Highway geological disaster lightweight detection method in alpine region

Abstract

According to the geological disaster universal lightweight monitoring method for the rural highway in the alpine region, the machine vision monitoring unit, the Beidou monitoring unit and the NB-IoT-based long stay wire displacement monitoring unit are used for cooperative work, and multi-dimensional and low-power-consumption observation of slope deformation is achieved. The method comprises the following steps: firstly, arranging a high-contrast target at a key slope position, and constructing a slope grid displacement tendency chart by combining a camera; secondly, edge calculation is carried out by integrating a Beidou receiver and an inclination angle and rainfall sensor front end, and high-precision displacement and attitude information can be obtained; and meanwhile, a universal pulley block and an NB-IoT module are additionally arranged on the basis of the stay wire type displacement meter, so that a single stay wire can cover multiple potential slippage points, each monitoring unit adopts a cold-resistant material and a local storage strategy, the monitoring units can continuously work under the conditions of unstable network and extreme temperature, the deployment and maintenance cost is reduced, and the reliability and real-time performance of disaster monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the field of highway geological disaster monitoring technology, and is particularly suitable for long-term, low-power consumption monitoring of geological disasters on slopes and slope surfaces along rural roads in high-altitude areas. It can efficiently and stably carry out geological disaster early warning and risk prevention and control in environments such as severe cold, high altitude, and unstable communications. Background Art

[0002] In high-altitude mountainous areas, due to complex geological structures, frequent extreme weather, and significant impacts of frozen soil and ice and snow, the slopes of rural roads are often threatened by geological disasters such as landslides, mudslides, and collapses. Traditional monitoring methods, such as single inclinometers, manual inspections, or large-scale manual visual inspections, are often inefficient and costly, and are easily affected by climate in extreme environments, resulting in monitoring gaps or increased misjudgment rates.

[0003] In recent years, with the rapid development of sensor technology, machine vision, Beidou Positioning System (BDS) and Internet of Things (IoT), multi-source fusion monitoring has become an effective way to deal with geological disaster prevention and control. However, the deployment of related equipment in high-altitude cold areas needs to overcome multiple constraints such as low temperature, low air pressure, poor network and insufficient power supply over long distances. Therefore, the development of a universal, lightweight and low-power highway slope monitoring technology is of great significance to ensure the safe passage and maintenance of rural roads in high-altitude cold areas. Summary of the invention

[0004] The present invention aims to provide a universal lightweight monitoring method for geological disasters on rural roads in high-altitude and cold areas. By coordinating three technologies: fixed-point target recognition monitoring based on machine vision, slope monitoring of the low-cost Beidou system, and long-wire displacement monitoring based on NB-IoT, multi-dimensional and long-period automatic observation and deformation warning of slopes along the highway can be achieved, reducing the intensity and cost of manual inspections and improving the real-time and accuracy of monitoring.

[0005] To achieve the above object, the present invention provides the following technical solutions, which mainly include:

[0006] A universal lightweight monitoring method for geological disasters on rural roads in high-cold areas, comprising the following steps:

[0007] 1) Monitoring system layout:

[0008] Machine vision monitoring units, Beidou monitoring units, and NB-IoT-based long-wire displacement monitoring units are deployed on target slopes along rural roads in high-cold areas;

[0009] The machine vision monitoring unit sets fixed point targets at key positions on the slope and deploys cameras at visible angles;

[0010] The Beidou monitoring unit integrates the Beidou receiver with a micro-electromechanical accelerometer, a tilt sensor and an RS485 protocol sensor;

[0011] The long-pull-wire displacement monitoring unit is based on the traditional pull-wire displacement meter, and is equipped with a universal pulley set and a low-power NB-IoT module to achieve multi-point or block displacement monitoring;

[0012] 2) Data collection and preprocessing:

[0013] The machine vision monitoring unit acquires a video or image sequence according to a set time interval, identifies the target position and converts it into three-dimensional coordinates;

