CN114781169A - Forest fire spreading simulation method based on cellular automaton - Google Patents
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Abstract
The invention discloses a forest fire spreading simulation method based on a cellular automaton, which comprises the steps of 1, obtaining digital elevation data, vegetation coverage data and natural meteorological data of a simulation area; step 2, dividing the simulation area into regular grid spaces, wherein each grid unit is used as a cell; step 3, calculating the spreading speed of the neighborhood cells to the central cells; step 4, dividing the combustion state of the cells into unburnt state, initial combustion and full combustion, and igniting the surrounding cells, gradually extinguishing and completely extinguishing, calculating the combustion state of the cells at the next moment based on the cellular automata, and realizing the updating of the combustion state of the cells; step 5, outputting the combustion states of different cells in different time periods; and 6, realizing visual display of forest fire spreading, dividing the cellular state into five combustion states of unburnt combustion, initial combustion and full combustion, and igniting surrounding cells, gradually extinguishing and completely extinguishing, and improving the accuracy and the practicability of the forest fire spreading model.
Description
Technical Field
The invention relates to the technical field of forest fire spreading simulation, in particular to a forest fire spreading simulation method based on a cellular automata.
Background
Forest fire refers to the action of forest fire which loses artificial control, freely spreads and expands in forest lands and brings certain harm and loss to forests, forest ecosystems and human beings. The forest fire is a natural disaster which has strong burst property and large destructiveness and is difficult to deal with and rescue, and the large-space forest fire often causes great life and property loss and natural ecological destruction. The method has the advantages that the occurrence, development and evolution rules of forest fires are deeply researched, the understanding and mastering of forest fire behaviors are improved, the method plays an important role in preventing, monitoring and controlling forest fires, and scientific and effective aid decision support can be provided for comprehensive forest fire prevention and disaster reduction.
To date, scholars at home and abroad have conducted a great deal of scientific research on the forest fire spreading process and have proposed a number of scientifically effective forest fire simulation models: the fire spread empirical model mainly analyzes the fire spread rule according to fire history statistical data and fire experiment simulation data, but the calculation is complex, and the migration applicability of the model is not high; the fire spreading probability model mainly uses a mathematical probability method to carry out statistical analysis and description on a fire spreading process, carries out mathematical probabilistic modeling on the random simulation process of fire spreading, analyzes the internal law of fire spreading from the perspective of probability theory, but is not suitable for the simulation of a large scene.
The cellular automaton is a space-time discrete local dynamics model, is a typical method for complex system research, is particularly suitable for space-time dynamic simulation research of a space complex system, can easily integrate dynamic variables influencing a fire spreading process, such as atmospheric environment, landform and landform, and the like, and supports visual simulation of the fire spreading process, and can intuitively demonstrate the fire spreading process. However, the existing cells generally only contain two combustion conditions of unburnt combustion and burning, and the states of the cells cannot be completely expressed, so that the accuracy and the practicability of a forest fire spreading model are not high.
Disclosure of Invention
According to the defects of the prior art, the invention aims to provide a forest fire spreading simulation method based on a cellular automaton, which divides cellular states into five combustion states of unburnt, initial combustion and full combustion, and can ignite surrounding cellular states, gradually extinguish and completely extinguish, thereby improving the accuracy and the practicability of a forest fire spreading model.
In order to solve the technical problems, the invention adopts the technical scheme that:
a forest fire spreading simulation method based on cellular automata comprises the following steps:
step 2, dividing the simulation area into regular grid spaces, wherein each grid unit is used as a cell, each cell comprises a state space vector, and the state space vector comprises the combustion state, digital elevation data, vegetation coverage data and natural meteorological data of the cell;
step 3, calculating the spreading speed of the neighborhood cells to the central cells;
step 4, designing a state transition rule in the process from initial burning to extinguishing of an actual tree, dividing the burning state of the cells into unburnt state, initial burning, full burning and the possibility of igniting the surrounding cells, gradually extinguishing and completely extinguishing, calculating the burning state of the cells at the next moment according to the forest fire spreading speed, the size of the cells and the burning state of the instant cells based on a cellular automaton, and realizing the updating of the burning state of the cells;
step 5, outputting the combustion states of different cells in different time periods;
and 6, rendering the combustion states of the cells in different time sequences to realize the visual display of the forest fire spread.
