A Smart Forestry IoT Patrol Method and System for Nature Reserves

Abstract

This invention discloses a smart forestry IoT patrol method and system for nature reserves. It acquires abnormal coordinates of patrol personnel when GPS signals are lost, determines the preceding and following positions of these abnormal coordinates, and then precisely corrects the abnormal positions based on the determined preceding and following positions and the patrol personnel's movement speed. The smart forestry IoT patrol method and system for nature reserves provided by this invention effectively solves the problem of GPS location deviation for forest rangers during patrols, ultimately establishing accurate trajectory positions and effectively and accurately protecting the work results of forest rangers. It also helps managers to conduct more precise supervision of forest rangers.

Description

Technical Field

[0001] This invention relates to the field of forestry resource patrol and management technology, and in particular discloses a smart forestry Internet of Things patrol method and system for nature reserves. Background Technology

[0002] When patrolling and managing forestry resources in nature reserves, the information software installed on forest rangers' mobile phones may lose GPS (Global Positioning System) signals, causing the recorded patrol points to deviate and resulting in off-track patrol routes. Therefore, it is necessary to correct the abnormal coordinates of the forest rangers. The usual approach is either not to correct the track points or to use a simplistic method: directly removing outliers from the software and skipping them when connecting the tracks. While this method is simple and can prevent the patrol track from deviating, it results in missing points and cannot accurately describe the movement rate between consecutive points. This is especially problematic when point acquisition takes a long time or the movement rate is high, leading to discrepancies between the patrol track and the actual route.

[0003] Therefore, the existing method of directly removing outliers and skipping outlier points to connect trajectories has the problem of missing points and cannot accurately describe the movement rate between two points, especially when the point acquisition time is long or the movement rate is high. This can lead to a certain difference between the patrol trajectory and the actual route, which is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] This invention provides a smart forestry IoT patrol method and system for nature reserves, aiming to solve the technical problems of existing methods that directly remove outliers through software and skip outlier points to connect trajectories, resulting in missing points and inaccurate description of the movement rate between two points, especially when the point acquisition time is long or the movement rate is high, which leads to a certain difference between the patrol trajectory and the actual route.

[0005] One aspect of the present invention relates to a smart forestry IoT patrol method for nature reserves, comprising the following steps:

[0006] Obtain the abnormal coordinates of patrol personnel when GPS signals are lost, and determine the positions of the points before and after the abnormal coordinates.

[0007] Based on the determined positions of the preceding and following points and the movement speed of the patrol personnel, the abnormal points are precisely corrected.

[0008] Furthermore, the steps for obtaining the abnormal coordinates of patrol personnel when GPS signals are lost, and determining the positions of the abnormal coordinates before and after the loss of these coordinates, include:

[0009] Detect abnormal coordinate points where patrol personnel lose GPS signal within a set interval;

[0010] Based on the detected abnormal coordinate points, determine the positions of the points before and after the abnormal coordinate points.

[0011] Furthermore, based on the determined positions of the preceding and following points and the movement speed of the patrol personnel, the steps for precisely correcting abnormal points include:

[0012] Calculate the distance between the abnormal point and the points before and after it, as well as the average speed of the patrol personnel before and after the abnormal coordinate point occurred, to determine the range of the actual point.

[0013] Take N points before and after the abnormal point, calculate the included angle formed by the line connecting every three adjacent points and the average value of all included angles, and use this to correct the true point location.

[0014] Furthermore, the steps to determine the range of the true location by calculating the distance between the abnormal point and the points before and after it, as well as the average speed of the patrol personnel during the time before and after the abnormal coordinate point occurred, include:

[0015] The system records the coordinates of patrol personnel at set intervals and automatically detects the distance between abnormal points and the points before and after them.

[0016] Based on the distance between the automatically detected abnormal point and the two points before and after it, as well as the average running speed of the patrol personnel in the time before and after the abnormal coordinate point occurred, it is determined whether the abnormal point is an abnormal point M.

