CN111930049A - Forestry forest fire prevention safety intelligent monitoring management system based on thing networking - Google Patents
Forestry forest fire prevention safety intelligent monitoring management system based on thing networking Download PDFInfo
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Abstract
The invention discloses an Internet-of-things-based forestry forest fire safety intelligent monitoring management system, which comprises a plant distribution region dividing module, a region plant density statistical module, a region monitoring point arranging module, a region environment parameter acquisition module, an environment parameter preprocessing module, a plant parameter database, a manual input module, a fire risk evaluation coefficient statistical module, a management server, an early warning module and a remote monitoring platform, wherein the system can predict the possibility of fire of each subregion in advance by identifying plant types and dividing the plant distribution region of the whole forest region, arranging a plurality of monitoring points in each subregion at the same time, acquiring and processing environment parameters at each monitoring point, further counting the fire risk evaluation coefficient of each subregion, and performing prevention processing on the subregion of which the fire risk evaluation coefficient is greater than the preset safe fire risk evaluation coefficient, the fire disaster is avoided to the maximum extent, and further the damage to forest resources is reduced.
Description
Technical Field
The invention belongs to the technical field of forestry fire prevention management, and relates to an Internet of things-based forestry forest fire prevention safety intelligent monitoring management system.
Background
Forest resources in China are quite rich, and become an important component of ecological environment and economic construction along with the enhancement of the continuous utilization of resources and the comprehensive utilization consciousness of resources in China. In recent years, with global warming, natural forest fires frequently occur, forest resources are seriously damaged, and the development of national economy is directly influenced, so that the strengthening of forest fire prevention has important significance.
At present, the fire prevention supervision of the forest only carries out prevention control on small-range fire which occurs, although the fire is finally controlled, the forest resource is inevitably damaged, the supervision level is limited, and the advance prediction on the forest fire cannot be carried out. In view of the above, the invention provides an intelligent forestry forest fire prevention monitoring and management system based on the internet of things, which can predict forest fires in advance, prevent and treat forest regions with fire risks, kill flames in a cradle before the occurrence of a fire and realize zero damage to forest resources.
Disclosure of Invention
The invention aims to provide an intelligent monitoring and management system for forest fire prevention safety based on the Internet of things, which solves the problems in the background technology.
The purpose of the invention is realized by adopting the following technical scheme:
an Internet-of-things-based intelligent monitoring and management system for forest fire prevention safety of forestry comprises a plant distribution area dividing module, an area plant density statistical module, an area monitoring point arrangement module, an area environment parameter acquisition module, an environment parameter preprocessing module, a plant parameter database, a fire risk evaluation coefficient statistical module, a management server, an early warning module and a remote monitoring platform, wherein the plant distribution area dividing module is used for acquiring and analyzing the whole forest area according to the image acquisition and analysis of the whole forest area to acquire the plant types distributed in the whole forest area and the area distribution of each plant type, the area plant density statistical module is connected with the plant distribution area dividing module, the area monitoring point arrangement module is connected with the area plant density statistical module, the area environment parameter acquisition module is connected with the area monitoring point arrangement module, the environment parameter preprocessing module is connected with the area environment parameter acquisition module, the fire risk evaluation coefficient statistic module is respectively connected with the plant distribution region division module, the region plant density statistic module, the environmental parameter preprocessing module and the plant parameter database, the management server is connected with the fire risk evaluation coefficient statistic module, and the early warning module and the remote monitoring platform are connected with the management server;
the plant distribution area dividing module comprises a forest area image acquisition module, an image preliminary processing module and an image comparison dividing module, wherein the image preliminary processing module is connected with the forest area image acquisition module, and the image comparison dividing module is connected with the image preliminary processing module;
the forest area image acquisition module comprises an image collector and is used for collecting the whole forest area image;
the image primary processing module is used for carrying out digital processing on the whole forest region image to obtain a digital forest region image, carrying out image enhancement and image sharpening on the obtained digital forest region image by using a digital image processing technology, and enhancing the detail edge of the forest region image to obtain a high-definition forest region image;
the image contrast and division module extracts plant appearance characteristics of the high-definition forest region image, wherein the plant appearance characteristics comprise the height of a plant trunk, the color and the shape of the plant trunk, and the color and the shape of plant leaves, and extracting the appearance characteristics corresponding to various plants in the plant parameter database for comparing and matching one by one, so as to identify various plant species in the whole forest area and send the identified plant species to a fire risk evaluation coefficient statistical module, meanwhile, according to the identified plant species, boundary lines of distribution areas of different species of plants are extracted through area scanning, performing image segmentation on the forest area image according to the extracted boundary lines to obtain a plurality of plant distribution subregions, wherein each plant distribution subregion corresponds to a plant type, and the plant distribution subregions are numbered according to a set sequence and are respectively 1,2.
