Forest fire monitoring system based on Internet of Things and network construction method

Abstract

The invention discloses a forest fire monitoring system based on the Internet of Things and a network construction method, and relates to the technical field of forest fire monitoring. Comprising a point location acquisition module used for acquiring data information of a forest, obtaining forest information items, laying erection point locations through a laying method based on the forest information items, and obtaining a monitoring erection point set which at least comprises one monitoring erection point location; and the equipment laying module is used for respectively laying at least one conventional monitoring equipment and at least one scanning monitoring equipment based on the monitoring erection point set. According to the method, the forest vegetation condition is divided into three grades, and the grade of the forest area is judged, so that the scanning interval is adjusted, the key scanning effect on different areas of the forest is realized, and under the special condition, for example, when leaves in a certain area are blown to be overlapped together, according to the overlapping degree, the forest vegetation condition is determined. And the area is frequently and emphatically scanned, so that the accuracy of a scanning result is ensured as much as possible.

Description

Technical Field

[0001] The present invention relates to the technical field of forest fire monitoring, and in particular to a forest fire monitoring system based on the Internet of Things and a network construction method. Background Art

[0002] A forest fire refers to a fire caused by natural or human factors in a forest or other vegetation-covered area. Such a fire not only causes serious damage to the ecological environment, but may also threaten human life and property safety. The Internet of Things refers to connecting various physical devices, sensors, software and other technologies through the Internet to achieve data collection, exchange and analysis, thereby improving efficiency, improving services and creating new business models. The Internet of Things is often connected to various fire monitoring equipment for more comprehensive monitoring of fires. The forest fire monitoring system based on the Internet of Things can achieve early warning and rapid response to fires, thereby effectively protecting forest resources and the ecological environment.

[0003] Patent publication number CN118298569A is a forest fire monitoring device and method based on multi-spectral infrared imaging. By setting up a satellite monitoring system and a ground monitoring system, the two are used in combination to achieve a combination of large-scale real-time monitoring and small-scale precise monitoring, thereby enhancing the timeliness of fire point discovery. At the same time, wireless communication components and color communication components are set in the ground monitoring system to effectively reduce the impact of geographical location on the timeliness of communication, so that the staff of the ground processing station can receive warning information in time, providing convenience for fire prevention and control. During the monitoring process of the satellite monitoring system, the floating signal ball transmits warning information to the satellite monitoring system through its color and the logo set on its surface, and forwards it to the ground processing station via the satellite monitoring system, and cooperates with the eruption of colored smoke to further ensure that the warning information can be identified and sent, thereby improving the accuracy of fire information transmission.

[0004] When the above and similar technical solutions are used to monitor forest fires, the conditions of the trees in the forest will change under different circumstances. For example, in windy weather, the trees in the forest will flutter in the wind, and the leaves will overlap and block each other. This will cause certain interference to the monitoring equipment that is in normal detection. Generally, monitoring equipment performs normal detection on the conditions in the area, and the detection frequency will not change. This leads to the risk that some areas may not be fully detected when the vegetation conditions in the forest change. In addition, since the signal strength in different areas of the forest is inconsistent, and the monitoring equipment or systems in different areas are directly connected to the host for signal connection, when the signal strength between an area and the host is poor or unstable, it is easy to cause the fire monitoring information to fail to be transmitted in time. Summary of the invention

[0005] The purpose of the present invention is to provide a forest fire monitoring system and a network construction method based on the Internet of Things to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a forest fire monitoring system based on the Internet of Things, comprising: Point acquisition module: obtains forest data information, obtains forest information items, and based on the forest information items, lays out the points through a laying method to obtain a monitoring erection point set, wherein the monitoring erection point set includes at least one monitoring erection point; Equipment laying module: based on the monitoring installation point set, at least one conventional monitoring device and at least one scanning monitoring device are laid respectively, and at least one secondary terminal is laid, the conventional monitoring device and the scanning monitoring device are connected to the secondary terminal through the Internet of Things, and the secondary terminal is connected to the final terminal based on the Internet of Things; A level classification module; obtaining the growth state of forest vegetation, and dividing the growth state of vegetation into at least two levels by a division method, thereby obtaining at least two vegetation state level items; Scanning monitoring module: set the scanning interval, and the scanning monitoring device performs an all-round scan of the forest according to the scanning interval, obtains the real-time status of the forest vegetation based on the scanning monitoring device, obtains the real-time status items, analyzes and processes the real-time status items according to the processing method, and performs targeted scanning on different conditions in different areas of the forest; The processing methods include: Step 1: grade determination: based on the real-time status item, the real-time growth status of the vegetation in the forest is graded to obtain the real-time grade determination item; Step 2: Position acquisition: based on the real-time determination level item, the growth state of the forest vegetation is divided into regions to obtain the real-time determination position item; Step 3: Adjustment setting, setting the adjustment item, obtaining at least two adjustment ranges based on the vegetation state level item, and then obtaining the adjustment range item; Step 4: Interval adjustment: Based on the real-time determination level item and the adjustment range item, the scanning interval is adjusted to obtain the interval adjustment result item, and based on the real-time determination position item, targeted scanning is performed according to the interval adjustment result item.