[0017] Furthermore, taking N points before and after the abnormal point, calculating the angle formed by the line connecting every three adjacent points and the average of all angles, the steps for correcting the true point position include:

[0018] Based on the positions of N points before and after the abnormal coordinate point and the average running speed within the time before and after the abnormal coordinate point occurred, calculate the maximum allowable total length of the line connecting the real point and the two points before and after, and define the possible range value A of the real point.

[0019] Take N points before and after the abnormal point, calculate the angle formed by the line connecting every three adjacent points, and take the average value B of all angles;

[0020] Within the defined possible range value A, calculate the angle formed by the line connecting the actual point and each of the three adjacent points. The closer the angle is to the value B, the more accurate it is, and the specific location of the actual point can be determined.

[0021] Another aspect of the present invention relates to a smart forestry Internet of Things (IoT) patrol system for nature reserves, comprising:

[0022] The judgment module is used to obtain the abnormal coordinates of the patrol personnel when they lose GPS signal and to determine the positions of the abnormal coordinates before and after the abnormal coordinates.

[0023] The correction module is used to accurately correct abnormal points based on the determined positions of the preceding and following points and the movement speed of the patrol personnel.

[0024] Furthermore, the judgment module includes:

[0025] The detection unit is used to detect abnormal coordinate points when patrol personnel lose GPS signals within a set interval.

[0026] The judgment unit is used to determine the position of the abnormal coordinate point before and after the detected abnormal coordinate point.

[0027] Furthermore, the correction module includes:

[0028] The determination unit is used to calculate the distance between the abnormal point and the points before and after it, as well as the average speed of the patrol personnel in the time before and after the abnormal coordinate point occurred, to determine the range of the actual point.

[0029] The correction unit is used to take N points before and after the abnormal point, calculate the included angle formed by the line connecting each three adjacent points and the average value of all included angles, and correct the true point position.

[0030] Furthermore, the defined unit includes:

[0031] The detection subunit is used to record the coordinates of patrol personnel at set time intervals and automatically detect the distance between abnormal points and the points before and after it.

[0032] The interpretation subunit determines whether the abnormal point is an abnormal point M based on the distance between the automatically detected abnormal point and the two points before and after it, as well as the average running speed of the patrol personnel in the time before and after the abnormal coordinate point occurred.

[0033] Furthermore, the correction unit includes:

[0034] The frame sub-unit is used to calculate the maximum allowable total length of the line connecting the real point and the two points before and after the abnormal coordinate point based on the positions of N points before and after the abnormal coordinate point and the average running speed within the time before and after the abnormal coordinate point occurs, and to frame the possible range value A of the real point.

[0035] Select a sub-unit to take N points before and after the abnormal point, calculate the included angle formed by the line connecting each three adjacent points, and take the average value B of all included angles;

[0036] The sub-cell is determined to calculate the angle between the actual point and the line connecting each of the three adjacent points within the defined possible range value A. The closer the angle is to the value B, the more accurate it is, and the specific location of the actual point is finally determined.

[0037] The beneficial effects achieved by this invention are as follows:

[0038] This invention provides a smart forestry IoT patrol method and system for nature reserves. It acquires abnormal coordinates of patrol personnel when GPS signals are lost, determines the preceding and following positions of these abnormal coordinates, and then precisely corrects the abnormal locations based on the determined preceding and following positions and the patrol personnel's movement speed. This smart forestry IoT patrol method and system for nature reserves effectively solves the problem of GPS location deviation during forestry patrols, ultimately establishing accurate trajectory positions and effectively and accurately protecting the work results of forest rangers. It also helps managers to conduct more precise supervision of forest rangers. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating an embodiment of a smart forestry IoT patrol method for nature reserves provided by the present invention.

[0040] Figure 2 for Figure 1 The diagram shows a detailed flowchart of an embodiment of the steps for obtaining abnormal coordinate points when patrol personnel lose GPS signals and determining the positions of the points before and after the abnormal coordinate points.