The plant parameter database stores appearance characteristics corresponding to various plant species, stores flammability coefficients corresponding to various plant species, stores plant distribution density ranges corresponding to various flammability density influence factors, and stores flammability standard environment parameters corresponding to various plant species in different seasons, wherein the various plant species comprise shrubs, trees, mosses, ferns and herbaceous plants;
the regional plant density statistical module comprises three steps of regional area acquisition, regional plant number statistics and plant distribution density calculation, wherein the specific implementation method of each step is as follows:
the method comprises the following steps: acquiring the area of each divided plant distribution subarea, acquiring the area of each plant distribution subarea by using a land area measuring instrument, and sending the area of each plant distribution subarea to a region monitoring point distribution module;
step two: counting the number of the plants in the region, namely counting the number of the plants in each divided plant distribution subregion to obtain the number of the plants in each plant distribution subregion;
step three: calculating the density of each plant, calculating the plant distribution density of each plant distribution subregion by using a density calculation formula according to the area of each plant distribution subregion and the number of plants in each plant distribution subregion, and sending the obtained plant distribution density of each plant distribution subregion to a fire risk assessment coefficient statistical module, wherein the density formula is
Q is expressed as the number of plants in the plant distribution subarea, and S is expressed as the area of the plant distribution subarea;
the area monitoring point distribution module receives the area of each plant distribution subarea sent by the area plant density statistical module and distributes environment monitoring points, and the area monitoring point distribution module comprises a monitoring point number statistical unit, a monitoring area dividing unit and a monitoring point distribution unit;
the monitoring point number counting unit is used for counting the number of monitoring points to be distributed in each plant distribution subregion according to a preset single monitoring point monitoring area and the area of each plant distribution subregion;
the monitoring area dividing unit is used for dividing each plant distribution subregion into a plurality of monitoring subregions according to the number of the monitoring points to be distributed in each plant distribution subregion and the preset monitoring area of a single monitoring point;
the monitoring point arrangement unit is used for setting a monitoring point in the center of each monitoring subarea in each divided plant distribution subarea, the monitoring points correspond to the monitoring subareas one by one, and the monitoring points arranged in each plant distribution subarea are numbered according to a preset sequence and are respectively marked as 1,2.. j.. m;
the regional environment parameter acquisition module comprises a plurality of environment parameter acquisition terminals, is respectively arranged at the positions of monitoring points in each plant distribution subregion, is used for detecting the environment parameters of the monitoring points in each plant distribution subregion, comprises the temperature, the humidity, the oxygen concentration, the carbon dioxide concentration, the wind speed and the illumination intensity, and sends the detected environment parameters of the monitoring points in each plant distribution subregion to the environment parameter preprocessing module;
the environmental parameter preprocessing module receives the values corresponding to the environmental parameters of the monitoring points in the plant distribution subareas and sent by the regional environmental parameter acquisition module, and stores the values corresponding to the environmental parameters of the monitoring points in the same plant distribution subarea in a unified manner to form a monitoring point environmental parameter set Qiw(qiw1,qiw2,...,qiwj,...,qiwm)，qiwj represents a numerical value corresponding to a w-th environmental parameter monitored by a j-th monitoring point of the ith plant distribution subregion, w represents an environmental parameter, w is r1, r2, r3, r4, r5, r6, r1, r2, r3, r4, r5 and r6 respectively represent temperature, humidity, oxygen concentration, carbon dioxide concentration, wind speed and illumination intensity, so as to obtain a monitoring point environmental parameter set of each plant distribution subregion, and the environmental parameter preprocessing module sends the obtained monitoring point environmental parameter set of each plant distribution subregion to the fire risk assessment coefficient statistical module;
the fire risk evaluation coefficient counting module receives plant distribution densities of all plant distribution sub-regions sent by the regional plant density counting module, receives monitoring point environment parameter sets of all plant distribution sub-regions sent by the environment parameter preprocessing module, receives all plant types distributed in the whole forest region sent by the plant distribution region dividing module, extracts combustible standard environment parameters corresponding to different plant types in different seasons, combustible coefficients corresponding to various plants and plant distribution density ranges corresponding to combustible density influence factors in the plant parameter database, respectively counts fire risk evaluation coefficients of all plant distribution sub-regions in the current season, and sends the fire risk evaluation coefficients to the management server;
the management server receives the fire risk evaluation coefficients of all plant distribution subregions in the current season, which are sent by the fire risk evaluation coefficient statistical module, compares the fire risk evaluation coefficients with preset safety fire risk evaluation coefficients, if the fire risk evaluation coefficient of a certain plant distribution subregion is larger than the safety fire risk evaluation coefficient, the condition that the subregion has fire risks is indicated, the plant distribution subregions with the fire risks in the whole forest region are counted, the counted plant distribution subregions with the fire risks are sent to a remote monitoring platform, and meanwhile, an early warning instruction is sent to the early warning module;
the early warning module receives an early warning instruction sent by the management server and sends an early warning signal;
and the remote monitoring platform receives the plant distribution subregion number with the fire risk sent by the management server and arranges related personnel for preventive treatment.
Preferably, the flammable standard environmental parameters include a flammable standard temperature, a flammable standard humidity, a flammable standard oxygen concentration, a flammable standard carbon dioxide concentration, a flammable standard wind speed, and a flammable standard illumination intensity.
Preferably, the calculation formula of the number of the monitoring points to be distributed in each plant distribution subregion is
In the formula KiExpressed as the number of monitoring points to be laid in the ith plant distribution subregion SiExpressed as the area of the ith plant distribution subregion, sSheetThe area is monitored as a single monitoring point.
