A landslide crack displacement self-adaptive monitoring system

Abstract

The application discloses a landslide crack displacement self-adaptive monitoring system and relates to the technical field of geological disaster monitoring. The system comprises a multi-source data acquisition module, which is responsible for collecting multi-dimensional data such as crack displacement; a data processing module, which performs smoothing, normalization and other processing on the data; an intelligent early warning module, which calculates landslide probability and divides four levels of early warning; a disaster deduction module, which calculates landslide speed and impact range; and a linkage control and management module, which triggers an alarm and implements traffic control. The application guarantees comprehensive and accurate data through multi-source data acquisition and special equipment, optimizes data quality through data processing, and provides support for early warning; the intelligent early warning module dynamically adjusts the benchmark, reduces false positives and false negatives, and adapts to different geological conditions. Meanwhile, the disaster deduction and linkage control module cooperates, quantitatively calculates the risk, automatically triggers the alarm and interfaces with the traffic control, forms a complete closed loop, and improves the early warning efficiency and the disaster emergency response capability.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring technology, specifically to an adaptive monitoring system for landslide crack displacement. Background Technology

[0002] Landslides, a frequent geological hazard along mountainous highways, pose a serious threat to the operational safety of transportation infrastructure and the safety of pedestrians due to their suddenness and destructiveness. The displacement evolution of slope cracks is a core precursor signal in the landslide development process. Accurate monitoring of crack displacement rates and simultaneous acquisition of related parameters affecting slope stability are key technical aspects for early landslide identification and mitigation of major disaster losses. As my country's transportation network extends into complex geological areas, the demand for landslide monitoring along highways is upgrading from traditional manual inspections to automated and intelligent systems. This requires building a monitoring system capable of integrating multi-dimensional data—not only collecting crack displacement rates in real time, but also simultaneously acquiring environmental parameters such as rainfall, soil moisture content, and slope vibration acceleration, combined with geophysical parameters such as slope lithology, dip angle, cohesion, and density, as well as engineering parameters such as the impact resistance strength of the affected facilities, to comprehensively characterize the changing patterns of slope stability.

[0003] Existing landslide crack displacement monitoring technologies have many limitations, making it difficult to meet the needs of accurate monitoring and efficient response in complex traffic scenarios. At the data acquisition level, some systems rely solely on a single sensor to collect crack displacement data, lacking simultaneous monitoring of key environmental parameters such as rainfall and soil moisture content, or using immature sensor principles, resulting in missing data dimensions and insufficient measurement accuracy. Geological parameters such as slope lithology and cohesion often rely on empirical estimations, failing to be accurately calibrated through on-site geological surveys and laboratory tests, leading to low reliability of subsequent analysis results. In the data processing and early warning stages, traditional systems mostly use static thresholds to determine warning levels, without introducing dynamic threshold self-learning mechanisms. This prevents adjustments to the warning benchmark based on differences in slope lithology and changes in environmental parameters, particularly in shale, granite, and other lithological slopes. The system is prone to false alarms or missed alarms; although some systems attempt multi-parameter early warning, they have not established a quantitative landslide probability calculation model, and the classification of early warning levels lacks a scientific basis, making it difficult to accurately reflect the risk of landslides occurring within 24 hours; in addition, most systems lack disaster simulation functions, and cannot calculate the sliding speed and impact range of the landslide body when a high-level early warning is triggered. Furthermore, the linkage control link has not achieved standardized docking with the traffic control system, and early warning signals need to be manually transmitted a second time, resulting in delays in response measures such as road closures and navigation adjustments, and failing to form an effective closed loop of "monitoring-early warning-response". Therefore, an adaptive monitoring system for landslide crack displacement is to be developed to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an adaptive monitoring system for landslide crack displacement. This system comprehensively acquires multi-dimensional data such as crack displacement and rainfall through a multi-source data acquisition module to ensure data accuracy; a data processing module optimizes data quality to support early warning; an intelligent early warning module uses dynamic thresholds and multi-parameter coupling formulas to reduce false alarms and missed alarms; a disaster simulation module quantitatively calculates landslide velocity and impact range; and a linkage control and management module automatically triggers alarms and connects with traffic control to form a complete closed loop, effectively improving disaster emergency response capabilities.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an adaptive monitoring system for landslide crack displacement, the system comprising:

