CN115660428A - Forest and grassland fire risk assessment system based on geographic information - Google Patents
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Abstract
The invention discloses a forest and grassland fire risk assessment system based on geographic information, which comprises the steps of collecting forest region resource, weather and disaster data information, transmitting the collected data to a model construction system, respectively carrying out risk assessment and risk assessment on the modeled data, and carrying out data quantification on the risk assessment and the risk assessment to obtain the grade of a to-be-detected region. The method scientifically evaluates the dangerousness and disaster reduction capability of the forest and the grassland, provides data support for monitoring and early warning of forest and grassland fires and key hidden danger information, and carries out monitoring and early warning of the forest and grassland fires and disaster assessment.
Description
Technical Field
The invention relates to a fire risk assessment technology, in particular to a forest and grassland fire risk assessment system based on geographic information.
Background
In the prior art, fire prevention is difficult for places with large areas, such as forests and grasslands, and fire risk assessment is needed for early-stage work for prevention and control of forest fires. The fire risk assessment work firstly needs to establish an assessment index system, the establishment of a good index system depends on the accurate grasp of related concepts, and the specific fire risk assessment index system is established in different environmental occasions. The system has important influence on the occurrence and extinguishment of fire and casualties, and a fire risk assessment index system is required to be brought into, but the fire hazard caused by climate or local dryness is very difficult to assess, so that advance prevention cannot be realized, and the fire is difficult to fight due to large diffusion area when the fire occurs, so that the forest and grassland fire risk assessment system based on geographic information is required to facilitate early warning of the fire risk.
Disclosure of Invention
The invention aims to provide a forest and grassland fire risk assessment system based on geographic information.
In order to achieve the purpose, the invention is implemented according to the following technical scheme:
the invention comprises a data information acquisition unit, a model construction system and a risk evaluation unit, wherein the data information acquisition unit acquires vegetation, geography, weather, historical disaster and water area data of a forest or grassland area and transmits the acquired data to the model construction system, the model construction system carries out modeling according to the data acquired by the data information acquisition unit, the risk evaluation unit carries out risk evaluation according to a model of the model construction system, and the risk evaluation unit carries out risk evaluation specifically as follows:
constructing a forest fire risk assessment model:
h = P × D × E/(1 + R) where H is the risk of forest fire; p is the probability of occurrence; d is the vulnerability of the disaster-bearing body; e is exposition; r is disaster resistance. Because the space range is large, the fireproof capacity difference of all places is obvious, and in order to avoid overlarge calculation result difference, the denominator of the formula (1) is (1 + R);
comprehensive evaluation is carried out on all factors influencing the fire risk by adopting an analytic hierarchy process, each evaluation index is divided into 3 levels according to the size of categories and the membership relation, the priority among all indexes is determined by a grading method according to professional knowledge, a judgment matrix is constructed, and the relative importance degree of all elements in each layer relative to a certain element on the upper layer is represented. The maximum eigenvalue (λ max) of each judgment matrix is obtained, and the unit eigenvector corresponding to λ max is the weight of each index. Finally, determining the weight lambda of each index according to the contribution rate of each index to the fire risk j
When the matrixes are judged to be completely consistent, λ max = n; when the judgment matrix is not completely consistent, the lambda max is more than or equal to n. A randomness index (CR) is used as an index for the consistency check. The consistency checking method of the judgment matrix comprises the following steps:
CR＝CI/RI
CI＝(λ max -n)/(n-1)
in the formula, n is the order number of the judgment matrix; λ max is the maximum eigenvalue of the judgment matrix; RI is a constant that varies with the degree of the decision matrix. RI values corresponding to n values of 1, 2, 3, 4, 5, 6, 7 and 8 are 0, 0.58, 0.91, 1.12, 1.24, 1.32 and 1.41 respectively. When CR is less than 0.1, judging that the matrix achieves the satisfactory effect, otherwise, readjusting the element scale value until the consistency is satisfactory.