More preferably, environmental parameter acquisition terminal includes temperature sensor, humidity transducer, gas sensor, air velocity transducer and light intensity sensor, temperature sensor is used for detecting the ambient temperature of monitoring point, humidity transducer is used for detecting the ambient humidity of monitoring point, gas sensor is arranged in detecting the air oxygen concentration and the carbon dioxide concentration of monitoring point, air velocity transducer is used for detecting the wind speed of monitoring point, light intensity sensor is used for detecting the illumination intensity of monitoring point.
Preferably, the system further comprises a manual input module, which is used for acquiring the current season of the forest at the moment and sending the current season of the forest to the fire risk assessment coefficient statistic module.
More preferably, the specific method for counting the fire risk assessment coefficients of the plant distribution subregions in the fire risk assessment coefficient statistical module in the current season includes the following steps:
s1: comparing the received various plant types distributed in the whole forest area with the corresponding flammability coefficients of various plants in the plant parameter database, and screening the flammability coefficients corresponding to various plants distributed in the whole forest area;
s2: receiving the current season of the forest sent by the manual input module, comparing the current season with the flammable standard environment parameters of different plant types in the plant parameter database, and selecting the flammable standard environment parameters of different plant types corresponding to the current season of the forest;
s3: comparing the received plant distribution density of each plant distribution subregion with the plant distribution density range corresponding to each inflammable density influence factor in the plant parameter database, and screening the inflammable density influence factors corresponding to the plant distribution density of each plant distribution subregion;
s4: comparing the received monitoring point environment parameter set of each plant distribution sub-area with inflammable standard environment parameters corresponding to different plant types in the current forest season to obtain a monitoring point environment parameter comparison set delta Qiw(Δqiw1,Δqiw2,...,Δqiwj,...,Δqiwm)，Δqiwj is expressed as the difference value between the numerical value corresponding to the w-th environmental parameter detected by the jth monitoring point of the ith plant distribution subarea and the w-th inflammable standard environmental parameter detected by the jth monitoring point corresponding to the current season;
s5: and counting the fire risk evaluation coefficients of the plant distribution subregions in the current season according to the obtained monitoring point environment parameter comparison set, the flammability coefficients corresponding to various plants and the flammability density influence factors corresponding to the plant distribution densities of the plant distribution subregions.
More preferably, the calculation formula of the fire risk assessment coefficient of each plant distribution subregion in the current season is
ξiExpressed as the fire risk assessment coefficient, gamma, of the ith plant distribution subregion in the current seasoniExpressed as flammability coefficient, μ, corresponding to the plant species distributed in the ith plant distribution subregioniExpressed as the flammability density influence factor, delta qi, corresponding to the plant distribution density of the ith plant distribution subregionr1j、Δqir2j、Δqir3j、Δqir4j、Δqir5j is respectively expressed as the difference value between the temperature, the humidity, the oxygen concentration, the carbon dioxide concentration, the wind speed and the illumination intensity monitored by the jth monitoring point of the ith plant distribution subarea and the flammable standard temperature, the flammable standard humidity, the flammable standard oxygen concentration, the flammable standard carbon dioxide concentration, the flammable standard wind speed and the flammable standard illumination intensity corresponding to the plant species distributed in the plant distribution subarea in the current season, qi isr10、qir20、qir30、qir40、qir50Respectively expressing the flammable standard temperature, flammable standard humidity, flammable standard oxygen concentration, flammable standard carbon dioxide concentration, flammable standard wind speed and flammable standard illumination intensity value corresponding to the plant species distributed in the ith plant distribution subregion in the current season.
The invention has the beneficial effects that:
(1) according to the invention, plant type identification and plant distribution area division are carried out on the whole forest area, the area and the plant distribution density of each plant distribution subarea are obtained, a plurality of environment monitoring points are distributed, the environment parameter acquisition processing is carried out on each monitoring point of each subarea by combining with the area environment parameter acquisition module, and then the fire risk evaluation coefficient of each subarea is counted, so that the quantitative display of the fire risk of each area of the forest is realized, the possibility of fire occurrence of each area of the forest can be predicted in advance, the problem that the existing forest fire prevention supervision can only carry out prevention control on the small-range fire which occurs is optimized, and the system carries out prevention processing on the subarea of which the fire risk evaluation coefficient is larger than the preset safe fire risk evaluation coefficient, thereby maximally avoiding the occurrence of the fire and further reducing the damage to forest resources.
(2) According to the invention, the environment parameters are detected by arranging the monitoring points in each divided plant distribution subarea, so that the error phenomenon caused by detection of a single monitoring point is avoided, the detected environment parameter value is closer to the true value, and reliable data support is provided for later fire risk evaluation coefficient calculation.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be derived on the basis of these drawings without inventive effort.
