CN114120565B - Forest fire early warning method - Google Patents
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Abstract
The application belongs to the technical field of environmental protection, and particularly relates to a forest fire early warning method. The traditional early warning method has high adaptation degree to smooth plains of terrains or slopes with strong directivity, but is difficult to make effective fire early warning for undulating landforms in complex mountain land terrain areas. The application provides a forest fire early warning method, wherein irregular grids are designed according to landforms of complex mountain land terrain areas, a spherical early warning device is arranged on each vertex of each grid from low to high, and after the arrangement is completed, the early warning center controls and operates the early warning device. Every fixed time interval T, each early warning device transmits the monitored temperature T to an early warning center i Air humidity H i Wind speed V i And the early warning nodes transmit the information sensed by the nodes mutually. The problem that forest fires are difficult to detect in a complex mountain land and terrain area can be effectively solved. Simple to operate, equipment is convenient, convenient operation.
Description
Technical Field
The application belongs to the technical field of environmental protection, and particularly relates to a forest fire early warning method.
Background
At present, forest resources are important national resources, and the forest fire burns a large forest area, which is one of important reasons for damaging the forest resources. Meanwhile, the forest is part of ecological construction, the effective prevention of forest fires is also required for protecting lives and properties of people in forest areas, and the promotion of national forest ecological construction is facilitated. The protection of the original and the reconstructed forest can provide important support for protecting forest resources.
Most forests are located environment topography complicacy, and its ground is undulant greatly, and relative difference in height is very big, and forest fire receives the topography influence easily in this type of environment, spreads fast and is difficult for discovering in low-lying mountain region environment. The forest fire rescue difficulty in the environment is higher than that in plain forests under the influence of mountain dense forests. Therefore, the fire early warning in the complex mountain land terrain area influences the forest resource protection level and the rescue timeliness in the special environment. The traditional early warning method has high adaptation degree to smooth plains of terrains or slopes with strong directivity, but is difficult to make effective fire early warning for undulating landforms in complex mountain land terrain areas.
Disclosure of Invention
1. Technical problem to be solved
Based on the traditional early warning method to the gentle plain of topography or the strong sloping field adaptation degree of directionality height, nevertheless to the problem that the effective condition of a fire early warning is difficult to make to the undulation landform in complicated mountain land topography district, the application provides a forest condition of a fire early warning method.
2. Technical scheme
In order to achieve the purpose, the application provides a forest fire early warning method, which comprises the following steps:
1) Acquiring data of a forest area to be monitored, and acquiring a fire risk area in the forest area according to the data; 2) Performing importance evaluation according to early warning indexes of forest fire conditions by combining with actual conditions of each fire risk area to obtain a judgment matrix of forest fire risk judgment indexes, and calculating early warning indexes Y of each risk area according to the judgment matrix i The method of (1); 3) According to the historical data of the monitored forest region, calculating the early warning index Y of each risk region i The method of (1) calculating the normal value Y of the early warning index of each fire risk area ref (ii) a 4) Carrying out meshing on the monitored forest region to form an irregular polygon; 5) Each irregular polygon vertex is an early warning node, a spherical early warning device is installed on each early warning node, each early warning device transmits monitored information to an early warning center, and the early warning nodes mutually transmit node induction information; 6) The early warning center calculates the early warning index Y of the current moment according to the early warning index monitored in real time i (t) according to the early warning index Y i (t) and the normal value of the early warning index Y ref Calculating forest fireThe condition danger coefficient I is used for judging whether the monitored area is abnormal or not according to the forest fire condition danger coefficient I and the monitored information; 7) And processing according to the judgment result.
Another embodiment provided by the present application is: the data comprises a satellite map, topographic and geomorphic data, vegetation types and coverage conditions, and year-round temperature, air humidity and wind speed.
Another embodiment provided by the present application is: the fire risk areas include areas where a fire has occurred, hill and valley areas, areas where lightning strikes have occurred, and areas that are more susceptible to human factors.
Another embodiment provided by the present application is: the early warning indexes comprise monitoring values of all points, vegetation conditions of all risk areas and human factors; the judgment matrix is the influence degree of the change of a certain evaluation index on other elements.
