CN114638736A - Forest fire prevention data analysis system and method based on Internet of things - Google Patents
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Abstract
The invention discloses a forest fire prevention data analysis system and method based on the Internet of things, and belongs to the technical field of data analysis. According to the method, the intelligent terminal equipment is arranged on the basis of the Internet of things, forest fire is monitored in real time, personnel do not need to go deep into the forest for ground patrol, the forest is divided into the chequers, the monitoring range of the intelligent terminal equipment is taken as a unit, the intelligent equipment is convenient to position and set, the probability of fire in each chequer is calculated through the forest fire probability calculating unit, the forest fire probability calculating unit comprises the probability of artificial fire in the forest, the probability of natural fire in the forest and the fire key probability of the forest, the probability of fire in different chequers and the influence caused by the fire are digitalized, the importance of the forest fire in different chequers in the forest fire protection is convenient, the intelligent terminal equipment is set according to the importance of the different chequers, and the condition of the forest fire is monitored to the maximum in controllable equipment cost.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a forest fire prevention data analysis system and method based on the Internet of things.
Background
The forest is the lung of the earth, the forest is an ecological system formed by a plant community, animals living in the forest and a non-biological environment in the space, the life cycle of the forest is long, the productivity of the forest is high, the use is multiple, the benefit is large, but the forest fire which is artificially controlled is lost seriously destroys the forest trees and the forest environment accumulated for many years, so that the forest ecological system is out of balance, the performance of the soil of the forest is destroyed, the landform is changed, and the destruction of the forest fire to the ecology is most direct and has the greatest disaster; a large number of plants are burnt, which causes water and soil loss, aggravation of land desertification and other adverse effects, and finally, even can not maintain ecological balance;
the existing forest fire prevention system is combined with traditional forest fire prevention detection based on intelligent terminal equipment, the traditional forest fire prevention detection mainly adopts ground patrol and manual lookout desk observation, wherein the ground patrol monitoring range is small, and many places with inconvenient traffic are difficult to patrol; the observation effect of the lookout tower depends too much on the experience of lookout personnel, the lookout tower is low in accuracy, large in error, easy to be limited by terrain and topography, small in coverage range and dead angle for fire monitoring;
the intelligent terminal device system realizes the unmanned monitoring of forest fire through technologies such as visible light and thermal inductance imaging, greatly improves the efficiency of forest fire protection, has perfect equipment and high monitoring accuracy, and means the high price of the existing forest fire protection system; the forest is wide in area and rare in people, so that comprehensive monitoring of the forest is realized, high equipment cost is needed for monitoring the forest by large-area laying equipment, and meanwhile, low labor cost is needed for overhauling and maintaining the large-area laying equipment; the larger the forest area is, the more difficult the comprehensive fire prevention monitoring of the forest is to be realized, and under the condition that intelligent monitoring is infeasible to realize by comprehensively laying intelligent terminal equipment, how to better lay the intelligent terminal equipment is the problem that needs to be solved.
Disclosure of Invention
The invention aims to provide a forest fire prevention data analysis system and method based on the Internet of things, and aims to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the forest fire prevention data analysis method based on the Internet of things specifically comprises the following steps:
the method comprises the following steps: collecting forest data, wherein the forest data comprises a forest map, forest climate, artificial activity range of the forest and distribution of trees of the forest;
the artificial activity range of the forest comprises the range of forest production activity and an artificial walking path;
the forest climate comprises temperature, humidity, air pressure, wind direction and rainfall;
step two: dividing the forest map into checkerboards, establishing a coordinate system, and determining the coordinate of each checkerboard;
step three: calculating the probability P1 of artificial fire of each checkerboard forest;
various artificial activities can be generated in forests, including artificial production activities, tourists 'tours and hunting paths, forest trees in forests are bushy and difficult to walk on roads, if people are moving, the artificial production activities generally need effective fire prevention measures in fixed areas and roads, and at the junction of the artificial activities and the original forest environment, the junction of the production activities and the tourists' tours and hunting paths are included, so that due to the fact that personnel are complex and begin to cross the forests, the personnel easily generate private feelings, and therefore the alertness of fire prevention is relaxed, and the attention is needed.