FIG. 1 is a block diagram of the modules of the present invention;
FIG. 2 is a schematic diagram of a plant distribution region dividing module according to the present invention;
FIG. 3 is a schematic diagram of a module for laying area monitoring points according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-3, an intelligent monitoring and management system for forest fire prevention safety of forestry based on internet of things comprises a plant distribution area division module, an area plant density statistics module, an area monitoring point arrangement module, an area environment parameter acquisition module, an environment parameter preprocessing module, a plant parameter database, an artificial input module, a fire risk assessment coefficient statistics module, a management server, an early warning module and a remote monitoring platform, wherein the plant distribution area division module is used for acquiring and analyzing the whole forest area according to the image acquisition and analysis processing of the whole forest area to acquire the plant types planted in the whole forest area and the area distribution of the plant types, the area plant density statistics module is connected with the plant distribution area division module, the area monitoring point arrangement module is connected with the area plant density statistics module, the area environment parameter acquisition module is connected with the area monitoring point arrangement module, the environmental parameter preprocessing module is connected with the regional environmental parameter acquisition module, the fire risk assessment coefficient statistical module is respectively connected with the plant distribution region dividing module, the regional plant density statistical module, the manual input module, the environmental parameter preprocessing module and the plant parameter database, the management server is connected with the fire risk assessment coefficient statistical module, and the early warning module and the remote monitoring platform are connected with the management server.
The plant distribution area dividing module comprises a forest area image acquisition module, an image primary processing module and an image comparison dividing module, wherein the image primary processing module is connected with the forest area image acquisition module, and the image comparison dividing module is connected with the image primary processing module.
The forest area image acquisition module comprises an image collector and is used for collecting the whole forest area image;
the image primary processing module is used for carrying out digital processing on the whole forest region image to obtain a digital forest region image, carrying out image enhancement and image sharpening on the obtained digital forest region image by using a digital image processing technology, and enhancing the detail edge of the forest region image to obtain a high-definition forest region image;
the image comparison and division module extracts plant appearance features of the high-definition forest area image, the appearance features comprise plant trunk height, plant trunk color and shape and plant leaf color and shape, the appearance features corresponding to various plants in the plant parameter database are extracted to be compared and matched one by one, the matching method specifically comprises the steps of matching the captured plant appearance features with the appearance features corresponding to the various plants, counting the similarity between the captured plant appearance features and the appearance features corresponding to the various plants, if the counted similarity is larger than a preset similarity threshold value, the captured plant appearance features are successfully matched with the plants, so that the plant types in the forest whole area are identified, the identified plant types are sent to the fire risk evaluation coefficient counting module, and meanwhile according to the identified plant types, boundary lines of different types of plant distribution areas are extracted through area scanning, image segmentation is carried out on a forest area image according to the extracted boundary lines, a plurality of plant distribution subregions are obtained, each plant distribution subregion corresponds to one plant type, and the plant distribution subregions are numbered according to a set sequence and are respectively 1,2.
The plant parameter database stores appearance characteristics corresponding to various plant types, wherein the various plant types comprise shrubs, trees, mosses, ferns and herbaceous plants, inflammable coefficients corresponding to the various plant types are stored, plant distribution density ranges corresponding to the various inflammable density influence factors are stored, and inflammable standard environment parameters corresponding to the various plant types in different seasons are stored, and the inflammable standard environment parameters comprise inflammable standard temperature, inflammable standard humidity, inflammable standard oxygen concentration, inflammable standard carbon dioxide concentration, inflammable standard wind speed and inflammable standard illumination intensity.
The regional plant density statistical module comprises three steps of regional area acquisition, regional plant number statistics and plant distribution density calculation, wherein the specific implementation method of each step is as follows:
the method comprises the following steps: the method comprises the steps of firstly, obtaining position coordinates of boundaries of each plant distribution sub-region by using the land area measuring instrument, then converting longitude and latitude coordinates of obtained boundary end points into plane coordinates, then connecting each end point of the boundaries of the plant distribution sub-regions with an origin of coordinates, dividing the plant distribution sub-regions into a plurality of triangles, finally, summing the areas of the triangles, obtaining the areas of the plant distribution sub-regions, and sending the areas of the plant distribution sub-regions to a region monitoring point distribution module;
step two: counting the number of the plants in the region, namely counting the number of the plants in each divided plant distribution subregion to obtain the number of the plants in each plant distribution subregion;
step three: calculating the density of each plant, calculating the plant distribution density of each plant distribution subregion by using a density calculation formula according to the area of each plant distribution subregion and the number of plants in each plant distribution subregion, and sending the obtained plant distribution density of each plant distribution subregion to a fire risk assessment coefficient statistical module, wherein the density formula is
Q is expressed as the number of plants in the plant distribution subregion, and S is expressed as the area of the plant distribution subregion.
The area monitoring point distribution module receives the area of each plant distribution subarea sent by the area plant density statistical module and distributes environment monitoring points, and the area monitoring point distribution module comprises a monitoring point number statistical unit, a monitoring area dividing unit and a monitoring point distribution unit;
the monitoring point number counting unit is used for counting the number of monitoring points to be distributed in each plant distribution subregion according to a preset single monitoring point monitoring area and each plant distribution subregion area
In the formula KiExpressed as the number of monitoring points to be laid in the ith plant distribution subregion SiExpressed as the area of the ith plant distribution subregion, sSheetExpressed as a single monitoring point monitoring area;
the monitoring area dividing unit is used for dividing each plant distribution subregion into a plurality of monitoring subregions according to the number of the monitoring points to be distributed in each plant distribution subregion and the preset monitoring area of a single monitoring point;
the monitoring point arrangement unit is used for setting a monitoring point in the center of each monitoring subarea in each divided plant distribution subarea, the monitoring points correspond to the monitoring subareas one by one, and the monitoring points arranged in each plant distribution subarea are numbered according to a preset sequence and are respectively marked as 1,2.