[0041] Figure 3 for Figure 1 The diagram shows a detailed process of an embodiment of the steps for accurately correcting abnormal points based on the determined positions of the preceding and following points and the movement speed of the patrol personnel.

[0042] Figure 4 for Figure 3 The diagram shows a detailed flowchart of an embodiment of the steps to determine the range of the true location by calculating the distance between the abnormal location and the locations before and after it, as well as the average speed of the patrol personnel during the time before and after the abnormal coordinate point occurred.

[0043] Figure 5 for Figure 3 The diagram shows a detailed flowchart of an embodiment of the step of taking N points before and after the abnormal point, calculating the included angle formed by the line connecting each three adjacent points and the average value of all included angles, and using this step to correct the true point.

[0044] Figure 6 A functional block diagram of an embodiment of a smart forestry Internet of Things patrol system for nature reserves provided by the present invention;

[0045] Figure 7 for Figure 6 A schematic diagram of the functional modules of one embodiment of the judgment module shown in the figure;

[0046] Figure 8 for Figure 6 A functional module diagram of one embodiment of the correction module shown;

[0047] Figure 9 for Figure 8 A schematic diagram of the functional modules of one embodiment of the determining unit shown;

[0048] Figure 10 for Figure 8 The diagram shows a functional module schematic of one embodiment of the correction unit.

[0049] Explanation of icon numbers:

[0050] 10. Judgment Module; 20. Correction Module; 11. Detection Unit; 12. Judgment Unit; 21. Determination Unit; 22. Correction Unit; 211. Detection Subunit; 212. Interpretation Subunit; 221. Frame Subunit; 222. Selection Subunit; 223. Determination Subunit. Detailed Implementation

[0051] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0052] like Figure 1 As shown, the first embodiment of the present invention proposes a smart forestry IoT patrol method for nature reserves, including the following steps:

[0053] Step S100: Obtain the abnormal coordinates of the patrol personnel when the GPS signal is lost, and determine the positions of the points before and after the abnormal coordinates.

[0054] The system monitors the patrol routes of patrol personnel, obtains abnormal coordinates when patrol personnel lose GPS signals, and determines the positions of points before and after the abnormal coordinates based on the obtained abnormal coordinates.

[0055] Step S200: Based on the determined positions of the preceding and following points and the movement speed of the patrol personnel, the abnormal points are precisely corrected.

[0056] Based on the determined positions of the preceding and following points and the movement speed of the patrol personnel, the abnormal points are precisely corrected. For example, the average angle between any two adjacent points is taken to further correct the actual position of the points.

[0057] The smart forestry IoT patrol method for nature reserves provided in this embodiment, compared with existing technologies, obtains the abnormal coordinates of patrol personnel when GPS signals are lost, determines the positions before and after the abnormal coordinates, and accurately corrects the abnormal positions based on the determined positions and the patrol personnel's movement speed. This smart forestry IoT patrol method for nature reserves effectively solves the problem of GPS position deviation for forest rangers during patrols, ultimately forming a true trajectory position, effectively and accurately protecting the work results of forest rangers; it also helps managers to conduct more precise supervision of forest rangers.

[0058] Further, please see Figure 2 , Figure 2 for Figure 1 The diagram shows a detailed flow chart of one embodiment of step S100. In this embodiment, step S100 includes:

[0059] Step S110: Detect abnormal coordinate points where patrol personnel lose GPS signal within a set interval.

[0060] The system monitors the patrol routes of personnel and detects abnormal coordinates when a patrolman loses GPS signal at set intervals. For example, the set interval could be 6 seconds.

[0061] Step S120: Determine the positions of the points before and after the detected abnormal coordinate points.

[0062] Based on the abnormal coordinates of the patrol personnel when their GPS signals are lost, the positions of the points before and after the abnormal coordinates are determined.