[0014] The Beidou monitoring unit obtains multiple parameters such as high-precision coordinates, acceleration, inclination and rainfall, and performs edge computing at the front end to reduce the amount of data;

[0015] The long cable displacement monitoring unit reports the displacement reading of each cable node through the NB-IoT network;

[0016] 3) Multi-source data fusion:

[0017] Establish a unified time-space reference on the back-end platform to align the coordinates and synchronize the timing of the target displacement of the machine vision unit, the dynamic coordinates of the Beidou monitoring unit, and the measurement values ​​of the long-pull wire displacement;

[0018] Use data correction strategies to compare multi-source observations at the same location or in the same area. If the difference is too large, it will indicate data anomalies or require manual verification.

[0019] 4) Disaster risk identification and early warning:

[0020] Conduct time series analysis on multi-source data and establish a judgment model based on indicators such as slope deformation rate, inclination, acceleration, etc.

[0021] When a sudden change in displacement rate or a rapid expansion of slip range is detected, an early warning is triggered and a danger level is generated, and an alarm message is sent to the highway management department or emergency system according to the threshold setting;

[0022] 5) Adaptation and maintenance to high-altitude cold environment:

[0023] The camera, target and Beidou receiver housing are made of cold-resistant materials, and antifreeze components are installed at the universal pulley assembly;

[0024] The camera frame rate, NB-IoT transmission interval, Beidou sampling frequency and other parameters can be dynamically adjusted through the remote management platform to ensure continuous operation even in extremely cold and communication-restricted conditions.

[0025] In a specific embodiment, the machine vision monitoring unit deploys multiple high-contrast or reflective targets after moderately cleaning the vegetation on the slope. The camera selects a fixed viewing angle and establishes a mapping relationship with a reference coordinate system; and identifies the position change of each target in the video sequence to form a gridded displacement trend diagram of the slope.

[0026] In a specific embodiment, the Beidou monitoring unit includes multiple sensor modules such as Beidou receiver, MEMS accelerometer, inclinometer and rain gauge, and places part of the data processing at the front end for edge computing to reduce the bandwidth and power consumption requirements of long-distance transmission in high-altitude and cold areas.

[0027] In a specific embodiment, the long-pull wire displacement monitoring unit adds a multifunctional universal pulley set on the basis of the traditional pull wire displacement meter, so that a single pull wire can monitor multiple key points in series, and adopts NB-IoT communication method to realize low-power and long-distance data uploading.

[0028] In a specific embodiment, the multi-source data fusion process includes:

[0029] The three-dimensional coordinate sequence of the target output by the machine vision monitoring unit, the dynamic positioning and inclination information output by the Beidou monitoring unit, and the multi-point pull-wire length change value output by the long pull-wire displacement monitoring unit are uniformly converted into coordinates;

[0030] If there are more than two observation methods in the same monitoring area, the observation results will be cross-checked or complementary fused. If the difference exceeds the preset threshold, it will be marked as abnormal data and manual inspection or system recalibration will be initiated.

[0031] In a specific embodiment, the disaster risk identification and early warning comprises the following steps:

[0032] Construct deformation rate or cumulative displacement curves for multi-source time series data, and monitor acceleration or angle mutations;

[0033] Set multiple thresholds, such as safety threshold, warning threshold, and danger threshold. Once the deformation rate or inclination angle exceeds the corresponding threshold, a graded warning will be issued to the monitoring center or highway management department.

[0034] Combined with numerical simulation or geological background information, the overall stability of the slope can be further judged to provide a reference for emergency decision-making.

[0035] In a specific embodiment, the high-cold environment adaptation and maintenance method includes:

[0036] The camera, Beidou receiver and cable sensor housings are designed to be windproof, cold-proof and moisture-proof. If necessary, low-power heating elements are installed inside the equipment.

[0037] Clear the snow and ice on the target regularly and ensure the flexibility of the moving parts of the pulley block;

[0038] Cooperate with the remote management platform to inspect and maintain the power supply status, communication quality, sensor calibration, etc. of each monitoring unit.