Regional environmental parameter collection module, including a plurality of environmental parameter collection terminals, it settles each monitoring point position in each plant distribution subregion respectively, and environmental parameter collection terminal includes temperature sensor, humidity transducer, gas sensor, air velocity transducer and light intensity sensor, temperature sensor is used for detecting the ambient temperature of monitoring point, humidity transducer is used for detecting the ambient humidity of monitoring point, oxygen concentration and carbon dioxide concentration in the air of monitoring point are used for detecting the gas sensor, air velocity transducer is used for detecting the wind speed of monitoring point, light intensity sensor is used for detecting the light intensity of monitoring point to send the temperature, humidity, oxygen concentration, carbon dioxide concentration, wind speed and the light intensity of each monitoring point in each plant distribution subregion that will detect to environmental parameter preprocessing module.
As a technical optimization scheme of the invention, a plurality of monitoring points are distributed in each divided plant distribution subarea to detect the environmental parameters, so that the error phenomenon caused by the detection of a single monitoring point is avoided, the detected environmental parameter value is closer to the true value, and reliable data support is provided for the subsequent fire risk evaluation coefficient calculation.
The environmental parameter preprocessing module receives the values corresponding to the environmental parameters of the monitoring points in the plant distribution subareas and sent by the regional environmental parameter acquisition module, and stores the values corresponding to the environmental parameters of the monitoring points in the same plant distribution subarea in a unified manner to form a monitoring point environmental parameter set Qiw(qiw1,qiw2,...,qiwj,...,qiwm)，qiwj is a numerical value corresponding to the w-th environmental parameter monitored by the j-th monitoring point of the ith plant distribution subarea, w is an environmental parameter, and w is r1, r2, r3, r4, r5, r6, r1, r2, r3, r4, r5 and r6 are respectively represented by temperature, humidity, oxygen concentration, carbon dioxide concentration, wind powerThe monitoring point environment parameter sets of all plant distribution sub-areas are obtained according to the speed and the illumination intensity, and the environment parameter preprocessing module sends the obtained monitoring point environment parameter sets of all plant distribution sub-areas to the fire risk evaluation coefficient statistical module.
As a technical optimization scheme, the method obtains the plant types and the plant distribution densities of all plant distribution subregions, and provides the flammability coefficient and flammability density influence factors for the subsequent fire risk assessment coefficient statistics for bedding.
And the manual input module is used for acquiring the current season of the forest at the moment and sending the current season of the forest to the fire risk evaluation coefficient statistical module.
The fire risk evaluation coefficient statistic module receives the plant distribution density of each plant distribution subregion sent by the regional plant density statistic module, receives the monitoring point environment parameter set of each plant distribution subregion sent by the environment parameter preprocessing module, receives each plant type of the whole forest regional distribution sent by the plant distribution regional division module, and receives the current season of the forest sent by the manual input module, meanwhile, combustible standard environment parameters corresponding to different plant types in different seasons, combustible coefficients corresponding to various plants and plant distribution density ranges corresponding to various combustible density influence factors are extracted from a plant parameter database, the fire risk evaluation coefficients of various plant distribution subregions in the current season are respectively counted, and sending the fire risk evaluation coefficient to a management server, wherein the specific fire risk evaluation coefficient statistics method comprises the following steps:
s1: comparing the received various plant types distributed in the whole forest area with the corresponding flammability coefficients of various plants in the plant parameter database, and screening the flammability coefficients corresponding to various plants distributed in the whole forest area;
s2: receiving the current season of the forest sent by the manual input module, comparing the current season with the flammable standard environment parameters of different plant types in the plant parameter database, and selecting the flammable standard environment parameters of different plant types corresponding to the current season of the forest;
s3: comparing the received plant distribution density of each plant distribution subregion with the plant distribution density range corresponding to each inflammable density influence factor in the plant parameter database, and screening the inflammable density influence factors corresponding to the plant distribution density of each plant distribution subregion;
s4: comparing the received monitoring point environment parameter set of each plant distribution sub-area with inflammable standard environment parameters corresponding to different plant types in the current forest season to obtain a monitoring point environment parameter comparison set delta Qiw(Δqiw1,Δqiw2,...,Δqiwj,...,Δqiwm)，Δqiwj is expressed as the difference value between the numerical value corresponding to the w-th environmental parameter detected by the jth monitoring point of the ith plant distribution subarea and the w-th inflammable standard environmental parameter detected by the jth monitoring point corresponding to the current season;
s5: according to the obtained monitoring point environment parameter comparison set, the flammability coefficients corresponding to various plants and the flammability density influence factors corresponding to the plant distribution densities of the plant distribution subregions, the fire risk evaluation coefficients of the plant distribution subregions in the current season are counted
ξiExpressed as the fire risk assessment coefficient, gamma, of the ith plant distribution subregion in the current seasoniExpressed as flammability coefficient, μ, corresponding to the plant species distributed in the ith plant distribution subregioniExpressed as the flammability density influence factor, delta qi, corresponding to the plant distribution density of the ith plant distribution subregionr1j、Δqir2j、Δqir3j、Δqir4j、Δqir5j is respectively expressed as the temperature, the humidity, the oxygen concentration, the carbon dioxide concentration, the wind speed and the illumination intensity monitored by the jth monitoring point of the ith plant distribution subarea and the inflammable standard temperature, the inflammable standard humidity, the inflammable standard oxygen concentration, the inflammable standard carbon dioxide concentration, the inflammable standard wind speed and the inflammable standard illumination intensity corresponding to the plant species distributed in the plant distribution subarea in the current seasonDifference between, qir10、qir20、qir30、qir40、qir50Respectively expressing the flammable standard temperature, flammable standard humidity, flammable standard oxygen concentration, flammable standard carbon dioxide concentration, flammable standard wind speed and flammable standard illumination intensity value corresponding to the plant species distributed in the ith plant distribution subregion in the current season.