[0006] Multi-source data acquisition module: Collects crack displacement rate, rainfall, soil moisture content, slope vibration acceleration, slope lithology parameters, slope dip angle, slope cohesion, slope density and impact resistance of the impacted facilities, and transmits the multi-dimensional data to the data processing module;

[0007] Data processing module: Performs sliding mean filtering on crack displacement rate to obtain smooth displacement rate, normalizes rainfall and soil moisture content to obtain standardized parameters, performs registration and lightweight processing on 3D terrain data, stores and outputs the processed data to intelligent early warning module.

[0008] The intelligent early warning module first calculates the dynamic displacement rate threshold using a dynamic threshold self-learning formula. Then, it combines the smooth displacement rate, standardized parameters, and slope vibration acceleration to calculate the 24-hour landslide probability using a multi-parameter coupled landslide probability formula. It then divides the warning into four levels: blue, yellow, orange, and red, and transmits the information to the disaster simulation module and the linkage control and management module.

[0009] Disaster simulation module: When the warning level is red, the disaster simulation module calculates the instantaneous sliding speed of the landslide body using the landslide sliding speed formula. Then, combined with the instantaneous sliding speed, the horizontal distance of the facilities, the mass of the landslide body, etc., it calculates the impact range using the landslide impact range formula and transmits it to the linkage control and management module.

[0010] Linkage control and management module: Based on the warning level and impact range, it triggers on-site audible and visual alarms, variable message signs, and emergency broadcasts, and connects to the traffic control system through standardized interfaces to implement road closures and navigation adjustments.

[0011] Furthermore, the data acquisition methods of the multi-source data acquisition module are as follows: a laser displacement meter is used to collect crack displacement rate, a rainfall sensor is used to collect rainfall, a soil moisture sensor using the time-domain reflectometry principle is used to collect soil moisture content, and an acceleration sensor is used to collect slope vibration acceleration; a ground-based lidar collects three-dimensional point cloud data of the landslide area, and a drone equipped with a full-frame camera acquires image data of the landslide area, and the two work together to determine the slope inclination angle; slope lithology parameters are determined through on-site geological surveys and then entered into the system; slope cohesion and slope density are determined through laboratory tests after on-site sampling and then entered into the system; and the impact resistance strength of the impacted facilities is determined by reviewing the facility design documents and then entered into the system.

[0012] Furthermore, the data processing module uses a moving average filter to calculate the smoothed displacement rate, and the calculation formula is as follows: ,in, To smooth the displacement rate; Let be the crack displacement rate at the i-th sampling point; the standardized rainfall calculation formula is: ,in, Standardized rainfall; This refers to the cumulative rainfall over 24 hours. The maximum 24-hour rainfall event occurring once every 50 years in the monitoring area; the standardized soil moisture content calculation formula is: ,in, To standardize soil moisture content; Real-time soil moisture content; This represents the saturated water content of the slope soil.

[0013] Furthermore, the dynamic threshold self-learning calculation formula in the intelligent early warning module is as follows: ,in, The dynamic displacement rate threshold; This represents the highest safe displacement rate in history. The slope lithology coefficient; Environmental coupling factor; It is a deformation inhibition factor; It is a natural constant; To smooth the displacement rate; Standardized rainfall; To standardize soil moisture content

[0014] Furthermore, the multi-parameter coupled landslide probability calculation formula in the intelligent early warning module is as follows: ,in, The probability of a landslide occurring in 24 hours; This is the probability amplification factor; Environmental sensitivity coefficient; The vibration contribution coefficient; This refers to the acceleration due to slope vibration. The threshold for slope stability vibration; To smooth the displacement rate; The dynamic displacement rate threshold; Standardized rainfall; To standardize soil moisture content; It is a natural constant.