Step four: calculating the probability P2 of the natural fire of the forest of each checkerboard;
the forest has natural forest fire, is full of oxygen, contains a lot of nutrient substances, and is fermented in a slightly humid environment to generate heat, so that water in the environment is evaporated, and dark fire gradually generates open fire, so that the natural forest fire is further caused. In the year, the forest fire naturally generated by deeply digging the forest is documented to be the self quick update of the forest, which is helpful for the forest to suppress the pest and disease damage and is a new living opportunity, but the forest fire is also in a small range, does not need to be artificially generated, but needs artificial supervision, and the forest fire is controlled in a proper range, so that the forest fire naturally generated also needs powerful supervision action.
Step five: calculating fire emphasis probability P3 of the forest of each checkerboard;
step six: outputting the fire occurrence of the forest, wherein the fire probability of each checkerboard is the fire key monitoring grid, and the checkerboard exceeding the threshold value is the fire key monitoring grid;
step seven: and configuring perfect intelligent terminal equipment in the fire key monitoring grid, wherein the intelligent terminal equipment monitors the regional fire condition in real time.
And in the second step, the size of the checkerboard is consistent with the range of the fireproof terminal monitoring equipment.
The specific contents of the probability P1 for calculating the artificial fire of the forest in the third step comprise:
dividing a forest map into three organization parts according to the artificial activity range of the forest: the method comprises the following steps of (1) carrying out personnel activity intensive area, junction of personnel activity and forest and unmanned activity area;
the personnel activity intensive area is a personnel production activity area, the personnel are intensive and the trees are rare, and the probability of the artificial fire in the personnel activity intensive area is inversely related to the perfection degree of the fire-proof measures in the area:
wherein, P1aIndicating the probability of an artificial fire in an area with dense activities of people, KreRepresenting the fire-prevention measure coefficient, re representing the completeness of the fire-prevention measure;
the junction of the personnel activity and the forest is the personThe junction of the personnel activity and the forest primary environment comprises the junction of the personnel production activity area and the forest primary environment and the walking path of personnel entering the forest, and the probability P1 of artificial fire at the junction of the personnel activity and the forestbProportional to the density of the forest at the intersection of the personnel activity and the forest:
P1b＝Kfd*fd
wherein, P1bRepresenting the probability of an artificial fire at the junction of a person's activity and a forest, KfdExpressing a forest density coefficient, and fd expressing the forest density at the junction of the personnel activity and the forest;
the unmanned active area is a forest original environment without personnel activity, and the probability P1 of artificial fire in the unmanned active areacIs zero.
The specific contents of the probability P2 for calculating the natural fire of the forest in the fourth step comprise:
according to the distribution of trees in the forest, calculating the oil content of the trees in each checkerboard, wherein the specific calculation formula is as follows:
wherein oc represents the average oil content of the tree of the species, H represents the height of the plants, H represents the average height of the tree of the species, and N represents the number of all plants in the checkerboard.
The distribution of the types of the plants of the forest is fixed, the growth range of the animals and the reptiles which are attached to the growth of the plants is also fixed, the plants are rich in grease, the grease content of the fallen leaves and dead branches generated by the plants is higher, the grease content of the animals and the reptiles which grow on the plants and the grease content of excrement of the animals and the reptiles which grow on the plants are higher, and therefore the grease content of the trees can represent the content of combustion-supporting nutrient substances in the checkerboard.
The specific contents of the probability P2 for calculating the natural fire of the forest in the fourth step further comprise:
the probability P2 of the natural fire of the forest is calculated by the following formula:
P2＝(A*Oilc+FHR)*B*SDI
wherein SDI represents forest stand density, FHR represents local fire risk weather grade, A represents vegetation coefficient, and B represents fire risk grade coefficient.
The national forest grassland fire prevention department establishes a forest fire weather grade forecast in a government network of the national forestry grassland bureau, forecasts the forest fire weather forecast in the whole country in real time, and calculates the probability P2 of natural fire of the forest in the checkerboard according to the local fire weather grade forecast; the forest stand density is increased to serve as a coefficient, the vegetation density is increased and decreased due to the influence of human activities, when the vegetation is felled due to the human activities in the forest, the tree density is sharply reduced, and the probability of natural fire of the forest is sharply reduced at the moment.
