A method for air-ground-ground collaborative sensing and early warning of mountain landslides

Abstract

The present invention discloses a method for cooperatively sensing and warning of mountain landslides in the air, space and land, which belongs to the field of early warning technology and includes the following steps: setting targets, obtaining images of identifying several targets at regular intervals through satellite remote sensing technology, connecting each target with several adjacent targets according to the distance from small to large, and forming a monitoring and early warning network; step 3, triggering early warning: measuring the length of each line segment and / or the angle of each line segment in the monitoring and early warning network through image recognition technology; comparing each measured value with the initial value, and triggering the first early warning command when the change rate of the line segment length exceeding A% is greater than B%, and / or the change rate of the angle exceeding C% is greater than D%. The satellite remote sensing technology of the invention provides large-scale and high-precision monitoring data, the drone inspection provides real-time on-site information, and the ground-based monitoring focuses on key factors such as rainfall. The three work together to improve the accuracy and timeliness of the early warning.

Description

Technical Field

[0001] The present invention belongs to the field of early warning technology, and in particular relates to an air-ground-ground collaborative sensing early warning method for mountain landslides. Background Art

[0002] The coordinated sensing and early warning system for mountain landslides is a system that uses a variety of technical means to conduct all-round and multi-level monitoring and early warning of mountain landslides. This system integrates the monitoring data of space-based (satellite remote sensing), air-based (drones, aerial photogrammetry, etc.) and ground-based (ground monitoring equipment, sensor networks, etc.) to achieve real-time monitoring, risk assessment and early warning of mountain landslide disasters.

[0003] In terms of space-based monitoring, satellite remote sensing technology has the advantages of wide coverage and fast data update, and can realize macro-monitoring and long-term tracking of landslide risk areas. Through high-resolution optical satellites and radar satellites, key information such as topography, vegetation coverage, and surface deformation in the landslide area can be obtained, providing an important basis for the identification and early warning of landslide disasters. Air-based monitoring mainly relies on drones and aerial photogrammetry technology. Drones have the characteristics of strong flexibility, low cost, and low risk. They can go deep into landslide-prone areas for high-resolution image shooting and three-dimensional modeling, providing strong support for the refined monitoring and assessment of landslide disasters. At the same time, aerial photogrammetry technology can also provide a wide range of landslide surface deformation information, providing important data support for macro-monitoring and early warning of landslide disasters. Ground-based monitoring is the last line of defense for landslide disaster early warning. By deploying various monitoring equipment on the ground, such as displacement sensors, stress sensors, and water content sensors, real-time monitoring and data analysis of the interior and surface of the landslide body can be achieved. These monitoring data can reflect the deformation trend and stability state of the landslide body, providing an important reference for early warning and emergency response of landslide disasters.

[0004] However, the above problem is that the detection data of the existing mountain landslide air-ground-space collaborative perception and early warning system is usually simply combined, or even the three-party data is not combined, but at least one of the three-party data reaches a preset value to trigger an early warning. This method has a low utilization rate of the three-party data. How to integrate the three-party data for accurate early warning is an urgent problem that needs to be solved. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a method for early warning of mountain landslides by collaborative perception of air, ground and space, which has the advantages of collaborative perception of mountain landslides by air, ground and space, and solves the problems existing in the prior art.

[0006] The present invention is implemented as follows: a method for air-ground-ground collaborative sensing and early warning of mountain landslides, comprising the following steps:

[0007] Step 1, setting targets: setting a number of targets at set locations on the mountain slope;

[0008] Step 2, satellite remote sensing monitoring: Through satellite remote sensing technology, images of several targets are regularly acquired and identified, and each target is connected to several adjacent targets in ascending distance to form a monitoring and early warning network;

[0009] Step 3, triggering an early warning: using image recognition technology to measure the length of each line segment and / or the angle of each line segment in the monitoring and early warning network; comparing each measured value with the initial value, when the line segment length change rate exceeding A% is greater than B%, and / or the angle change rate exceeding C% is greater than D%, triggering the first early warning command;

[0010] Step 4, air-based monitoring: using a camera or laser radar carried by a drone to regularly patrol along the monitoring and early warning network to capture the position change information of the target for correction of the monitoring and early warning network;

[0011] Step 5, foundation monitoring: a rain gauge is set at a set position of the slope, and when the monitoring value of the rain gauge reaches a first threshold value E, at least one value among A, B, C, and D is automatically reduced.