[0015] Furthermore, the triggering conditions for the four-level early warning system of the intelligent early warning module are based on the 24-hour landslide occurrence probability. Confirmed: When A blue alert is triggered when the value is <0.3; a blue alert is triggered when 0.3 ≤ A yellow alert is triggered when the value is <0.5; a yellow alert is triggered when the value is ≤0.5. When <0.7, an orange alert is triggered; when A red alert is triggered when the value is ≥0.7.

[0016] Furthermore, the formula for calculating the landslide sliding velocity in the disaster simulation module is as follows: ,in, The instantaneous sliding velocity of the landslide body; s is the smooth displacement acceleration; s is the sliding distance of the landslide body; The initial sliding velocity; For slope cohesion; The slope angle; The density of the slope; It is the acceleration due to gravity; This is the sliding resistance attenuation coefficient.

[0017] Furthermore, the formula for calculating the landslide impact range in the disaster simulation module is as follows: ,in, The impact range of the landslide; The horizontal distance from the initial sliding position of the landslide body to the target facility; Estimate the mass of the landslide; For energy transfer efficiency; The impact resistance strength of the facility being impacted; This represents the instantaneous sliding velocity of the landslide body.

[0018] Furthermore, in the linkage control and management module, the audible and visual alarm covers the key area surrounding the landslide area, the variable message sign displays early warning content including the landslide impact range, the emergency broadcast supports bilingual broadcasting in Mandarin and the local dialect of the monitoring area, and the evacuation instructions are repeatedly played during a red alert.

[0019] Compared with existing technologies, this adaptive monitoring system for landslide crack displacement has the following advantages:

[0020] I. This invention comprehensively collects multi-dimensional data such as crack displacement rate, rainfall, and soil moisture content through a multi-source data acquisition module. It relies on specialized equipment such as laser displacement meters and time-domain reflectometry soil moisture sensors to ensure the accuracy of basic data. Combined with ground-based lidar and UAV collaboration, it determines the slope inclination angle, achieving comprehensive and accurate data acquisition. In the data processing stage, moving average filtering is used to eliminate displacement data interference, and normalization processing unifies environmental parameter dimensions. Three-dimensional data registration technology optimizes terrain data quality, providing high-quality data support for subsequent early warning. The intelligent early warning module innovatively uses a dynamic threshold self-learning formula and a multi-parameter coupled landslide probability formula, breaking through the limitations of traditional static thresholds. It can dynamically adjust the early warning benchmark based on slope lithology and environmental parameters, and divides the warning into four levels based on the 24-hour landslide occurrence probability, significantly reducing the risk of false alarms and missed alarms. This makes the early warning more closely aligned with the actual deformation patterns of the slope and adaptable to monitoring needs under different geological conditions.

[0021] Second, this invention, through the collaborative design of a disaster simulation module and a linkage control and management module, quantitatively calculates the instantaneous sliding velocity of the landslide body based on the landslide sliding velocity formula when a red alert is triggered. Combined with the landslide impact range formula, it clarifies the impact range on surrounding facilities, solving the problem of ambiguity in traditional simulations and providing clear risk boundaries for emergency decision-making. The linkage control and management module can automatically trigger on-site audible and visual alarms, variable message signs, and emergency broadcasts based on the alert level and impact range. Simultaneously, it connects to the traffic control system through standardized interfaces to quickly implement road closures and navigation adjustments, forming a complete closed loop of "monitoring-early warning-simulation-response." Furthermore, the emergency broadcast supports bilingual broadcasts in Mandarin and local dialects, and repeatedly plays evacuation instructions during a red alert, further improving the efficiency of early warning information transmission, effectively reducing the safety threat of landslides to traffic routes and surrounding facilities, and enhancing disaster emergency response capabilities.

