CN116612598A - Multi-point monitoring type forest fire prevention monitoring system based on Internet of things - Google Patents
Multi-point monitoring type forest fire prevention monitoring system based on Internet of things Download PDFInfo
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Abstract
The invention relates to the technical field of forest fire prevention monitoring, and particularly discloses a multi-point monitoring type forest fire prevention monitoring system based on the Internet of things, which comprises a ground defoliation information monitoring module, a vegetation fire inflammable analysis module, a meteorological information monitoring analysis module, a forest fire early warning analysis module, an early warning terminal and a cloud database; according to the invention, the defoliation layer, the different types of vegetation layers and the meteorological layers are monitored and analyzed, the forest fire early warning coefficient of each monitoring area is calculated, and the fire early warning evaluation is carried out according to the forest fire early warning coefficient, so that the multidimensional analysis of the forest fire early warning of each monitoring area is realized, the timeliness and the real-time performance of forest manager on the detection of forest fire abnormal conditions are improved, the limitation of the forest fire monitoring analysis according to the open flame condition, the meteorological conditions and the vegetation distribution condition at present is effectively solved, and the life and property safety of human beings is ensured.
Description
Technical Field
The invention relates to the technical field of forest fire prevention monitoring, in particular to a multipoint monitoring type forest fire prevention monitoring system based on the Internet of things.
Background
Forest is one of the most important ecological systems, provides rich key ecological functions such as biodiversity, oxygen supply, water circulation, soil protection, carbon absorption and the like, and forest fires threaten human life and property safety while causing burning of a large number of trees, death of animals and plants and loss of biodiversity, so forest fire prevention monitoring is needed.
The existing forest is mainly used for carrying out fireproof monitoring according to open flame conditions, meteorological conditions and vegetation distribution conditions, and obviously, the fireproof monitoring mode has the following problems: 1. the forest fire prevention monitoring is carried out through the open fire condition, the delay of fire disaster discovery exists, and simultaneously forest management personnel carry out the fire prevention monitoring on site, so that the forest fire prevention effect cannot be guaranteed.
2. The thickness condition of fallen leaves is only considered at present, the inflammability condition and the distribution condition of fallen leaves are not subjected to deep analysis, the pertinence of forest fire prevention monitoring is reduced, and the accuracy of forest fire prevention is reduced.
3. The influences of varieties of vegetation, grease amount and water content on the possibility of fire occurrence are not considered, the inflammable conditions of the vegetation of different varieties are different, the coverage of the current forest fire prevention monitoring is insufficient, the hidden danger of forest fire is increased, the stability of a forest ecological system is damaged, and the life and property safety of human beings is threatened.
Disclosure of Invention
In view of this, in order to solve the problems set forth in the background art, a multi-point monitoring type forest fire prevention monitoring system based on the internet of things is provided.
The aim of the invention can be achieved by the following technical scheme: the invention provides a multipoint monitoring type forest fire prevention monitoring system based on the Internet of things, which comprises the following steps: the ground defoliation information monitoring module is used for dividing the target forest into areas to obtain divided monitoring areas, extracting the area of each monitoring area, and monitoring the number of defoliation color types in each monitoring area and the coverage area corresponding to each defoliation color type.
The vegetation information monitoring module is used for monitoring the types of the low-level vegetation and the high-level vegetation in each monitoring area, the occupation area of each low-level vegetation type and each high-level vegetation type and the vegetation number, and monitoring the outline volume, the height, the leaf color of each low-level vegetation in each low-level vegetation type and the breast diameter and the branch color of each high-level vegetation in each high-level vegetation type.
The vegetation fire inflammability analysis module is used for calculating fire inflammability coefficients of the vegetation corresponding to the apparent layer surface and the non-apparent layer surface of each monitoring area so as to analyze the forest fire inflammability coefficients of the vegetation corresponding to the vegetation layer surface of each monitoring area, wherein />Indicating the number of the monitored area,/-, and>。
the meteorological information monitoring and analyzing module is used for monitoring the temperature, the humidity and the sunlight intensity of each monitoring area so as to analyze the forest fire inflammability coefficient of the corresponding meteorological layer of each monitoring area。
The cloud database is used for storing inflammable fallen leaf color types, branches and leaves, storing oil and water content of each low-level vegetation type and each high-level vegetation type corresponding to each age, and storing conventional growth rate of each high-level vegetation type.
The forest fire early warning analysis module is used for calculating forest fire early warning coefficients of all monitoring areas。
And the early warning terminal is used for carrying out forest fire early warning when the forest fire early warning coefficient of a certain monitoring area is greater than or equal to the forest fire early warning coefficient of the set reference.