[0012] As preferred embodiment of the present invention, in step 1, setting the target includes the following steps:

[0013] Step 1.1: Select location: Based on geological survey results and slope stability analysis, select a setting location in the landslide-prone area of ​​the mountain slope;

[0014] Step 1.2: Install the target: Install a solid, easily identifiable target at the selected location so that the target can be clearly displayed in the satellite remote sensing image;

[0015] Step 1.3: Record information: Record the specific location, installation time, model information of each target, and establish a target database.

[0016] As a preferred embodiment of the present invention, in step 2, satellite remote sensing monitoring includes the following steps:

[0017] Step 2.1: Acquire images: Use satellite remote sensing technology to acquire images containing the target at preset time intervals;

[0018] Step 2.2: Image preprocessing: denoising and contrast enhancement operations are performed on the acquired image to improve image quality;

[0019] Step 2.3: Target recognition: Use image recognition technology to identify the target in the preprocessed image and determine its location;

[0020] Step 2.4: Construct a monitoring and early warning network: Connect each target with several adjacent targets according to the principle of distance from small to large to form a monitoring and early warning network.

[0021] As a preferred embodiment of the present invention, in step S3, triggering the early warning includes the following steps:

[0022] Step 3.1: Measure changes: Use image recognition technology to measure the length of each line segment and / or the angle of each line segment in the monitoring and early warning network;

[0023] Step 3.2: Change analysis: Calculate the rate of change of each line segment length and angle, and compare each measured value with the initial value;

[0024] Step 3.3: Early warning judgment: When the length change rate of the line segment exceeding A% is greater than B%, or the angle change rate of the angle exceeding C% is greater than D%, it is judged as a potential landslide risk and the first early warning command is triggered.

[0025] As a preferred embodiment of the present invention, in step 4, the air-based monitoring includes the following steps:

[0026] Step 4.1: Plan the inspection route: Plan the drone inspection route based on the monitoring and early warning network;

[0027] Step 4.2: Perform inspection tasks: Use the camera or lidar carried by the drone to inspect along the planned route and capture the target’s position change information;

[0028] Step 4.3: Data processing and analysis: Process the images or point cloud data obtained by the drone, extract the target location information, compare it with the satellite remote sensing data, and correct the monitoring and early warning network.

[0029] As a preferred embodiment of the present invention, in step 5, the foundation monitoring includes the following steps:

[0030] Step 5.1: Install rain gauge: Install rain gauge at the set location on the slope to monitor rainfall;

[0031] Step 5.2: Real-time monitoring: The rain gauge monitors rainfall in real time and transmits the data to the early warning system in real time;

[0032] Step 5.3: Adjust the threshold: When the monitoring value of the rain gauge reaches the first threshold E, at least one value among A, B, C, and D is automatically reduced according to the impact of rainfall on slope stability to lower the warning threshold and improve the warning sensitivity.

[0033] As a preferred embodiment of the present invention, the first threshold E is a step threshold, and the first threshold E is inversely proportional to at least one value among A, B, C, and D.

[0034] As a preferred embodiment of the present invention, in step 4.1: the line segment whose length change rate exceeds B1% is used as the navigation path of the drone, and the drone flies along the navigation path and collects images.

[0035] As a preferred embodiment of the present invention, the UAV turns at the angle of the monitoring and early warning network during navigation, and the steering angle is the same as the angle. When the angle change rate is greater than D1%, the UAV automatically triggers a command to take pictures or record videos when passing the angle.

[0036] As a preferred embodiment of the present invention, the line segment length of the monitoring and early warning network is L;

[0037] The target is provided with an angle adjustment seat, and a rangefinder is installed on the angle adjustment seat; the rangefinder measures the distance from the target to the adjacent target, and the distance measurement value of the rangefinder is M.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. Comprehensive perception and early warning: Combine multiple monitoring methods in the air, ground and space to achieve comprehensive perception and timely early warning of landslide risks. Satellite remote sensing technology provides large-scale, high-precision monitoring data, drone inspections provide real-time on-site information, and ground-based monitoring focuses on key factors such as rainfall. The three work together to greatly improve the accuracy and timeliness of early warnings.