[0007] Furthermore, the forest information item includes forest area information, and the laying method includes: S1: Image acquisition, obtaining the overall area image information of the forest based on the target device to obtain the forest image item; S2: Range setting, setting the division range, dividing the forest image item into regions based on the division range, and obtaining multiple forest region items; S3: point determination: based on the forest area item, the midpoint coordinate information of the forest area item is obtained to obtain multiple regional midpoint items, and then the regional midpoint items are used as monitoring installation points to obtain a monitoring installation point set.

[0008] Furthermore, the conventional monitoring equipment includes a temperature sensor and a smoke sensor, and the laying method of the conventional monitoring equipment includes: M1: Regional subdivision: based on the forest area item, set the subdivision ratio, perform regional subdivision on the forest area item, and obtain multiple regional subdivision items; M2: Equipment installation, based on the area subdivision items, each area subdivision item is set with at least two temperature sensors and smoke sensors, and the coverage of the temperature sensor and the smoke sensor is obtained to obtain the temperature range item and the smoke range item; M3: Range inventory, based on the temperature range items and smoke range items, determine whether the temperature range items and smoke range items overlap each other. When the temperature range items and smoke range items overlap each other, it is determined that the installation of conventional monitoring equipment is completed, otherwise repeat M2-M3.

[0009] Furthermore, the scanning monitoring equipment includes a laser infrared radar and a high-definition camera, and the method for laying the scanning monitoring equipment includes: location selection, obtaining the installation location of the scanning monitoring equipment based on the midpoint item of the area, and laying at least two laser infrared radars and high-definition cameras based on the midpoint item of the area.

[0010] Furthermore, the growth state of the vegetation includes the growth cycle of the vegetation, and the division method includes: N1: Information acquisition, obtaining vegetation information of the target forest, obtaining vegetation information items, and obtaining the height limit of the target vegetation information based on the vegetation information, obtaining the target vegetation height item; N2: Setting the first division item: setting the first division item, and dividing the height of the target vegetation based on the target vegetation height item and the first division item to obtain at least two height range items, and further obtain at least two vegetation state level items.

[0011] Furthermore, the growth state of the vegetation includes the leaf stacking density of the vegetation, and the division method also includes: setting a second division item, setting the second division item, dividing the growth state of the vegetation based on the second division item, obtaining at least two leaf density ranges, and then obtaining at least two vegetation state level items.

[0012] Furthermore, the method for adjusting the setting includes: X1: first adjustment item matching, based on the set adjustment item, matching with the height range item to obtain at least two first adjustment items; X2: second adjustment item matching, based on the set adjustment item, matching with the blade density range to obtain at least two second adjustment items; X3: Adjustment combination, based on the combination result of the first adjustment item and the second adjustment item, at least four adjustment ranges are obtained, and then the adjustment range item is obtained.

[0013] A method for constructing a forest fire monitoring network based on the Internet of Things, comprising: Z1: Connection range setting, starting from the target secondary terminal, setting the connection range, based on the connection range, obtaining the secondary terminals, conventional monitoring devices and scanning monitoring devices within the connection range, and obtaining the auxiliary connection items; Z2: Auxiliary connection, based on the auxiliary connection item, build a monitoring network of the auxiliary connection item and the target secondary terminal to obtain an expanded connection network, thereby forming a mesh monitoring network layout.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The forest fire monitoring system and network construction method based on the Internet of Things sets a scanning interval, obtains the real-time situation in the forest according to the scanning interval, divides the forest vegetation situation into three levels, and determines the level of the forest area according to the real-time data obtained by the scanning monitoring equipment, so as to adjust the scanning interval, thereby achieving the effect of focusing on scanning different areas of the forest. In special circumstances, such as when the leaves in a certain area are overlapped by the wind, the area is frequently and focusedly scanned according to the degree of overlap, so as to ensure the accuracy of the scanning result as much as possible.