Specifically, the fire inflammability coefficient of the vegetation corresponding apparent layer of each monitoring area is calculated by the following specific calculation process: a1, matching each fallen leaf color type in each monitoring area with each inflammable fallen leaf color type stored in a cloud database to obtain the inflammable fallen leaf color type number in each monitoring area, and recording as。
A2, accumulating the coverage areas corresponding to the inflammable fallen leaf color types in each monitoring area to obtain inflammable fallen leaf coverage total areas, and marking as。
A3, respectively recording the area of each monitoring area and the number of fallen leaf color types in each monitoring area as and />。
A4, calculating fire inflammability coefficients of apparent layers corresponding to fallen leaf layers in all monitoring areas，。
wherein , and />Respectively representing the inflammable defoliation color category number ratio and defoliation coverage total area ratio of the set reference, +.> and />Fire flammability assessment duty weight of the apparent layer corresponding to the set number of the flammable fallen leaf color types and the total fallen leaf coverage area duty ratio respectively, and +.>And the fire flammability assessment correction factors of the set falling leaf layers corresponding to the apparent layers are shown.
A5, calculating fire flammability coefficients of apparent layers corresponding to the low vegetation layers of the monitoring areas according to the vegetation numbers of the low vegetation types of the monitoring areas and the leaf colors of the low vegetation types。
A6, according to the vegetation number of each height vegetation type and the branch color of each height vegetation in each height vegetation type of each monitoring area, calculating the fire flammability coefficient of the apparent layer corresponding to the height vegetation layer of each monitoring area in the same way according to the calculation mode of the fire flammability coefficient of the apparent layer corresponding to the low height vegetation layer of each monitoring area。
A7, calculating fire inflammability coefficients of the apparent layers corresponding to the vegetation in each monitoring area。
Specifically, the fire flammability coefficient of the corresponding apparent layer of the low vegetation layer of each monitoring area is calculated, and the specific calculation process is as follows: b1, the vegetation number of each low-level vegetation type in each monitoring area is recorded as, wherein ,/>Indicating low vegetation category number->。
B2, calculating the density of low vegetation in each monitoring area，/>。
And B3, if the color of the blade of a certain low-level vegetation in each low-level vegetation type of each monitoring area is successfully matched with the color of each flammable blade stored in the cloud database, marking the low-level vegetation as flammable vegetation, and counting the number of flammable vegetation in each low-level vegetation type of each monitoring area.
B4, accumulating the flammable vegetation numbers in the low-level vegetation types of the monitoring areas to obtain the total flammable low-level vegetation numbers of the monitoring areas, and recording the total flammable low-level vegetation numbers as。
B5, calculating fire inflammability coefficients of apparent layers corresponding to the low vegetation layers of each monitoring area，。
wherein , and />Respectively representing the total vegetation number ratio of the density and the inflammable low level of the set reference, +.> and />Respectively representing the set density and the fire flammability assessment duty ratio weight of the total number of the flammable low-level vegetation to the corresponding apparent layer,and the fire flammability assessment correction factors of the corresponding apparent layers of the set low-level vegetation layers are shown.
Specifically, the calculation formula of the fire flammability coefficient of the vegetation corresponding apparent layer of each monitoring area is as follows:, wherein ,/> and />Fire flammability evaluation occupancy weights respectively representing set falling leaf level, low vegetation level and high vegetation level corresponding to apparent level>Representing natural constants.
Specifically, the fire flammability coefficient of the vegetation corresponding to the non-apparent layer of each monitoring area is calculated by the specific calculation process: c1, the breast diameter of each height vegetation in each height vegetation type of each monitoring area is recorded as, wherein ,/>Indicates the high vegetation type number->，/>Number indicating height vegetation->。
C2, extracting the conventional growth rate of each high vegetation type from the cloud database and marking as。
C3, calculating the estimated age of each height vegetation in each height vegetation type of each monitoring area，。
And C4, positioning the oil and water content of each height vegetation in each height vegetation type of each monitoring area from the cloud database according to the expected age of each height vegetation in each height vegetation type of each monitoring area.
C5, extracting the maximum oil from the oil of each height vegetation type in each monitoring area, and recording asCalculating the average water content of each vegetation in each vegetation type in each monitoring area to obtain the average water content of each vegetation type in each monitoring area, and marking the average water content as +.>。
C6, calculating fire flammability coefficients of the non-apparent layers corresponding to the vegetation types at each height in each monitoring area，。
wherein , and />Respectively indicate the +.>Maximum fat content and average water content of seed height vegetation, +.> and />Fire flammability assessment occupancy weight for maximum grease content and average water content of respectively representing set height vegetation corresponding to non-apparent bedding surface, ++>And the fire flammability assessment correction factors which indicate the set height vegetation types correspond to the non-apparent layers.