Further, in step 1, the vegetation coverage data includes a vegetation coverage type, and the natural meteorological data includes a wind power level, a wind speed, a wind direction, a temperature and a humidity.
Further, in step 3, the spreading speed of the adjacent cells to the central cell is obtained according to the initial forest fire spreading speed, the wind adjusting coefficient, the terrain slope adjusting coefficient and the combustible index.
Further, the initial forest fire spreadSpeed is related to wind power level, temperature and humidity, and initial forest fire spreading speed R0The calculation formula of (2) is as follows:
R0＝0.03T+0.05W+0.01h-0.3
wherein T is temperature in units of deg.C, h is daily minimum humidity in units of RH%, and W is Typha wind class;
the calculation formula of the Typfu wind level W is as follows:
wherein V is wind speed, the unit is m/s, and int represents an integer;
adjusting the coefficient K by windwObtaining the gain effect of wind speed and wind direction on forest fire spreading speed, and wind adjusting coefficient KwThe calculation method of (A) is as follows:
Kw＝e0.1783V′
wherein V' is the wind speed of the neighborhood cells;
adjusting coefficient K through terrain gradientΦObtaining the gain effect of the terrain on the forest fire spreading speed:
by combustible index KsAcquiring the influence of combustible vegetation types on forest fire spreading speed;
the propagation speed of fire from the central cell to the neighboring cells
The calculation formula of (2) is as follows:
further, spreading the combustible index K of the pine needlessIs 0.8, combustible index K of Korean pine and Chinese pines1.0, flammability index of dry branches and fallen leaves Ks1.2, flammability index of Imperata and weeds KsIs 1.6, flammability index K of Cyperus rotundus and Betula platyphyllas1.8, combustible index K of pasture and grasslandsIs 2.0.
Further, the spread direction of the fire from the central unit cell to the adjacent unit cells is divided into northwest direction, north direction, northeast direction, east direction, southeast direction, southwest direction and west direction, the unit cells in the north, east, south and west directions of the central unit cell are taken as the adjacent unit cells of the central unit cell, and the unit cells in the northwest, northeast, southeast and southwest directions are taken as the sub-adjacent unit cells of the central unit cell, wherein the spread direction of the fire from the central unit cell to the adjacent unit cells is divided into northwest direction, north direction, northeast direction, southwest direction and west direction
The component direction of the speed of the central unit cell spreading to the surrounding 8 directions of the domain unit cell,
representing any wind direction in the four quadrants, the velocity components of the 8 field cells of the central cell in the northwest, northeast, southeast, southwest and west directions
Respectively expressed as:
further, of the neighbourhood cell (k, l) relative to the central cell (i, j)
Can be expressed as:
Wherein h isk，1And hi，jHeight values representing the center positions of the neighboring cells (k, l) and the center cell (i, j), assuming that the height values are the same within one cell,a represents the size of the side length of the unit cell,
representing the diagonal length of the cell, using a calculation formula when the cell (k, l) is a neighbor of the central cell (i, j)
Using a calculation formula when the neighbor cell (k, l) is the central cell (i, j) secondary neighbor cell
When the temperature is higher than the set temperature
When the value G is 0, the enhancement effect of the uphill slope on the spreading speed is represented; when the temperature is higher than the set temperature
When the value of G is 1, the effect of the downhill on suppressing the propagation speed is shown, when
When the value G is 0, the enhancement effect of the uphill slope on the spreading speed is represented; when the temperature is higher than the set temperature
When the value G is 1, the effect of the downhill slope on suppressing the propagation speed is shown.
Further, projecting the wind direction to the 8 domain directions of the central unit cell, we can obtain the propagation speeds of the fire from the central unit cell to the 8 domain unit cells in the northwest direction, the north direction, the northeast direction, the east direction, the southeast direction, the south direction, the southwest direction and the west direction as follows:
wherein, theta is an included angle between the wind direction and the due north direction.
Further, in the step 4, the cellular automaton is expressed as { ZnS, N, f }, wherein ZnThe method comprises the steps of representing an N-dimensional cellular space, representing a state by S, representing the number of neighbor cells by N, and representing a state conversion rule by f, and representing a forest fire spreading geographic cellular automaton.