[0039] In a specific embodiment, in order to cope with unstable communications or data interruptions in high-altitude cold regions, the monitoring system is also equipped with a local storage and delayed upload mechanism, which supports automatic retransmission of key observation data after network recovery to ensure the integrity of the deformation monitoring sequence.

[0040] In a specific embodiment, the method also includes an optimized deployment step for the network. According to factors such as road risk level, vegetation distribution, geological conditions and traffic flow, differentiated monitoring density is adopted to densely deploy machine vision and Beidou equipment in high-risk areas, and long-wire displacement monitoring is used as a supplement to large-scale coverage.

[0041] In a specific embodiment, the multi-source monitoring data generated by the method is connected to the GIS platform to construct a three-dimensional visualization interface and record the displacement time series, providing auxiliary decision-making for comprehensive assessment of disasters along the highway, formulation of emergency plans and maintenance plans.

[0042] The above technical solution uses machine vision monitoring units, low-cost Beidou monitoring units, and NB-IoT-based long-pull displacement units to comprehensively apply three means in the monitoring of geological disasters on rural roads in high-cold areas, forming a three-dimensional, multi-source complementary, low-power, and extreme environment-adaptable geological disaster early warning system. Its beneficial effects are specifically reflected in:

[0043] Complementarity and high accuracy of multi-source observation: The three monitoring methods focus on "surface", "point" and "line" respectively, which effectively improves the comprehensiveness and temporal and spatial accuracy of slope monitoring;

[0044] High-cold adaptability and easy maintenance: Through cold-resistant materials, antifreeze design and local storage, it can continue to work in environments with limited communication and extremely low temperatures;

[0045] Hierarchical warning and automatic management: unified space-time fusion and multi-dimensional deformation analysis can identify disaster signs at an early stage and provide accurate warnings to emergency departments and highway maintenance units;

[0046] Low-cost, lightweight and scalable: It adopts standardized modules and low-energy communication technology, and can be quickly laid and promoted along a large range of rural roads in accordance with local conditions.

[0047] In summary, this method provides a feasible, universal and efficient technical path for the prevention and control of geological disaster risks on rural roads in high-altitude and cold areas. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0049] Embodiment 1

[0050] 1. Implementation Environment and Objectives

[0051] Application scenario: Along a county-level rural road in a high-altitude and cold region, the terrain is undulating and the vegetation is dense. The slopes are scattered and there is a perennial freeze-thaw cycle on the slopes. There are cracks and collapse risks in some sections of the road.

[0052] Implementation goal: Use machine vision monitoring units, Beidou monitoring units and long-pull wire displacement monitoring units based on NB-IoT to form a multi-source complementary lightweight slope monitoring network, complete the continuous observation of the three-level "surface-point-line" geological disaster deformation, and maintain low power consumption and stable operation in high-altitude and harsh environments.

[0053] 2. Technical Solution Implementation Process

[0054] 1. Monitoring system deployment

[0055] 1) Machine vision monitoring unit:

[0056] For a high and steep slope area, select several key locations (such as the top of the slope, the middle of the slope, and the slippery area), clean the surface vegetation appropriately, and then set up 5 high-contrast targets;

[0057] Install a cold-resistant camera and a windproof and moisture-proof housing at the corner of the road at the foot of the mountain, select a fixed camera angle so that all targets are within the visible range, and establish a mapping coordinate system with the camera by measuring the reference point;

[0058] Expected effect: It can capture the 2D / 3D displacement of multiple target points in a “large range facing the slope”.

[0059] 2) Beidou monitoring unit:

[0060] A BeiDou receiver is installed at the top of the slope and near the roadbed, with built-in MEMS accelerometer, inclinometer, rain gauge and other sensors, and an external low-temperature resistant insulation shell;

[0061] Beidou receivers have preliminary data filtering and compression functions, which can process multiple parameters (displacement, inclination, rainfall) before sending them, reducing bandwidth consumption in high-altitude and cold areas;

[0062] Expected effect: Carry out "high-precision multi-parameter" monitoring of "key points" to supplement the gaps in machine vision monitoring of minor deformations and meteorological factors.