[0022] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart of an adaptive monitoring system for landslide crack displacement;

[0025] Figure 2 This is a framework diagram of an adaptive monitoring system for landslide crack displacement. Detailed Implementation

[0026] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0027] Example 1: Landslide Monitoring Scenario on Mountain Highways

[0028] A landslide occurred at kilometer marker K25+300 on a mountainous section of the G318 highway. This section is located in a geological zone where shale and granite intersect, experiencing concentrated and heavy rainfall during the rainy season. Rainwater infiltration easily reduces the cohesion of the slope, and two minor landslides have occurred previously, posing a threat to the safety of the highway mainline and adjacent ramps. This system is required to achieve 24-hour automated monitoring, proactively identify landslide risks, and coordinate response to prevent traffic disruptions and casualties. The specific implementation process is as follows... Figure 1 As shown.

[0029] Deployment and data acquisition of multi-source data acquisition modules

[0030] Laser displacement gauges were deployed along the main crack at the landslide site, with a crack length of approximately 60 meters, to collect the crack displacement rate in real time. Rainfall sensors were installed at an open, unobstructed area at the top of the slope to collect 24-hour cumulative rainfall. Soil moisture sensors using the time-domain reflectometry method were deployed vertically along the slope to obtain soil moisture content at different depths. Accelerometers were fixed on stable rock surfaces in the middle of the slope to collect slope vibration acceleration. Simultaneously, ground-based lidar was used twice weekly to scan the landslide area for 3D point cloud data. Within two hours after rainfall, a drone equipped with a full-frame camera acquired image data of the landslide area; these two methods were used together to determine the slope inclination. On-site geological surveys were conducted to determine the slope lithology and obtain lithological parameters. Shale and granite mixed soil samples were taken on-site and sent to the laboratory for direct shear tests to determine the slope cohesion and density. Design documents for highway guardrails and bridge piers were reviewed to obtain the impact resistance of the impacted structures. All these parameters were entered into the system. All collected data were transmitted in real time to the data processing module via industrial-grade wireless transmission components.

[0031] Data processing module

[0032] After receiving multi-source data, the data processing module performs noise reduction processing on the crack displacement rate collected by the laser displacement gauge using a moving average filter to eliminate instantaneous data fluctuations caused by environmental vibrations, resulting in a smooth displacement rate. The 24-hour cumulative rainfall collected by the rainfall sensor and the real-time soil moisture content collected by the soil moisture sensor are normalized to obtain standardized rainfall and standardized soil moisture content. A moving average filter is then used to calculate the smooth displacement rate, using the following formula: ,in, To smooth the displacement rate; Let be the crack displacement rate at the i-th sampling point; the standardized rainfall calculation formula is: ,in, Standardized rainfall; This refers to the cumulative rainfall over 24 hours. The maximum 24-hour rainfall event occurring once every 50 years in the monitoring area; the standardized soil moisture content calculation formula is: ,in, To standardize soil moisture content; Real-time soil moisture content; The soil saturation moisture content of the slope is determined; the 3D terrain data acquired by ground lidar and UAV are registered and lightweighted, and integrated into a unified format of 3D terrain data; the processed smooth displacement rate, standardized parameters, and 3D terrain data are stored in the corresponding database and simultaneously output to the intelligent early warning module.