The data information acquisition unit acquires vegetation, geography and water area data through the unmanned aerial vehicle, and the unmanned aerial vehicle path planning needs to meet the following principle:
the unmanned aerial vehicle flight starting point is recorded as S, the ending point is recorded as T, and the task planning should obtain a path from the task starting point to the task ending point; when the unmanned aerial vehicle avoids a no-fly zone, the planned path is ensured to be optimized as much as possible, and the shortest flight distance requirement of the unmanned aerial vehicle is met;
the unmanned aerial vehicle path planning is mainly realized by the following steps;
environment modeling: according to the actual environment of the unmanned aerial vehicle; establishing a proper model in a simulation system; abstracting an actual physical space into a virtual space which can be solved by a mathematical model;
path searching: planning a task route from any starting point S to any terminal point T according to the task conditions; the path cost is shortest while task condition constraint and obstacle avoidance requirements are met;
firstly, inputting a flight starting point S of the unmanned aerial vehicle, and establishing two arrays C1 and C2 at an ending point T for storing information of nodes where the unmanned aerial vehicle passes.
D i ＝f i +g i
In the above formula f i Represents the effective distance traveled from the starting point S to the node i, wherein the effective distance traveled refers to the distance g of a route that has successfully avoided an obstacle i Represents the straight-line distance from the node i to the terminal point T, wherein the straight-line distance refers to the straight-line distance from the node i to the terminal point T when the obstacle is not considered, namely the residual minimum distance, which is a minimum estimated value D i Is f i And g i Represents the current minimum total cost value of node i.
The algorithm of the unmanned aerial vehicle comprises the following important steps:
and step1, inputting coordinates of a starting point and an end point, and storing the starting point into C1.
And step2, traversing the C1, finding out the node corresponding to the minimum Dmin, expanding the node, namely finding out the obstacle closest to the node, acquiring the node information of the obstacle, adding the available node of the obstacle into the C1, and moving the expanded point in the C1 to the C2.
And Step3, repeating Step2 until the line connecting the current expansion point and the terminal point does not pass through any obstacle any more, stopping when the terminal point can be directly reached, or stopping the circulation until the C1 is empty.
When the unmanned aerial vehicle acquires data, the unmanned aerial vehicle adopts an infrared camera to inspect and measure temperature, and the unmanned aerial vehicle comprises an abrupt heating source in a visible range, and if the temperature rise delta is more than or equal to 80% compared with the ambient temperature, the unmanned aerial vehicle judges that the temperature is abnormal
δ t ＝(τ 1 -τ 2 )/τ 1 ×100％＝(T 1 -T 2 )/(T 1 -T 0 )×100％
Wherein:
τ 1 and T 1 -temperature rise and temperature of the heat generating spot;
τ 2 and T 2 -temperature rise and temperature of the normal corresponding point;
T 0 -temperature of the ambient temperature reference subject.
The model building system builds a map model as follows: the terrain structure, vegetation and water area in the environment are respectively represented by models with different shapes, and the models are represented as follows:
z 1 (x,y)＝h i ,r＜R i
z 2 (x,y)＝R i -r 2 ,r＜R i
z(x,y)＝max(z 1 (x,y),z 2 (x,y))
wherein z (x, y) is the map midpoint, the height value of (x, y), z 1 (x, y) is a terrain height value, z 2 (x, y) is the vegetation height value, (x) i ,y i ) Is the coordinates of the center point of the ith terrain,
is the distance from the model midpoint (x, y) to the center of the ith terrain or vegetation, h i Height of vegetation, R i Is the radius of the footprint of the vegetation.
The beneficial effects of the invention are:
the invention relates to a forest and grassland fire risk assessment system based on geographic information. The method scientifically evaluates the danger of the forest and the grassland, has strong and weak disaster reduction capability, provides data support for monitoring and early warning of the forest and the grassland fire and key hidden danger information, and develops monitoring and early warning of the forest and the grassland fire and disaster assessment.
Drawings
Figure 1 is a drone routing diagram of the present invention;
fig. 2 is a drone routing diagram of an embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and specific embodiments, which are provided herein for the purpose of illustrating the invention and are not to be construed as limiting the invention.
The system comprises a data information acquisition unit, a model construction system and a risk assessment unit, wherein the data information acquisition unit acquires vegetation, geography, weather, historical disaster and water area data of a forest or grassland area and transmits the acquired data to the model construction system, the model construction system carries out modeling according to the data acquired by the data information acquisition unit, and the risk assessment unit carries out risk assessment according to a model of the model construction system.