Analyzing different chequers as fire points, the same wind speed, simulating the process of fire spreading according to different seasons and terrains, calculating the speed of fire spreading, counting the fire spread before the rescue team arrives, calculating different fire points, calculating the burning frequency of each chequer before the rescue team develops the rescue, and outputting the fire key probability of a single chequer;
the fifth step of calculating the fire emphasis probability P3 of the forest of each checkerboard comprises the following specific steps:
step 1: dividing local seasons according to local wind directions;
the forest seasons are divided according to different wind directions, the wind directions can change due to seasons, and different wind directions can lead to different fire spreading results.
Step 2: simulating the process of fire spreading in different seasons at the same wind speed by simulating different chequers as fire points, and counting the number of the burnt chequers and the number of the burnt chequers before the rescue team arrives;
step 3: simulating different chessboards as fire points, the same wind speed and different seasons, simulating the process of fire spreading, and counting the burning frequency of each chessboard before the rescue team arrives;
step 4: calculating the fire emphasis probability P3 of the forest of each checkerboard, wherein the specific calculation formula is as follows:
wherein P3 represents the fire emphasis probability of the forest of each checkerboard, Q represents the number of checkerboards burned before the rescue team arrives, NqThe total number of the chequers is represented, J represents the number of forest seasons, and R represents the frequency of burning the chequers.
The sixth specific content of the step comprises:
the fire probability P of a single checkerboard is P1+ P2+ P3, the higher the fire probability P value of the single checkerboard is, the higher the possibility of fire occurrence of the checkerboard is, the greater the influence caused after the fire occurrence is, the fire probability P threshold value of the single checkerboard is set, and the checkerboard exceeding the threshold value is a fire key monitoring grid.
The forest fire prevention data analysis system based on the Internet of things comprises intelligent terminal equipment, a data acquisition unit, an intelligent terminal distribution unit, a forest fire probability calculation unit and an intelligent alarm unit;
the data acquisition unit comprises a probability calculation data acquisition unit and an intelligent equipment real-time data acquisition unit, the probability calculation data acquisition unit acquires forest data and inputs the forest data into the forest fire probability calculation unit, the intelligent equipment real-time data acquisition unit is connected with intelligent terminal equipment, and the acquired intelligent terminal equipment data are input into the intelligent alarm unit;
the forest fire probability calculating unit calculates the probability of fire occurrence in a single checkerboard and outputs an intelligent terminal distribution unit, the forest fire probability calculating unit comprises the probability of artificial fire of a forest, the probability of natural fire of the forest and the fire key probability of the forest, the probability of the artificial fire of the forest divides the checkerboard in a forest map into three categories according to artificial activities, and the probability of the fire occurrence of the single checkerboard due to the artificial activities is calculated respectively;
calculating the probability of the natural fire of the forest in a single checkerboard according to the probability of the natural fire of the forest, acquiring the local forest fire risk meteorological rating, and outputting the probability of the natural fire of the single checkerboard by combining the forest stand density and the tree oil content in the single checkerboard;
the key probability of the forest fire is that each chequer point is used as a fire point, the process of spreading the forest fire is simulated according to the local wind direction and the ground movement of the forest, and the key probability of the forest fire is calculated according to the spreading range of the fire and the burning frequency of the chequer grids at different fire points;
the terminal equipment distribution module is used for intensively distributing terminal equipment in the checkerboards with high fire probability according to the fire probability of each checkerboard;
the intelligent terminal device detects whether a fire disaster occurs in a single checkerboard in real time and inputs a fire disaster result to the intelligent alarm unit;
the intelligent alarm unit monitors and timely stores and backs up real-time monitoring data, positions in time when a fire occurs, and gives an alarm to management personnel.