Further, when S is 0, it means unburned, S is 1, S is 2, it means full combustion and can ignite surrounding cells, S is 3, it means gradually extinguished, and S is 4, it means completely extinguished;
and traversing cells of which the combustion state is possibly changed in a fire scene to meet the following conditions:
wherein,&&to show the results of the processes of,
indicating presence, | | indicates or, when the state of a cell is S ═ 1 and a cell with S ═ 2 exists in its neighborhood, or the state of a cell is S ═ 2 or S ═ 3, the state of a cell may change;
if S is 0, the combustible initial-combustion cells with the adjacent cells in the existing state of S being 1 are obtained, the cell value of the central cell at the next moment is calculated, and the formula is applied:
wherein, it is to
And
taking an integer, wherein L represents the side length of the cell, and delta t is a time step and represents the time interval for updating the combustion state of the cell; t is the current moment, and t + delta t is the next moment; (i, j) represents the geographic location of the central cell;
indicating the status of the immediate central cell (i, j),
represents the state of the central cell (i, j) at the next moment;
expressing the spreading speed of fire from the central cellular to 4 field cellular in the northwest direction, the northeast direction, the southeast direction and the southwest direction at the current moment;
the spreading speed of fire from the central cellular to 4 field cellular in the north direction, the east direction, the south direction and the west direction at the current moment is shown; rmaxThe maximum spreading speed of the cells in 8 fields around the central cell; m is a step coefficient;
if S is just initially burnt, the next time S is 2 and the fuel is completely burnt;
if S is 2 and the adjacent cells S is more than or equal to 2 or incombustible, S is 3 and the lamp is gradually extinguished;
when S is 3 at this time, the next time S is 4, and the lamp is completely turned off.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the forest fire spreading simulation method based on the cellular automata fully combines the characteristics of the cellular automata, divides the states of the cells into unburnt state, initial burning state, full burning state, ignition of surrounding cells, gradual extinguishment and full extinguishment, fully considers various states of the cell burning state, divides 5 states more accurately, has the characteristics of small calculated amount, high calculating speed, high model precision, good visualization effect and wide application in forest fire simulation under large and small scene scales, keeps the balance of algorithm efficiency and simulation precision, and provides an effective method for the field of forest fire spreading simulation.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention.
Fig. 2 is a schematic diagram of the invention for dividing the simulation area into regular grid spaces.
FIG. 3 is a schematic diagram of an eight-neighborhood cell of the present invention.
FIG. 4(a) shows the present invention using (i, j) as the ignition point, which is fully ignited and has the ability to ignite surrounding cells.
FIG. 4(b) is a schematic diagram of the present invention igniting the surrounding 8 neighborhood at time (i, j) t + Δ t at a certain speed.
FIG. 4(c) is a schematic diagram of the present invention for a full combustion in the neighborhood of time t +2 Δ t at time 8.
FIG. 4(d) is a schematic diagram of the present invention showing the fade-out at time (i, j) at t +3 Δ t, with 8 neighbors igniting each at 8 neighbors at a certain rate.
Fig. 5(a) is a simulation diagram of the present invention showing forest fire spreading with (i, j) as fire point visualization.
Fig. 5(b) is a simulation diagram for visually displaying forest fire spreading at the time t + Δ t according to the invention.
Fig. 5(c) is a simulation diagram for visually displaying forest fire spreading at the time t +2 Δ t according to the invention.
Fig. 5(d) is a simulation diagram for visually displaying forest fire spreading at the time t +3 Δ t according to the invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
A forest fire spreading simulation method based on cellular automata comprises the following steps:
step 2, dividing the simulation area into regular grid spaces, wherein each grid unit is used as a cell, each cell comprises a state space vector, and the state space vector comprises a combustion state, digital elevation data, vegetation coverage data and natural meteorological data of the cell;
step 3, calculating the spreading speed of the neighborhood cells to the central cells;
step 4, designing a state transition rule in the process from initial burning to extinguishing of an actual tree, dividing the burning state of the cells into unburnt state, initial burning, full burning and the possibility of igniting the surrounding cells, gradually extinguishing and completely extinguishing, calculating the burning state of the cells at the next moment according to the forest fire spreading speed, the size of the cells and the burning state of the instant cells based on a cellular automaton, and realizing the updating of the burning state of the cells;
step 5, outputting the combustion states of different cells in different time periods;
and 6, rendering the combustion states of the cells in different time sequences to realize the visual display of the forest fire spread.