[0040] 2. Dynamically adjust the warning threshold: Dynamically adjust the warning threshold according to actual conditions such as rainfall, making the warning system more flexible and intelligent. When the rainfall increases, lower the warning threshold and issue a warning signal in advance to buy more time for disaster prevention and mitigation work.

[0041] 3. Improve disaster prevention and mitigation capabilities: Through timely and accurate early warning, relevant departments and personnel can take measures in advance to reduce losses caused by natural disasters such as landslides. At the same time, it also provides rich data support for scientific research, which is helpful for in-depth exploration of the mechanism of landslide occurrence and optimization of prediction models. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flowchart of the air-ground-ground collaborative sensing and early warning method for mountain landslides provided by an embodiment of the present invention;

[0043] Figure 2 is a flowchart of setting a target provided by an embodiment of the present invention;

[0044] Figure 3 is a flowchart of satellite remote sensing monitoring provided by an embodiment of the present invention;

[0045] Figure 4 is a flowchart of air-based monitoring provided by an embodiment of the present invention;

[0046] Figure 5 is a flowchart of foundation monitoring provided by an embodiment of the present invention;

[0047] Figure 6 is a flowchart of satellite remote sensing monitoring provided by an embodiment of the present invention;

[0048] Figure 7 1 is a schematic diagram of a top view of the structure of an initial monitoring and early warning network provided by an embodiment of the present invention;

[0049] Figure 8 It is a schematic diagram of a top view structure of a monitoring and early warning network taken at a certain time provided by an embodiment of the present invention;

[0050] Fig. 9 It is a schematic diagram of the main structure of a target on a slope provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to further understand the content, features and effects of the present invention, the following embodiments are given as examples and described in detail with reference to the accompanying drawings.

[0052] The structure of the present invention is described in detail below in conjunction with the accompanying drawings.

[0053] See also Figure 1-Figure 9 The embodiment of the present invention provides a method for air-ground-ground collaborative sensing and early warning of mountain landslides, comprising the following steps:

[0054] Step 1, setting targets: setting a number of targets at a set location on the mountain slope [e.g., on a dangerous rock];

[0055] Step 2, satellite remote sensing monitoring: Using satellite remote sensing technology, regularly obtain images of several targets, and connect each target with several adjacent targets (for example, three) in ascending distance to form a monitoring and early warning network;

[0056] Step 3, triggering an early warning: using image recognition technology to measure the length of each line segment and / or the angle of each line segment in the monitoring and early warning network; comparing each measured value with the initial value, when the line segment length change rate exceeding A% is greater than B%, and / or the angle change rate exceeding C% is greater than D%, triggering the first early warning command;

[0057] Step 4, air-based monitoring: using a camera or laser radar carried by a drone to regularly patrol along the monitoring and early warning network to capture the position change information of the target for correction of the monitoring and early warning network;

[0058] Step 5, foundation monitoring: a rain gauge is set at a set position of the slope, and when the monitoring value of the rain gauge reaches a first threshold value E, at least one value among A, B, C, and D is automatically reduced.

[0059] The working principle of the above scheme is:

[0060] First, a monitorable early warning network is established by setting targets on high-risk mountain slopes. Satellite remote sensing technology regularly captures images of these targets, and uses image recognition technology to measure changes in the length of line segments and angles between targets. When these change rates exceed the preset thresholds (A%, B%, C%, D%), the system will automatically trigger the first early warning command, indicating the risk of a possible landslide. At the same time, the camera or lidar carried by the drone will regularly patrol along the monitoring and early warning network to capture the actual position change information of the target, calibrate and correct the satellite remote sensing data, and improve the accuracy of the early warning. In addition, ground-based monitoring monitors rainfall conditions in real time through rain gauges set on the slopes. When the rainfall reaches the preset threshold E, the early warning threshold (at least one of A, B, C, and D) is automatically adjusted to adapt to changes in landslide risks under different rainfall conditions.