[0015] At the same time, the connection range is set, and the conventional monitoring equipment and scanning monitoring equipment within the connection range are respectively connected and transmitted with the secondary terminal, and the secondary terminal is connected to the host, so that the conventional monitoring equipment and scanning monitoring equipment in the forest are constructed in a mesh manner. When the signal is unstable in some areas, the data can still be transmitted in separate paths. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the relationship between the point acquisition module, the equipment laying module, the grade classification module and the scanning monitoring module of the present invention; Figure 2 It is a schematic diagram of the processing method of the present invention; Figure 3 A schematic diagram of the relationship between the regional midpoint item and the forest image item of the present invention; Figure 4 It is a schematic diagram of the relationship between the regional subdivision items of the present invention; Figure 5 A schematic diagram of the installation position of the scanning monitoring device of the present invention; Figure 6 The figure is a schematic diagram of the network construction of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] In the process of monitoring forest fires, the situation in the forest is relatively complex, which will cause certain interference to the monitoring. For example, in different areas of the forest, due to the inconsistent light intensity, the growth of trees will also be inconsistent. At this time, during the normal monitoring process, the growth of trees may affect the monitoring of some areas with lush growth, which may lead to inadequate monitoring, resulting in potential safety hazards. The technical solution provided by the present application first sets up points in the forest to obtain multiple monitoring points, and lays conventional monitoring equipment based on the monitoring points. The monitoring equipment includes temperature sensors and smoke sensors. The conventional monitoring equipment is used for all-weather monitoring of forest abnormalities, and scanning monitoring equipment is laid based on the monitoring points. The scanning monitoring equipment includes a lidar and a rotatable high-definition camera. The conventional monitoring equipment and the scanning monitoring equipment are connected via the Internet of Things. The setting Scanning interval, according to which real-time conditions in the forest are obtained at regular intervals, forest obstruction conditions are divided into at least two levels, and the level of the forest area is determined according to the real-time data obtained by the scanning monitoring equipment, so as to adjust the scanning interval, which can achieve the effect of focusing on scanning different areas of the forest. In special circumstances, such as when the leaves in a certain area are overlapped by the wind, the area is scanned frequently and focused according to the degree of overlap to ensure the accuracy of the scanning results as much as possible. In terms of network construction, secondary terminals are set according to the monitoring installation points, and the connection range is set. Conventional monitoring equipment and scanning monitoring equipment within the connection range are respectively connected and transmitted with the secondary terminals, and the secondary terminals are connected to the host, so that the conventional monitoring equipment and scanning monitoring equipment in the forest are constructed in a mesh manner. When the signal is unstable in some areas, the data can still be transmitted in separate paths.

[0019] like Figure 1-Figure 3As shown, the present invention provides a technical solution: a forest fire monitoring system based on the Internet of Things, including: a point acquisition module: acquiring forest data information, obtaining forest information items, and based on the forest information items, laying out points through a laying method to obtain a monitoring erection point set, and the monitoring erection point set has at least one monitoring erection point; an equipment laying module: based on the monitoring erection point set, laying out at least one conventional monitoring device and at least one scanning monitoring device, and laying out at least one secondary terminal, the conventional monitoring device and the scanning monitoring device are both connected to the secondary terminal through the Internet of Things, and the secondary terminal is connected to the final terminal based on the Internet of Things; a level division module: acquiring the growth state of forest vegetation, and dividing the growth state of vegetation into at least two levels through a division method, and obtaining at least two vegetation state level items; a scanning monitoring module: setting a scanning interval, and the scanning monitoring device monitors the forest according to the scanning interval The forest is scanned in all directions, and the real-time status of the forest vegetation is obtained based on the scanning monitoring equipment to obtain the real-time status item. Based on the real-time status item, the real-time status item is analyzed and processed according to the processing method, and targeted scanning is performed on different situations in different areas of the forest; Processing method: Step 1: Level determination, based on the real-time status item, the real-time growth status of the vegetation in the forest is determined by level, and the real-time determination level item is obtained; Step 2: Position acquisition, based on the real-time determination level item, the growth status of the forest vegetation is divided into regions to obtain the real-time determination position item; Step 3: Adjustment setting, setting the adjustment item, based on the vegetation status level item, at least two adjustment ranges are obtained, and then the adjustment range item is obtained; Step 4: Interval adjustment, based on the real-time determination level item and the adjustment range item, the scanning interval is adjusted to obtain the interval adjustment result item, and based on the real-time determination position item, targeted scanning is performed according to the interval adjustment result item.