The data information acquisition unit acquires vegetation, geography and water area data through the unmanned aerial vehicle, and the unmanned aerial vehicle path planning needs to meet the following principles:
the unmanned aerial vehicle flight starting point is recorded as S, the ending point is recorded as T, and the task planning should obtain a path from the task starting point to the task ending point; when the unmanned aerial vehicle avoids a no-fly zone, the planned path is ensured to be optimized as much as possible, and the shortest flight distance requirement of the unmanned aerial vehicle is met;
the unmanned aerial vehicle path planning is mainly realized by the following steps; and (3) environment modeling: according to the actual environment of the unmanned aerial vehicle; establishing a proper model in the simulation system; abstracting an actual physical space into a virtual space which can be solved by a mathematical model; path searching: planning a task route from any starting point S to any terminal point T according to the task conditions; the path cost is shortest while the task condition constraint and the obstacle avoidance requirement are met;
firstly, inputting an unmanned aerial vehicle flight starting point S, and establishing two arrays C1 and C2 at an end point T, wherein the two arrays are used for storing information of nodes where the unmanned aerial vehicle passes.
D i ＝f i +g i
In the above formula f i Represents the effective distance traveled from the starting point S to the node i, wherein the effective distance traveled refers to the distance g of the route that has successfully avoided the obstacle i Represents the straight-line distance from the node i to the terminal point T, wherein the straight-line distance refers to the straight-line distance from the node i to the terminal point T when the obstacle is not considered, namely the residual minimum distance, which is a minimum estimated value D i Is f i And g i Represents the current minimum total cost value of node i.
The algorithm steps of the unmanned aerial vehicle are as follows:
and step1, inputting coordinates of a starting point and an end point, and storing the starting point into C1.
And step2, traversing the C1, finding out the node corresponding to the minimum Dmin, expanding the node, namely finding out the obstacle closest to the node, acquiring the node information of the obstacle, adding the available node of the obstacle into the C1, and moving the expanded point in the C1 to the C2.
And Step3, repeating Step2 until the connecting line of the current expansion point and the terminal point does not pass through any obstacle any more, stopping when the terminal point is directly reached, or stopping the circulation until the C1 is empty.
When the unmanned aerial vehicle acquires data, the unmanned aerial vehicle adopts an infrared camera to inspect and measure temperature, the infrared camera comprises an abrupt heating source in a visible range, and if the temperature rise delta is more than or equal to 80% compared with the ambient temperature, the temperature is judged to be abnormal
δ t ＝(τ 1 -τ 2 )/τ 1 ×100％＝(T 1 -T 2 )/(T 1 -T 0 )×100％
Wherein:
τ 1 and T 1 -temperature rise and temperature of the heat generating spot;
τ 2 and T 2 -temperature rise and temperature of the normal corresponding point;
T 0 -temperature of the ambient temperature reference subject.
As shown in fig. 1: assuming that 3 existing polygonal obstacles, 3 circular obstacles, an initial point of S and a terminal point of T are created C1 and C2, and the stored node information comprises f value, g value, node coordinate and upper node coordinate; starting from a starting point S, storing S into C1, traversing C1 and finding D min The corresponding node S is expanded, barrier nodes P4, P5 and P6 which are closest to the S are found, available nodes in the nodes P4, P5 and P6 are added into the cellular array C1, and the expanded point S in the C1 is moved to the C2; go through C1 again to find D min The corresponding node P6 continuously searches for the nearest barrier to the point P6, and stores the available node information of the barrier into the node C1, wherein when the found nearest barrier to the point P6 is a circle, two left and right tangent lines between the point P6 and the terminal point T are obtained, and the available tangent points are stored into the node C1 as alternative points; and traversing again and repeating the steps until the connecting line of the current expansion point and the terminal point T does not pass through any obstacle any more, and then obtaining the optimal path from S to T.