[0063] The smart forestry IoT patrol method for nature reserves provided in this embodiment, compared with existing technologies, detects abnormal coordinate points when patrol personnel lose GPS signals within a set interval; based on the detected abnormal coordinate points, it determines the positions before and after the abnormal coordinate points. This smart forestry IoT patrol method for nature reserves provided in this embodiment can effectively solve the problem of GPS point deviation during forestry patrols, ultimately forming a true trajectory position, effectively and accurately protecting the work results of forest rangers; and it can help managers to conduct more precise supervision of forest rangers.

[0064] Preferably, please see Figure 3 , Figure 3 for Figure 1 The diagram shows a detailed flow chart of one embodiment of step S200. In this embodiment, step S200 includes:

[0065] Step S210: Calculate the distance between the abnormal point and the points before and after it, as well as the average speed of the patrol personnel before and after the abnormal coordinate point occurred, to determine the range of the actual point.

[0066] Calculate the distance between the abnormal location and the locations before and after it, as well as the average speed of the patrol personnel within the time before and after the abnormal coordinate location occurred (e.g., within one minute before and after), to determine the range of the actual location.

[0067] Step S220: Take N points before and after the abnormal point, calculate the included angle formed by the line connecting each three adjacent points and the average value of all included angles, and use them to correct the true point.

[0068] Take N points before and after the abnormal point, calculate the angle formed by the line connecting every three adjacent points and the average of all angles to further correct the position of the true point. In this embodiment, the N points before and after the abnormal point can be 5 points before and after the abnormal point.

[0069] The smart forestry IoT patrol method for nature reserves provided in this embodiment, compared with existing technologies, determines the range of the true location by calculating the distance between the abnormal point and the points before and after it, as well as the average speed of the patrol personnel within the time before and after the abnormal coordinate point. It then takes N points before and after the abnormal point and calculates the angle formed by the line connecting every three adjacent points and the average value of all angles to correct the true location. This smart forestry IoT patrol method for nature reserves effectively solves the problem of GPS point deviation for forest rangers during forestry patrols, ultimately forming a true trajectory location, effectively and accurately protecting the work results of forest rangers; it also helps managers to conduct more precise supervision of forest rangers.

[0070] Further, see Figure 4 , Figure 4 for Figure 3 The diagram shows a detailed flow chart of one embodiment of step S210. In this embodiment, step S210 includes:

[0071] Step S211: Record the coordinates of patrol personnel at set time intervals and automatically detect the distance between abnormal points and the points before and after them.

[0072] The system records the coordinates of patrol personnel at set intervals (e.g., every 6 seconds) and automatically detects the distance between abnormal locations and the points before and after them.

[0073] Step S212: Based on the distance between the automatically detected abnormal point and the two points before and after it, and the average running speed of the patrol personnel before and after the abnormal coordinate point occurred, determine whether the abnormal point is an abnormal point M.

[0074] Based on the distance between the automatically detected abnormal point and the two points before and after it, as well as the average running speed of the patrol personnel within the time before and after the abnormal coordinate point (e.g., within one minute before and after), it is determined whether it is an abnormal point M.

[0075] The smart forestry IoT patrol method for nature reserves provided in this embodiment, compared with the prior art, records the coordinates of patrol personnel at set time intervals, automatically detects the distance between abnormal points and the points before and after them, and determines whether the abnormal point is an abnormal point M based on the automatically detected distance between the abnormal point and the points before and after it and the average running speed of the patrol personnel in the time before and after the abnormal coordinate point occurred. The smart forestry IoT patrol method for nature reserves provided in this embodiment can effectively solve the problem of GPS point deviation of forest rangers during forestry patrols, and finally form a true trajectory position, effectively and accurately protecting the work results of forest rangers; it can help managers to conduct more precise supervision of forest rangers.

[0076] Preferably, please see Figure 5 , Figure 5 for Figure 3 The detailed flowchart of step S220 shown in the figure is illustrated in this embodiment. In this embodiment, step S220 includes:

[0077] Step S221: Based on the positions of N points before and after the abnormal coordinate point and the average running speed within the time before and after the abnormal coordinate point, calculate the maximum allowable total length of the line connecting the real point and the two points before and after, and define the possible range value A of the real point.

