Mountain fire risk analysis method based on multi-element grid fusion model

Abstract

The invention discloses a forest fire risk analysis method based on a multi-element grid fusion model, and relates to the field of forest fire analysis, and the method comprises the steps: S1, data collection, S2, data processing, S3, weight setting, S4, building of the multi-element grid fusion model, dividing forest fire risk values into different grades, and S5, calculation of forest fire risk values. The multi-element grid fusion model can determine the mountain fire risk level of each grid; and S5, equipment matching and result output: matching the edge information of the latitude and longitude coordinate information of the power grid equipment obtained in the step S1.4 with map grids, determining the forest fire grade of the equipment, and obtaining forest fire monitoring information, meteorological information monitored by an automatic meteorological station and a power grid company GIS according to the matching condition. The system automatically calculates forest fire risk information of the area where the power transmission and distribution line is located. The forest fire risk can be evaluated more accurately, and a scientific basis is provided for forest fire prevention and management.

Description

Technical Field

[0001] The present invention relates to the field of wildfire analysis, and more specifically, to a wildfire risk analysis method based on a multi-element raster fusion model. Background Art

[0002] Wildfire is a serious natural disaster that poses a huge threat to the ecological environment, people's lives and property safety, and social and economic development. Accurate assessment of wildfire risk is crucial to the formulation of effective wildfire prevention and management measures. Traditional wildfire risk assessment methods mainly rely on manual experience and simple statistical analysis, and there are problems such as inaccurate assessment results and poor timeliness. With the development of geographic information system (GIS) technology, more and more researchers have begun to use GIS technology to conduct wildfire risk assessment. However, large-scale professional geographic information software such as ARCGIS is complex to operate and has high hardware requirements, making it inconvenient for widespread application.

[0003] Therefore, this study constructed a comprehensive wildfire risk quantitative analysis model based on raster data of multiple element layers such as fire point density, combustible load, and vegetation type. Then, a Python program was independently developed to simulate large-scale professional geographic information analysis processes such as ARCGIS to achieve raster-level wildfire risk value calculation and wildfire risk level classification. Finally, the software matched the latitude and longitude coordinate data of power grid equipment (transmission line towers, etc.) and directly output the wildfire risk distribution segment data of the power grid equipment according to the template. Summary of the invention

[0004] The embodiment of the present invention provides a wildfire risk analysis method of a multi-factor raster fusion model, which uses multi-factor raster data to construct an accurate wildfire risk quantification model. By simulating the analysis process of large professional geographic information systems such as ARCGIS, it is possible to calculate the wildfire risk value at the raster level and divide the wildfire risk level accordingly. Finally, the method outputs the analysis results in an intuitive way, providing a scientific basis for wildfire risk assessment and management.

[0005] To achieve the above object, the present invention provides a wildfire risk analysis method based on a multi-factor raster fusion model, comprising:

[0006] S1. Data collection, including the following sub-steps:

[0007] S1.1. Collect fire point density data;

[0008] S1.2. Collect combustible load data;

[0009] S1.3. Collect vegetation types;

[0010] S1.4. Collect longitude and latitude coordinate information of power grid equipment;

[0011] S2. Data processing: calculating the fire point density, fuel load, and vegetation type risk based on the data collected in steps S1.1, S1.2, and S1.3;

[0012] S3. Weight setting, including the following sub-steps:

[0013] S3.1 Establish a wildfire risk hierarchy model, wherein the wildfire risk hierarchy model includes a target layer, a criterion layer, and a scheme layer;

[0014] S3.2. Construct a judgment matrix for each factor in the criterion layer, compare the factors pairwise, and determine their relative importance to the target layer;

[0015] S3.3. Calculate the eigenvector of the judgment matrix, each component of which is the weight of the corresponding factor;

[0016] S4. Establish a multi-factor raster fusion model to divide wildfire risk values ​​into different levels. The multi-factor raster fusion model can determine the wildfire risk level of each raster;

[0017] S5. Equipment matching and result output: The edge information of the longitude and latitude coordinate information of the power grid equipment obtained in step S1.4 is matched with the map grid to determine the wildfire level of the equipment. The wildfire monitoring information, the meteorological information monitored by the automatic weather station and the power grid company GIS system are obtained according to the matching situation. The system automatically calculates the wildfire risk information of the area where the transmission and distribution lines are located.