[0061] The beneficial effects of the above scheme are:

[0062] 1. Comprehensive perception and early warning: Combine multiple monitoring methods in the air, ground and space to achieve comprehensive perception and timely early warning of landslide risks. Satellite remote sensing technology provides large-scale, high-precision monitoring data, drone inspections provide real-time on-site information, and ground-based monitoring focuses on key factors such as rainfall. The three work together to greatly improve the accuracy and timeliness of early warnings.

[0063] 2. Dynamically adjust the warning threshold: Dynamically adjust the warning threshold according to actual conditions such as rainfall, making the warning system more flexible and intelligent. When the rainfall increases, lower the warning threshold and issue a warning signal in advance to buy more time for disaster prevention and mitigation work.

[0064] 3. Improve disaster prevention and mitigation capabilities: Through timely and accurate early warning, relevant departments and personnel can take measures in advance to reduce losses caused by natural disasters such as landslides. At the same time, it also provides rich data support for scientific research, which is helpful for in-depth exploration of the mechanism of landslide occurrence and optimization of prediction models.

[0065] It should be noted that only the rate of change of the line segment length can be used as the standard for triggering the first warning command, or only the rate of change of the angle can be used as the standard for triggering the first warning command, or both can be combined as the standard, for example, the command is triggered only when both standards are met.

[0066] It should be noted that the line segment length change rate is the ratio of the absolute value of the difference between the distance value and the initial length of the line segment to the initial length of the line segment. The angle change rate is the ratio of the absolute value of the difference between the measured value and the initial angle to the initial angle.

[0067] It should be noted that A, B, C, and D can be any value between 0 and 100. For example, if A, B, C, and D are all 20, then when more than 20% of the line segment length change rates are greater than 20%, and more than 20% of the angle change rates are greater than 20%, the first warning command is triggered.

[0068] Furthermore, if the rainfall increases and the monitoring value of the rain gauge reaches the first threshold E, the standard for triggering the first warning command is lowered. For example, the value of A is reduced from 20 to 15, that is, when the change rate of the line segment length exceeds 15% and the change rate of the angle exceeds 20%, the first warning command is triggered.

[0069] Specifically, in step 1, setting the target includes the following steps:

[0070] Step 1.1: Select the location: Based on the geological survey results and slope stability analysis, select the setting location in the landslide-prone area of ​​the mountain slope (such as dangerous rocks, potential sliding surfaces, etc.);

[0071] Step 1.2: Install the target: Install a sturdy and easily identifiable target at the selected location so that the target can be clearly displayed in the satellite remote sensing image; the target should have characteristics such as weathering resistance and corrosion resistance.

[0072] Step 1.3: Record information: Record the specific location, installation time, and model information of each target, and establish a target database to facilitate subsequent management and data analysis.

[0073] Preferably, the target is provided with a signal transmitting device for accurately locating the position of the target by satellite to improve the accuracy of monitoring.

[0074] Specifically, in step 2, satellite remote sensing monitoring includes the following steps:

[0075] Step 2.1: Acquire images: Use satellite remote sensing technology to acquire images containing the target at preset time intervals;

[0076] Step 2.2: Image preprocessing: denoising and contrast enhancement operations are performed on the acquired image to improve image quality;

[0077] Step 2.3: Target recognition: Use image recognition technology to identify the target in the preprocessed image and determine its location;

[0078] Step 2.4: Construct a monitoring and early warning network: Connect each target with several adjacent targets according to the principle of distance from small to large to form a monitoring and early warning network.

[0079] Specifically, in step S3, triggering the warning includes the following steps:

[0080] Step 3.1: Measure changes: Use image recognition technology to measure the length of each line segment and / or the angle of each line segment in the monitoring and early warning network;

[0081] Step 3.2: Change analysis: Calculate the rate of change of each line segment length and angle, and compare each measured value with the initial value;

[0082] Step 3.3: Early warning judgment: When the length change rate of the line segment exceeding A% is greater than B%, or the angle change rate of the angle exceeding C% is greater than D%, it is judged as a potential landslide risk and the first early warning command is triggered.