[0033] Intelligent early warning module early warning judgment

[0034] The intelligent early warning module first calls upon the smoothed displacement rate, standardized rainfall, standardized soil moisture content, and preset historical maximum safe displacement rate and slope lithology coefficient output by the data processing module. It then calculates the current dynamic displacement rate threshold of the slope using a dynamic threshold self-learning formula. The dynamic threshold self-learning calculation formula is as follows: ,in, The dynamic displacement rate threshold; This represents the highest safe displacement rate in history. The slope lithology coefficient; Environmental coupling factor; It is a deformation inhibition factor; It is a natural constant; To smooth the displacement rate; Standardized rainfall; To standardize soil moisture content, the probability of a landslide occurring within 24 hours was calculated using a multi-parameter coupled landslide probability formula, which combines smoothed displacement rate, dynamic displacement rate threshold, standardized parameters, and slope vibration acceleration collected by acceleration sensors. The multi-parameter coupled landslide probability calculation formula is as follows: ,in, The probability of a landslide occurring in 24 hours; This is the probability amplification factor; Environmental sensitivity coefficient; The vibration contribution coefficient; This refers to the acceleration due to slope vibration. The threshold for slope stability vibration; To smooth the displacement rate; The dynamic displacement rate threshold; Standardized rainfall; To standardize soil moisture content; The probability is a natural constant; the warning levels are divided according to the probability results: when the probability is <0.3, a blue warning is triggered, indicating that the slope is stable; when the probability is 0.3 ≤ probability <0.5, a yellow warning is triggered, reminding maintenance personnel to strengthen monitoring; when the probability is 0.5 ≤ probability <0.7, an orange warning is triggered, notifying the transportation department to prepare for closure; when the probability rises to 0.72 after a certain rainfall, a red warning is triggered, and the warning signal is synchronously transmitted to the disaster simulation module and the linkage control and management module.

[0035] Disaster simulation module: disaster range calculation

[0036] After receiving a red alert signal, the disaster simulation module calls upon the smooth displacement acceleration, slope sliding distance, initial sliding velocity, and preset slope cohesion, dip angle, density, and sliding resistance attenuation coefficient from the data processing module. It then calculates the instantaneous sliding velocity of the landslide using the landslide sliding velocity formula: ,in, The instantaneous sliding velocity of the landslide body; s is the smooth displacement acceleration; s is the sliding distance of the landslide body; The initial sliding velocity; For slope cohesion; The slope angle; The density of the slope; It is the acceleration due to gravity; The sliding resistance attenuation coefficient is used. Combined with the instantaneous sliding velocity, the horizontal distance from the initial sliding position of the landslide to the highway guardrail and bridge pier, the estimated mass of the landslide, energy transfer efficiency, and the impact resistance strength of the impacted facilities, the landslide impact range is calculated using the landslide impact range formula. This calculation shows that the landslide impact range will cover the guardrail section from K25+280 to K25+320 of the highway and one bridge pier. The impact range result is then transmitted to the linkage control and management module. The landslide impact range calculation formula is: ,in, The impact range of the landslide; The horizontal distance from the initial sliding position of the landslide body to the target facility; Estimate the mass of the landslide; For energy transfer efficiency; The impact resistance strength of the facility being impacted; This represents the instantaneous sliding velocity of the landslide body.

[0037] Emergency Response of Linkage Control and Management Module

[0038] Based on the red alert level and impact range, the linkage control and management module immediately triggers the on-site audible and visual alarms, activating a high-volume alarm sound and flashing red lights; updates the variable message signs along the route, displaying "Landslide risk ahead from K25+280 to K25+320, passage prohibited, please detour via K30 interchange"; broadcasts evacuation instructions in both Mandarin and the local dialect in a loop via emergency broadcast, reminding vehicles already in the section to leave immediately; simultaneously, it connects to the highway traffic control system through a standardized interface, pushing information on the impact range and alert level, triggering the closure of the main line and adjacent ramps from K25+280 to K25+320, and simultaneously pushing detour routes to navigation software to ensure orderly traffic flow.