[0018] In particular, in the step S1.1, the fire point density data is collected, specifically through satellite remote sensing monitoring and ground monitoring; in the step S1.2, the combustible load data is collected, specifically through field investigation and model estimation; in the step S1.3, the vegetation type is collected, specifically through satellite remote sensing image interpretation and field investigation; in the step S1.4, the longitude and latitude coordinate information of the power grid equipment is collected, specifically through the geographic information system of the power grid company.

[0019] In particular, in step S2, the fire point density is calculated using the formula:

[0020]

[0021] Where C is the density of fire points, Q is the total number of fire points, and D is the total area of ​​the study area.

[0022] In particular, in step S2, the combustible load is calculated using the formula:

[0023] A=a*NDVI b (2);

[0025] In the formula, A is the fuel load, NDVI is the normalized difference vegetation index, which reflects the vegetation production status and vegetation coverage; a is the research factor, which reflects different vegetation types, climatic conditions, and topographic factors; b is the influence of NDVI on the fuel load.

[0026] In particular, in step S2, the risk of vegetation type number is calculated using the formula:

[0027] V=V b *I (3);

[0029] Where V is the risk of vegetation type, V b is the basic vegetation impact factor; I is the vegetation impact type impact value.

[0030] In particular, in step S3.2, the judgment matrix can be expressed as:

[0031]

[0032] The comparison results can be expressed by numbers 1-9 and their reciprocals, where 1 means that the two factors are equally important and 9 means that one factor is extremely important than the other.

[0033] In particular, in step S3.3, the eigenvector w of the judgment matrix is ​​calculated, and the vector is normalized to obtain the weight vector W, and the formula is:

[0034] W=(w1,w2,w3) (5);

[0035] Among them, w1, w2, w3 are the first eigenvector, the second eigenvector, and the third eigenvector respectively.

[0036] In particular, step S4 establishes a multi-factor raster fusion model as follows:

[0037] RI=w1*C q +w2*A p +w3*V e (6);

[0038] In the formula, RI is the comprehensive risk index, and q, p, and t are different index parameters used to adjust the degree of influence of each factor. These parameters can be adjusted according to the characteristics of actual data and analysis requirements; the wildfire risk value is divided into different levels to determine the wildfire risk level of each grid.

[0039] In particular, the wildfire risk level includes level 1, level 2, level 3, and level 4: when RI ≥ T h ; Secondary risk level: When T m ≤RI≤T h; Level 3 risk level: When T l ≤RI≤T m , Level 4 risk level: RI≤T l ; T h 、T m are the thresholds for high risk and medium risk respectively; RI is the comprehensive risk index.

[0040] The beneficial effect of the present invention is that the present invention proposes a method for wildfire risk analysis based on a multi-factor grid fusion model, which integrates multi-factor layer shp data such as fire point density, combustible load, vegetation type, and longitude and latitude coordinate information of power grid equipment (transmission line towers, etc.), performs a graded analysis on wildfire risks, and directly outputs the segment data of each wildfire risk level according to the template. Through this method, wildfire risks can be assessed more accurately, providing a scientific basis for wildfire prevention and management. At the same time, the method has the advantages of simple operation, low cost, strong timeliness, etc., and is convenient for wide application. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments are briefly introduced below.

[0042] Figure 1 A flow chart of a wildfire risk analysis method based on a multi-factor raster fusion model provided by the present invention. DETAILED DESCRIPTION

[0043] The technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiment 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.

[0044] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they cannot be understood as limitations on the present invention.

[0045] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0046] like Figure 1 As shown, the present invention provides a wildfire risk analysis method based on a multi-factor raster fusion model, comprising steps S1-S5:

[0047] S1. Data collection, including the following sub-steps:

[0048] S1.1. Collect fire point density data;

[0049] S1.2. Collect combustible load data;

[0050] S1.3. Collect vegetation types;

[0051] S1.4. Collect longitude and latitude coordinate information of power grid equipment;

[0052] In step S1.1, fire point density data is collected, specifically through satellite remote sensing monitoring and ground monitoring; in step S1.2, combustible load data is collected, specifically through field investigation and model estimation; in step S1.3, vegetation type is collected, specifically through satellite remote sensing image interpretation and field investigation; in step S1.4, longitude and latitude coordinate information of power grid equipment is collected, specifically through the geographic information system of the power grid company.