C7, calculating fire flammability coefficients of the non-apparent layers corresponding to the height vegetation of each monitoring area。
C8, calculating the estimated age of each low vegetation in each low vegetation type of each monitoring area according to the contour volume and the height of each low vegetation in each low vegetation type of each monitoring area, wherein ,/>A number representing the low level vegetation,。
c9, according to the expected age of each low-level vegetation in each low-level vegetation type of each monitoring area, calculating the fire flammability coefficient of each low-level vegetation of each monitoring area corresponding to the non-apparent layer according to the calculation mode of the fire flammability coefficient of each high-level vegetation of each monitoring area corresponding to the non-apparent layerThereby calculating the fire flammability coefficient of the vegetation corresponding to the non-apparent layer of each monitoring area>。
Specifically, the fire flammability coefficient of the non-apparent layer corresponding to the height vegetation of each monitoring area is calculated by the specific calculation process: d1, extracting the maximum fire flammability factor from the fire flammability factors of the non-apparent layers corresponding to the vegetation types at each height in each monitoring area, and marking as。
D2, if the fire flammability factor of the non-apparent layer corresponding to the vegetation type at a certain height in a certain monitoring area is greater than or equal to the flammability factor of the fire with a set reference, marking the vegetation type at the height as flammable vegetation type, thereby counting the comprehensive occupied area of the vegetation type at the flammable height in each monitoring area, and marking as。
D3, calculating fire flammability coefficients of the non-apparent layers corresponding to the height vegetation of each monitoring area，。
wherein , and />Respectively representing the maximum fire flammability coefficient and the comprehensive floor area of the height vegetation for setting reference,/-> and />Fire flammability evaluation duty ratio weights respectively representing maximum fire flammability coefficient of set height vegetation and corresponding non-apparent level of comprehensive occupied area, ++>And the fire flammability assessment correction factors of the set height vegetation corresponding to the non-apparent layers are shown.
Specifically, the calculation formula of the fire flammability coefficient of the vegetation corresponding to the non-apparent layer in each monitoring area is as follows:，/> and />And respectively representing the fire flammability evaluation duty ratio weights of the set low vegetation and the set high vegetation corresponding to the non-apparent layers.
Specifically, the calculation formula of the forest fire flammability coefficient of the vegetation layer corresponding to each monitoring area is as follows:, wherein ,/> and />And respectively representing the set apparent and non-apparent bedding surfaces corresponding to the forest fire flammability evaluation duty ratio weights.
Specifically, the analysis of each monitoring area corresponds to a meteorological levelThe forest fire inflammability coefficient comprises the following specific analysis processes: e1, respectively recording the temperature, humidity and sunlight intensity of each monitoring area as and />。
E2, calculating the forest fire flammability coefficient of each monitoring area corresponding to the meteorological layer，。
wherein , and />Respectively indicating the temperature, humidity and sunlight intensity of the set reference,/-> and />Respectively representing the set temperature, humidity and sunlight intensity corresponding to the flammable duty ratio weight of forest fire, and the weight of the forest fire>And the set meteorological-layer forest fire flammability evaluation correction factor is represented.
Specifically, the calculation formula of the forest fire early warning coefficient of each monitoring area is as follows:, wherein ,/> and />And respectively representing the set forest fire early warning evaluation duty ratio weights corresponding to the vegetation layer and the meteorological layer.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects: (1) According to the invention, through monitoring and analyzing the fallen leaf layer, the vegetation layer of different types and the meteorological layer, the forest fire early warning coefficient of each monitoring area is calculated, and the fire early warning evaluation is carried out according to the forest fire early warning coefficient, so that the multidimensional analysis of the forest fire early warning of each monitoring area is realized, the timeliness and the instantaneity of the forest manager for detecting the abnormal forest fire situations are improved, the rationality and the suitability of the forest fire plan are improved, and the occurrence probability of the forest fire is reduced.
(2) According to the invention, the fire flammability coefficient analysis of the fallen leaves is carried out according to the color types of the fallen leaves and the coverage areas of the color types of the fallen leaves, so that the pertinence and the accuracy of the fire flammability analysis of the apparent vegetation layer are improved, and a reliable data support basis is provided for subsequent forest fire prevention early warning.
(3) When the fire flammability coefficient analysis of the vegetation level is carried out, the dual analysis of the vegetation level is realized by respectively carrying out the fire flammability coefficient analysis of the apparent level and the non-apparent level on the low-level vegetation and the high-level vegetation, the limitation of the forest fire prevention monitoring analysis only according to the vegetation distribution condition at present is effectively solved, the forest fire hidden danger is effectively reduced, the stability of a forest ecological system is improved, and the life and property safety of human beings is ensured.
(4) When the fire combustible coefficient analysis is carried out on the non-apparent layers corresponding to the low-level vegetation and the high-level vegetation, the fire combustible coefficient analysis is carried out on the non-apparent layers corresponding to the vegetation in each monitoring area through the analysis of parameters such as the types, the occupied areas and the number of the vegetation, so that the sufficiency and the credibility of the fire combustible coefficient analysis are improved, and the forest fire prevention effect is ensured.
(5) According to the invention, the vegetation age estimation is carried out from the contour volume and the height of the low-level vegetation and the breast diameter of the high-level vegetation, so that the water content and the grease content of the vegetation are confirmed, the fire flammability coefficient of each monitoring area vegetation corresponding to the non-apparent layer is analyzed, the accuracy and the rationality of the fire flammability coefficient confirmation of the non-apparent layer of the vegetation are improved, and a more stable supporting basis is provided without subsequent forest fire prevention early warning coefficient confirmation.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram showing the connection of the system modules according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a multi-point monitoring type forest fire prevention monitoring system based on the internet of things, comprising: the system comprises a ground fallen leaf information monitoring module, a vegetation fire inflammable analysis module, a meteorological information monitoring and analysis module, a forest fire early warning analysis module, an early warning terminal and a cloud database.