In step 1, vegetation coverage data includes vegetation coverage type, and natural meteorological data includes wind power level, wind speed, wind direction, temperature, and humidity.
In step 3, the influencing parameters influencing the forest fire comprise an initial forest fire spreading speed, a wind adjusting coefficient, a terrain slope adjusting coefficient and a combustible index, and the spreading speed of the neighborhood cells to the center cells is obtained.
Specifically, the initial forest fire spread rate is related to the wind power level, temperature and humidity, and the initial forest fire spread rate R0The calculation formula of (c) is:
R0＝0.03T+0.05W+0.01h-0.3
wherein T is temperature in units of deg.C, h is minimum humidity in units of RH%, and W is Typha wind level;
the calculation formula of the Typfu wind level W is as follows:
wherein V is wind speed, the unit is m/s, and int represents an integer;
adjusting the coefficient K by windwObtaining the gain effect of wind speed and wind direction on forest fire spreading speed, and wind adjusting coefficient KwThe calculation method of (A) is as follows:
Kw＝e0.1783V′
wherein V' is the wind speed of the neighborhood cells;
adjusting coefficient K through terrain gradientΦObtaining the gain effect of the terrain on the forest fire spreading speed:
by combustible index KsAcquiring the influence of combustible vegetation types on forest fire spreading speed;
the calculation formula of the spreading speed R of the fire from the central cell to the adjacent cells is as follows:
R＝R0·Kw·KΦ·Ks
wherein the combustible index K of different combustible vegetation typessDifferent, common combustible index KsAs shown in the following table:
according to the invention, the spreading direction of fire from the central cell to the adjacent cells is divided into northwest direction, north direction, northeast direction, east direction, southeast direction, south direction, southwest direction and west direction, the central cell northwest direction, southeast direction, southwest direction and southwest direction are taken as the adjacent cells of the central cell, and the central cell northwest direction, northeast direction, southeast direction and southwest direction are taken as the secondary adjacent cells of the central cell, wherein the central cell is divided into northwest direction, northeast direction, southeast direction and southwest direction
Is the speed component direction of the central cellular element spreading to the peripheral field cellular elements in 8 directions,
representing any wind direction in the four quadrants, the velocity components of the 8 field cells of the central cell in the northwest, northeast, southeast, southwest and west directions
Respectively expressed as:
variations in terrain variation may result from grade changes. So that in the cellular space,
the expression method of (2) is improved, and each of the 8 field unit cells (k, l) has relative central unit cell
The values also all have a respective gradient value
Thus, the neighbor cells (k, l) are relative to the central cell (i, j)
Can be expressed as:
Wherein h isk，1And hi，jHeight values representing the center positions of the neighboring cells (k, l) and the center cell (i, j), assuming that the height values are the same within one cell, a represents the size of the side length of the cell,
representing the diagonal length of the cell, using a calculation formula when the cell (k, l) is a neighbor of the central cell (i, j)
Using a calculation formula when the neighbor cell (k, l) is the central cell (i, j) secondary neighbor cell
When in use
When the value G is 0, the enhancement effect of the uphill slope on the spreading speed is represented; when in use
When the value of G is 1, the effect of the downhill on suppressing the propagation speed is shown, when
When the value G is 0, the enhancement effect of the uphill slope on the spreading speed is represented; when in use
The value of G is 1, indicating the effect of the downhill slope on the speed of propagation.
The wind direction is projected to 8 field directions of the cellular, and the cosine of the included angle in the adjacent cell direction is obtained as shown in table 1.
TABLE 1 cosine of angle in the direction of neighboring cells
Cosine of angle in the direction of sub-adjacent cells
According to table 1, the spreading speeds of fire from the central cell to 8 field cells in the northwest direction, the north direction, the northeast direction, the east direction, the southeast direction, the south direction, the southwest direction and the southwest direction can be respectively expressed as:
wherein, theta is an included angle between the wind direction and the due north direction.