[0063] 3) Long cable displacement monitoring unit:

[0064] On steep slopes or roads with many curves, the original cable displacement meter is retained and a multifunctional universal pulley set is added, so that a single cable can be connected to several potential deformation points.

[0065] With the NB-IoT module, data is reported in a "low power, long distance" manner;

[0066] Expected effect: Using “lines” to cover multiple potential slip locations, increasing coverage and reducing hardware costs.

[0067] Multi-source sensing collaboration brings more comprehensive slope deformation information;

[0068] The equipment is lightweight and can be assembled in a modular manner, which meets the real conditions of weak infrastructure and limited power supply in high-altitude and cold regions.

[0069] 2. Data Collection and Preprocessing

[0070] 1) Machine vision monitoring unit:

[0071] The camera takes a sequence of distant images at regular intervals of 4 hours;

[0072] Automatically identify the pixel coordinates of each target and convert them into the three-dimensional position of the target in combination with the reference coordinate system;

[0073] By comparing the target coordinates before and after, the displacement rate or cumulative displacement can be obtained.

[0074] 2) Beidou monitoring unit:

[0075] Periodically sample and filter acceleration, inclination, rainfall and other data at the front end;

[0076] If a rapid change in inclination or a sudden change in acceleration is detected, a preliminary judgment can be made at the front end and a high-risk label can be applied.

[0077] 3) Long cable displacement monitoring unit:

[0078] The NB-IoT channel is used to report the length change of each pull-wire node (relative to the initial value) at a frequency of 6 hours;

[0079] If bandwidth is tight or weather is extreme, reduce the reporting frequency and record key moment data locally, and retransmit it after the network is restored.

[0080] Front-end filtering and offline storage reduce bandwidth usage for long-distance communications in high-altitude and cold regions;

[0081] The frequency of multi-source observations can be flexibly adjusted to improve energy utilization efficiency and power supply safety.

[0082] 3. Multi-source data fusion

[0083] 1) Unified space-time benchmark and coordinate conversion:

[0084] The backend platform converts each source data to the same coordinate system and timestamp based on the camera installation orientation, Beidou reference station differential information, and the initial reference length of the cable;

[0085] The macro-displacement results of machine vision in the same area are compared with the micro-displacement solution results of Beidou.

[0086] 2) Data correction and anomaly detection:

[0087] If the difference between the displacement values ​​measured by machine vision and Beidou at the same point exceeds the set threshold, or the displacement of the long pull line changes suddenly and significantly, it will be marked as suspicious, triggering manual verification or automatic inspection commands;

[0088] If the consistency of multi-source results is good, normal records are made and the displacement trend curve is updated regularly.

[0089] Improve accuracy through interactive verification of multi-source data to avoid misjudgment by a single sensor due to ice and snow interference or equipment failure;

[0090] Construct comprehensive "displacement field" information to provide rich data support for subsequent disaster risk assessment.

[0091] 4. Disaster risk identification and early warning

[0092] 1) Deformation timing and threshold setting:

[0093] Perform time-series superposition analysis on the curves of machine vision grid displacement, Beidou inclination angle and acceleration, and cable node length change to identify acceleration or rate inflection points;

[0094] Set multiple critical values ​​for safety threshold, warning threshold, and danger threshold.

[0095] 2) Early warning execution:

[0096] If any monitoring means is found to reach or exceed the threshold, the system will enter the corresponding warning state (yellow / orange / red) and send an alarm message to the highway management center;

[0097] Combined with simulation calculations or geological background data, the overall stability of the slope can be further judged. If necessary, maintenance and emergency departments can be notified to strengthen inspections or close road sections.

[0098] Diverse monitoring variables can capture potential signs of landslides or collapses earlier, reducing human and property losses when disasters occur;

[0099] Freeze-thaw cycles are prone to rock and soil loosening in high-altitude areas, and graded warnings can enable timely intervention in critical states.