The system comprises a ground fallen leaf information monitoring module, a vegetation information monitoring module and a cloud database, wherein the ground fallen leaf information monitoring module, the vegetation information monitoring module and the cloud database are connected with a vegetation fire inflammable analysis module, the vegetation fire inflammable analysis module and the weather information monitoring analysis module are connected with a forest fire early warning analysis module, and the forest fire early warning analysis module is connected with an early warning terminal.
The ground defoliation information monitoring module is used for dividing a target forest into areas to obtain divided monitoring areas, extracting the area of each monitoring area, and monitoring the number of defoliation color types in each monitoring area and the coverage area corresponding to each defoliation color type.
It should be noted that, the defoliation information of each monitoring area, the low-level vegetation information and the high-level vegetation information mentioned later are all obtained through monitoring by the cameras carried by the unmanned aerial vehicle.
The vegetation information monitoring module is used for monitoring the types of the low-level vegetation and the high-level vegetation in each monitoring area, the occupation area of each low-level vegetation type and each high-level vegetation type and the vegetation number, and monitoring the outline volume, the height, the leaf color of each low-level vegetation in each low-level vegetation type and the breast diameter and the branch color of each high-level vegetation in each high-level vegetation type.
It should be noted that, the leaf color is the main body leaf color of low vegetation, and the evaluation mode of the main body leaf color is: the color with the largest ratio of the low-level vegetation leaf color is used as the main body leaf color.
The vegetation fire inflammability analysis module is used for calculating fire inflammability coefficients of the vegetation corresponding to the apparent layer and the non-apparent layer of each monitoring area so as to analyze forest fire inflammability coefficients of the vegetation corresponding to each monitoring area, wherein />Indicating the number of the monitored area,/-, and>。
in a specific embodiment of the invention, the fire flammability coefficient of the vegetation corresponding to the apparent layer of each monitoring area is calculated by the following specific calculation process: a1, matching each fallen leaf color type in each monitoring area with each inflammable fallen leaf color type stored in a cloud database to obtain the inflammable fallen leaf color type number in each monitoring area, and recording as。
A2, accumulating the coverage areas corresponding to the inflammable fallen leaf color types in each monitoring area to obtain inflammable fallen leaf coverage total areas, and marking as。
A3, respectively recording the area of each monitoring area and the number of fallen leaf color types in each monitoring area as and />。
A4, calculating fire inflammability coefficients of apparent layers corresponding to fallen leaf layers in all monitoring areas，。
wherein , and />Respectively representing the inflammable defoliation color category number ratio and defoliation coverage total area ratio of the set reference, +.> and />Fire flammability assessment duty weight of the apparent layer corresponding to the set number of the flammable fallen leaf color types and the total fallen leaf coverage area duty ratio respectively, and +.>And the fire flammability assessment correction factors of the set falling leaf layers corresponding to the apparent layers are shown.
According to the embodiment of the invention, the fire flammability coefficient analysis of the fallen leaves is carried out according to the color types of the fallen leaves and the coverage areas of the color types of the fallen leaves, so that the pertinence and the accuracy of the fire flammability analysis of the apparent vegetation layers are improved, and a reliable data support basis is provided for subsequent forest fire prevention early warning.
A5, calculating fire flammability coefficients of apparent layers corresponding to the low vegetation layers of the monitoring areas according to the vegetation numbers of the low vegetation types of the monitoring areas and the leaf colors of the low vegetation types。
In a specific embodiment of the invention, the fire flammability coefficient of the low vegetation level of each monitoring area corresponding to the apparent level is calculated by the following specific calculation process: b1, the vegetation number of each low-level vegetation type in each monitoring area is recorded as, wherein ,/>Indicating low vegetation category number->。
B2, calculating the density of low vegetation in each monitoring area，/>。
And B3, if the color of the blade of a certain low-level vegetation in each low-level vegetation type of each monitoring area is successfully matched with the color of each flammable blade stored in the cloud database, marking the low-level vegetation as flammable vegetation, and counting the number of flammable vegetation in each low-level vegetation type of each monitoring area.
B4, accumulating the inflammable vegetation numbers in the low-level vegetation types of the monitoring areas to obtain the total inflammable low-level vegetation numbers of the monitoring areas, and recordingIs that。
B5, calculating fire inflammability coefficients of apparent layers corresponding to the low vegetation layers of each monitoring area，。
wherein , and />Respectively representing the total vegetation number ratio of the density and the inflammable low level of the set reference, +.> and />Respectively representing the set density and the fire flammability assessment duty ratio weight of the total number of the flammable low-level vegetation to the corresponding apparent layer,and the fire flammability assessment correction factors of the corresponding apparent layers of the set low-level vegetation layers are shown.