The forest fire spreading geographic CA algorithm may be described as: the forest fire spreading system is characterized in that a geographical cellular space consisting of burning states at different geographical positions and different fire situation cellular cells continuously changes the burning state of each tree along with the advance of discrete time according to the forest fire spreading rule, the state change of each tree is only related to the state of each tree and the state of the trees in the Moore neighborhood, and the conversion rule drives the core of the operation of the whole system. Since in the algorithm, the state change is only related to the state of the tree in the neighborhood of the tree and the state of the tree in the neighborhood of the mole, the state obtained by adopting the 8 neighborhood is more accurate than the state obtained by adopting the 4 neighborhood.
Any one of the 8 neighbor cells (K, l) has a respective (K, K) relative to the central firing cellΦ)k，lThe values also all have a respective gradient value
Thus, K of the neighbor cell (K, l) relative to the central combustion cell (i, j)ΦThe traditional calculation mode needs to be changed, and the simulation precision can be improved.
The cellular automata is defined in a cellular space composed of discrete finite-state cells, and evolves in discrete time according to a certain local ruleMechanical system, abstractable as { ZnS, N, f, i.e., { N-dimensional cell space, state, neighborhood number, state transition rule }. The forest fire spreading geographic cellular automata algorithm can be described as follows: a geographical cellular space (Z) consisting of cells of different geographical burning states (S) and different firesn) According to the forest fire spreading rule (f), the burning state of each tree continuously changes along with the advance of discrete time, and the state change of each tree is only related to the state of each tree and the state of the trees (N) in the Moore neighborhood, wherein the rule S is convertedt+Δt＝f(StAnd N) is the core driving the whole system to operate, and N is 8 which is a molar neighborhood.
In step 4, the state of the cells is divided into unburnt state, initial burning state, full burning state and surrounding cells capable of being ignited, and extinguished, S ═ 0 represents unburnt state, S ═ 1 represents initial burning state, S ═ 2 represents full burning state and surrounding cells capable of being ignited, S ═ 3 represents extinguished, S ═ 4 represents extinguished;
traversing cells of which the combustion state is possibly changed in a fire scene, and meeting the following conditions:
wherein,&&to show the results of the processes of,
indicating presence, | | indicates or, when the state of a cell is S ═ 1 and a cell with S ═ 2 exists in its neighborhood, or the state of a cell is S ═ 2 or S ═ 3, the state of a cell may change.
If the combustible initial-combustion cells with the adjacent cells in the existence state of S-1 are combustible when S is 0, calculating the cell value of the central cell at the next moment, and applying the formula:
wherein, it is to
And
taking an integer, wherein L represents the side length of the cell, and delta t is a time step and represents the time interval for updating the combustion state of the cell; t is the current moment, and t + delta t is the next moment; (i, j) represents the center cell geographic location;
representing the state of the immediate central cell (i, j),
represents the state of the central cell (i, j) at the next time;
representing the spreading speed of fire from the central cellular to the 4 field cellular in the northwest direction, the northeast direction, the southeast direction and the southwest direction at the current moment;
representing the spreading speed of fire from the central cellular to 4 field cellular in the north direction, the east direction, the south direction and the west direction at the current moment; rmaxThe maximum spreading speed of the cells in 8 fields around the central cell is obtained; m is a step size coefficient.
If S is just initially burnt, the next time S is 2 and the fuel is completely burnt;
if S is 2 and the adjacent cells S is more than or equal to 2 or incombustible, S is 3 and the lamp is gradually extinguished;
when S is 3 at this time, the next time S is 4, and the lamp is completely turned off.
The larger m, the more efficient the algorithm and the lower the simulation accuracy, here taken to be 0.125.
The existing forest fire spreading simulation method based on cellular automata generally divides the combustion state of cells into three states of unburnt state, burning state and extinguishing state, but the state conversion from unburnt state to burning state does not conform to the actual situation, whether the surrounding cells have the capability of igniting or not is not considered, the surrounding cells can not be ignited in a certain period, if the forest fire spreading condition is directly entered into the combustion state, the forest fire spreading condition cannot be accurately shown, and the error is large. The present invention divides the state of the cells into unburnt state, initial burning state, full burning state, and the state that the cells around can be ignited, gradually extinguished and completely extinguished. Various states of cell combustion are fully considered, and division of 5 states is more accurate.