The method comprises the steps of carrying out simulation experiments on an algorithm, wherein a PC system used in the experiments is Windows10, a processor model is Inte1 (R) Core (TM) i7-8700, the master frequency is 3.19GHz, the development environment is Matlab2016b, the simulation experiment process is realized through Matlab programming language, and Matlab is used for drawing a planning environment and planned flight paths.
The model building system builds a map model as follows: the terrain structure, vegetation and water area in the environment are respectively represented by models with different shapes, and the models are represented as follows:
z 1 (x,y)＝h i ,r＜R i
z 2 (x,y)＝R i -r 2 ,r＜R i
z(x,y)＝max(z 1 (x,y),z 2 (x,y))
wherein z (x, y) is the map midpoint, the height value of (x, y), z 1 (x, y) is a terrain height value, z 2 (x, y) is the vegetation height value, (x) i ,y i ) Is the coordinates of the center point of the ith terrain,
is the distance from the model midpoint (x, y) to the center of the ith terrain or vegetation, h i Height of vegetation, R i Is the radius of the footprint of the vegetation.
When the path planning is carried out, the fitness function is used for evaluating the quality degree of the generated path and is also a basis for iterative evolution of the algorithm population, and the quality of the fitness function determines the efficiency and quality of algorithm execution. In order to better judge the path quality, the fitness function is constructed by comprehensively considering the length cost, the obstacle risk cost and the path smoothness of the path. Assuming C paths, each consisting of n points, there are g spherical and cylindrical obstacles in the environment in total.
The risk assessment unit specifically performs risk assessment by:
constructing a forest fire risk assessment model:
H＝P×D×E/(1+R)
h is the risk of forest fire; p is the probability of occurrence; d is the vulnerability of the disaster-bearing body; e is exposition; r is the disaster resistance. Because the space range is large, the difference of the fireproof capacity of each place is obvious, and the difference of the calculation results is too large, the denominator of the formula (1) is (1 + R);
comprehensive evaluation is carried out on all factors influencing the fire risk by adopting an analytic hierarchy process, each evaluation index is divided into 3 levels according to the size of categories and the membership relation, the priority among all indexes is determined by a grading method according to professional knowledge, a judgment matrix is constructed, and the relative importance degree of all elements in each layer relative to a certain element on the upper layer is represented. The maximum characteristic value (lambda max) of each judgment matrix is obtained, and the unit characteristic vector corresponding to the lambda max is eachThe weight of the index. Finally, determining the weight lambda of each index according to the contribution rate of each index to the fire risk j
When the matrixes are judged to be completely consistent, λ max = n; when the judgment matrix is not completely consistent, the lambda max is more than or equal to n. The randomness index (CR) was used as an index for the consistency check. The consistency checking method of the judgment matrix comprises the following steps:
CR＝CI/RI
CI＝(λ max -n)/(n-1)
in the formula, n is the order number of the judgment matrix; λ max is the maximum eigenvalue of the judgment matrix; RI is a constant that varies with the degree of the decision matrix. RI values corresponding to n values of 1, 2, 3, 4, 5, 6, 7 and 8 are 0, 0.58, 0.91, 1.12, 1.24, 1.32 and 1.41 respectively. When CR is less than 0.1, judging that the matrix achieves the satisfactory effect, otherwise, readjusting the element scale value until the element scale value has satisfactory consistency.
Grading of forest fire risks
The forest fire risk assessment indexes are firstly normalized, then the occurrence probability, the fragility, the disaster prevention capability and the forest fire risk index of the forest fire are respectively calculated, the calculation results of the 4 climate scenes are normalized again, and the classification is carried out by a natural breakpoint classification method. The method is classified by the minimum variance, the difference between the classes is obvious, and the difference inside the classes is small. And determining the grading threshold value of each index according to the calculation result of the observation time period, and respectively classifying the data lower than the minimum value and higher than the maximum value into the lowest grade and the highest grade. And dividing results according to the forest fire risk grades under the historical and future 4 climate scenes.