[0020] It should be noted that the setting points are first set according to the forest information obtained, and conventional monitoring equipment and scanning monitoring equipment are laid at the setting points respectively. Conventional monitoring equipment includes temperature sensors and smoke sensors. Conventional monitoring equipment is always used to monitor abnormal conditions in the forest. For example, when abnormal temperature is detected somewhere or smoke is present in the air, a corresponding alarm can be issued. The scanning monitoring equipment scans the conditions in the area intermittently. For example, the scanning monitoring equipment includes a laser infrared radar and a high-definition camera. The laser infrared radar and the high-definition camera scan and monitor the conditions in the area according to the set time interval. During the scanning process, the vegetation conditions in the area have been graded in advance. Therefore, during the scanning process, when different levels of vegetation conditions are found in the area, in order to make the scanning result more accurate, the scanning frequency is changed by adjusting the time interval, so as to perform high-frequency scanning according to different levels of vegetation conditions to enhance the scanning accuracy. The set scanning interval is 90 minutes. Every 90 minutes, the laser infrared radar and the high-definition camera will scan the area.

[0021] Forest information item forest area information, laying method: S1: image acquisition, based on the target device to obtain the overall forest area image information, to obtain the forest image item; S2: range setting, set the division range, based on the division range to divide the forest image item into regions, to obtain multiple forest area items; S3: point determination, based on the forest area item, obtain the midpoint coordinate information of the forest area item, to obtain multiple regional midpoint items, and then use the regional midpoint items as monitoring installation points to obtain a monitoring installation point set.

[0022] like Figure 4 As shown, it should be noted that the target equipment includes remote sensing equipment, etc. According to the acquired area image of the target forest, the division range is set. The division range is a fixed value, which is usually determined according to the effective monitoring range of conventional monitoring equipment and scanning monitoring equipment. Therefore, the set division range is a square area with a side length of 1 km. The obtained forest image data is divided based on the division range, and the midpoint coordinate information of the forest area after each division is obtained, and the midpoint position is used as the monitoring setting point.

[0023] In the specific implementation process, the specific area image of a forest was obtained through remote sensing equipment. At this time, the forest was divided into ten areas according to the division range, and ten monitoring points were obtained according to the midpoint coordinate information of each area.

[0024] Conventional monitoring equipment temperature sensors and smoke sensors, and the laying method of conventional monitoring equipment: M1: area subdivision, based on the forest area item, set the subdivision ratio, perform regional subdivision on the forest area item, and obtain multiple regional subdivision items; M2: equipment installation, based on the regional subdivision items, set at least two temperature sensors and smoke sensors for each regional subdivision item, and obtain the coverage of the temperature sensor and the smoke sensor to obtain the temperature range item and the smoke range item; M3: range inventory, based on the temperature range item and the smoke range item, determine whether the temperature range item and the smoke range item overlap each other. When the temperature range item and the smoke range item overlap each other, it is determined that the laying of conventional monitoring equipment is completed, otherwise repeat M2-M3.

[0025] like Figure 5 As shown, it should be noted that since the effective monitoring range of the temperature sensor and the smoke sensor is small, after obtaining the divided forest area, it is necessary to set the subdivision ratio and subdivide the divided forest area again. At this time, the subdivision ratio is 25%, so the divided forest area is divided into four equal parts again, and at least two temperature sensors and smoke sensors are set in each area, which are divided into positive and negative respectively. When there is a damaged temperature sensor or smoke sensor, there is a replacement option, and according to the effective range of the temperature sensor or smoke sensor, the temperature sensor or smoke sensor is set in the four-divided forest area, and the effective range of the temperature sensor or smoke sensor in each subdivided area can overlap each other.

[0026] The scanning monitoring equipment includes a laser infrared radar and a high-definition camera. The method for laying the scanning monitoring equipment includes: position selection, obtaining the installation position of the scanning monitoring equipment based on the midpoint item of the area, and laying at least two laser infrared radars and high-definition cameras based on the midpoint item of the area.

[0027] It should be noted that since the effective range of laser infrared radar and high-definition camera is relatively long, they can be installed based on the midpoint items in each area.

[0028] The growth state and growth cycle of vegetation are divided in the following ways: N1: information acquisition, obtaining vegetation information of the target forest to obtain vegetation information items, and obtaining the height limit of the target vegetation information based on the vegetation information to obtain the target vegetation height item; N2: setting the first division item, setting the first division item, and dividing the height of the target vegetation based on the target vegetation height item and the first division item to obtain at least two height range items, and then obtaining at least two vegetation state level items.