As a technical optimization scheme, the fire risk evaluation coefficient obtained by the method realizes the quantitative display of the fire risk of each area of the forest, so that relevant personnel can conveniently and visually know the fire risk evaluation coefficient, the possibility of fire occurrence of each area of the forest can be predicted in advance according to the fire risk evaluation coefficient, and the higher the fire risk evaluation coefficient value is, the higher the possibility of the fire risk is.
The management server receives the fire risk evaluation coefficients of all plant distribution subregions in the current season, which are sent by the fire risk evaluation coefficient statistical module, compares the fire risk evaluation coefficients with preset safety fire risk evaluation coefficients, if the fire risk evaluation coefficient of a certain plant distribution subregion is larger than the safety fire risk evaluation coefficient, the fact that fire risks exist in the subregion is indicated, the plant distribution subregions with the fire risks in the whole forest region are counted, the counted plant distribution subregion numbers with the fire risks are sent to the remote monitoring platform, and meanwhile, an early warning instruction is sent to the early warning module.
The early warning module receives an early warning instruction sent by the management server, and sends an early warning signal to prompt related personnel of fire risk.
The remote monitoring platform receives the plant distribution subregion number with the fire risk sent by the management server, arranges related personnel for preventive treatment, can avoid the occurrence of fire to the maximum extent, and then reduces the damage to forest resources.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (7)
1. The utility model provides a forestry forest fire prevention safety intelligent monitoring management system based on thing networking which characterized in that: the system comprises a plant distribution area dividing module, an area plant density statistical module, an area monitoring point laying module, an area environment parameter acquisition module, an environment parameter preprocessing module, a plant parameter database, a fire risk evaluation coefficient statistical module, a management server, an early warning module and a remote monitoring platform, wherein the plant distribution area dividing module is used for acquiring plant types planted in an integral forest area and area distribution of each plant type according to image acquisition and analysis processing of the integral forest area, the area plant density statistical module is connected with the plant distribution area dividing module, the area monitoring point laying module is connected with the area plant density statistical module, the area environment parameter acquisition module is connected with the area monitoring point laying module, the environment parameter preprocessing module is connected with the area environment parameter acquisition module, the fire risk evaluation coefficient statistical module is respectively connected with the plant distribution area dividing module, The regional plant density statistical module, the environmental parameter preprocessing module and the plant parameter database are connected, the management server is connected with the fire risk evaluation coefficient statistical module, and the early warning module and the remote monitoring platform are connected with the management server;
the plant distribution area dividing module comprises a forest area image acquisition module, an image preliminary processing module and an image comparison dividing module, wherein the image preliminary processing module is connected with the forest area image acquisition module, and the image comparison dividing module is connected with the image preliminary processing module;
the forest area image acquisition module comprises an image collector and is used for collecting the whole forest area image;
the image primary processing module is used for carrying out digital processing on the whole forest region image to obtain a digital forest region image, carrying out image enhancement and image sharpening on the obtained digital forest region image by using a digital image processing technology, and enhancing the detail edge of the forest region image to obtain a high-definition forest region image;
the image contrast and division module extracts plant appearance characteristics of the high-definition forest region image, wherein the appearance characteristics comprise the height of a plant trunk, the color and the shape of the plant trunk and the color and the shape of plant leaves, and extracting the appearance characteristics corresponding to various plants in the plant parameter database for comparing and matching one by one, so as to identify various plant species in the whole forest area and send the identified plant species to a fire risk evaluation coefficient statistical module, meanwhile, according to the identified plant species, boundary lines of distribution areas of different species of plants are extracted through area scanning, performing image segmentation on the forest area image according to the extracted boundary lines to obtain a plurality of plant distribution subregions, wherein each plant distribution subregion corresponds to a plant type, and the plant distribution subregions are numbered according to a set sequence and are respectively 1,2.