[0078] After identifying the anomaly point M, calculate the maximum allowable total length of the line connecting the real point and the two points before and after the anomaly point, based on the positions of N points before and after the anomaly point and the average velocity within the time before and after the anomaly point (e.g., within one minute before and after the anomaly point), and define the possible range value A of the real point.

[0079] Step S222: Take N points before and after the abnormal point, calculate the included angle formed by the line connecting each three adjacent points, and take the average value B of all included angles.

[0080] Take N points before and after the abnormal point (for example, take 5 points before and after the abnormal point, for a total of 10 points), calculate the included angle formed by the line connecting each three adjacent points, and take the average value B of all included angles.

[0081] Step S223: Within the defined possible range value A, calculate the angle formed by the line connecting the actual point and each of the three adjacent points. The closer the angle is to the value B, the more accurate it is. Finally, determine the specific location of the actual point.

[0082] Within the defined area A, calculate the angle formed by the line connecting the actual point and each of the three adjacent points. The closer the angle is to the value of B, the more accurate it is, thus finally determining the specific location of the actual point.

[0083] The smart forestry IoT patrol method for nature reserves provided in this embodiment, compared with existing technologies, calculates the maximum allowable total length of the line connecting the real point to the points before and after the anomaly based on the positions of N points before and after the anomaly and the average running speed within the time before and after the anomaly, thus defining the possible range value A of the real point. It then takes N points before and after the anomaly, calculates the angle formed by the line connecting each pair of adjacent points, and takes the average value B of all angles. Within the defined possible range value A, it calculates the angle formed by the line connecting the real point to each pair of adjacent points; the closer the angle is to value B, the more accurate it is, ultimately determining the specific location of the real point. This smart forestry IoT patrol method for nature reserves provided in this embodiment can effectively solve the problem of GPS point deviation during forest patrols, ultimately forming a true trajectory location, effectively and accurately protecting the work results of forest rangers; and it can help managers to conduct more precise supervision of forest rangers.

[0084] like Figure 6 As shown, Figure 6 This is a functional block diagram of an embodiment of a smart forestry IoT patrol system for nature reserves provided by the present invention. In this embodiment, the smart forestry IoT patrol system for nature reserves includes a judgment module 10 and a correction module 20. The judgment module 10 is used to obtain the abnormal coordinates of the patrol personnel when GPS signals are lost and to determine the positions of the abnormal coordinates before and after the abnormal coordinates. The correction module 20 is used to accurately correct the abnormal points based on the determined positions of the previous and next points and the movement speed of the patrol personnel.

[0085] In the judgment module 10, the patrol trajectory of the patrol personnel is monitored, the abnormal coordinate points when the patrol personnel lose GPS signal are obtained, and the positions of the points before and after the abnormal coordinate points are determined based on the obtained abnormal coordinate points.

[0086] In the correction module 20, based on the determined positions of the preceding and following points and the movement speed of the patrol personnel, the abnormal points are precisely corrected. For example, the average angle between any two adjacent points is taken to further correct the position of the true point.

[0087] The smart forestry IoT patrol system for nature reserves provided in this embodiment, compared with existing technologies, employs a judgment module 10 and a correction module 20. It acquires the abnormal coordinates of patrol personnel when GPS signals are lost, determines the preceding and following positions of these abnormal coordinates, and accurately corrects the abnormal positions based on the determined preceding and following positions and the patrol personnel's movement speed. This smart forestry IoT patrol system for nature reserves effectively solves the problem of GPS location deviation for forest rangers during patrols, ultimately establishing accurate trajectory positions and effectively and accurately protecting the work results of forest rangers. It also helps managers to conduct more precise supervision of forest rangers.

[0088] Further, please see Figure 7 , Figure 7 for Figure 6 The diagram shows a functional module of an embodiment of the judgment module. In this embodiment, the judgment module 10 includes a detection unit 11 and a judgment unit 12. The detection unit 11 is used to detect abnormal coordinate points when the patrol personnel lose GPS signal within a set interval. The judgment unit 12 is used to determine the position of the abnormal coordinate points before and after the detected abnormal coordinate points.