[0053] S2. Data processing: calculating the fire point density, fuel load, and vegetation type risk based on the data collected in steps S1.1, S1.2, and S1.3;

[0054] In step S2, the fire point density is calculated using the formula:

[0055]

[0056] Where C is the density of fire points, Q is the total number of fire points, and D is the total area of ​​the study area.

[0057] In step S2, the combustible load is calculated using the formula:

[0058] A=a*NDVI b (2);

[0060] In the formula, A is the fuel load, NDVI is the normalized difference vegetation index, which reflects the vegetation production status and vegetation coverage; a is the research factor, which reflects different vegetation types, climatic conditions, and topographic factors; b is the influence of NDVI on the fuel load.

[0061] In step S2, the risk of vegetation type number is calculated using the formula:

[0062] V=V b *I (3);

[0064] Where V is the risk of vegetation type, V b is the basic vegetation impact factor; I is the vegetation impact type impact value.

[0065] S3. Weight setting, including the following sub-steps:

[0066] S3.1 Establish a wildfire risk hierarchy model, wherein the wildfire risk hierarchy model includes a target layer, a criterion layer, and a scheme layer;

[0067] S3.2. Construct a judgment matrix for each factor in the criterion layer, compare the factors pairwise, and determine their relative importance to the target layer;

[0068] S3.3. Calculate the eigenvector of the judgment matrix, each component of which is the weight of the corresponding factor;

[0069] In step S3.2, the judgment matrix can be expressed as:

[0070]

[0071] The comparison results can be expressed by numbers 1-9 and their reciprocals, where 1 means that the two factors are equally important and 9 means that one factor is extremely important than the other.

[0072] In step S3.3, the eigenvector w of the judgment matrix is ​​calculated, and the vector is normalized to obtain the weight vector W, which is:

[0073] W=(w1,w2,w3) (5);

[0074] Among them, w1, w2, w3 are the first eigenvector, the second eigenvector, and the third eigenvector respectively.

[0075] S4. Establish a multi-factor raster fusion model to divide wildfire risk values ​​into different levels. The multi-factor raster fusion model can determine the wildfire risk level of each raster;

[0076] Step S4 establishes a multi-factor raster fusion model as follows:

[0077] RI=w1*C q +w2*A p +w3*V e (6);

[0078] In the formula, RI is the comprehensive risk index, and q, p, and t are different index parameters used to adjust the degree of influence of each factor. These parameters can be adjusted according to the characteristics of actual data and analysis requirements; the wildfire risk value is divided into different levels to determine the wildfire risk level of each grid.

[0079] The wildfire risk levels include level 1, level 2, level 3, and level 4. When RI ≥ T h ; Secondary risk level: When T m ≤RI≤T h ; Level 3 risk level: When T l ≤RI≤T m , Level 4 risk level: RI≤T l ; T h 、T m are the thresholds for high risk and medium risk respectively; RI is the comprehensive risk index.

[0080] S5. Equipment matching and result output: The edge information of the longitude and latitude coordinate information of the power grid equipment obtained in step S1.4 is matched with the map grid to determine the wildfire level of the equipment. The wildfire monitoring information, the meteorological information monitored by the automatic weather station and the power grid company GIS system are obtained according to the matching situation. The system automatically calculates the wildfire risk information of the area where the transmission and distribution lines are located.

[0081] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A wildfire risk analysis method based on a multi-factor raster fusion model, comprising: S1. Data collection, including the following sub-steps: S1.

1. Collect fire point density data; S1.

2. Collect combustible load data; S1.

3. Collect vegetation types; S1.

4. Collect longitude and latitude coordinate information of power grid equipment; S2. Data processing: calculating the fire point density, fuel load, and vegetation type risk based on the data collected in steps S1.1, S1.2, and S1.3; S3. Weight setting, including the following sub-steps: S3.1 Establish a wildfire risk hierarchy model, wherein the wildfire risk hierarchy model includes a target layer, a criterion layer, and a scheme layer; S3.