A6, according to the vegetation number of each height vegetation type and the branch color of each height vegetation in each height vegetation type of each monitoring area, calculating the fire flammability coefficient of the apparent layer corresponding to the height vegetation layer of each monitoring area in the same way according to the calculation mode of the fire flammability coefficient of the apparent layer corresponding to the low height vegetation layer of each monitoring area。
A7, calculating fire inflammability coefficients of the apparent layers corresponding to the vegetation in each monitoring area。
In a specific embodiment of the present invention, a calculation formula of the fire flammability coefficient of the vegetation corresponding to the apparent layer of each monitoring area is:, wherein ,/> and />Fire flammability evaluation occupancy weights respectively representing set falling leaf level, low vegetation level and high vegetation level corresponding to apparent level>Representing natural constants.
In a specific embodiment of the invention, the fire flammability coefficient of the vegetation corresponding to the non-apparent layer of each monitoring area is calculated by the following specific calculation process: c1, the breast diameter of each height vegetation in each height vegetation type of each monitoring area is recorded as, wherein ,/>Indicates the high vegetation type number->，/>Number indicating height vegetation->。
C2, extracting the conventional growth rate of each high vegetation type from the cloud database and marking as。
C3, calculating the estimated age of each height vegetation in each height vegetation type of each monitoring area，。
And C4, positioning the oil and water content of each height vegetation in each height vegetation type of each monitoring area from the cloud database according to the expected age of each height vegetation in each height vegetation type of each monitoring area.
C5, extracting the maximum oil from the oil of each height vegetation type in each monitoring area, and recording asCalculating the average water content of each vegetation in each vegetation type in each monitoring area to obtain the average water content of each vegetation type in each monitoring area, and marking the average water content as +.>。
C6, calculating fire flammability coefficients of the non-apparent layers corresponding to the vegetation types at each height in each monitoring area，。
wherein , and />Respectively indicate the +.>Maximum fat content and average water content of seed height vegetation, +.> and />Fire flammability assessment occupancy weight for maximum grease content and average water content of respectively representing set height vegetation corresponding to non-apparent bedding surface, ++>And the fire flammability assessment correction factors which indicate the set height vegetation types correspond to the non-apparent layers.
C7, calculating fire flammability coefficients of the non-apparent layers corresponding to the height vegetation of each monitoring area。
In a specific embodiment of the present invention, the calculating the fire flammability coefficient of the height vegetation corresponding to the non-apparent layer in each monitoring area includes the following steps: d1, extracting the maximum fire flammability factor from the fire flammability factors of the non-apparent layers corresponding to the vegetation types at each height in each monitoring area, and marking as。
D2, if the fire flammability factor of the non-apparent layer corresponding to the vegetation type at a certain height in a certain monitoring area is greater than or equal to the flammability factor of the fire with a set reference, marking the vegetation type at the height as flammable vegetation type, thereby counting the comprehensive occupied area of the vegetation type at the flammable height in each monitoring area, and marking as。
D3, calculating fire flammability coefficients of the non-apparent layers corresponding to the height vegetation of each monitoring area，。
wherein , and />Respectively representing the maximum fire flammability coefficient and the comprehensive floor area of the height vegetation for setting reference,/-> and />Fire flammability evaluation duty ratio weights respectively representing maximum fire flammability coefficient of set height vegetation and corresponding non-apparent level of comprehensive occupied area, ++>And the fire flammability assessment correction factors of the set height vegetation corresponding to the non-apparent layers are shown.
C8, calculating the estimated age of each low vegetation in each low vegetation type of each monitoring area according to the contour volume and the height of each low vegetation in each low vegetation type of each monitoring area, wherein ,/>A number representing the low level vegetation,。
it should be noted that, the calculating the estimated age of each low-level vegetation in each low-level vegetation type of each monitoring area specifically includes: f1, respectively marking the contour volume and the height of each low-level vegetation in each low-level vegetation type of each monitoring area as and />。
F2, slave cloudExtracting conventional growth rate of each low-level vegetation type from a database and marking the conventional growth rate as。
F3, calculating the estimated age of each low vegetation in each low vegetation type of each monitoring area，, wherein ,/> and />The set contour volume and the height of the low vegetation are respectively represented by the duty ratio weights of the predicted ages.
C9, according to the expected age of each low-level vegetation in each low-level vegetation type of each monitoring area, calculating the fire flammability coefficient of each low-level vegetation of each monitoring area corresponding to the non-apparent layer according to the calculation mode of the fire flammability coefficient of each high-level vegetation of each monitoring area corresponding to the non-apparent layerThereby calculating the fire flammability coefficient of the vegetation corresponding to the non-apparent layer of each monitoring area>。
According to the embodiment of the invention, the age estimation of the vegetation is carried out from the contour volume and the height of the low-level vegetation and the breast diameter of the high-level vegetation, so that the water content and the oil content of the vegetation are confirmed, the fire flammability coefficient of the vegetation in each monitoring area corresponding to the non-apparent layer is analyzed, the accuracy and the rationality of the fire flammability coefficient confirmation of the vegetation in the non-apparent layer are improved, and a more stable supporting basis is provided for the follow-up forest fire prevention early warning coefficient confirmation.