The possibility of forest fires, the vulnerability, the exposure and the disaster resistance are selected, and the fire danger index and the forest fire occurrence density are selected to calculate the possibility of forest fires. The combustible types are divided according to the vegetation types, the weights of different vegetation types are determined according to the combustibility of the vegetation types, the forests are divided into evergreen coniferous forests, evergreen broadleaf forests, deciduous coniferous forests, deciduous broadleaf forests, coniferous mixed forests and shrubs, and the weights relative to the vulnerability index are respectively 0.4221, 0.0254, 0.2729, 0.0407, 0.0811 and 0.1578 (Table 1). The disaster-resistant capability is determined according to the historical experience of fighting forest fires and the indexes of available vehicles, airplanes and the like.
TABLE 1 Chinese forest fire Risk evaluation index System and weight
Comprehensively evaluating according to a forest fire risk evaluation model and a forest fire risk observed historically, wherein regions with high forest fire probability and high forest fire probability are mainly distributed in northeast and southwest regions and respectively account for 13.1% and 4.0% of the forest area; areas with high fragility and high grade are mainly distributed in the great XingAnLing area and the Changbai mountain area in the northeast, and the southern and southwest forest areas, and account for 6.8 percent and 22.4 percent respectively; while the areas with high level of exposure are mainly located in great and lesser Khingan areas in northeast, and 16.6% and 0.4% respectively. The disaster resistance is mainly calculated according to the statistical data of provinces (regions), major forest fires occur in the provinces such as Fujian province, zhejiang province and Guizhou province, local fire protection agencies have abundant fire suppression experiences, and historically, the average unit area forest in the fire suppression process has more available fire suppression resources, so the disaster resistance of the regions is stronger. The comprehensive evaluation of forest fire risk indexes in China has high risk and high-risk areas mainly including northeast great XingAnLing and Changbai mountain areas, southwest and southern areas. The areas with moderate or low risk of fire represent 72.7%, and the areas with high and very high risk represent 21.2% and 6.2%, respectively (table 2).
TABLE 2 area proportion of forest fire danger in each grade
Areas with low, low and medium risk of forest fires are acceptable risks, areas with high risk and high risk level are key fire-prevention areas, necessary control measures need to be taken according to forest fire management targets, the prevention and control capacity of forest fires is improved, and property and economic losses possibly caused by potential forest fires are reduced.
The technical solution of the present invention is not limited to the above-mentioned specific embodiments, and all technical modifications made according to the technical solution of the present invention fall within the protection scope of the present invention.
Claims (4)
1. The utility model provides a forest and grassland fire risk assessment system based on geographic information which characterized in that: including data information acquisition unit, model construction system and risk evaluation unit, data information acquisition unit gathers vegetation, geography, weather, historical disaster, waters data in forest or grassland area, with the data transmission who gathers extremely model construction system, model construction system basis data acquisition unit gathers the data and models, risk evaluation unit basis model construction system's model carries out the risk assessment, risk evaluation unit risk assessment specifically does:
constructing a forest fire risk assessment model:
H＝P×D×E/(1+R)
in the formula: h is the risk of forest fire; p is the probability of occurrence; d is the vulnerability of the disaster-bearing body; e is exposition; r is the disaster resistance. Because the space range is large, the difference of the fireproof capacity of each place is obvious, and the difference of the calculation results is too large, the denominator of the formula (1) is (1 + R);
comprehensively judging factors influencing the fire risk by adopting an analytic hierarchy process, dividing each judgment index into 3 levels according to the size of categories and the membership, determining the priority among the indexes by a grading method according to professional knowledge, constructing a judgment matrix, representing the relative importance degree of each element in each layer relative to a certain element on the upper layer, solving the maximum characteristic value (lambda max) of each judgment matrix, wherein the unit characteristic vector corresponding to the lambda max is the weight of each index, and finally determining the weight lambda of each index according to the contribution rate of each index to the fire risk j
When the matrixes are judged to be completely consistent, λ max = n; when the judgment matrix is not completely consistent, the lambda max is more than or equal to n. A randomness index (CR) is used as an index for the consistency check. The consistency checking method for the judgment matrix comprises the following steps:
CR＝CI/RI
CI＝(λ max -n)/(n-1)
in the formula: n is the order of the judgment matrix; λ max is the maximum eigenvalue of the judgment matrix; RI is a constant that varies with the degree of the decision matrix. RI values corresponding to n values of 1, 2, 3, 4, 5, 6, 7 and 8 are 0, 0.58, 0.91, 1.12, 1.24, 1.32 and 1.41 respectively. When CR is less than 0.1, judging that the matrix achieves the satisfactory effect, otherwise, readjusting the element scale value until the consistency is satisfactory.