[0100] 5. Adaptation and maintenance to high-altitude cold environment

[0101] 1) Equipment cold-resistant design:

[0102] The housings of the camera and Beidou receiver are made of composite thermal insulation materials, and low-power heating elements can be placed inside;

[0103] The cable pulley assembly is treated with enhanced antifreeze and lubrication, and snow is cleared regularly.

[0104] 2) Remote parameter adjustment:

[0105] The backend management platform can dynamically adjust the camera shooting frame rate, NB-IoT upload cycle, and Beidou sampling frequency according to the season and network conditions;

[0106] When communication is temporarily interrupted, each unit saves key monitoring data locally and automatically retransmits it after the network is restored to ensure the continuity of the data chain.

[0107] 3) Regular inspection and calibration:

[0108] It is recommended to review the target integrity, sensor calibration status, etc. every quarter;

[0109] Clear areas that may be blocked by vegetation growth or snow to ensure observation visibility and flexibility of wire pulling operations.

[0110] Equipment anti-cold, anti-humidity and anti-freeze measures ensure long-term working stability in harsh climates;

[0111] Through remote dynamic adjustment and local storage mechanisms, the difficulty of frequent maintenance in high-altitude and cold areas can be greatly reduced, and the sustainable operation level of the system can be improved.

[0112] The three types of monitoring equipment mostly use standardized, lightweight hardware and can be deployed in combination as needed;

[0113] Remote parameter adjustment and edge computing fully reduce energy consumption and transmission traffic, and can maintain long-term operation under conditions such as unstable communications and power supply difficulties in high-altitude mountainous areas.

[0114] Timely warning and emergency response support

[0115] By providing graded early warnings based on time series features such as deformation rate and acceleration, maintenance and emergency response departments can be alerted at the early stages of geological disasters, reducing passivity and losses when road sections are closed or disasters break out.

[0116] It provides visual displacement trend graphs and 3D GIS interfaces, which can be used to formulate emergency plans and optimize maintenance resource allocation according to risk levels.

[0117] The equipment uses common hardware modules such as general cameras, Beidou receivers, NB-IoT modules, and improved wire-type sensors, with unified data standardization and moderate installation and maintenance difficulty;

[0118] The overall cost is significantly lower than large-scale manual inspections and high-precision customized instruments, and is suitable for universal geological disaster monitoring on vast high-altitude rural roads.

[0119] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0120] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A universal lightweight monitoring method for geological disasters on rural roads in high-cold areas, characterized in that: The following steps are involved: 1) Monitoring system layout: Machine vision monitoring units, Beidou monitoring units, and NB-IoT-based long-wire displacement monitoring units are deployed on target slopes along rural roads in high-cold areas; The machine vision monitoring unit sets fixed point targets at key positions on the slope and deploys cameras at visible angles; The Beidou monitoring unit integrates the Beidou receiver with a micro-electromechanical accelerometer, a tilt sensor and an RS485 protocol sensor; The long-pull-wire displacement monitoring unit is based on the traditional pull-wire displacement meter, and is equipped with a universal pulley set and a low-power NB-IoT module to achieve multi-point or block displacement monitoring; 2) Data collection and preprocessing: The machine vision monitoring unit acquires a video or image sequence according to a set time interval, identifies the target position and converts it into three-dimensional coordinates; The Beidou monitoring unit obtains multiple parameters such as high-precision coordinates, acceleration, inclination and rainfall, and performs edge computing at the front end to reduce the amount of data; The long cable displacement monitoring unit reports the displacement reading of each cable node through the NB-IoT network; 3) Multi-source data fusion: Establish a unified time-space reference on the back-end platform to align the coordinates and synchronize the timing of the target displacement of the machine vision unit, the dynamic coordinates of the Beidou monitoring unit, and the measurement values ​​of the long-pull wire displacement; Use data correction strategies to compare multi-source observations at the same location or in the same area. If the difference is too large, it will indicate data anomalies or require manual verification. 4) Disaster risk identification and early warning: Conduct time series analysis on multi-source data and establish a judgment model based on indicators such as slope deformation rate, inclination, acceleration, etc. When a sudden change in displacement rate or a rapid expansion of slip range is detected, an early warning is triggered and a danger level is generated, and an alarm message is sent to the highway management department or emergency system according to the threshold setting; 5) Adaptation and maintenance to high-altitude cold environment: The camera, target and Beidou receiver housing are made of cold-resistant materials, and antifreeze components are installed at the universal pulley assembly; The camera frame rate, NB-IoT transmission interval, Beidou sampling frequency and other parameters can be dynamically adjusted through the remote management platform to ensure continuous operation even in extremely cold and communication-restricted conditions.