[0029] It should be noted that the growth limit height of the vegetation in the target forest is obtained, and the first division item is set. The first division item is 33.33%. Therefore, the entire target vegetation height item can be divided into three parts, namely low, medium and high, to obtain three height range items, among which the low level is 0-33.33%, the medium level is 33.33%-66.66%, and the high level is >66.66%, thereby obtaining three vegetation status level items.

[0030] In the specific implementation process, the vegetation in the target forest is oak, and its maximum height is generally 40m. At this time, it is divided according to the first division item, and then divided into low-level 0-13.33m, middle-level 13.33m-26.66m, and high-level >26.66m.

[0031] The growth state of vegetation and the leaf stacking density of vegetation, the division method also includes: setting a second division item, setting the second division item, dividing the growth state of vegetation based on the second division item, obtaining at least two leaf density ranges, and then obtaining at least two vegetation state level items.

[0032] It should be noted that the second classification item set is the leaf stacking density of vegetation, which is also divided into low, medium and high levels, among which the low level is <225g / m², the medium level is 225g / m²-375g / m², and the high level is >375g / m², thus also obtaining three vegetation status level items.

[0033] Adjustment setting method: X1: first adjustment item matching, based on the set adjustment item, matching with the height range item, to obtain at least two first adjustment items; X2: second adjustment item matching, based on the set adjustment item, matching with the blade density range, to obtain at least two second adjustment items; X3: adjustment combination, based on the combination result of the first adjustment item and the second adjustment item, to obtain at least four adjustment ranges, and then to obtain the adjustment range item.

[0034] It should be noted that the set adjustment item is 10%. Since there are three height range items, the first adjustment items corresponding to the low, medium and high levels are 10%, 20% and 30% respectively. Similarly, since there are three blade density ranges, the second adjustment items corresponding to the low, medium and high levels are 10%, 20% and 30% respectively, thus obtaining nine adjustment ranges.

[0035] In the specific implementation process, the real-time growth status of the vegetation in the forest is graded according to the real-time status items, where the height is at a medium level and the leaf stacking density is at a medium level. The corresponding adjustment range is 20%+20%=40%. Since the set scanning interval is 90 minutes, after adjustment, the scanning interval is 90-(90*40%)=54. Therefore, according to the real-time determination position item obtained, the scanning interval is shortened and the real-time determination position item is scanned every 54 minutes.

[0036] A method for constructing a forest fire monitoring network based on the Internet of Things, comprising: Z1, connection range setting, taking a target secondary terminal as a starting point, setting a connection range, based on the connection range, acquiring secondary terminals, conventional monitoring equipment and scanning monitoring equipment within the connection range, and obtaining auxiliary connection items; Z2: auxiliary connection, based on the auxiliary connection items, constructing a monitoring network of the auxiliary connection items and the target secondary terminals, obtaining an expanded connection network, and then forming a meshed monitoring network layout.

[0037] like Figure 6 As shown, it should be noted that the connection range is set as a circular area with a diameter of 2km. The target secondary terminal is taken as the starting point. The secondary terminals, conventional monitoring equipment and scanning monitoring equipment within the connection range are connected to the target secondary terminal, thereby forming a mesh network. When the network in some areas is unstable and the signal transmission is affected, there are still other branch networks that can be transmitted.

[0038] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. A forest fire monitoring system based on the Internet of Things, characterized in that: include: Point acquisition module: obtains forest data information, obtains forest information items, and based on the forest information items, lays out the points through a laying method to obtain a monitoring erection point set, wherein the monitoring erection point set includes at least one monitoring erection point; Equipment laying module: based on the monitoring installation point set, at least one conventional monitoring device and at least one scanning monitoring device are laid respectively, and at least one secondary terminal is laid, the conventional monitoring device and the scanning monitoring device are connected to the secondary terminal through the Internet of Things, and the secondary terminal is connected to the final terminal based on the Internet of Things; A level classification module; obtaining the growth state of forest vegetation, and dividing the growth state of vegetation into at least two levels by a division method, thereby obtaining at least two vegetation state level items; Scanning monitoring module: set the scanning interval, and the scanning monitoring device performs an all-round scan of the forest according to the scanning interval, obtains the real-time status of the forest vegetation based on the scanning monitoring device, obtains the real-time status items, analyzes and processes the real-time status items according to the processing method, and performs targeted scanning on different conditions in different areas of the forest; The processing methods include: Step 1: grade determination: based on the real-time status item, the real-time growth status of the vegetation in the forest is graded to obtain the real-time grade determination item; Step 2: Position acquisition: based on the real-time determination level item, the growth state of the forest vegetation is divided into regions to obtain the real-time determination position item; Step 3: Adjustment setting, setting the adjustment item, obtaining at least two adjustment ranges based on the vegetation state level item, and then obtaining the adjustment range item; Step 4: Interval adjustment: Based on the real-time determination level item and the adjustment range item, the scanning interval is adjusted to obtain the interval adjustment result item, and based on the real-time determination position item, targeted scanning is performed according to the interval adjustment result item.