[0039] In summary, in the landslide monitoring scenario of mountainous highways, a multi-source data acquisition module is used to strategically deploy equipment such as laser displacement gauges and time-domain reflectometry soil moisture sensors. Combined with on-site surveys and laboratory tests, slope parameters are obtained, enabling comprehensive multi-dimensional data collection. The data processing module filters, normalizes, and registers the data in three dimensions, providing high-quality data for subsequent analysis. The intelligent early warning module, relying on a dynamic threshold self-learning formula and a multi-parameter coupled landslide probability formula, accurately triggers a four-level early warning. During a red alert, the disaster simulation module clarifies the risk boundary using landslide velocity and impact range formulas, and the linkage control and management module quickly connects to the traffic control system to implement road closures and detour guidance. The entire process forms a closed loop of "collection-processing-early warning-simulation-response," effectively addressing the risk of landslides during the rainy season on mountainous highways and ensuring road operation safety.

[0040] Example 2: Monitoring scenario of landslides on scenic slopes

[0041] The slope below a viewing platform in a 5A-level scenic area is a mixed sandstone and shale slope, approximately 25 meters high. Below it lies the main tourist trail and rest platform, receiving an average of 2000 visitors daily. During the rainy season, the slope is prone to cracking and widening due to rainwater immersion, posing a risk of landslides impacting the trails and tourists. This system is needed for real-time monitoring and emergency evacuation guidance. The specific implementation process is as follows... Figure 2 As shown.

[0042] Deployment and data acquisition of multi-source data acquisition modules

[0043] One laser displacement meter was installed at each of the three main cracks that had appeared on the slope below the viewing platform to collect the crack displacement rate in real time. A rainfall sensor was installed next to the visitor rest pavilion at the top of the slope to record the cumulative rainfall over 24 hours. A time-domain reflectometry (TDAR) soil moisture sensor was deployed along the vertical direction of the slope to monitor changes in soil moisture at different depths. An accelerometer was installed on the stable rock surface at the bottom of the slope, away from the walkway, to collect the slope vibration acceleration. The slope's three-dimensional point cloud data was scanned by ground-based lidar every three days, increasing to once a day before and after holidays. A drone was used to acquire slope image data once a week. The two methods were used together to determine the slope inclination angle. The distribution ratio of sandstone and shale on the slope was determined through on-site geological surveys to obtain slope lithology parameters. Three sets of soil samples were taken on-site and sent to the laboratory for density and cohesion tests. The impact resistance strength of the impacted facilities was obtained by referring to the design drawings of the scenic walkway and rest platform. All parameters were entered into the system. The collected data was transmitted to the data processing module in real time via a wireless transmission module.

[0044] Data processing module

[0045] The data processing module performs sliding mean filtering on the crack displacement rate collected by the laser displacement meter to remove minor vibration interference caused by tourists walking, resulting in a smooth displacement rate. It normalizes the rainfall and soil moisture data to convert them into standardized rainfall and standardized soil moisture. It registers the 3D data acquired by the ground lidar and UAV to eliminate coordinate deviations between different devices, while also performing lightweight processing to reduce data storage and transmission pressure. All processed data is then categorized, stored, and output to the intelligent early warning module.

[0046] Intelligent early warning module early warning judgment

[0047] The intelligent early warning module first utilizes the smoothed displacement rate, standardized rainfall, and standardized soil moisture content output by the data processing module, combined with the historical maximum safe displacement rate and slope lithology coefficient of the scenic area slope, to calculate the current dynamic displacement rate threshold of the slope using a dynamic threshold self-learning formula. Then, it substitutes the smoothed displacement rate, dynamic displacement rate threshold, standardized parameters, and slope vibration acceleration collected by the acceleration sensor into a multi-parameter coupled landslide probability formula to calculate the 24-hour landslide probability. When the probability is 0.25, a blue alert is triggered, requiring only routine monitoring by scenic area maintenance personnel; when the probability rises to 0.45, a yellow alert is triggered, and warning signs are set up at the trail entrance; when the probability reaches 0.65, an orange alert is triggered, restricting the number of people passing through the trail; after a period of continuous rainfall, the probability rises to 0.75, triggering a red alert, and the warning signal is sent to the disaster simulation module and the linkage control and management module.