Another embodiment provided by the present application is: the early warning index Y i The calculation comprises the steps of determining a weight vector theory by using a feature vector method in an analytic hierarchy process; calculating a feature vector corresponding to the maximum feature value of the judgment matrix, and normalizing to obtain the feature vector, namely each element omega in the weight vectors omega, omega i Namely the weight of each corresponding evaluation index, and the early warning index Y of each risk area is constructed and calculated i ：Y i ＝f(ω i ·m i )。
Another embodiment provided by the present application is: the grid division adopts a Voronoi graph algorithm to divide the forest zones to be monitored, the division method comprises the steps that each fire risk area is regarded as a point which is discretely distributed in the forest zones to be monitored, and each adjacent fire risk area is sequentially connected to form a triangular network which is closely connected, namely a Delaunay triangular network in the algorithm is constructed; numbering each fire risk area and the formed triangulation network, and recording which three fire risk areas each triangulation network consists of; calculating the center of a circumscribed circle of each triangular net, and recording the center of the circle; traversing the triangular linked list, and searching adjacent triangular nets TriA, triB and TriC which are shared with the three edges of the current triangular net pTri; if the triangle is found, connecting the circle center of the circumscribed circle of the triangle with the circle center of the circumscribed circle of the pTri, and storing the circle center into a Voronoi edge linked list; if the ray can not be found, making the perpendicular bisector ray of each side of the triangle and storing the perpendicular bisector ray into a Voronoi side linked list; after the traversal is finished, finding all the Voronoi edges, and drawing a Voronoi graph according to the edges; finally obtaining all Voronoi irregular polygons which can go over the edge of the monitoring area step by step; mounting a spherical early warning device at the vertex of each obtained Voronoi polygon; the center of a circumcircle of each triangular net formed by the risk areas is one vertex of a Voronoi polygon related to the triangular net, and each Voronoi irregular polygon only comprises one fire risk area.
Another embodiment provided by the present application is: when two adjacent risk areas are far away from each other, a secondary risk area is arranged between the two adjacent risk areas with larger distance for enabling the effective radius of the spherical early warning device to cover the whole monitoring forest area.
Another embodiment provided by the present application is: the forest fire hazard coefficient
And when the forest fire danger coefficient I is larger than the minimum threshold value a of the forest fire danger coefficient, the monitored area is an abnormal area.
Another embodiment provided by the present application is: and the processing according to the judgment result comprises observing on a GIS map of a background of the early warning center, confirming the specific position of the abnormal area, judging the spreading speed and the spreading direction on the GIS map according to the wind direction, deploying in time and dispatching the unmanned aerial vehicle for confirmation.
Another embodiment provided by the present application is: the early warning center can manually require the spherical early warning device to upload monitoring data through manual intervention in the conventional monitoring process, and effective measures are taken when the forest fire danger coefficient I of a certain risk area is found to be larger than 0.5 time of the minimum threshold value a of the forest fire danger coefficient.
3. Advantageous effects
Compared with the prior art, the forest fire early warning method provided by the application has the beneficial effects that:
the application provides a forest fire early warning method, which is a forest fire early warning method for the forest fire prevention of complex mountain land topography and carries out targeted forest fire early warning design on landform with high fluctuation degree.
According to the forest fire early warning method, the network layout is reasonable, the distributed irregular polygons can cover the whole forest monitoring area, a risk area is arranged in each selected irregular polygon, and the spherical early warning device is arranged on each vertex, so that the timeliness and the accuracy of early warning of the whole forest area are guaranteed; more scientific, introduces early warning indexes Y of each region i Is calculated by i ＝f(ω i ·m i ) Each evaluation index has different weight, so that the problem that one evaluation index is easily influenced by objective factors is avoided.
The forest fire early warning method is efficient, labor-saving, visualized by means of the GIS map, forest fire danger early warning spatial distribution information of relevant areas can be generated quickly, and the background can be deployed in time according to the early warning information.
Drawings
FIG. 1 is a schematic diagram of the (sub) risk zones and the constructed Delaunay triangulation network of the present application;
FIG. 2 is a schematic view of an irregular polygon arrangement of the present application;
fig. 3 is a schematic diagram of the internal structure of the spherical early warning device of the present application;
FIG. 4 is a schematic flow chart of a forest fire warning method according to the present application.
Detailed Description
Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings, and it will be apparent to those skilled in the art from this detailed description that the present application can be practiced. Features from different embodiments may be combined to yield new embodiments, or certain features may be substituted for certain embodiments to yield yet further preferred embodiments, without departing from the principles of the present application.
