obtain the spatiotemporal distribution of the calculation results of different influencing factors, and the comprehensive factor results are calculated for each single influencing factor according to the set weights; The wildfire risk is assessed based on the comprehensive factor results obtained: The human factor evaluation index, terrain factor evaluation index, climate factor evaluation index and vegetation factor evaluation index are calculated separately; after obtaining each individual evaluation index, a weighted summary is performed to obtain a comprehensive risk assessment result.

[0071] Comprehensive risk assessment results , as follows:

[0072] in, L(X), B(X), S(X), P(X) are the evaluation index of human factors, terrain factors, climate factors and vegetation factors respectively, and W l , W b , W s , W p , which respectively represent the weight of the human factor evaluation index, the weight of the terrain factor evaluation index, the weight of the climate factor evaluation index, and the weight of the vegetation factor evaluation index.

[0073] Human Factors Evaluation Index L(X) :

[0074] Topographic factor evaluation index B(X) :

[0075] Climate Factor Evaluation Index S(X) :

[0076] Vegetation factor evaluation index P(X) :

[0077] in, is the weight of each indicator; L in (X), B in (X), S in (X) and P in (X) is the normalized value of each index.

[0078] The evaluation module evaluates wildfire risk based on the comprehensive factor results obtained, and realizes dynamic monitoring of wildfire risk values.

[0079] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, graphics processors (GPU), tensor processors (TPU), digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of a static wildfire risk assessment method based on multi-source data and remote sensing technology, including: Establish an indicator system for the target area and collect data; pre-process the collected data; use the pre-processed data to calculate the influencing factors and indicator weights, determine the spatiotemporal distribution of the calculation results of different influencing factors, and calculate the comprehensive factor results for each single influencing factor according to the set weights; evaluate the wildfire risk based on the obtained comprehensive factor results, and realize dynamic monitoring of wildfire risk values.

[0080] See also Figure 2 , the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the static wildfire risk assessment method based on multi-source data and remote sensing technology in the embodiment is implemented. To avoid repetition, it is not described here one by one. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the static wildfire risk assessment system based on multi-source data and remote sensing technology in the embodiment are implemented. To avoid repetition, it is not described here one by one.

[0081] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 2This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0082] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, graphics processing units (GPU), tensor processing units (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0083] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0084] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0085] See also Figure 3 The terminal device is an electronic device 600, which is in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0086] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0087] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0088] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0089] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0090] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0091] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0092] The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the static wildfire risk assessment method based on multi-source data and remote sensing technology in the above embodiment; the processor may load and execute the following steps: Establish an indicator system for the target area and collect data; pre-process the collected data; use the pre-processed data to calculate the influencing factors and indicator weights, determine the spatiotemporal distribution of the calculation results of different influencing factors, and calculate the comprehensive factor results for each single influencing factor according to the set weights; evaluate the wildfire risk based on the obtained comprehensive factor results, and realize dynamic monitoring of wildfire risk values.

[0093] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0094] The best embodiment of the present invention can be applied to wildfire safety risk analysis and monitoring and disaster prevention and mitigation facilities in power transmission channels. The following are specific application examples: Example 1: Study on the spatiotemporal pattern changes of static wildfire risk in Zhejiang Province Zhejiang Province's natural geographical conditions: Zhejiang Province has a complex terrain, mainly mountains and hills. The western part is mostly mountainous, while the eastern and coastal areas are plains and hills. Zhejiang Province has a subtropical monsoon climate with four distinct seasons and warm and humid weather. Zhejiang Province's geographical and climatic conditions make it have a certain risk of wildfires, especially in winter and spring and in dry and hot weather. Zhejiang Province has a high forest coverage rate and dense vegetation, especially subtropical evergreen broad-leaved forests. The terrain characteristics of mountains and hills make it easy for fires to spread under certain conditions, especially in strong winds.

[0095] Selection of evaluation indicators: The influencing factors of wildfire disasters analyzed in this study mainly include four aspects: human, topography, climate and vegetation, with a total of 14 indicator layers.

[0096] Table 2 Wildfire disaster risk assessment index table

[0097] The specific implementation steps are as follows: Step 1: Collect meteorological data from the meteorological department, including but not limited to temperature, relative humidity, wind speed, wind direction, etc., collect key geospatial data from the geographic information database, such as topographic maps, and collect remote sensing product data such as land use type and vegetation index; Step 2: Process the collected data, including resampling, interpolation, and cropping. Since the units of different data vary greatly, all data are normalized for positive and negative indicators according to the evaluation index system. At the same time, the entropy weight method and hierarchical analysis method are combined to confirm the indicator weights for Zhejiang Province and obtain the weights of each influencing factor; Step 3: Calculate the impact factor and weight according to the formula to obtain the spatiotemporal distribution of the calculation results of different impact factors in Zhejiang Province, and calculate the comprehensive factor result according to a certain weight for each single impact factor; Step 4: Input meteorological factors, land use and vegetation indexes of Zhejiang Province in different years and months to calculate the spatiotemporal change assessment of wildfire risk in the study area; Step 5: Based on the spatiotemporal risk pattern of the comprehensive wildfire assessment results, take necessary measures in a timely manner to focus on areas with high wildfire risk in Zhejiang Province and provide necessary risk prediction support to power companies.

[0098] In summary, the present invention provides a static wildfire risk assessment method and system based on multi-source data and remote sensing technology, which fully considers various factors causing wildfires, improves the accuracy of wildfire prediction, and reduces errors; realizes monthly spatiotemporal prediction of wildfire risks, and monitors the spatiotemporal changes and seasonal differences of wildfire risks; provides important decision-making support information for power infrastructure construction and wildfire disaster risk investigation, and helps to take timely measures to ensure power transmission, as well as the safety of life and property.