[0083] Specifically, in step 4, air-based monitoring includes the following steps:

[0084] Step 4.1: Plan patrol routes: Plan drone patrol routes based on the monitoring and early warning network to ensure comprehensive coverage of all targets;

[0085] Step 4.2: Perform inspection tasks: Use the camera or lidar carried by the drone to inspect along the planned route and capture the target’s position change information;

[0086] Step 4.3: Data processing and analysis: Process the images or point cloud data obtained by the drone, extract the target location information, compare it with the satellite remote sensing data, and correct the monitoring and early warning network (replace satellite remote sensing data with data collected by the drone).

[0087] Specifically, in step 5, the foundation monitoring includes the following steps:

[0088] Step 5.1: Install rain gauge: Install rain gauge at the set location on the slope to monitor rainfall;

[0089] Step 5.2: Real-time monitoring: The rain gauge monitors rainfall in real time and transmits the data to the early warning system in real time;

[0090] Step 5.3: Adjust the threshold: When the monitoring value of the rain gauge reaches the first threshold E, at least one value among A, B, C, and D is automatically reduced according to the impact of rainfall on slope stability to lower the warning threshold and improve the warning sensitivity.

[0091] Further, the first threshold E is a step threshold, and the first threshold E is inversely proportional to at least one value among A, B, C, and D.

[0092] For example, the first threshold E includes successively increasing step-like values ​​E1, E2, E3, E4... The larger the monitoring value of the rain gauge is, the smaller at least one value among A, B, C, and D is. Which values ​​among A, B, C, and D change, and the magnitude of the change can be set by the user.

[0093] Furthermore, in step 4.1: the line segments whose length change rate exceeds B1% are used as the navigation path of the UAV, and the UAV flies along the navigation path and collects images. Among them, B1 is less than B. In this way, the parts that have reached a certain degree of deformation can be monitored in detail. It should be noted that B can be 0. When B1 is 0, all line segments are used as the navigation path of the UAV.

[0094] Furthermore, when the drone is navigating, it turns at the angle of the monitoring and early warning network, and the steering angle is the same as the angle. When the angle change rate is greater than D1%, the drone automatically triggers the command to take pictures or record videos when passing through the angle. Through this setting, the position around the target that has reached a certain degree of displacement can be monitored in detail.

[0095] Furthermore, the line segment length of the monitoring and early warning network is L;

[0096] The target is provided with an angle adjustment seat, and a rangefinder is installed on the angle adjustment seat; the rangefinder measures the distance from the target to the adjacent target, and the distance measurement value of the rangefinder is M.

[0097] The above settings have two functions:

[0098] First, the true distance between adjacent targets can be measured, thereby accurately determining the distance change between the targets.

[0099] Second, it can determine the height and slope of the mountain slope, among which the height .

[0100] The rangefinder can be set as a laser rangefinder, and the angle of the angle adjustment seat in the horizontal position can be adjusted according to the angle of the monitoring and early warning network, and then aligned with the target through fine-tuning.

[0101] Preferably, the change in the height or inclination of the slope is further adjusted to at least one value of A, B, C, and D, for example, as the inclination increases, at least one value of A, B, C, and D decreases. Because the probability of landslide increases as the inclination increases.

[0102] Working principle of the present invention:

[0103] When in use, first, by setting targets on high-risk mountain slopes, a monitorable early warning network is established. Satellite remote sensing technology regularly captures images of these targets, and uses image recognition technology to measure changes in the length of line segments and angles between targets. When these change rates exceed the preset thresholds (A%, B%, C%, D%), the system will automatically trigger the first early warning command, indicating the risk of possible landslides. At the same time, the camera or lidar carried by the drone will regularly patrol along the monitoring and early warning network, capture the actual position change information of the target, calibrate and correct the satellite remote sensing data, and improve the accuracy of the early warning. In addition, the ground-based monitoring monitors the rainfall in real time through the rain gauge set on the slope. When the rainfall reaches the preset threshold E, the early warning threshold (at least one of A, B, C, and D) is automatically adjusted to adapt to the changes in landslide risks under different rainfall conditions.

[0104] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0105] 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 defined by the appended claims and their equivalents.