Referring to FIGS. 1-4, the present applicationA method for early warning forest fire includes such steps as acquiring the satellite map of forest area to be monitored, collecting the topographic and geomorphic data, vegetation type, coverage and temp (T) over the years i ) Air humidity (H) i ) Wind speed (V) i ) And so on. According to the obtained data, finding out fire risk areas in the forest area, including: areas where fire had occurred, hill valley areas, areas where lightning had been struck, areas that were more affected by human factors (near villages, roads, and bridges). When two adjacent risk areas are far away, in order to enable the effective radius R of the spherical early warning device to cover the whole monitoring forest area, a secondary risk area is arranged between the two adjacent risk areas with larger distance according to actual conditions. Early warning index m according to forest fire i The method comprises the following steps: monitoring value (temperature T) of each point i Air humidity H i Wind speed V i ) Vegetation situation (P) of each risk area i ) And human factors (D) i ) (such as the distance between the forest and a road or a bridge), making importance evaluation by professional evaluators of each forest district in combination with the specifications of forest fire regulations, national forest fire danger division grade of the national forestry industry standard (LY/T1063-2008) of the people's republic of China and the like to obtain a judgment matrix of forest fire danger judgment indexes, wherein the matrix is the influence degree of the change of certain evaluation index on other elements, determining a weight vector theory by using a feature vector method in an analytic hierarchy process, calculating a feature vector corresponding to the maximum feature value of the judgment matrix by using MATLAB software, and normalizing to obtain the feature vector which is the weight vector omega, wherein each element omega in omega is the weight vector omega i Namely the weight of each corresponding evaluation index, and the early warning index Y of each risk area is constructed and calculated i Method Y i ＝f(ω i ·m i ) (ii) a Respectively calculating the normal value Y of the early warning index of each risk area according to the acquired historical data of the monitoring area and 12 months per year ref ；
Carrying out meshing on the monitored forest zones by a Voronoi graph algorithm, and specifically comprising the following steps:
1. and regarding each risk area as a discretely distributed point in the monitored forest region, and sequentially connecting each adjacent risk area to construct a triangulation network, namely a Delaunay triangulation network in the construction algorithm. The risk areas and the formed triangulation networks are numbered and it is recorded which three risk areas each triangulation network is composed of.
2. And calculating the center of a circumscribed circle of each triangular net and recording.
3. And traversing the triangle linked list, and searching adjacent triangles TriA, triB and TriC which are shared with the three edges of the current triangular net pTri.
4. If the triangle is found, the circumcenter of the found triangle is connected with the circumcenter of the pTri and stored in the Voronoi edge chain table. If not, the perpendicular bisector ray at the outermost edge is solved and stored in the Voronoi edge linked list.
5. And ending the traversal until all the Voronoi edges are found, and drawing a Voronoi diagram according to the edges. The drawn Voronoi irregular grid covers the edges of the monitored forest area.
Each vertex of the Voronoi irregular polygon is an early warning node, a spherical early warning device is arranged on each early warning node, and each early warning device transmits the monitored temperature T to an early warning center at fixed time intervals T i Air humidity H i And wind speed V i And the early warning nodes transmit the information sensed by the nodes mutually.
The early warning center 9 contains a server and a display device, and the server is used for receiving, storing and analyzing the temperature, the wind speed, the wind direction and the position information transmitted by the early warning device; the display device is used for displaying a high-definition map of the monitored complex mountain land area, and the acquired data and the analysis result of the server.
The early warning center 9 background server receives the early warning index m i From the early warning index Y i Is calculated by i ＝f(ω i ·m i ) Calculating the early warning index Y of the current moment i (t), calculating a forest fire hazard coefficient I;
when I>When a% is reached (a is confirmed according to the condition of each monitored forest region), the ith monitoring region is considered as an abnormal regionAnd observing on a GIS map of a background of the early warning center, confirming the specific position of the abnormal area, judging the spread range on the GIS map according to the wind direction, deploying in time and dispatching the unmanned aerial vehicle for confirmation.
a is the minimum threshold value of the forest fire hazard coefficient, and a is set by the staff in each forest area according to the historical fire conditions of different forest areas and according to the early warning index Y i The calculation method of the method is used for calculating independently, and if the monitored forest region has no historical fire, similar forest regions are selected according to the actual situation of the forest region to obtain an average value.
The early warning center 9 can send instructions to each early warning device through the background of the early warning center in the conventional monitoring process, require to upload monitoring data, and take effective measures when finding that a certain risk area I is greater than 0.5a percent, such as: an unmanned aerial vehicle or an artificial water supply device is sent out to irrigate, so that the forest fire is prevented, more reasonable forest fire prevention is achieved, and unnecessary loss is reduced.