In one embodiment of the present invention, fig. 4(a) -4 (d) illustrate the process of simulating forest fire spreading by cellular automata algorithm. FIG. 4(a) shows that (i, j) is the fire point, which is fully ignited and has the ability to ignite surrounding cells; FIG. 4(b) is a schematic diagram of the ignition of the surrounding 8 neighborhood at time (i, j) t + Δ t at a certain velocity; FIG. 4(c) is a schematic diagram of a full combustion in the neighborhood of time 8 at t +2 Δ t; fig. 4(d) is a schematic diagram of the 8 neighbors being lit up at a rate at time (i, j) that is t +3 Δ t.
Fig. 5(a) -5 (d) show the states of the cellular automata at various moments of time for simulating forest fire spread. In the simulation, the air temperature was set to 25 ℃, the relative humidity was 30%, the wind speed was 5m/s, and the wind direction was the north direction. By adopting the forest fire spreading simulation method based on the cellular automata, provided by the invention, the forest fire spreading condition can be clearly displayed.
In summary, the invention is based on the cellular automata principle, fully considers the influence of combustible, wind, temperature, humidity, gradient and other factors on the forest fire spreading speed aiming at the defects of the existing forest fire spreading model, combines the spatial information technology of remote sensing and geographic information system core, and utilizes a computer to realize real-time dynamic forest fire spreading simulation: firstly inputting digital altitude data, vegetation coverage data and natural meteorological data of a simulation area, then carrying out regular grid division on the simulation area, and comprising combustion state, digital elevation, vegetation coverage type, humidity, temperature, wind speed, wind direction and wind power grade state values of each unit, then respectively calculating initial forest fire spreading speed, wind adjustment coefficient and terrain slope adjustment coefficient by using the state values and an empirical model, obtaining a combustible index according to the vegetation coverage type, calculating spreading speed of neighborhood cells to central cells by using four obtained values and a forest fire spreading correction model, traversing cellular cells with possibly changed combustion state in a fire scene according to cellular state conversion rules, and updating the cellular state, wherein for the cellular cells which are combustible and have initial combustion cellular cells with S1 in the neighborhood, the cellular state is required to be updated according to the forest fire spreading speed, And calculating the size of the cells and the current state to obtain the state of the next moment, finally outputting different point burning levels in different time periods, and rendering to realize the visual display of forest fire spreading.
The method fully combines the characteristics of the cellular automata, fully utilizes data such as wind direction, wind speed, temperature, humidity, gradient and the like, has the characteristics of small calculated amount, high calculating speed, high model precision, good visualization effect and wide application to forest fire simulation under large and small scene scales, keeps the balance of algorithm efficiency and simulation precision, and provides an effective method for the field of forest fire spreading simulation.
In conclusion, the method is reliable and practical, has better accuracy and practicability for simulating forest fire spreading, has better effect and better practicability and feasibility by comparing and verifying with the practice.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A forest fire spreading simulation method based on cellular automata is characterized by comprising the following steps:
step 1, acquiring digital elevation data, vegetation coverage data and natural meteorological data of a simulation area;
step 2, dividing the simulation area into regular grid spaces, wherein each grid unit is used as a cell, each cell comprises a state space vector, and the state space vector comprises the combustion state, digital elevation data, vegetation coverage data and natural meteorological data of the cell;
step 3, calculating the spreading speed of the neighborhood cells to the central cells;
step 4, designing a state transition rule in the process from initial burning to extinguishing of an actual tree, dividing the burning state of the cells into unburnt state, initial burning, full burning and the possibility of igniting the surrounding cells, gradually extinguishing and completely extinguishing, calculating the burning state of the cells at the next moment according to the forest fire spreading speed, the size of the cells and the burning state of the instant cells based on a cellular automaton, and realizing the updating of the burning state of the cells;
step 5, outputting the combustion states of different cells in different time periods;
and 6, rendering the combustion states of the cells in different time sequences to realize the visual display of forest fire spreading.
2. The forest fire spread simulation method based on cellular automata according to claim 1, wherein: in step 1, vegetation coverage data includes vegetation coverage type, and natural meteorological data includes wind power level, wind speed, wind direction, temperature, and humidity.
3. The forest fire spread simulation method based on the cellular automata according to claim 1, wherein: in step 3, the spreading speed of the neighborhood cells to the center cells is obtained according to the initial forest fire spreading speed, the wind adjusting coefficient, the terrain slope adjusting coefficient and the combustible index.