[0089] The detection unit 11 monitors the patrol personnel's patrol trajectory and detects abnormal coordinates when the patrol personnel lose GPS signals at set intervals. For example, the set interval could be 6 seconds.

[0090] In the judgment unit 12, the position of the previous and next points under the abnormal coordinate point is determined based on the abnormal coordinate point when the patrol personnel lose their GPS signal.

[0091] The smart forestry IoT patrol system for nature reserves provided in this embodiment, compared with existing technologies, employs a detection unit 11 and a judgment unit 12. It detects abnormal coordinate points where patrol personnel lose GPS signals within a set interval; based on the detected abnormal coordinate points, it determines the positions before and after the abnormal coordinate points. This smart forestry IoT patrol system for nature reserves effectively solves the problem of GPS point deviation during forestry patrols, ultimately forming a true trajectory position, effectively and accurately protecting the work results of forest rangers; it also helps managers to conduct more precise supervision of forest rangers.

[0092] Preferably, see Figure 8 , Figure 8 for Figure 6The diagram shows a functional module schematic of one embodiment of the correction module. In this embodiment, the correction module 20 includes a determining unit 21 and a correcting unit 22. The determining unit 21 is used to calculate the distance between the abnormal point and the points before and after it, as well as the average speed of the patrol personnel before and after the occurrence of the abnormal coordinate point, to determine the range value of the true point. The correcting unit 22 is used to take N points before and after the abnormal point, calculate the included angle formed by the line connecting each three adjacent points and the average value of all included angles, and correct the true point.

[0093] In the determination unit 21, the distance between the abnormal point and the points before and after it is calculated, as well as the average speed of the patrol personnel within the time before and after the abnormal coordinate point occurs (e.g., within one minute before and after), to determine the range value of the true point.

[0094] In the correction unit 22, N points before and after the abnormal point are taken, and the included angle formed by the line connecting every three adjacent points and the average value of all included angles are calculated to further correct the position of the true point. In this embodiment, the N points before and after the abnormal point can be 5 points before and after the abnormal point.

[0095] The smart forestry IoT patrol system for nature reserves provided in this embodiment, compared with existing technologies, employs a determining unit 21 and a correcting unit 22. It determines the range of the true location by calculating the distance between the abnormal point and the points before and after it, as well as the average speed of the patrol personnel within the time before and after the abnormal coordinate point. It then takes N points before and after the abnormal point and calculates the angle formed by connecting any three adjacent points and the average value of all angles to correct the true location. This smart forestry IoT patrol system for nature reserves effectively solves the problem of GPS point deviation during forestry patrols, ultimately forming a true trajectory location, effectively and accurately protecting the work results of forest rangers; it also helps managers to conduct more precise supervision of forest rangers.

[0096] Further, see Figure 9 , Figure 9 for Figure 8 The diagram shows a functional module of an embodiment of the determining unit. In this embodiment, the determining unit 21 includes a detection subunit 211 and a judgment subunit 212. The detection subunit 211 is used to record the coordinates of the patrol personnel at set time intervals and automatically detect the distance between the abnormal point and the two points before and after it. The judgment subunit 212 determines whether the abnormal point is an abnormal point M based on the distance between the automatically detected abnormal point and the two points before and after it and the average running speed of the patrol personnel in the time before and after the abnormal coordinate point occurred.

[0097] In the detection subunit 211, the coordinates of the patrol personnel are recorded at set time intervals (e.g., every 6 seconds), and the distance between abnormal points and the points before and after them is automatically detected.

[0098] In the interpretation subunit 212, the distance between the automatically detected abnormal point and the two points before and after it, as well as the average running speed of the patrol personnel in the time before and after the abnormal coordinate point (e.g., within one minute before and after), is used to determine whether it is an abnormal point M.