Claims

1. A method for air-ground-ground collaborative sensing and early warning of mountain landslides, characterized in that: The following steps are involved: Step 1, setting targets: setting a number of targets at set locations on the mountain slope; Step 2, satellite remote sensing monitoring: Through satellite remote sensing technology, images of several targets are regularly acquired and identified, and each target is connected to several adjacent targets in ascending distance to form a monitoring and early warning network; Step 3, triggering an early warning: measuring the length of each line segment and / or the angle of each line segment in the monitoring and early warning network by using image recognition technology; comparing each measured value with the initial value, when the line segment length change rate exceeding A% is greater than B%, and / or the angle change rate exceeding C% is greater than D%, triggering the first early warning command, wherein A, B, C and D are early warning thresholds; Step 4, air-based monitoring: using a camera or laser radar carried by a drone to regularly patrol along the monitoring and early warning network to capture the position change information of the target for correction of the monitoring and early warning network; Step 5, foundation monitoring: a rain gauge is set at a set position of the slope, and when the monitoring value of the rain gauge reaches a first threshold value E, at least one value among A, B, C, and D is automatically reduced; In step 4, air-based monitoring includes the following steps: Step 4.1: Plan the inspection route: Plan the drone inspection route based on the monitoring and early warning network; Step 4.2: Perform inspection tasks: Use the camera or lidar carried by the drone to inspect along the planned route and capture the target’s position change information; Step 4.3: Data processing and analysis: Process the images or point cloud data obtained by the drone, extract the target location information, compare it with the satellite remote sensing data, and correct the monitoring and early warning network; In step 5, foundation monitoring includes the following steps: Step 5.1: Install rain gauge: Install rain gauge at the set location on the slope to monitor rainfall; Step 5.2: Real-time monitoring: The rain gauge monitors rainfall in real time and transmits the data to the early warning system in real time; Step 5.3: Adjust the threshold: When the monitoring value of the rain gauge reaches the first threshold E, at least one value among A, B, C, and D is automatically reduced according to the influence of rainfall on slope stability, so as to lower the warning threshold and improve the warning sensitivity; In step 4.1: the line segments whose length change rate exceeds B1% are used as the navigation path of the UAV, and the UAV flies along the navigation path and collects images; wherein B1 is less than B, and when B1 is 0, all the line segments are used as the navigation path of the UAV; wherein B1 is the warning threshold; When the UAV is navigating, it turns at the angle of the monitoring and early warning network, and the steering angle is the same as the angle. When the angle change rate is greater than D1%, the UAV automatically triggers the command to take pictures or record videos when passing the angle, where D1 is the early warning threshold.

2. The method for air-ground-ground collaborative sensing and early warning of mountain landslides according to claim 1, characterized in that: In step 1, setting the target includes the following steps: Step 1.1: Select location: Based on geological survey results and slope stability analysis, select a setting location in the landslide-prone area of ​​the mountain slope; Step 1.2: Install the target: Install a solid, easily identifiable target at the selected location so that the target can be clearly displayed in the satellite remote sensing image; Step 1.3: Record information: Record the specific location, installation time, model information of each target, and establish a target database.

3. The method for air-ground-ground collaborative sensing and early warning of mountain landslides according to claim 1, characterized in that: In step 2, satellite remote sensing monitoring includes the following steps: Step 2.1: Acquire images: Use satellite remote sensing technology to acquire images containing the target at preset time intervals; Step 2.2: Image preprocessing: denoising and contrast enhancement operations are performed on the acquired image to improve image quality; Step 2.3: Target recognition: Use image recognition technology to identify the target in the preprocessed image and determine its location; Step 2.4: Construct a monitoring and early warning network: Connect each target with several adjacent targets according to the principle of distance from small to large to form a monitoring and early warning network.

4. The method for air-ground-ground collaborative sensing and early warning of mountain landslides according to claim 1, characterized in that: The first threshold E is a step threshold, and the first threshold E is inversely proportional to at least one value of A, B, C, and D.

5. The method for air-ground-ground collaborative sensing and early warning of mountain landslides according to claim 1, characterized in that: The line length of the monitoring and early warning network is L; The target is provided with an angle adjustment seat, and a rangefinder is installed on the angle adjustment seat; the rangefinder measures the distance from the target to the adjacent target, and the distance measurement value of the rangefinder is M.

Images