4. A forest fire spread simulation method based on cellular automata according to claim 3, characterized in that: the initial forest fire spreading speed is related to the wind power grade, the temperature and the humidity, and the initial forest fire spreading speed R0The calculation formula of (2) is as follows:
R0＝0.03T+0.05W+0.01W+0.01h-0.3
wherein T is temperature in units of deg.C, h is daily minimum humidity in units of RH%, and W is Typha wind class;
the calculation formula of the Typfu wind level W is as follows:
wherein V is wind speed, the unit is m/s, and int represents an integer;
adjusting the coefficient K by windwObtaining the gain effect of wind speed and wind direction on forest fire spreading speed, and adjusting the wind coefficient KwThe calculation method of (A) is as follows:
Kw＝e0.1783V′
wherein V' is the wind speed of the neighborhood cells;
adjusting coefficient K through terrain gradientΦObtaining the gain effect of the terrain on the forest fire spreading speed:
by combustible index KsAcquiring the influence of combustible vegetation types on forest fire spreading speed;
the propagation speed of fire from the central cell to the adjacent cells
The calculation formula of (2) is as follows:
5. the forest fire spread simulation method based on cellular automata according to claim 4, wherein: combustible index K of flat laying pine needlessIs 0.8, combustible index K of Korean pine and Pinus armandis1.0, flammability index of dry branches and fallen leaves Ks1.2, flammability index of Imperata and weeds KsA flammability index K of 1.6, Cyperus rotundus and Betula betulas1.8, combustible index K of pasture and grasslandsWas 2.0.
6. Cellular automaton according to claim 4The forest fire spreading simulation method is characterized by comprising the following steps: dividing the fire into northwest direction, northeast direction, eastern direction, southeast direction, southwest direction and westward direction according to the spreading direction of the fire from the central cellular cell to the adjacent cellular cells, taking the central cellular cell in the northwest direction, the eastern direction, the southeast direction and the southwest direction as the adjacent cellular cell of the central cellular cell, and taking the central cellular cell in the northwest direction, the northeast direction, the southeast direction and the southwest direction as the next adjacent cellular cell of the central cellular cell, wherein the fire spreading direction is divided into northwest direction, northeast direction, southeast direction, eastern direction, southeast direction and southwest direction
The component direction of the speed of the central unit cell spreading to the surrounding 8 directions of the domain unit cell,
representing any wind direction in four quadrants, the velocity components of 8 field cells of the central cell in the northwest direction, the north direction, the northeast direction, the east direction, the southeast direction, the south direction, the southwest direction and the west direction
Respectively expressed as:
7. the forest fire spread simulation method based on cellular automata according to claim 6, wherein: of neighbouring cells (k, l) relative to the central cell (i, j)
Can be expressed as:
Wherein h isk，1And hi，jHeight values representing the center positions of the neighboring cells (k, l) and the center cell (i, j), assuming that the height values are the same within one cell, a represents the size of the side length of the cell,
representing the diagonal length of the cell, using a calculation formula when the cell (k, l) is a neighbor of the central cell (i, j)
Using a calculation formula when the neighbor cell (k, l) is the central cell (i, j) secondary neighbor cell
When in use
When the value G is 0, the enhancement effect of the uphill slope on the spreading speed is represented; when the temperature is higher than the set temperature
When the value of G is 1, the effect of downhill slope on the inhibition of the propagation speed is shown, when
When the value G is 0, the enhancement effect of the uphill slope on the spreading speed is represented; when in use
When the value G is 1, the effect of the downhill slope on suppressing the propagation speed is shown.
8. The forest fire spread simulation method based on cellular automata according to claim 7, wherein: projecting the wind direction to 8 field directions of the central unit cell, the spreading speed of fire from the central unit cell to the 8 field unit cells in the northwest direction, the northeast direction, the southeast direction, the southwest direction and the southwest direction can be respectively expressed as:
wherein, theta is an included angle between the wind direction and the due north direction.
9. The forest fire spread simulation method based on the cellular automata according to claim 1, wherein: in the step 4, the cellular automaton is expressed as { ZnS, N, f }, wherein ZnThe method comprises the steps of representing an N-dimensional cellular space, representing a state by S, representing the number of neighbor cells by N, and representing a state conversion rule by f, and representing a forest fire spreading geographic cellular automaton.