The terminal equipment distribution module sets a fire probability P grade threshold value of the checkerboards, and sets different intelligent terminal equipment setting schemes according to the fire probability grade of the checkerboards:
when the fire probability P value of the chessboard is (0, P)1) The fire probability level of the checkerboards is one level, the fire probability of the checkerboards is within a checkerboard safety value, and the checkerboard safety does not need to be provided with intelligent terminal equipment for monitoring the occurrence of forest fires;
when the fire probability P of the checkerboard is in (P)1，P2) Setting a preliminary intelligent terminal device setting scheme, wherein the preliminary intelligent terminal device setting scheme comprises a video monitoring unit, and the video monitoring unit identifies visible light smoke and fire and monitors occurrence of forest fire;
when the fire probability P of the checkerboard is in (P)2，PMAX) The fire probability grade of the checkerboards is three, and the fire probability of the checkerboards is higher than the danger value of the checkerboardsAnd the checkerboard is dangerous, a final version of intelligent terminal device setting scheme is set, the final version of intelligent terminal device setting scheme comprises an infrared thermal imaging temperature measuring unit, the infrared thermal imaging temperature measuring unit identifies abnormal high temperature, identifies hidden fire in a single checkerboard, prevents forest fire and prevents the forest fire from happening in the bud.
Compared with the prior art, the invention has the following beneficial effects: set up intelligent terminal equipment based on the thing networking, real-time monitoring forest fire, need not personnel and go deep into the forest and carry out ground patrol and protect, divide the forest into the check, use intelligent terminal equipment's monitoring range as the unit, be convenient for intelligent equipment's location and setting, calculate the probability of the conflagration breaing out in the single check through forest fire probability calculating unit, the probability of the different check conflagrations of numeralization takes place the influence that the conflagration breaing out and cause, be convenient for audio-visual different check boards in the importance of forest fire protection, set up intelligent terminal equipment according to the importance of different check boards, in controllable equipment cost, the condition of the monitoring forest fire of maximize.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow diagram of a forest fire prevention data analysis method based on the Internet of things;
fig. 2 is a schematic structural diagram of the forest fire prevention data analysis system based on the internet of things.
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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution: the first embodiment is as follows: the forest fire prevention data analysis method based on the Internet of things specifically comprises the following steps:
the method comprises the following steps: collecting forest data, wherein the forest data comprises a forest map, forest climate, artificial activity range of the forest and distribution of trees of the forest;
the artificial movement range of the forest comprises the range of forest production movement and an artificial walking path;
the forest climate comprises temperature, humidity, air pressure, wind direction and rainfall;
step two: dividing the forest map into chequers, establishing a coordinate system, and determining the coordinate of each chequer;
step three: calculating the probability P1 of artificial fire of each checkerboard forest;
various artificial activities can be generated in the forest, including artificial production activities, tourists 'sightseeing and hunting paths, forest trees are bushy and roads in the forest are difficult to operate, if someone is moving, the artificial production activities generally need effective fire prevention measures in fixed areas and roads, and at the junction of the artificial activities and the original forest environment, including the junction of the production activities and the tourists' sightseeing and hunting paths, because the personnel are complex and start to be connected with the forest, the personnel easily generate private feelings, thereby the vigilance for fire prevention is relaxed, and instead, the attention is needed.
Step four: calculating the probability P2 of the natural fire of the forest of each checkerboard;
the forest has natural forest fire, is full of oxygen, contains a lot of nutrient substances, and is fermented in a slightly humid environment to generate heat, so that water in the environment is evaporated, and dark fire gradually generates open fire, so that the natural forest fire is further caused. In recent years, the forest fire naturally generated by deeply digging a forest is documented, is a new life for self-renewal of the forest and is beneficial to the suppression of plant diseases and insect pests, but the forest fire is also a small-range forest fire, does not need artificial occurrence, but needs artificial supervision to control the forest fire in a proper range, so that the forest fire naturally generated also needs powerful supervision action.
Step five: calculating fire emphasis probability P3 of the forest of each checkerboard;
step six: outputting the fire occurrence of the forest, wherein the fire probability of each checkerboard is the fire key monitoring grid, and the checkerboard exceeding the threshold value is the fire key monitoring grid;
step seven: and (4) configuring perfect intelligent terminal equipment in the fire key monitoring grid, and monitoring the regional fire condition in real time by the intelligent terminal equipment.
And in the second step, the size of the checkerboard is consistent with the range of the fireproof terminal monitoring equipment.