2. The forest and grassland fire risk assessment system based on geographical information according to claim 1, wherein: the data information acquisition unit acquires vegetation, geography and water area data through the unmanned aerial vehicle, and the unmanned aerial vehicle path planning needs to meet the following principles:
recording the starting point of the unmanned aerial vehicle flight as S, recording the ending point as T, and obtaining a path from the task starting point to the task ending point by the task planning; when the unmanned aerial vehicle avoids a no-fly zone, the planned path is ensured to be optimized as much as possible, and the shortest flight distance requirement of the unmanned aerial vehicle is met;
the unmanned aerial vehicle path planning is mainly realized by the following steps; environment modeling: according to the actual environment of the unmanned aerial vehicle; establishing a proper model in the simulation system; abstracting an actual physical space into a virtual space which can be solved by a mathematical model; path searching: planning a task route from any starting point S to any terminal point T according to the task conditions; the path cost is shortest while the task condition constraint and the obstacle avoidance requirement are met;
firstly, inputting an unmanned aerial vehicle flight starting point S, and establishing two arrays C1 and C2 at an end point T, wherein the two arrays are used for storing information of nodes where the unmanned aerial vehicle passes.
D i ＝f i +g i
In the above formula f i Represents the effective distance traveled from the starting point S to the node i, wherein the effective distance traveled refers to the distance g of a route that has successfully avoided an obstacle i Represents the straight-line distance from the node i to the terminal point T, wherein the straight-line distance refers to the straight-line distance from the node i to the terminal point T when the obstacle is not considered, namely the residual minimum distance, which is a minimum estimated value D i Is f i And g i Represents the current minimum total cost value of node i.
The algorithm steps of the unmanned aerial vehicle are as follows:
and step1, inputting coordinates of a starting point and an end point, and storing the starting point into C1.
And step2, traversing the C1, finding out the node corresponding to the minimum Dmin, expanding the node, namely finding out the obstacle closest to the node, acquiring the node information of the obstacle, adding the available node of the obstacle into the C1, and moving the expanded point in the C1 to the C2.
And Step3, repeating Step2 until the line connecting the current expansion point and the terminal point does not pass through any obstacle any more, stopping when the terminal point can be directly reached, or stopping the circulation until the C1 is empty.
3. A forest and grassland fire risk assessment system based on geographical information according to claim 2, wherein: when the unmanned aerial vehicle acquires data, the unmanned aerial vehicle adopts an infrared camera to inspect and measure temperature, and the unmanned aerial vehicle comprises an abrupt heating source in a visible range, and if the temperature rise delta is more than or equal to 80% compared with the ambient temperature, the unmanned aerial vehicle judges that the temperature is abnormal
δ t ＝(τ 1 -τ 2 )/τ 1 ×100％＝(T 1 -T 2 )/(T 1 -T 0 )×100％
Wherein:
τ 1 and T 1 -temperature rise and temperature of the heat generating spot;
τ 2 and T 2 -temperature rise and temperature of the normal corresponding point;
T 0 -temperature of the ambient temperature reference subject.
4. The forest and grassland fire risk assessment system based on geographical information according to claim 1, wherein: the model building system builds a map model as follows: the terrain structure, vegetation and water area in the environment are respectively represented by models with different shapes, and the models are represented as follows:
z 1 (x，y)＝h i ，r＜R i
z 2 (x，y)＝R i -r 2 ，r＜R i
z(x，y)＝max(z 1 (x，y)，z 2 (x，y))
wherein z (x, y) is the map midpoint, the height value of (x, y), z 1 (x, y) is a terrain height value, z 2 (x, y) is the vegetation height value, (x) i ，y i ) Is the coordinates of the center point of the ith terrain,
is the distance from the model midpoint (x, y) to the center of the ith terrain or vegetation, h i Height of vegetation, R i Is the land occupation radius of the vegetation.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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