10. The forest fire spread simulation method based on cellular automata according to claim 8, wherein: let S ═ 0 denote no combustion, S ═ 1 denote initial combustion, S ═ 2 denote full combustion and can ignite surrounding cells, S ═ 3 denote extinction, S ═ 4 denote extinction;
traversing cells of which the combustion state is possibly changed in a fire scene, and meeting the following conditions:
wherein,&&to show the results of the processes of,
indicating presence, | | indicates or, when the state of a cell is S ═ 1 and a cell with S ═ 2 exists in its neighborhood, or the state of a cell is S ═ 2 or S ═ 3, the state of a cell may change;
if the combustible initial-combustion cells with the adjacent cells in the existence state of S-1 are combustible when S is 0, calculating the cell value of the central cell at the next moment, and applying the formula:
wherein, it is to
And
taking an integer, wherein L represents the side length of the cell, and delta t is a time step and represents the time interval for updating the combustion state of the cell; t is the current moment, and t + delta t is the next moment; (i, j) represents the center cell geographic location;
indicating the status of the immediate central cell (i, j),
represents the state of the central cell (i, j) at the next time;
expressing the spreading speed of fire from the central cellular to 4 field cellular in the northwest direction, the northeast direction, the southeast direction and the southwest direction at the current moment;
the spreading speed of fire from the central cellular to 4 field cellular in the north direction, the east direction, the south direction and the west direction at the current moment is shown; rmaxThe maximum spreading speed of the cells in 8 fields around the central cell is obtained; m is a step coefficient;
if S is just initially burnt, the next time S is 2 and the fuel is completely burnt;
if S is 2 and the adjacent cells S is more than or equal to 2 or incombustible, S is 3 and the lamp is gradually extinguished;
when S is 3 at this time, the next time S is 4, and the lamp is completely turned off.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116386252A (en) * | 2023-04-11 | 2023-07-04 | 东北林业大学 | Combustible monitoring method based on forest area division |
| CN116415712A (en) * | 2023-02-14 | 2023-07-11 | 武汉大学 | Fire spread prediction method and system based on multiple data sources |
| CN117557559A (en) * | 2024-01-11 | 2024-02-13 | 青岛山科智汇信息科技有限公司 | Impact point determining method, medium and system for fire extinguishing bomb |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108710718A (en) * | 2018-03-30 | 2018-10-26 | 国网安徽省电力有限公司 | Mountain fire sprawling analog simulation method based on cellular automata and system |
| KR20190129468A (en) * | 2018-05-11 | 2019-11-20 | 대한민국(산림청 국립산림과학원장) | Method for realtime forest fire danger rating forecasting in north korea |
| CN114386819A (en) * | 2021-12-27 | 2022-04-22 | 北京工业大学 | Forest fire spreading simulation method based on cellular automaton |
-
2022
- 2022-04-29 CN CN202210474421.2A patent/CN114781169B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108710718A (en) * | 2018-03-30 | 2018-10-26 | 国网安徽省电力有限公司 | Mountain fire sprawling analog simulation method based on cellular automata and system |
| KR20190129468A (en) * | 2018-05-11 | 2019-11-20 | 대한민국(산림청 국립산림과학원장) | Method for realtime forest fire danger rating forecasting in north korea |
| CN114386819A (en) * | 2021-12-27 | 2022-04-22 | 北京工业大学 | Forest fire spreading simulation method based on cellular automaton |
Non-Patent Citations (1)
| Title |
|---|
| 惠珊;芮小平;李尧;: "一种耦合元胞自动机的改进林火蔓延仿真算法", 武汉大学学报(信息科学版), no. 10, 5 October 2016 (2016-10-05) * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116415712A (en) * | 2023-02-14 | 2023-07-11 | 武汉大学 | Fire spread prediction method and system based on multiple data sources |
| CN116386252A (en) * | 2023-04-11 | 2023-07-04 | 东北林业大学 | Combustible monitoring method based on forest area division |
| CN116386252B (en) * | 2023-04-11 | 2024-04-02 | 东北林业大学 | Combustible monitoring method based on forest area division |
| CN117557559A (en) * | 2024-01-11 | 2024-02-13 | 青岛山科智汇信息科技有限公司 | Impact point determining method, medium and system for fire extinguishing bomb |
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