The specific contents of the probability P1 for calculating the artificial fire of the forest in the third step comprise:
dividing a forest map into three organization parts according to the artificial activity range of the forest: the method comprises the following steps of (1) carrying out personnel activity intensive area, junction of personnel activity and forest and unmanned activity area;
the personnel activity dense area is a personnel production activity area, the personnel are dense and the trees are rare, and the probability of the artificial fire in the personnel activity dense area is inversely related to the perfection degree of the fire prevention measures in the area:
wherein, P1aIndicating the probability of an artificial fire in an area with dense activities of people, KreRepresenting the fire-prevention measure coefficient, re representing the completeness of the fire-prevention measure;
the junction of the personnel activity and the forest is the junction of the personnel activity and the original forest environment, including the junction of a personnel production activity area and the original forest environment and the walking path of personnel entering the forest, and the probability P1 of artificial fire at the junction of the personnel activity and the forestbProportional to the density of the forest at the junction of the human activity and the forest:
P1b＝Kfd*fd
wherein, P1bRepresenting the probability of an artificial fire at the junction of a person's activity and a forest, KfdRepresenting a forest density coefficient, and fd representing the forest density at the junction of the personnel activity and the forest;
the unmanned active area is the original forest environment, no personnel activity exists, and the probability P1 of artificial fire in the unmanned active areacIs zero.
The specific contents of the probability P2 for calculating the natural fire of the forest in the fourth step comprise:
according to the distribution of the trees in the forest, calculating the oil content of the trees in each checkerboard, wherein the specific calculation formula is as follows:
wherein oc represents the average oil content of the tree of the species, H represents the height of the plants, H represents the average height of the tree of the species, and N represents the number of all plants in the checkerboard.
The distribution of the types of the plants of the forest is fixed, the growth range of the animals and the reptiles which are attached to the growth of the plants is also fixed, the plants are rich in grease, the grease content of the fallen leaves and dead branches generated by the plants is higher, the grease content of the animals and the reptiles which grow on the plants and the grease content of excrement of the animals and the reptiles which grow on the plants are higher, and therefore the grease content of the trees can represent the content of combustion-supporting nutrient substances in the checkerboard.
The specific contents of the probability P2 for calculating the natural fire of the forest in the fourth step further comprise:
the probability P2 of natural fire of forest is calculated by the following formula:
P2＝(A*Oilc+FHR)*B*SDI
wherein SDI represents forest stand density, FHR represents local fire risk weather grade, A represents vegetation coefficient, and B represents fire risk grade coefficient.
The national forest grassland fire prevention department establishes a forest fire weather grade forecast in a government network of the national forestry grassland bureau, forecasts the forest fire weather forecast in the whole country in real time, and calculates the probability P2 of natural fire of the forest in the checkerboard according to the local fire weather grade forecast; the forest stand density is increased as a coefficient, the vegetation density is increased or decreased due to the influence of human activities, when the forest is artificially moved, vegetation is felled, the tree density is sharply reduced, and the probability of natural fire of the forest is sharply reduced at the moment.
Analyzing different chequers as fire points, the same wind speed, simulating the process of fire spreading according to different seasons and terrains, calculating the speed of fire spreading, counting the fire spread before the rescue team arrives, calculating different fire points, calculating the burning frequency of each chequer before the rescue team develops the rescue, and outputting the fire key probability of a single chequer;
in the fifth step, the specific step of calculating the fire emphasis probability P3 of the forest of each checkerboard comprises the following steps:
step 1: dividing local seasons according to local wind directions;
the forest seasons are divided according to different wind directions, the wind directions can change due to seasons, and different wind directions can lead to different fire spreading results.
Step 2: simulating the process of fire spreading in different seasons at the same wind speed by simulating different chequers as fire points, and counting the number of the burnt chequers and the number of the burnt chequers before the rescue team arrives;
step 3: simulating different chessboards as fire points, the same wind speed and different seasons, simulating the process of fire spreading, and counting the burning frequency of each chessboard before the rescue team arrives;
step 4: calculating the fire emphasis probability P3 of the forest of each checkerboard, wherein the specific calculation formula is as follows:
wherein P3 represents the fire emphasis probability of the forest of each checkerboard, and Q represents the chess burnt before the rescue team arrivesNumber of trays, NqThe total number of the chequers is represented, J represents the number of forest seasons, and R represents the frequency of burning the chequers.