The spherical early warning device in this application is latticed from low to high installation on the undulation landform in complicated mountain region topography district. Each spherical early warning device comprises a wind speed and direction sensor 1, a Beidou positioning system 2, a storage device 3, an external USB connection 4, an internal power supply 5, an omnibearing infrared temperature sensor 8, a wireless transmission module 7 and a shell 6. The Beidou positioning system 2 provides coordinate and elevation information of adjacent nodes of the Beidou positioning system, the position information of the adjacent nodes of the Beidou positioning system is input into the storage device 3 through the external USB connection 4, and the Beidou positioning system is communicated with adjacent devices and the early warning center 9 through the wireless transmission module 7. And at a fixed time interval t, the early warning nodes mutually transmit fire information sensed by the nodes.
When arranging spherical early warning device in complicated mountain land topography district, the connection can be designed into irregular hexagon according to the topography between the device, and when taking place the condition of a fire, early warning device carries out data interchange along between the topography, and position coordinates, communication address and the temperature data of its own and adjacent three device have been preserved at least to every device to carry out data transmission to early warning center 9.
The temperature, wind speed, wind direction and position information of the early warning device received by the early warning center 9 are received, stored and analyzed by a server, and a data analysis result and a specific position of fire occurrence are obtained by utilizing a GIS system; the display device visually displays the GIS map, accurately positions the ignition point, judges the fire spreading speed and the fire spreading direction, sends out early warning information and plans a nearest fire fighting route.
In the alternative of this application, each module is arranged respectively in the different isolation cabins of different spherical devices, prevents because the whole problem of becoming invalid that local temperature is too high to cause, and every early warning device is to three device timing transmission information on every side, and the temperature of other directions is judged suddenly to the problem on the one hand in advance, stops to burn out certain early warning device because of forest fire on the one hand and leads to whole net to become invalid.
The network layout is reasonable, the arranged Voronoi irregular polygons can cover the whole forest area to be monitored, a risk area is arranged in each selected Voronoi irregular polygon, and a spherical early warning device is arranged on each vertex, so that the timeliness and the accuracy of early warning of the whole forest area are guaranteed; points located on the edges of the Voronoi irregular polygon are equidistant from discrete points on either side of the Voronoi irregular polygon. The monitoring in the risk area is ensured to be uniform; more scientific, and introduces early warning indexes Y of each region i Is calculated by i ＝f(ω i ·m i ) Each evaluation index has different weight, so that the problem that one evaluation index is easily influenced by objective factors is avoided, and the fire warning is more reasonable; the early warning center 9 can send instructions to all spherical early warning devices through the background of the early warning center in the conventional monitoring process, require to upload monitoring data and discover a certain risk area I>At 0.5a%, effective measures are taken. Such as: an unmanned aerial vehicle or a person is dispatched to irrigate, so that the trouble is prevented in the bud, and unnecessary loss is reduced; the spherical device is convenient to install and low in cost, and a single early warning device comprises a Beidou positioning system, an omnidirectional infrared temperature sensor, a battery and a wireless transmission module. The early warning device is of a spherical structure, the total volume is small, and when the early warning device is installed in a complicated mountain land area, the early warning device only needs to be installed on the ground or trees at the position to be monitored; the multi-directional backup is realized, each spherical early warning device transmits information to the early warning center and the surrounding three devices at intervals of t time, and the data caused by that a certain spherical early warning device is damaged by forest fire is prevented from being damagedThe absence causes the entire irregular grid to fail.
The method has the advantages of high efficiency, labor saving, utilization of GIS map visualization, rapid generation of forest fire early warning spatial distribution information of relevant areas, and timely deployment of the background according to early warning information.
Although the present application has been described above with reference to specific embodiments, those skilled in the art will recognize that many changes may be made in the configuration and details of the present application within the principles and scope of the present application. The scope of protection of the application is determined by the appended claims, and all changes that come within the meaning and range of equivalency of the technical features are intended to be embraced therein.