The plant parameter database stores appearance characteristics corresponding to various plant species, stores flammability coefficients corresponding to various plant species, stores plant distribution density ranges corresponding to various flammability density influence factors, and stores flammability standard environment parameters corresponding to various plant species in different seasons, wherein the various plant species comprise shrubs, trees, mosses, ferns and herbaceous plants;
the regional plant density statistical module comprises three steps of regional area acquisition, regional plant number statistics and plant distribution density calculation, wherein the specific implementation method of each step is as follows:
the method comprises the following steps: acquiring the area of each divided plant distribution subarea, acquiring the area of each plant distribution subarea by using a land area measuring instrument, and sending the area of each plant distribution subarea to a region monitoring point distribution module;
step two: counting the number of the plants in the region, namely counting the number of the plants in each divided plant distribution subregion to obtain the number of the plants in each plant distribution subregion;
step three: calculating the plant distribution density, calculating the plant distribution density of each plant distribution subregion by using a density calculation formula according to the area of each plant distribution subregion and the number of plants in each plant distribution subregion, and calculating the plant distribution density of each plant distribution subregionThe obtained plant distribution density of each plant distribution subregion is sent to a fire risk evaluation coefficient statistical module, and the density formula is
Q is expressed as the number of plants in the plant distribution subarea, and S is expressed as the area of the plant distribution subarea;
the area monitoring point distribution module receives the area of each plant distribution subarea sent by the area plant density statistical module and distributes environment monitoring points, and the area monitoring point distribution module comprises a monitoring point number statistical unit, a monitoring area dividing unit and a monitoring point distribution unit;
the monitoring point number counting unit is used for counting the number of monitoring points to be distributed in each plant distribution subregion according to a preset single monitoring point monitoring area and the area of each plant distribution subregion;
the monitoring area dividing unit is used for dividing each plant distribution subregion into a plurality of monitoring subregions according to the number of the monitoring points to be distributed in each plant distribution subregion and the preset monitoring area of a single monitoring point;
the monitoring point arrangement unit is used for setting a monitoring point in the center of each monitoring subarea in each divided plant distribution subarea, the monitoring points correspond to the monitoring subareas one by one, and the monitoring points arranged in each plant distribution subarea are numbered according to a preset sequence and are respectively marked as 1,2.. j.. m;
the regional environment parameter acquisition module comprises a plurality of environment parameter acquisition terminals, is respectively arranged at the positions of monitoring points in each plant distribution subregion, is used for detecting the environment parameters of the monitoring points in each plant distribution subregion, comprises the temperature, the humidity, the oxygen concentration, the carbon dioxide concentration, the wind speed and the illumination intensity, and sends the detected environment parameters of the monitoring points in each plant distribution subregion to the environment parameter preprocessing module;
the environmental parameter preprocessing module receives all monitors in all plant distribution sub-areas sent by the area environmental parameter acquisition moduleThe values corresponding to the environmental parameters of the measuring points are uniformly stored for the values corresponding to the environmental parameters of all the monitoring points in the same plant distribution subregion to form a monitoring point environmental parameter set Qiw(qiw1,qiw2,...,qiwj,...,qiwm)，qiwj represents a numerical value corresponding to a w-th environmental parameter monitored by a j-th monitoring point of the ith plant distribution subregion, w represents an environmental parameter, w is r1, r2, r3, r4, r5, r6, r1, r2, r3, r4, r5 and r6 respectively represent temperature, humidity, oxygen concentration, carbon dioxide concentration, wind speed and illumination intensity, so as to obtain a monitoring point environmental parameter set of each plant distribution subregion, and the environmental parameter preprocessing module sends the obtained monitoring point environmental parameter set of each plant distribution subregion to the fire risk assessment coefficient statistical module;
the fire risk evaluation coefficient counting module receives plant distribution densities of all plant distribution sub-regions sent by the regional plant density counting module, receives monitoring point environment parameter sets of all plant distribution sub-regions sent by the environment parameter preprocessing module, receives all plant types distributed in the whole forest region sent by the plant distribution region dividing module, extracts combustible standard environment parameters corresponding to different plant types in different seasons, combustible coefficients corresponding to various plants and plant distribution density ranges corresponding to combustible density influence factors in the plant parameter database, respectively counts fire risk evaluation coefficients of all plant distribution sub-regions in the current season, and sends the fire risk evaluation coefficients to the management server;
the management server receives the fire risk evaluation coefficients of all plant distribution subregions in the current season, which are sent by the fire risk evaluation coefficient statistical module, compares the fire risk evaluation coefficients with preset safety fire risk evaluation coefficients, if the fire risk evaluation coefficient of a certain plant distribution subregion is larger than the safety fire risk evaluation coefficient, the condition that the subregion has fire risks is indicated, the plant distribution subregions with the fire risks in the whole forest region are counted, the counted plant distribution subregions with the fire risks are sent to a remote monitoring platform, and meanwhile, an early warning instruction is sent to the early warning module;
the early warning module receives an early warning instruction sent by the management server and sends an early warning signal;
and the remote monitoring platform receives the plant distribution subregion number with the fire risk sent by the management server and arranges related personnel for preventive treatment.
2. The forestry forest fire prevention safety intelligent monitoring management system based on the internet of things as claimed in claim 1, characterized in that: the flammable standard environmental parameters include a flammable standard temperature, a flammable standard humidity, a flammable standard oxygen concentration, a flammable standard carbon dioxide concentration, a flammable standard wind speed, and a flammable standard illumination intensity.