In a specific embodiment of the invention, the vegetation pairs of each monitoring areaThe calculation formula of the fire flammability factor of the non-apparent layer is as follows:，/> and />And respectively representing the fire flammability evaluation duty ratio weights of the set low vegetation and the set high vegetation corresponding to the non-apparent layers.
When the fire flammability coefficient analysis is carried out on the non-apparent layers corresponding to the low-level vegetation and the high-level vegetation, the fire flammability coefficient analysis is carried out on the non-apparent layers corresponding to the vegetation in each monitoring area through analysis of parameters such as the types, the occupied areas and the number of the vegetation, so that the fullness and the credibility of the fire flammability coefficient analysis on the non-apparent layers corresponding to the vegetation in each monitoring area are improved, and the forest fire prevention effect is ensured.
In a specific embodiment of the present invention, a calculation formula of a forest fire flammability coefficient of each monitored area corresponding to a vegetation level is:, wherein ,/> and />And respectively representing the set apparent and non-apparent bedding surfaces corresponding to the forest fire flammability evaluation duty ratio weights.
When the fire flammability coefficient analysis of the vegetation level is carried out, the dual analysis of the vegetation level is realized by respectively carrying out the fire flammability coefficient analysis of the apparent level and the non-apparent level on the low-level vegetation and the high-level vegetation, the limitation of forest fire prevention monitoring analysis only according to the vegetation distribution condition at present is effectively solved, the forest fire hidden danger is effectively reduced, the stability of a forest ecological system is improved, and the life and property safety of human beings is ensured.
The meteorological information monitoring and analyzing module is used forMonitoring the temperature, humidity and sunlight intensity of each monitoring area so as to analyze the forest fire flammability coefficient of each monitoring area corresponding to the meteorological layer。
The temperature, humidity and sunlight intensity of each monitoring area are obtained by monitoring a temperature sensor, a humidity sensor and a sunlight intensity sensor which are arranged at the central point of each monitoring area.
In a specific embodiment of the invention, the analysis of the forest fire flammability coefficient of each monitoring area corresponding to the meteorological layer comprises the following specific analysis processes: e1, respectively recording the temperature, humidity and sunlight intensity of each monitoring area as and />。
E2, calculating the forest fire flammability coefficient of each monitoring area corresponding to the meteorological layer，/>。
wherein , and />Respectively indicating the temperature, humidity and sunlight intensity of the set reference,/-> and />Respectively representing the set temperature, humidity and sunlight intensity corresponding to the flammable duty ratio weight of forest fire, and the weight of the forest fire>And the set meteorological-layer forest fire flammability evaluation correction factor is represented.
The cloud database is used for storing inflammable fallen leaf color types, branches and leaves, storing oil and water content of each low-level vegetation type and each high-level vegetation type corresponding to each age, and storing conventional growth rate of each high-level vegetation type.
The forest fire early warning analysis module is used for calculating forest fire early warning coefficients of all monitoring areas。
In a specific embodiment of the present invention, a calculation formula of the forest fire early warning coefficient of each monitoring area is:, wherein ,/> and />And respectively representing the set forest fire early warning evaluation duty ratio weights corresponding to the vegetation layer and the meteorological layer.
The early warning terminal is used for carrying out forest fire early warning when the forest fire early warning coefficient of a certain monitoring area is larger than or equal to the forest fire early warning coefficient of a set reference.
According to the embodiment of the invention, the defoliation layer, the vegetation layer of different types and the meteorological layer are monitored and analyzed, the forest fire early warning coefficient of each monitoring area is calculated, and the fire early warning evaluation is carried out according to the forest fire early warning coefficient, so that the multidimensional analysis of the forest fire early warning of each monitoring area is realized, the timeliness and the real-time performance of forest manager on the detection of abnormal forest fire prevention conditions are improved, the rationality and the suitability of the forest fire prevention plan are improved, and the probability of forest fire occurrence is reduced.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (10)
1. Multi-point monitoring formula forest fire prevention monitored control system based on thing networking, its characterized in that includes:
the ground defoliation information monitoring module is used for dividing a target forest into areas to obtain divided monitoring areas, extracting the area of each monitoring area, and monitoring the number of defoliation color types in each monitoring area and the coverage area corresponding to each defoliation color type;
the vegetation information monitoring module is used for monitoring the types of the low-level vegetation and the high-level vegetation in each monitoring area, the occupation area of each low-level vegetation type and each high-level vegetation type and the vegetation number, and monitoring the outline volume, the height, the leaf color of each low-level vegetation in each low-level vegetation type and the breast diameter and the branch color of each high-level vegetation in each high-level vegetation type;
the vegetation fire inflammability analysis module is used for calculating fire inflammability coefficients of the vegetation corresponding to the apparent layer surface and the non-apparent layer surface of each monitoring area so as to analyze the forest fire inflammability coefficients of the vegetation corresponding to the vegetation layer surface of each monitoring area, wherein />Indicating the number of the monitored area,/-, and>；
the meteorological information monitoring and analyzing module is used for monitoring the temperature, the humidity and the sunlight intensity of each monitoring area so as to analyze the forest fire inflammability coefficient of the corresponding meteorological layer of each monitoring area；
The cloud database is used for storing inflammable fallen leaf color types, branches and leaves, storing oil and water content of each low-level vegetation type and each high-level vegetation type corresponding to each age, and storing conventional growth rates of each low-level vegetation type and each high-level vegetation type;
the forest fire early warning analysis module is used for calculating forest fire early warning coefficients of all monitoring areas；
And the early warning terminal is used for carrying out forest fire early warning when the forest fire early warning coefficient of a certain monitoring area is greater than or equal to the forest fire early warning coefficient of the set reference.