Claims (9)
1. A forest fire early warning method is characterized by comprising the following steps: the method comprises the following steps:
1) Acquiring data of a forest area to be monitored, and acquiring a fire risk area in the forest area according to the data;
2) Performing importance evaluation according to early warning indexes of forest fire conditions by combining with actual conditions of each fire risk area to obtain a judgment matrix of forest fire risk judgment indexes, and calculating early warning indexes Y of each risk area according to the judgment matrix i The method of (1);
3) According to the historical data of the monitored forest regions, calculating the early warning index Y of each risk region i The method of (1) calculates the normal value Y of the early warning index of each fire risk area ref ；
4) Carrying out meshing on the monitored forest region to form an irregular polygon;
5) Each irregular polygon vertex is an early warning node, a spherical early warning device is installed on each early warning node, each early warning device transmits monitored information to an early warning center, and the early warning nodes mutually transmit node induction information;
6) The early warning center calculates the early warning index Y of the current moment according to the early warning index monitored in real time i (t) according to the early warning index Y i (t) and the normal value of the early warning index Y ref Calculating a forest fire danger coefficient I, and judging whether the monitored area is abnormal or not according to the forest fire danger coefficient I and the monitored information;
7) Processing according to the judgment result; the grid division adopts a Voronoi graph algorithm to divide the forest zones to be monitored, the division method comprises the steps of regarding each fire risk area as a point which is discretely distributed in the forest zones to be monitored, and sequentially connecting each adjacent fire risk area to construct a triangular net, namely a Delaunay triangular net in the construction algorithm; numbering each fire risk area and the formed triangulation network, and recording which three fire risk areas each triangulation network consists of; calculating the center of a circumscribed circle of each triangular net, and recording the center of the circle; traversing the triangular linked list, and searching adjacent triangular nets TriA, triB and TriC which are shared with the three edges of the current triangular net pTri; if the triangle is found, connecting the circle center of the circumscribed circle of the triangle and the circle center of the circumscribed circle of the pTri, and storing the circle centers into the Voronoi edge chain table; if the ray can not be found, solving the perpendicular bisector ray at the outermost edge and storing the perpendicular bisector ray in the Voronoi edge linked list; after the traversal is finished, finding all the Voronoi edges, and drawing a Voronoi graph according to the edges; finally obtaining all Voronoi irregular polygons which can go over the edge of the monitoring area step by step; and each Voronoi irregular polygon only contains one fire risk area;
each vertex of the Voronoi irregular polygon is an early warning node, a spherical early warning device is arranged on each early warning node, and each early warning device transmits the monitored temperature T to an early warning center at fixed time intervals T i Air humidity H i And wind speed V i And the early warning nodes mutually transmit node-induced information.
2. A forest fire warning method as claimed in claim 1, characterised in that: the data comprises a satellite map, topographic and geomorphic data, vegetation types and coverage conditions, and year-round temperature, air humidity and wind speed.
3. A forest fire warning method as claimed in claim 1, wherein: the fire risk areas include areas where a fire has occurred, hill and valley areas, areas where lightning strikes have occurred, and areas that are more susceptible to human factors.
4. A forest fire warning method as claimed in claim 1, wherein: the early warning indexes comprise monitoring values of all points, vegetation conditions of all risk areas and human factors; the judgment matrix is the influence degree of the change of a certain evaluation index on other elements.
5. A forest fire warning method as claimed in claim 1, characterised in that: the early warning index Y i The calculation comprises the step of determining a weight vector theory by using a feature vector method in an analytic hierarchy process; calculating a characteristic vector corresponding to the maximum characteristic value of the judgment matrix, and normalizing to obtain the characteristic vector which is each element omega in the weight vector omega, omega i Namely the weight of each corresponding evaluation index, thereby constructing and calculating the early warning index Y of each risk area i ：Y i ＝f(ω i ·m i )。
6. A forest fire warning method as claimed in claim 1, wherein: when two adjacent risk areas are far away from each other, a secondary risk area is arranged between the two adjacent risk areas with larger distance for enabling the effective radius of the spherical early warning device to cover the whole monitoring forest area.
7. A forest fire warning method as claimed in claim 5, wherein: the forest fire hazard coefficient
And when the forest fire danger coefficient I is greater than the minimum threshold value a of the forest fire danger coefficient, the monitored area is an abnormal area.
8. A forest fire early warning method as claimed in any one of claims 1 to 7, wherein: and the processing according to the judgment result comprises observing on a GIS map of a background of the early warning center, confirming the specific position of the abnormal area, judging the spreading speed and the spreading direction on the GIS map according to the wind direction, deploying in time and dispatching the unmanned aerial vehicle for confirmation.
9. A forest fire warning method as claimed in claim 8, wherein: the early warning center can send instructions to all spherical early warning devices through the early warning center background in the conventional monitoring process, requires to upload monitoring data, and takes effective measures when finding that the forest fire danger coefficient I of a certain risk area is greater than 0.5 times of the minimum threshold value a of the forest fire danger coefficient.
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| CN115640496B (en) * | 2022-12-16 | 2023-03-10 | 天津众联智能科技有限责任公司 | Electrical hidden danger judgment method based on improved analytic hierarchy process |
| CN116701409B (en) * | 2023-08-07 | 2023-11-03 | 湖南永蓝检测技术股份有限公司 | Sensor data storage method for intelligent on-line detection of environment |
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