3. The forestry forest fire prevention safety intelligent monitoring management system based on the internet of things as claimed in claim 1, characterized in that: the number calculation formula of the monitoring points to be distributed in each plant distribution subregion is
In the formula KiExpressed as the number of monitoring points to be laid in the ith plant distribution subregion SiExpressed as the area of the ith plant distribution subregion, sSheetThe area is monitored as a single monitoring point.
4. The forestry forest fire prevention safety intelligent monitoring management system based on the internet of things as claimed in claim 1, characterized in that: the environmental parameter acquisition terminal includes temperature sensor, humidity transducer, gas sensor, air velocity transducer and light intensity sensor, temperature sensor is used for detecting the ambient temperature of monitoring point, humidity transducer is used for detecting the ambient humidity of monitoring point, gas sensor is arranged in detecting the air oxygen concentration and the carbon dioxide concentration of monitoring point, air velocity transducer is used for detecting the wind speed of monitoring point, light intensity sensor is used for detecting the illumination intensity of monitoring point.
5. The forestry forest fire prevention safety intelligent monitoring management system based on the internet of things as claimed in claim 1, characterized in that: the system also comprises a manual input module which is used for obtaining the current season of the forest at the moment and sending the current season of the forest to the fire risk evaluation coefficient statistical module.
6. The forestry forest fire prevention safety intelligent monitoring management system based on the internet of things as claimed in claim 1, characterized in that: the specific method for counting the fire risk evaluation coefficients of all plant distribution subregions in the current season in the fire risk evaluation coefficient counting module comprises the following steps of:
s1: comparing the received various plant types distributed in the whole forest area with the corresponding flammability coefficients of various plants in the plant parameter database, and screening the flammability coefficients corresponding to various plants distributed in the whole forest area;
s2: receiving the current season of the forest sent by the manual input module, comparing the current season with the flammable standard environment parameters of different plant types in the plant parameter database, and selecting the flammable standard environment parameters of different plant types corresponding to the current season of the forest;
s3: comparing the received plant distribution density of each plant distribution subregion with the plant distribution density range corresponding to each inflammable density influence factor in the plant parameter database, and screening the inflammable density influence factors corresponding to the plant distribution density of each plant distribution subregion;
s4: comparing the received monitoring point environment parameter set of each plant distribution sub-area with inflammable standard environment parameters corresponding to different plant types in the current forest season to obtain a monitoring point environment parameter comparison set delta Qiw(Δqiw1,Δqiw2,...,Δqiwj,...,Δqiwm)，Δqiwj is expressed as the difference value between the numerical value corresponding to the w-th environmental parameter detected by the jth monitoring point of the ith plant distribution subarea and the w-th inflammable standard environmental parameter detected by the jth monitoring point corresponding to the current season;
s5: and counting the fire risk evaluation coefficients of the plant distribution subregions in the current season according to the obtained monitoring point environment parameter comparison set, the flammability coefficients corresponding to various plants and the flammability density influence factors corresponding to the plant distribution densities of the plant distribution subregions.
7. The forestry forest fire prevention safety intelligent monitoring management system based on the internet of things as claimed in claim 6, characterized in that: the calculation formula of the fire risk evaluation coefficient of each plant distribution subregion in the current season is
ξiExpressed as the fire risk assessment coefficient, gamma, of the ith plant distribution subregion in the current seasoniExpressed as flammability coefficient, μ, corresponding to the plant species distributed in the ith plant distribution subregioniExpressed as the flammability density influence factor, delta qi, corresponding to the plant distribution density of the ith plant distribution subregionr1j、Δqir2j、Δqir3j、Δqir4j、Δqir5j is respectively expressed as the difference value between the temperature, the humidity, the oxygen concentration, the carbon dioxide concentration, the wind speed and the illumination intensity monitored by the jth monitoring point of the ith plant distribution subarea and the flammable standard temperature, the flammable standard humidity, the flammable standard oxygen concentration, the flammable standard carbon dioxide concentration, the flammable standard wind speed and the flammable standard illumination intensity corresponding to the plant species distributed in the plant distribution subarea in the current season, qi isr10、qir20、qir30、qir40、qir50Respectively expressing the flammable standard temperature, flammable standard humidity, flammable standard oxygen concentration, flammable standard carbon dioxide concentration, flammable standard wind speed and flammable standard illumination intensity value corresponding to the plant species distributed in the ith plant distribution subregion in the current season.
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| CN117993719A (en) * | 2024-02-29 | 2024-05-07 | 吉林省林业科学研究院(吉林省林业生物防治中心站) | Method for preventing forest fire from occurring by utilizing white clover |
| CN117993719B (en) * | 2024-02-29 | 2024-06-25 | 吉林省林业科学研究院(吉林省林业生物防治中心站) | A method for preventing forest fires by using white clover |
| CN118674266A (en) * | 2024-02-29 | 2024-09-20 | 吉林省林业科学研究院(吉林省林业生物防治中心站) | Method for preventing forest fire from happening and application thereof |
| CN118674266B (en) * | 2024-02-29 | 2024-11-12 | 吉林省林业科学研究院(吉林省林业生物防治中心站) | A method for preventing forest fires and its application |
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