2. The multi-point monitoring type forest fire prevention monitoring system based on the internet of things as set forth in claim 1, wherein: the fire inflammability coefficient of the vegetation corresponding apparent layer of each monitoring area is calculated, and the specific calculation process is as follows:
a1, matching each fallen leaf color type in each monitoring area with each inflammable fallen leaf color type stored in a cloud database to obtain the inflammable fallen leaf color type number in each monitoring area, and recording as；
A2, accumulating the coverage areas corresponding to the inflammable fallen leaf color types in each monitoring area to obtain inflammable fallen leaf coverage total areas, and marking as;
A3, respectively recording the area of each monitoring area and the number of fallen leaf color types in each monitoring area as and />；
A4, calculating fire inflammability coefficients of apparent layers corresponding to fallen leaf layers in all monitoring areas，；
wherein , and />Respectively representing the inflammable defoliation color category number ratio and defoliation coverage total area ratio of the set reference, +.> and />Fire flammability assessment duty weight of the apparent layer corresponding to the set number of the flammable fallen leaf color types and the total fallen leaf coverage area duty ratio respectively, and +.>A fire inflammability assessment correction factor representing the apparent layer corresponding to the set fallen leaf layer;
a5, calculating fire flammability coefficients of apparent layers corresponding to the low vegetation layers of the monitoring areas according to the vegetation numbers of the low vegetation types of the monitoring areas and the leaf colors of the low vegetation types；
A6, according to the vegetation number of each height vegetation type and the branch color of each height vegetation in each height vegetation type in each monitoring area, according to each monitoring areaThe fire inflammability coefficient of the apparent layer corresponding to the vegetation layer of each monitoring area is calculated in the same way by the calculation mode of the fire inflammability coefficient of the apparent layer corresponding to the vegetation layer of the low-level vegetation layer；
A7, calculating fire inflammability coefficients of the apparent layers corresponding to the vegetation in each monitoring area。
3. The multi-point monitoring type forest fire prevention monitoring system based on the internet of things as set forth in claim 2, wherein: the fire inflammability coefficient of the corresponding apparent layer of the low vegetation layer of each monitoring area is calculated, and the specific calculation process is as follows:
b1, the vegetation number of each low-level vegetation type in each monitoring area is recorded as, wherein ,/>Indicating low vegetation category number->；
B2, calculating the density of low vegetation in each monitoring area，/>；
B3, if the color of the blade of a certain low-level vegetation in each low-level vegetation type of each monitoring area is successfully matched with the color of each flammable blade stored in the cloud database, marking the low-level vegetation as flammable vegetation, and counting the number of flammable vegetation in each low-level vegetation type of each monitoring area;
b4, accumulating the flammable vegetation numbers in the low-level vegetation types of the monitoring areas to obtain the total flammable low-level vegetation numbers of the monitoring areas, and recording the total flammable low-level vegetation numbers as；
B5, calculating fire inflammability coefficients of apparent layers corresponding to the low vegetation layers of each monitoring area，;
wherein , and />Respectively representing the total vegetation number ratio of the density and the inflammable low level of the set reference, +.> and />Fire flammability assessment duty weight which respectively represents the set total vegetation number of density and flammability low level to the corresponding apparent layer>And the fire flammability assessment correction factors of the corresponding apparent layers of the set low-level vegetation layers are shown.
4. The multi-point monitoring type forest fire prevention monitoring system based on the internet of things as set forth in claim 2, wherein: the calculation formula of the fire flammability coefficient of the vegetation corresponding apparent layer of each monitoring area is as follows:, wherein ,/> and />Fire flammability evaluation occupancy weights respectively representing set falling leaf level, low vegetation level and high vegetation level corresponding to apparent level>Representing natural constants.