2. Construct a judgment matrix for each factor in the criterion layer, compare the factors pairwise, and determine their relative importance to the target layer; S3.

3. Calculate the eigenvector of the judgment matrix, each component of which is the weight of the corresponding factor; S4. Establish a multi-factor raster fusion model to divide wildfire risk values ​​into different levels. The multi-factor raster fusion model can determine the wildfire risk level of each raster; S5. Equipment matching and result output: The edge information of the longitude and latitude coordinate information of the power grid equipment obtained in step S1.4 is matched with the map grid to determine the wildfire level of the equipment. The wildfire monitoring information, the meteorological information monitored by the automatic weather station and the power grid company GIS system are obtained according to the matching situation. The system automatically calculates the wildfire risk information of the area where the transmission and distribution lines are located.

2. According to the wildfire risk analysis method of a multi-factor raster fusion model described in claim 1, in the step S1.1, the fire point density data is collected, specifically obtained through satellite remote sensing monitoring and ground monitoring; in the step S1.2, the combustible load data is collected, specifically obtained through field investigation and model estimation; in the step S1.3, the vegetation type is collected, specifically obtained through satellite remote sensing image interpretation and field investigation; in the step S1.4, the longitude and latitude coordinate information of the power grid equipment is collected, specifically obtained through the geographic information system of the power grid company.

3. According to the mountain fire risk analysis method of a multi-factor grid fusion model according to claim 1, in the step S2, the fire point density is calculated using the formula: Where C is the density of fire points, Q is the total number of fire points, and D is the total area of ​​the study area.

4. According to the wildfire risk analysis method of a multi-factor grid fusion model according to claim 1, in step S2, the combustible load is calculated using the formula: A=α*NDVI b (2) In the formula, A is the fuel load, NDVI is the normalized difference vegetation index, which reflects the vegetation production status and vegetation coverage; a is the research factor, which reflects different vegetation types, climatic conditions, and topographic factors; b is the influence of NDVI on the fuel load.

5. According to the method for analyzing wildfire risk using a multi-factor raster fusion model in claim 1, in step S2, the risk of vegetation type number is calculated using the formula: V=V b *I (3); Where V is the risk of vegetation type, V b is the basic vegetation impact factor; I is the vegetation impact type impact value.

6. According to the wildfire risk analysis method of a multi-factor grid fusion model according to claim 1, in step S3.2, the judgment matrix can be expressed as: The comparison results can be expressed by numbers 1-9 and their reciprocals, where 1 means that the two factors are equally important and 9 means that one factor is extremely important than the other.

7. According to the wildfire risk analysis method of a multi-factor raster fusion model according to claim 1, in the step S3.3, the eigenvector w of the judgment matrix is ​​calculated, and the vector is normalized to obtain the weight vector W, and the formula is: W=(w1,w2,w3) (5); in, w1, w2, w3 are the first eigenvector, the second eigenvector, and the third eigenvector respectively.

8. According to the method for analyzing wildfire risk using a multi-factor grid fusion model as claimed in claim 1, step S4 establishes a multi-factor grid fusion model as follows: RI=w1*C q +w2*A p +w3*V e (6); In the formula, RI is the comprehensive risk index, and q, p, and t are different index parameters used to adjust the degree of influence of each factor. These parameters can be adjusted according to the characteristics of actual data and analysis requirements; the wildfire risk value is divided into different levels to determine the wildfire risk level of each grid.

9. The wildfire risk analysis method of a multi-factor grid fusion model according to claim 1, wherein the wildfire risk level includes a first-level risk level, a second-level risk level, a third-level risk level, and a fourth-level risk level: when RI ≥ T h ; Secondary risk level: When T m ≤RI≤T h ; Level 3 risk level: When T l ≤RI≤T m , Level 4 risk level: RI≤T l ; T h , T m are the thresholds for high risk and medium risk respectively; RI is the comprehensive risk index.

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