[0099] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be 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 in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in 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, which will not be repeated here.

[0100] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0101] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention 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 constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0102] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, 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.

[0103] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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.

[0104] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0105] 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 processes 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, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0106] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0107] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0109] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A static wildfire risk assessment method based on multi-source data and remote sensing technology, characterized in that: The following steps are involved: Establish an indicator system for the target area and collect data; Preprocess the collected data; The impact factors and indicator weights are calculated using the preprocessed data, the spatial and temporal distribution of the calculation results of different impact factors is determined, and the comprehensive factor results are calculated for each single impact factor according to the set weights; The wildfire risk is evaluated based on the comprehensive factor results obtained, and dynamic monitoring of the wildfire risk value is achieved.

2. The static wildfire risk assessment method based on multi-source data and remote sensing technology according to claim 1 is characterized in that: For the target area of ​​study, geospatial data, meteorological station observation data, remote sensing land use data and vegetation data are collected, among which meteorological data include temperature, specific humidity, wind speed and wind direction.

3. The static wildfire risk assessment method based on multi-source data and remote sensing technology according to claim 1 is characterized in that: Preprocessing includes resampling, interpolation and cropping, as well as normalization of positive and negative indicators of all data according to the constructed evaluation index system.

4. The static wildfire risk assessment method based on multi-source data and remote sensing technology according to claim 3 is characterized in that: Normalization of positive indicators: Negative indicator normalization: in, is the original value of the indicator; is the maximum value of the original value of the indicator; The minimum value of the original indicator value.

5. The static wildfire risk assessment method based on multi-source data and remote sensing technology according to claim 3 is characterized in that: Positive indicators include distance to water bodies, slope, average daily temperature, maximum daily temperature and minimum daily temperature, and the rest are negative indicators.

6. The static wildfire risk assessment method based on multi-source data and remote sensing technology according to claim 1 is characterized in that: The calculation results of the comprehensive factors are as follows: The entropy weight method and hierarchical analysis method are combined to confirm the indicator weights and obtain the weights of each influencing factor; the influencing factors and weights are calculated to obtain the spatiotemporal distribution of the calculation results of different influencing factors, and the comprehensive factor results are calculated for each single influencing factor according to the set weights.

7. The static wildfire risk assessment method based on multi-source data and remote sensing technology according to claim 6 is characterized in that: The wildfire risk is assessed based on the comprehensive factor results obtained: The human factor evaluation index, terrain factor evaluation index, climate factor evaluation index and vegetation factor evaluation index are calculated separately; after obtaining each individual evaluation index, a weighted summary is performed to obtain a comprehensive risk assessment result.

8. The static wildfire risk assessment method based on multi-source data and remote sensing technology according to claim 7 is characterized in that: Comprehensive risk assessment results , as follows: in, L(X), B(X), S(X), P(X) are the evaluation index of human factors, terrain factors, climate factors and vegetation factors respectively, and W l , W b , W s , W p , which respectively represent the weight of the human factor evaluation index, the weight of the terrain factor evaluation index, the weight of the climate factor evaluation index, and the weight of the vegetation factor evaluation index.

9. The static wildfire risk assessment method based on multi-source data and remote sensing technology according to claim 8 is characterized in that: Human Factors Evaluation Index L(X) : Topographic factor evaluation index B(X) : Climate Factor Evaluation Index S(X) : Vegetation factor evaluation index P(X) : in, is the weight of each indicator; L in (X), B in (X), S in (X) and P in (X) is the normalized value of each index.

10. A static wildfire risk assessment system based on multi-source data and remote sensing technology, characterized in that: include: Build modules, establish indicator systems for target areas and collect data; A processing module pre-processes the collected data; The calculation module uses the obtained data to calculate the impact factors and indicator weights, obtains the temporal and spatial distribution of the calculation results of different impact factors, and calculates the comprehensive factor results of each single impact factor according to the set weight; The evaluation module evaluates wildfire risk based on the comprehensive factor results obtained, and realizes dynamic monitoring of wildfire risk values.

11. The static wildfire risk assessment system based on multi-source data and remote sensing technology according to claim 10, characterized in that: In the calculation module, the results of calculating the comprehensive factors are as follows: The index weights are confirmed by combining the entropy weight method and the hierarchical analysis method to obtain the weight of each influencing factor; the influencing factors and weights are calculated to obtain the spatiotemporal distribution of the calculation results of different influencing factors, and the comprehensive factor results are calculated for each single influencing factor according to the set weights; The wildfire risk is assessed based on the comprehensive factor results obtained: The human factor evaluation index, terrain factor evaluation index, climate factor evaluation index and vegetation factor evaluation index are calculated separately; after obtaining each individual evaluation index, a weighted summary is performed to obtain a comprehensive risk assessment result.

12. The static wildfire risk assessment method based on multi-source data and remote sensing technology according to claim 11, characterized in that: Comprehensive risk assessment results , as follows: in, L(X), B(X), S(X), P(X) are the evaluation index of human factors, terrain factors, climate factors and vegetation factors respectively, and W l , W b , W s , W p , which respectively represent the weight of the human factor evaluation index, the weight of the terrain factor evaluation index, the weight of the climate factor evaluation index, and the weight of the vegetation factor evaluation index.

13. The static wildfire risk assessment method based on multi-source data and remote sensing technology according to claim 12, characterized in that: Human Factors Evaluation Index L(X) : Topographic factor evaluation index B(X) : Climate Factor Evaluation Index S(X) : Vegetation factor evaluation index P(X) : in, is the weight of each indicator; L in (X), B in (X), S in (X) and P in (X) is the normalized value of each index.

14. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 9.

15. A computing device, characterized in that: include: One or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for executing the method described in any one of claims 1 to 9.

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