The sixth concrete content comprises the following steps:
the fire probability P of a single checkerboard is P1+ P2+ P3, the higher the fire probability P value of the single checkerboard is, the higher the possibility of fire of the checkerboard is, the greater the influence caused by the fire is, the fire probability P threshold value of the single checkerboard is set, and the checkerboard exceeding the threshold value is a fire key monitoring grid.
The forest fire prevention data analysis system based on the Internet of things comprises intelligent terminal equipment, a data acquisition unit, an intelligent terminal distribution unit, a forest fire probability calculation unit and an intelligent alarm unit;
the data acquisition unit comprises a probability calculation data acquisition unit and an intelligent equipment real-time data acquisition unit, the probability calculation data acquisition unit acquires forest data and inputs the forest data into the forest fire probability calculation unit, the intelligent equipment real-time data acquisition unit is connected with intelligent terminal equipment, and the acquired intelligent terminal equipment data are input into the intelligent alarm unit;
the method comprises the steps that a forest fire probability calculating unit calculates the probability of fire occurrence in a single checkerboard and outputs an intelligent terminal distribution unit, the forest fire probability calculating unit comprises the probability of artificial fire of a forest, the probability of natural fire of the forest and the fire emphasis probability of the forest, the probability of the artificial fire of the forest divides the checkerboard in a forest map into three categories according to artificial activities, and the probability of the fire occurrence of the single checkerboard due to the artificial activities is calculated respectively;
calculating the probability of natural fire of the forest in a single checkerboard according to the probability of the natural fire of the forest, acquiring the local forest fire risk meteorological rating, and outputting the probability of the natural fire of the single checkerboard by combining the forest stand density and the tree grease content in the single checkerboard;
the key probability of the forest fire is that each chequer point is used as a fire point, the process of spreading the forest fire is simulated according to the local wind direction and the ground movement of the forest, and the key probability of the forest fire is calculated according to the spreading range of the fire and the burning frequency of the checkerboard;
the terminal equipment distribution module is used for intensively distributing terminal equipment in the checkerboards with high fire probability according to the fire probability of each checkerboard;
the intelligent terminal device detects whether a fire disaster happens to a single checkerboard in real time and inputs a fire disaster result to the intelligent alarm unit, the intelligent terminal device comprises a video monitor and an infrared thermal imaging temperature measurement, the video monitor unit monitors visible light smoke and fire identification, and the infrared thermal imaging temperature measurement identifies abnormal high temperature; identify the hidden fire in a single checkerboard and prevent the hidden fire from happening in advance
The intelligent alarm unit monitors and stores and backs up real-time monitoring data in time, positions the position in time when a fire occurs, and gives an alarm to management personnel.
Set up intelligent terminal equipment based on the thing networking, real-time monitoring forest fire, need not personnel and go deep into the forest and carry out ground patrol and protect, divide the forest into the check board, use intelligent terminal equipment's monitoring range as the unit, be convenient for intelligent equipment's location and setting, calculate the probability of the conflagration breaing out in the single check board through forest fire probability calculation unit, the probability of the different check boards conflagration breaing out of numeralization and the influence that causes, be convenient for audio-visual different check boards in the importance of forest fire protection, set up intelligent terminal equipment according to the importance of different check boards, in controllable equipment cost, the condition of maximize monitoring forest fire.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The forest fire prevention data analysis method based on the Internet of things is characterized by comprising the following steps: the fire prevention data analysis method specifically comprises the following steps:
the method comprises the following steps: collecting forest data, wherein the forest data comprises a forest map, forest climate, artificial activity range of a forest and distribution of trees of the forest;
the artificial activity range of the forest comprises the range of forest production activity and an artificial walking path;
the forest climate comprises temperature, humidity, air pressure, wind direction and rainfall;
step two: dividing the forest map into checkerboards, establishing a coordinate system, and determining the coordinate of each checkerboard;
step three: calculating the probability P1 of artificial fire of each checkerboard forest;
step four: calculating the probability P2 of the natural fire of the forest of each checkerboard;
step five: calculating fire emphasis probability P3 of the forest of each checkerboard;
step six: outputting the fire occurrence of the forest, wherein the fire probability of each checkerboard is the fire key monitoring grid, and the checkerboard exceeding the threshold value is the fire key monitoring grid;
step seven: and configuring perfect intelligent terminal equipment in the fire key monitoring grid, wherein the intelligent terminal equipment monitors the regional fire condition in real time.