[0099] The smart forestry IoT patrol system for nature reserves provided in this embodiment, compared with the prior art, adopts a detection subunit 211 and a judgment subunit 212. It records the coordinates of patrol personnel at set time intervals, automatically detects the distance between abnormal points and the points before and after them, and judges whether the abnormal point is an abnormal point M based on the automatically detected distance between the abnormal point and the points before and after it, and the average running speed of the patrol personnel within the time before and after the abnormal coordinate point occurred. This smart forestry IoT patrol system for nature reserves provided in this embodiment can effectively solve the problem of GPS point deviation for forest rangers during forestry patrols, ultimately forming a true trajectory location, effectively and accurately protecting the work results of forest rangers; it can also help managers to conduct more precise supervision of forest rangers.

[0100] Preferably, see Figure 10 , Figure 10 for Figure 8 The diagram illustrates the functional modules of an embodiment of the correction unit shown. In this embodiment, the correction unit 22 includes a defining subunit 221, a selecting subunit 222, and a determining subunit 223. The defining subunit 221 is used to calculate the maximum allowable total length of the line connecting the real point and the points before and after the abnormal coordinate point based on the positions of N points before and after the abnormal coordinate point and the average running speed within the time before and after the abnormal coordinate point occurs, thus defining the possible range value A of the real point. The selecting subunit 222 is used to select N points before and after the abnormal point, calculate the angle formed by the line connecting each of the three adjacent points, and take the average value B of all the angles. The determining subunit 223 is used to calculate the angle formed by the line connecting the real point and each of the three adjacent points within the defined possible range value A. The closer the angle is to the value B, the more accurate it is, and finally, the specific location of the real point is determined.

[0101] In the frame subunit 221, after determining the abnormal point M, based on the positions of N points before and after the abnormal coordinate point and the average velocity within the time before and after the abnormal coordinate point (e.g., within one minute before and after), the maximum allowable total length of the line connecting the real point and the two points before and after is calculated, and the possible range value A of the real point is framed.

[0102] In the selected sub-unit 222, take N points before and after the abnormal point (for example, take 5 points before and after the abnormal point, for a total of 10 points), calculate the included angle formed by the line connecting each three adjacent points, and take the average value B of all included angles.

[0103] In determining subunit 223, within the defined range A, the angle formed by the line connecting the actual point and each of the three adjacent points is calculated. The closer the angle is to the value of B, the more accurate it is, thus finally determining the specific location of the actual point.

[0104] The smart forestry IoT patrol system for nature reserves provided in this embodiment, compared with the prior art, uses a correction unit 22 that employs a framing subunit 221, a selection subunit 222, and a determination subunit 223. By calculating the maximum allowable total length of the line connecting the real point to the points before and after the abnormal coordinate point, based on the positions of N points before and after the abnormal coordinate point and the average running speed within the time before and after the abnormal coordinate point, it defines the possible range value A of the real point. It then takes N points before and after the abnormal point, calculates the angle formed by the line connecting each pair of adjacent three points, and takes the average value B of all angles. Within the defined possible range value A, it calculates the angle formed by the line connecting the real point to each pair of adjacent three points; the closer the angle is to value B, the more accurate it is, ultimately determining the specific location of the real point. The smart forestry IoT patrol system for nature reserves provided in this embodiment can effectively solve the problem of GPS point deviation during forest patrols, ultimately forming a true trajectory location, effectively and accurately protecting the work results of forest rangers; it can also help managers to conduct more precise supervision of forest rangers.