5. The internet of things-based multipoint monitoring type forest fire prevention monitoring system as set forth in claim 4, wherein: the fire inflammable coefficient of the vegetation corresponding to the non-apparent layer of each monitoring area is calculated by the specific calculation process:
c1, the breast diameter of each height vegetation in each height vegetation type of each monitoring area is recorded as, wherein ,/>Indicates the high vegetation type number->，/>Number indicating height vegetation->；
C2, extracting the conventional growth rate of each high vegetation type from the cloud database and marking as；
C3, calculating the estimated age of each height vegetation in each height vegetation type of each monitoring area，/>；
C4, according to the expected age of each height vegetation in each height vegetation type of each monitoring area, positioning the oil content and the water content of each height vegetation in each height vegetation type of each monitoring area from a cloud database;
c5, extracting the maximum oil from the oil of each height vegetation type in each monitoring area, and recording asCalculating the average water content of each vegetation in each vegetation type in each monitoring area to obtain the average water content of each vegetation type in each monitoring area, and marking the average water content as +.>；
C6, calculating fire flammability coefficients of the non-apparent layers corresponding to the vegetation types at each height in each monitoring area，；
wherein , and />Respectively indicate the +.>Maximum fat content and average water content of seed height vegetation, +.> and />Fire flammability assessment occupancy weight for maximum grease content and average water content of respectively representing set height vegetation corresponding to non-apparent bedding surface, ++>A fire inflammability assessment correction factor representing the corresponding non-apparent layer of the set height vegetation type;
c7, calculating fire flammability coefficients of the non-apparent layers corresponding to the height vegetation of each monitoring area；
C8, calculating the estimated age of each low vegetation in each low vegetation type of each monitoring area according to the contour volume and the height of each low vegetation in each low vegetation type of each monitoring area, wherein ,/>A number representing the low level vegetation,；
c9, according to the expected age of each low-level vegetation in each low-level vegetation type of each monitoring area, calculating the fire flammability coefficient of each low-level vegetation of each monitoring area corresponding to the non-apparent layer according to the calculation mode of the fire flammability coefficient of each high-level vegetation of each monitoring area corresponding to the non-apparent layerThereby calculating the implantation of each monitoring areaIs corresponding to the fire flammability factor of the non-apparent layer>。
6. The internet of things-based multipoint monitoring type forest fire prevention monitoring system as set forth in claim 5, wherein: the fire inflammable coefficient of the non-apparent layer corresponding to the height vegetation of each monitoring area is calculated, and the specific calculation process is as follows:
d1, extracting the maximum fire flammability factor from the fire flammability factors of the non-apparent layers corresponding to the vegetation types at each height in each monitoring area, and marking as；
D2, if the fire flammability factor of the non-apparent layer corresponding to the vegetation type at a certain height in a certain monitoring area is greater than or equal to the flammability factor of the fire with a set reference, marking the vegetation type at the height as flammable vegetation type, thereby counting the comprehensive occupied area of the vegetation type at the flammable height in each monitoring area, and marking as；
D3, calculating fire flammability coefficients of the non-apparent layers corresponding to the height vegetation of each monitoring area，；
wherein , and />Respectively represents the maximum fire flammability coefficient and the comprehensive occupied area of the height vegetation of the set reference, and />Fire flammability evaluation duty ratio weights respectively representing maximum fire flammability coefficient of set height vegetation and corresponding non-apparent level of comprehensive occupied area, ++>And the fire flammability assessment correction factors of the set height vegetation corresponding to the non-apparent layers are shown.
7. The internet of things-based multipoint monitoring type forest fire prevention monitoring system as set forth in claim 5, wherein: the calculation formula of the fire flammability coefficient of the vegetation corresponding to the non-apparent layer in each monitoring area is as follows:，/> and />And respectively representing the fire flammability evaluation duty ratio weights of the set low vegetation and the set high vegetation corresponding to the non-apparent layers.
8. The internet of things-based multipoint monitoring type forest fire prevention monitoring system as set forth in claim 7, wherein: the calculation formula of the forest fire flammability coefficient of the vegetation layer corresponding to each monitoring area is as follows:, wherein ,/> and />And respectively representing the set apparent and non-apparent bedding surfaces corresponding to the forest fire flammability evaluation duty ratio weights.
9. The internet of things-based multipoint monitoring type forest fire prevention monitoring system as set forth in claim 4, wherein: the forest fire inflammability coefficient of each monitoring area corresponding to the meteorological layer is analyzed, and the specific analysis process is as follows:
e1, respectively recording the temperature, humidity and sunlight intensity of each monitoring area as and />；
E2, calculating the forest fire flammability coefficient of each monitoring area corresponding to the meteorological layer，；
wherein , and />Respectively indicating the temperature, humidity and sunlight intensity of the set reference,/-> and />Respectively representing the set temperature, humidity and sunlight intensity corresponding to the flammable duty ratio weight of forest fire, and the weight of the forest fire>And the set meteorological-layer forest fire flammability evaluation correction factor is represented.
10. The multi-point monitoring type forest fire prevention monitoring system based on the internet of things as set forth in claim 1, wherein: the calculation formula of the forest fire early warning coefficient of each monitoring area is as follows:, wherein , and />And respectively representing the set forest fire early warning evaluation duty ratio weights corresponding to the vegetation layer and the meteorological layer.
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