2. The forest fire prevention data analysis method based on the Internet of things as claimed in claim 1, wherein: and in the second step, the size of the checkerboard is consistent with the range of the fireproof terminal monitoring equipment.
3. The forest fire prevention data analysis method based on the Internet of things as claimed in claim 1, wherein: the specific contents of the probability P1 for calculating the artificial fire of the forest in the third step comprise:
dividing a forest map into three organization parts according to the artificial activity range of the forest: the method comprises the following steps of (1) carrying out personnel activity intensive area, junction of personnel activity and forest and unmanned activity area;
the personnel activity intensive area is a personnel production activity area, the personnel are intensive and the trees are rare, and the probability of the artificial fire in the personnel activity intensive area is inversely related to the perfection degree of the fire-proof measures in the area:
wherein, P1aIndicating the probability of an artificial fire in an area with dense activities of people, KreExpressing the fire-proof measure coefficient, re expressing the perfection of the fire-proof measure;
the junction of the personnel activities and the forest is the junction of the personnel activities and the original forest environment, and comprises the junction of a personnel production activity area and the original forest environment and a walking path of personnel entering the forest, and the probability P1 of artificial fire at the junction of the personnel activities and the forest isbProportional to the density of the forest at the junction of the human activity and the forest:
P1b＝Kfd*fd
wherein, P1bRepresenting the probability of an artificial fire at the junction of a person's activity and a forest, KfdRepresenting a forest density coefficient, and fd representing the forest density at the junction of the personnel activity and the forest;
the unmanned active area is a forest original environment without personnel activity, and the probability P1 of artificial fire in the unmanned active areacIs zero.
4. The forest fire prevention data analysis method based on the Internet of things as claimed in claim 1, wherein: the specific contents of the probability P2 for calculating the natural fire of the forest in the fourth step comprise:
according to the distribution of trees in the forest, calculating the oil content of the trees in each checkerboard, wherein the specific calculation formula is as follows:
wherein oc represents the average oil content of the tree of the species, H represents the height of the plants, H represents the average height of the tree of the species, and N represents the number of all plants in the checkerboard.
5. The Internet of things-based forest fire prevention data analysis method according to claim 4, wherein the method comprises the following steps: the specific contents of the probability P2 for calculating the natural fire of the forest in the fourth step further comprise:
the probability P2 of natural fire of forest is calculated by the following formula:
P2＝(A*Oilc+FHR)*B*SDI
wherein SDI represents forest stand density, FHR represents local fire danger weather grade, A represents vegetation coefficient, and B represents fire danger grade coefficient.
6. The forest fire prevention data analysis method based on the Internet of things as claimed in claim 1, wherein: the fifth step of calculating the fire emphasis probability P3 of the forest of each checkerboard comprises the following specific steps:
step 1: dividing local seasons according to local wind directions;
step 2: simulating the process of fire spreading in different seasons at the same wind speed by simulating different chequers as fire points, and counting the number of the burnt chequers and the number of the burnt chequers before the rescue team arrives;
step 3: simulating different chessboards as fire points, the same wind speed and different seasons, simulating the process of fire spreading, and counting the burning frequency of each chessboard before the rescue team arrives;
step 4: calculating the fire emphasis probability P3 of the forest of each checkerboard, wherein the specific calculation formula is as follows:
wherein P3 represents the fire emphasis probability of the forest of each checkerboard, Q represents the number of checkerboards burned before the rescue team arrives, NqThe total number of the chequers is represented, J represents the number of forest seasons, and R represents the frequency of burning the chequers.
7. The forest fire prevention data analysis method based on the Internet of things as claimed in claim 1, wherein: the sixth specific content of the step comprises:
the fire probability P of a single checkerboard is P1+ P2+ P3, the higher the fire probability P value of the single checkerboard is, the higher the possibility of fire occurrence of the checkerboard is, the greater the influence caused after the fire occurrence is, the fire probability P threshold value of the single checkerboard is set, and the checkerboard exceeding the threshold value is a fire key monitoring grid.