2. The forest fire monitoring system based on the Internet of Things according to claim 1 is characterized in that: The forest information item includes forest area information, and the laying method includes: S1: Image acquisition, obtaining the overall area image information of the forest based on the target device to obtain the forest image item; S2: Range setting, setting the division range, dividing the forest image item into regions based on the division range, and obtaining multiple forest region items; S3: point determination: based on the forest area item, the midpoint coordinate information of the forest area item is obtained to obtain multiple regional midpoint items, and then the regional midpoint items are used as monitoring installation points to obtain a monitoring installation point set.

3. The forest fire monitoring system based on the Internet of Things according to claim 2 is characterized in that: The conventional monitoring equipment includes a temperature sensor and a smoke sensor, and the laying method of the conventional monitoring equipment includes: M1: Regional subdivision: based on the forest area item, the subdivision ratio is set, and the forest area item is subdivided to obtain multiple regional subdivision items; M2: Equipment installation, based on the area subdivision items, each area subdivision item is set with at least two temperature sensors and smoke sensors, and the coverage of the temperature sensor and the smoke sensor is obtained to obtain the temperature range item and the smoke range item; M3: Range inventory, based on the temperature range items and smoke range items, determine whether the temperature range items and the smoke range items overlap each other. When the temperature range items and the smoke range items overlap each other, it is determined that the installation of conventional monitoring equipment is completed, otherwise repeat M2-M3.

4. The forest fire monitoring system based on the Internet of Things according to claim 2 is characterized in that: The scanning monitoring equipment includes a laser infrared radar and a high-definition camera. The method for laying the scanning monitoring equipment includes: position selection, obtaining the installation position of the scanning monitoring equipment based on the midpoint item of the area, and laying at least two laser infrared radars and high-definition cameras based on the midpoint item of the area.

5. The forest fire monitoring system based on the Internet of Things according to claim 1, characterized in that: The growth state of the vegetation includes the growth cycle of the vegetation, and the division method includes: N1: Information acquisition, obtaining vegetation information of the target forest, obtaining vegetation information items, and obtaining the height limit of the target vegetation information based on the vegetation information, obtaining the target vegetation height item; N2: Setting the first division item: setting the first division item, and dividing the height of the target vegetation based on the target vegetation height item and the first division item to obtain at least two height range items, and further obtain at least two vegetation state level items.

6. The forest fire monitoring system based on the Internet of Things according to claim 5 is characterized in that: The growth state of the vegetation includes the leaf stacking density of the vegetation, and the division method also includes: setting a second division item, setting the second division item, dividing the growth state of the vegetation based on the second division item, obtaining at least two leaf density ranges, and then obtaining at least two vegetation state level items.

7. The forest fire monitoring system based on the Internet of Things according to claim 6 is characterized in that: The method for adjusting the settings includes: X1: first adjustment item matching, based on the set adjustment item, matching with the height range item to obtain at least two first adjustment items; X2: second adjustment item matching, based on the set adjustment item, matching with the blade density range to obtain at least two second adjustment items; X3: Adjustment combination, based on the combination result of the first adjustment item and the second adjustment item, at least four adjustment ranges are obtained, and then the adjustment range item is obtained.

8. A method for constructing a forest fire monitoring network based on the Internet of Things, characterized in that: include: Z1: Connection range setting, starting from the target secondary terminal, setting the connection range, based on the connection range, obtaining the secondary terminals, conventional monitoring devices and scanning monitoring devices within the connection range, and obtaining the auxiliary connection items; Z2: Auxiliary connection, based on the auxiliary connection item, build a monitoring network of the auxiliary connection item and the target secondary terminal to obtain an expanded connection network, thereby forming a mesh monitoring network layout.

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