[0105] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A smart forestry Internet of Things patrol method for nature reserves, characterized in that, The method comprises the following steps: abnormal coordinate points of the patrol personnel when the GPS signal is lost are acquired, and the positions of the front and back points of the abnormal coordinate points are determined; the abnormal coordinate points are accurately corrected according to the positions of the front and back points determined and the moving speed of the patrol personnel; the step of acquiring the abnormal coordinate points of the patrol personnel when the GPS signal is lost and determining the positions of the front and back points of the abnormal coordinate points comprises: abnormal coordinate points of the patrol personnel when the GPS signal is lost are detected within a set interval time; the positions of the front and back points of the abnormal coordinate points are determined according to the detected abnormal coordinate points; the step of accurately correcting the abnormal coordinate points according to the positions of the front and back points determined and the moving speed of the patrol personnel comprises: the range value of the real coordinate point is determined by calculating the distance between the abnormal coordinate points and the positions of the front and back points and the average speed of the patrol personnel within the time before and after the abnormal coordinate points occur; N points before and after the abnormal coordinate points are taken, the included angle of each adjacent three points is calculated, and the average value of all the included angles is used to correct the real coordinate point; the step of calculating the distance between the abnormal coordinate points and the positions of the front and back points and the average speed of the patrol personnel within the time before and after the abnormal coordinate points occur to determine the range value of the real coordinate point comprises: the coordinate points of the patrol personnel are recorded every set timing time, and the distance between the abnormal coordinate points and the front and back two points is automatically detected; whether the abnormal coordinate points are abnormal points M is determined according to the distance between the abnormal coordinate points and the front and back two points automatically detected and the average running speed of the patrol personnel within the time before and after the abnormal coordinate points occur; the step of taking N points before and after the abnormal coordinate points, calculating the included angle of each adjacent three points, and taking the average value of all the included angles to correct the real coordinate point comprises: the maximum value of the total length of the line connecting the real coordinate point and the front and back two points is calculated according to the positions of the N points before and after the abnormal coordinate points and the average running speed of the patrol personnel within the time before and after the abnormal coordinate points occur, and the possible range value A of the real coordinate point is framed; N points before and after the abnormal coordinate points are taken, the included angle of each adjacent three points is calculated, and the average value B of all the included angles is taken; the included angle of the real coordinate point and each adjacent three points is calculated within the framed possible range value A, the closer to the value B, the more accurate the included angle is, and the specific position of the real coordinate point is finally determined.

2. A smart forestry Internet of Things patrol system for nature reserves, characterized in that, It comprises: a judgment module (10) for acquiring abnormal coordinate points of the patrol personnel when the GPS signal is lost and determining the positions of the front and back points of the abnormal coordinate points; a correction module (20) for accurately correcting the abnormal coordinate points according to the positions of the front and back points determined and the moving speed of the patrol personnel; the judgment module (10) comprises: a detection unit (11) for detecting abnormal coordinate points of the patrol personnel when the GPS signal is lost within a set interval time; a judgment unit (12) for determining the positions of the front and back points of the abnormal coordinate points according to the detected abnormal coordinate points; The correction module (20) comprises: A determination unit (21) for calculating the distance between the abnormal coordinate point and the front and rear point positions, and the average speed of the patrol personnel within the time before and after the abnormal coordinate point, and determining the range value of the real point; A correction unit (22) for taking each of the N points before and after the abnormal coordinate point, calculating the included angle formed by each adjacent three points, and calculating the average value of all the included angles, and correcting the real point; The determination unit (21) comprises: A detection subunit (211) for recording the coordinate point of the patrol personnel every set timing time, and automatically detecting the distance between the abnormal coordinate point and the two points before and after the abnormal coordinate point; An interpretation subunit (212) for interpreting whether the abnormal coordinate point is an abnormal point M according to the automatically detected distance between the abnormal coordinate point and the two points before and after the abnormal coordinate point, and the average running speed of the patrol personnel within the time before and after the abnormal coordinate point; The correction unit (22) comprises: A framing subunit (221) for calculating the maximum value of the total length of the line connecting the real point and the two points before and after the abnormal coordinate point according to the positions of the N points before and after the abnormal coordinate point and the average running speed within the time before and after the abnormal coordinate point, and framing the possible range value A of the real point; A selection subunit (222) for taking each of the N points before and after the abnormal coordinate point, calculating the included angle formed by each adjacent three points, and taking the average value B of all the included angles; A determination subunit (223) for calculating the included angle formed by the real point and each adjacent three points within the framed possible range value A, and the closer to the value B, the more accurate the included angle, and finally determining the specific position of the real point.

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