[0048] Disaster simulation module: disaster range calculation

[0049] After receiving a red alert, the disaster simulation module calls upon the smooth displacement acceleration, potential slope sliding distance, and initial sliding velocity from the data processing module. Combining these with the slope cohesion, dip angle, density, and sliding resistance attenuation coefficient, it calculates the instantaneous sliding velocity of the landslide using the landslide sliding velocity formula. Then, combining this velocity with the horizontal distance from the landslide's starting position to the walkway and rest platform, the estimated mass of the landslide, energy transfer efficiency, and the impact resistance of the walkway and platform, it calculates the impact range of the landslide within a 15-22m radius below the viewing platform using the landslide impact range formula. The impact range result is then transmitted to the linkage control and management module.

[0050] Emergency Response of Linkage Control and Management Module

[0051] Based on the red alert and impact range, the linkage control and management module immediately activates three audible and visual alarms around the slope, emitting a highly recognizable alarm sound and red light; updates three variable message signs within the scenic area, displaying "Landslide risk on the slope below the viewing platform, 15-22m walkway closed, please evacuate along the east side walkway" and "No stopping at the rest platform, evacuate immediately"; broadcasts evacuation instructions in both Mandarin and the local dialect of the scenic area through an emergency broadcast system, clearly guiding tourists to leave via the backup walkway outside the impact range; simultaneously, it connects to the scenic area management system through a standardized interface, pushing early warning information and the impact range, dispatching scenic area staff to the site to guide evacuation, and closing the walkway entrance gates to prevent tourists from entering the danger zone until the landslide risk is eliminated.

[0052] In summary, in the scenario of landslide monitoring on scenic slopes, considering the high density of people, a multi-source data acquisition module is deployed as needed to balance monitoring accuracy with the interference of tourist activities, simultaneously acquiring data such as crack displacement and slope parameters. After the data processing module optimizes data quality, the intelligent early warning module dynamically calculates the landslide probability and issues graded warnings, providing advance notice to operation and maintenance personnel. Once a red warning is triggered, the disaster simulation module accurately calculates the landslide impact range, and the linkage control and management module guides tourist evacuation through audible and visual alarms, bilingual broadcasts, and information boards, coordinating with the scenic area's system dispatch personnel. This implementation fully demonstrates the system's adaptability in densely populated areas, ensuring tourist safety while avoiding excessive disruption to scenic area operations, verifying its practical value in geological disaster prevention and control in tourism scenarios.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An adaptive monitoring system for landslide crack displacement, characterized in that, The system includes: Multi-source data acquisition module: Collects crack displacement rate, rainfall, soil moisture content, slope vibration acceleration, slope lithology parameters, slope dip angle, slope cohesion, slope density and impact resistance of the impacted facilities, and transmits the multi-dimensional data to the data processing module; Data processing module: Performs sliding mean filtering on crack displacement rate to obtain smooth displacement rate, normalizes rainfall and soil moisture content to obtain standardized parameters, performs registration and lightweight processing on 3D terrain data, stores and outputs the processed data to intelligent early warning module. Intelligent early warning module: First, the dynamic displacement rate threshold is calculated using a dynamic threshold self-learning formula. The dynamic threshold self-learning calculation formula is as follows: ,in, The dynamic displacement rate threshold; This represents the highest safe displacement rate in history. The slope lithology coefficient; Environmental coupling factor; It is a deformation inhibition factor; It is a natural constant; To smooth the displacement rate; Standardized rainfall; To standardize soil moisture content, and combining smoothed displacement rate, standardized parameters, and slope vibration acceleration, the 24-hour landslide probability is calculated using a multi-parameter coupled landslide probability formula. The multi-parameter coupled landslide probability calculation formula is as follows: ,in, The probability of a landslide occurring in 24 hours; This is the probability amplification factor; Environmental sensitivity coefficient; The vibration contribution coefficient; This refers to the acceleration due to slope vibration. The threshold for slope stability vibration; To smooth the displacement rate; The dynamic displacement rate threshold; Standardized rainfall; To standardize soil moisture content; It is a natural constant; it divides the warning into four levels: blue, yellow, orange, and red, and transmits the information to the disaster simulation module and the linkage control and management module; Disaster simulation module: When the warning level is red, the disaster simulation module calculates the instantaneous sliding speed of the landslide body using the landslide sliding speed formula, and then calculates the impact range using the landslide impact range formula by combining the instantaneous sliding speed, the horizontal distance of the facilities, and the mass of the landslide body, and transmits it to the linkage control and management module. Linkage control and management module: Based on the warning level and impact range, it triggers on-site audible and visual alarms, variable message signs, and emergency broadcasts, and connects to the traffic control system through standardized interfaces to implement road closures and navigation adjustments.