8. Forest fire prevention data analysis system based on thing networking, its characterized in that: the system comprises intelligent terminal equipment, a data acquisition unit, an intelligent terminal distribution unit, a forest fire probability calculation unit and an intelligent alarm unit;
the data acquisition unit comprises a probability calculation data acquisition unit and an intelligent equipment real-time data acquisition unit, the probability calculation data acquisition unit acquires forest data and inputs the forest data into the forest fire probability calculation unit, the intelligent equipment real-time data acquisition unit is connected with intelligent terminal equipment, and the acquired intelligent terminal equipment data are input into the intelligent alarm unit;
the forest fire probability calculating unit calculates the probability of fire occurrence in a single checkerboard and outputs an intelligent terminal distribution unit;
the terminal equipment distribution module is used for intensively distributing terminal equipment in the checkerboards with high fire probability according to the fire probability of each checkerboard;
the intelligent terminal device detects whether a fire disaster occurs in a single checkerboard in real time and inputs a fire disaster result to the intelligent alarm unit;
the intelligent alarm unit monitors and timely stores and backs up real-time monitoring data, positions in time when a fire occurs, and gives an alarm to management personnel.
9. The Internet of things-based forest fire prevention data analysis system of claim 8, wherein: the forest fire probability calculating unit comprises the probability of artificial fire of the forest, the probability of natural fire of the forest and the fire key probability of the forest, the probability of the artificial fire of the forest is divided into three categories according to artificial activities, and the probability of fire caused by the artificial activities of the single checkerboards is calculated respectively;
calculating the probability of the natural fire of the forest in a single checkerboard according to the probability of the natural fire of the forest, acquiring the local forest fire risk meteorological rating, and outputting the probability of the natural fire of the single checkerboard by combining the forest stand density and the tree oil content in the single checkerboard;
the key probability of the forest fire is that each chequer point is used as a fire point, the process of spreading the forest fire is simulated according to the local wind direction and the ground movement of the forest, and the key probability of the forest fire is calculated according to the spreading range of the fire and the burning frequency of the checkerboard.
10. The Internet of things-based forest fire prevention data analysis system of claim 8, wherein: the terminal equipment distribution module sets a fire probability P grade threshold value of the checkerboards, and sets different intelligent terminal equipment setting schemes according to the fire probability grade of the checkerboards:
when the fire probability P value of the checkerboard is (0, P)1) The fire probability grade of the checkerboards is one grade, the fire probability of the checkerboards is within the safety value of the checkerboards, and the checkerboards are safeNo intelligent terminal equipment is required to be arranged to monitor the occurrence of forest fires;
when the fire probability P of the checkerboard is in (P)1，P2) Setting a preliminary intelligent terminal device setting scheme, wherein the preliminary intelligent terminal device setting scheme comprises a video monitoring unit, and the video monitoring unit identifies visible light smoke and fire and monitors occurrence of forest fire;
when the fire probability P of the checkerboard is in (P)2，PMAX) The fire probability grade of the checkerboards is three-level, the checkerboard fire probability is higher than a checkerboard danger value, the checkerboards are dangerous, a setting scheme of intelligent terminal equipment of a final version is set, the setting scheme of the intelligent terminal equipment of the final version comprises an infrared thermal imaging temperature measuring unit, and the infrared thermal imaging temperature measuring unit identifies abnormal high temperature and prevents forest fires.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116612598A (en) * | 2023-07-18 | 2023-08-18 | 泰格森安(山东)物联科技有限公司 | Multi-point monitoring type forest fire prevention monitoring system based on Internet of things |
| CN117783453A (en) * | 2024-02-27 | 2024-03-29 | 杨凌职业技术学院 | Real-time monitoring system for forestry fire prevention |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116612598A (en) * | 2023-07-18 | 2023-08-18 | 泰格森安(山东)物联科技有限公司 | Multi-point monitoring type forest fire prevention monitoring system based on Internet of things |
| CN116612598B (en) * | 2023-07-18 | 2023-10-10 | 泰格森安(山东)物联科技有限公司 | Multi-point monitoring type forest fire prevention monitoring system based on Internet of things |
| CN117783453A (en) * | 2024-02-27 | 2024-03-29 | 杨凌职业技术学院 | Real-time monitoring system for forestry fire prevention |
| CN117783453B (en) * | 2024-02-27 | 2024-05-10 | 杨凌职业技术学院 | Real-time monitoring system for forestry fire prevention |
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