2. The method according to claim 1, characterized in that: The machine vision monitoring unit deploys a plurality of high-contrast or reflective targets after the vegetation on the slope is properly cleaned. The camera selects a fixed viewing angle and establishes a mapping relationship with a reference coordinate system. The position change of each target is identified in the video sequence to form a grid displacement trend diagram of the slope.

3. The method according to claim 1 or 2, characterized in that: The Beidou monitoring unit includes multiple sensor modules such as Beidou receiver, MEMS accelerometer, inclinometer and rain gauge, and places part of the data processing at the front end for edge computing to reduce the bandwidth and power consumption requirements for long-distance transmission in high-altitude and cold areas.

4. The method according to claim 1 or 2, characterized in that: The long-pull-wire displacement monitoring unit adds a multifunctional universal pulley set on the basis of the traditional pull-wire displacement meter, so that a single pull wire can monitor multiple key points in series, and adopts NB-IoT communication method to realize low-power and long-distance data uploading.

5. The method according to claim 1 or 2, characterized in that: The multi-source data fusion process includes: The three-dimensional coordinate sequence of the target output by the machine vision monitoring unit, the dynamic positioning and inclination information output by the Beidou monitoring unit, and the multi-point pull-wire length change value output by the long pull-wire displacement monitoring unit are uniformly converted into coordinates; If there are more than two observation methods in the same monitoring area, the observation results will be cross-checked or complementary fused. If the difference exceeds the preset threshold, it will be marked as abnormal data and manual inspection or system recalibration will be initiated.

6. The method according to claim 1, characterized in that: The disaster risk identification and early warning includes the following steps: Construct deformation rate or cumulative displacement curves for multi-source time series data, and monitor acceleration or angle mutations; Set multiple thresholds, such as safety threshold, warning threshold, and danger threshold. Once the deformation rate or inclination angle exceeds the corresponding threshold, a graded warning will be issued to the monitoring center or highway management department. Combined with numerical simulation or geological background information, the overall stability of the slope can be further judged to provide a reference for emergency decision-making.

7. The method according to claim 1, characterized in that: The high-cold environment adaptation and maintenance methods include: The camera, Beidou receiver and cable sensor housings are designed to be windproof, cold-proof and moisture-proof. If necessary, low-power heating elements are installed inside the equipment. Clear the snow and ice on the target regularly and ensure the flexibility of the moving parts of the pulley block; Cooperate with the remote management platform to inspect and maintain the power supply status, communication quality, sensor calibration, etc. of each monitoring unit.

8. The method according to claim 1, characterized in that: In order to cope with unstable communications or data interruptions in high-altitude and cold regions, the monitoring system is also equipped with a local storage and delayed upload mechanism, which supports automatic retransmission of key observation data after network recovery to ensure the integrity of the deformation monitoring sequence.

9. The method according to claim 1, characterized in that: The method also includes steps for optimizing the deployment of the network. According to factors such as road risk level, vegetation distribution, geological conditions and traffic flow, differentiated monitoring density is adopted to intensively deploy machine vision and Beidou equipment in high-risk areas, and long-wire displacement monitoring is used as a supplement to large-scale coverage.

10. The method according to claim 1, characterized in that: The multi-source monitoring data generated by the method are connected to the GIS platform to construct a three-dimensional visualization interface and record the displacement time series, providing auxiliary decision-making for comprehensive assessment of disasters along the highway, formulation of emergency plans and maintenance plans.