2. The landslide crack displacement self-adaptive monitoring system according to claim 1, characterized in that, The data acquisition methods of the multi-source data acquisition module are as follows: laser displacement gauges are used to collect crack displacement rates, rainfall sensors are used to collect rainfall, soil moisture sensors using the time-domain reflectometry principle are used to collect soil moisture content, and acceleration sensors are used to collect slope vibration acceleration; ground-based lidar collects three-dimensional point cloud data of the landslide area, and a drone equipped with a full-frame camera acquires image data of the landslide area. The two are used together to determine the slope inclination angle; slope lithology parameters are determined through on-site geological surveys and then entered into the system; slope cohesion and slope density are determined through laboratory tests after on-site sampling and then entered into the system; and the impact resistance strength of the impacted facilities is determined by reviewing the facility design documents and then entered into the system.

3. The landslide crack displacement self-adaptive monitoring system according to claim 1, characterized in that, The data processing module uses a moving average filter to calculate the smoothed displacement rate, and the calculation formula is as follows: ,in, To smooth the displacement rate; Let be the crack displacement rate at the i-th sampling point; the standardized rainfall calculation formula is: ,in, Standardized rainfall; This refers to the cumulative rainfall over 24 hours. The maximum 24-hour rainfall event occurring once every 50 years in the monitoring area; the standardized soil moisture content calculation formula is: ,in, To standardize soil moisture content; Real-time soil moisture content; This represents the saturated water content of the slope soil.

4. The landslide crack displacement self-adaptive monitoring system according to claim 1, characterized in that, The triggering conditions for the four-level warning system of the intelligent early warning module are based on the 24-hour landslide occurrence probability. Confirmed: When A blue alert is triggered when the value is less than 0.

3. When 0.3≤ <0.5, a yellow pre-warning is triggered; when 0.5≤ <0.7, an orange pre-warning is triggered; when ≥0.7, a red pre-warning is triggered.

5. The landslide crack displacement adaptive monitoring system according to claim 1, wherein, The formula for calculating the landslide sliding velocity in the disaster simulation module is as follows: ,in, The instantaneous sliding velocity of the landslide body; s is the smooth displacement acceleration; s is the sliding distance of the landslide body; The initial sliding velocity; For slope cohesion; The slope angle; The density of the slope; It is the acceleration due to gravity; This is the sliding resistance attenuation coefficient.

6. The landslide crack displacement adaptive monitoring system according to claim 1, wherein, The formula for calculating the landslide impact range in the disaster simulation module is as follows: ,in, The impact range of the landslide; The horizontal distance from the initial sliding position of the landslide body to the target facility; Estimate the mass of the landslide; For energy transfer efficiency; The impact resistance strength of the facility being impacted; This represents the instantaneous sliding velocity of the landslide body.

7. The landslide crack displacement adaptive monitoring system according to claim 1, wherein, In the linkage control and management module, the audible and visual alarm covers the key area around the landslide area, the variable message sign displays early warning content including the landslide impact range, the emergency broadcast supports bilingual broadcasting in Mandarin and the local dialect of the monitoring area, and the evacuation instruction is repeatedly played when a red alert is issued.

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