Landslide monitoring and early warning system and monitoring method

Abstract

The invention provides a landslide monitoring and early warning system and a monitoring method, and relates to the technical field of geological disaster monitoring and early warning, and the method comprises the steps: collecting a plurality of pieces of position data of a target monitoring region; based on the multiple pieces of position data, determining a displacement change condition of the target monitoring area; judging whether the displacement change condition meets a preset displacement change condition or not; when the displacement change condition meets a preset displacement change condition, collecting multiple pieces of conductivity data of the target monitoring area; according to the multiple pieces of conductivity data, calculating water level depth data of the target monitoring area; calculating a landslide generation coefficient of the target monitoring area based on the water level depth data; and when it is judged that the landslide occurrence coefficient is smaller than or equal to a preset threshold value and a prediction result corresponding to the landslide occurrence coefficient is consistent with the displacement change condition, performing early warning prompt. According to the invention, the accuracy of landslide prediction can be further improved.

Description

Technical Field

[0001] The present application relates to the technical field of geological disaster monitoring and early warning, and specifically to a landslide monitoring and early warning system and a monitoring method. Background Art

[0002] Landslides are natural disasters caused by the sudden loss of stability of soil, rocks and other materials on a hillside due to geological, meteorological or human factors, and they slide rapidly down the slope. This phenomenon is usually triggered by heavy rainfall, earthquakes, volcanic activity, or human engineering activities, and the consequences can be very serious, including damage to buildings, roads, bridges, and threats to human life. Landslides are sudden and destructive, so predicting landslides and taking preventive measures are crucial to reducing disaster risks.

[0003] At present, it is a common method to monitor and analyze landslide risk using the location data of landslide bodies. However, the analysis of landslide risk not only depends on the location data, but also needs to consider multiple factors, such as rainfall, groundwater level, soil properties, geological structure, etc. The location data alone may not be able to fully assess the landslide risk, especially in the case of compound landslides or the combined effect of multiple factors, and thus the accuracy of this monitoring method is not high enough. Summary of the invention

[0004] The present application provides a landslide monitoring and early warning system and a monitoring method, which can further improve the accuracy of landslide prediction.

[0005] In a first aspect of the present application, a landslide monitoring and early warning system is provided, the system comprising a GNSS displacement monitoring module, a processing module, a judgment module, an electroosmosis monitoring module and an early warning module, wherein:

[0006] The GNSS displacement monitoring module is used to collect multiple position data of the target monitoring area;

[0007] The processing module is used to determine the displacement change of the target monitoring area based on the plurality of position data;

[0008] The judging module is used to judge whether the displacement change condition satisfies a preset displacement change condition;

[0009] The electroosmosis monitoring module is used to collect multiple conductivity data of the target monitoring area when the displacement change condition meets the preset displacement change condition;

[0010] The processing module is further used to calculate the water level depth data of the target monitoring area according to the plurality of conductivity data;

[0011] The processing module is further used to calculate the landslide occurrence coefficient of the target monitoring area based on the water level depth data;

[0012] The early warning module is used to issue an early warning prompt when it is determined that the landslide occurrence coefficient is less than or equal to a preset threshold value and the prediction result corresponding to the landslide occurrence coefficient is consistent with the displacement change.

[0013] Optionally, the processing module is further used to divide the target monitoring area into a plurality of sliding surfaces;

[0014] The processing module is further used to calculate the pore water pressure of each sliding surface based on the water level depth data;

[0015] The processing module is further used to calculate the sliding force of each sliding surface and the anti-sliding force of each sliding surface based on the pore water pressure;

[0016] The processing module is further used to calculate the landslide occurrence coefficient according to the plurality of sliding forces and the plurality of anti-sliding forces;

[0017] The processing module is further used to calculate the landslide occurrence coefficient of the target monitoring area based on the multiple position data and the water level depth data.

[0018] Optionally, the processing module is further used to calculate the sliding force of each sliding surface and the anti-sliding force of each sliding surface based on the pore water pressure, and the anti-sliding force is calculated specifically by the following formula:

[0019] R=c·j+(W·cosα-τ·(Hd)·S)·tanΦ

[0020] Wherein, R is the anti-sliding force, c is the soil cohesion of the sliding surface, j is the length of the sliding surface, W is the gravity of the sliding surface, α is the angle between the sliding surface and the horizontal plane, τ is the unit weight of the groundwater corresponding to the target monitoring area, H is the water level depth data, d is the actual dry soil thickness from the sliding surface to the upper part of the soil, S is the projection area of ​​the orthographic projection of the sliding surface on the horizontal plane, and Φ is the internal friction angle;

[0021] The processing module is further used to calculate the sliding force specifically through the following formula:

[0022] S=W·sinα

[0023] Wherein, S is the sliding force, W is the gravity of the sliding surface, and α is the angle between the sliding surface and the horizontal plane.

[0024] Optionally, the processing module is further used to calculate the landslide occurrence coefficient according to the plurality of sliding forces and the plurality of anti-sliding forces, specifically by the following formula:

[0025]

[0026] Wherein, F is the landslide occurrence coefficient, c i is the soil cohesion of the i-th sliding surface, j i is the length of the i-th sliding surface, W i is the gravity of the i-th sliding surface, α i is the angle between the sliding surface and the horizontal plane, τ is the unit weight of groundwater corresponding to the target monitoring area, d i is the actual dry soil thickness from the i-th sliding surface to the top of the soil, H is the water level depth data, S i is the projection area of ​​the i-th sliding surface on the horizontal plane, and Φ is the internal friction angle.

[0027] Optionally, the processing module is further used to determine the measurement depth data of each electroosmosis sensor, and the electroosmosis sensor is a sensor corresponding to the electroosmosis monitoring module;

[0028] The processing module is also used to obtain the conductivity data sent by each of the electroosmotic sensors;

[0029] The processing module is further used to obtain the environmental coefficient of the target monitoring area, wherein the environmental coefficient includes soil porosity, soil type coefficient, soil temperature, ion concentration correction coefficient and aquifer coefficient;

[0030] The processing module is also used to calculate the water level depth data corresponding to each of the electroosmosis sensors based on the environmental coefficient, the conductivity data and the measured depth data.

[0031] Optionally, the processing module is further used to calculate the water level depth data corresponding to each of the electroosmotic sensors according to the conductivity data and the measured depth data, specifically by the following formula:

[0032]

[0033] Among them, H is the water level depth data, K s is the soil type coefficient, K a is the aquifer coefficient, n is the soil porosity, ρ is the conductivity data, β is the temperature sensitivity coefficient, T is the soil temperature, T0 is the reference temperature, C ion is the ion concentration correction factor.

[0034] Optionally, the GNSS displacement monitoring module is further used to collect a plurality of initial position data of the target monitoring area;

[0035] The GNSS displacement monitoring module is further used to correct each of the initial position data to obtain the position data;

[0036] The processing module is further used to obtain the plurality of position data sent by the GNSS displacement monitoring module (101);

[0037] The processing module is further used to perform time series analysis based on the plurality of position data to obtain the displacement change situation, wherein the displacement change situation includes the displacement amount, displacement speed and displacement acceleration of the target monitoring area.

[0038] In a second aspect of the present application, a landslide monitoring method is provided, the method being applied to a monitoring and early warning system, the method comprising:

[0039] Collect multiple location data of the target monitoring area;

[0040] Based on the plurality of position data, determining a displacement change of the target monitoring area;

[0041] Determining whether the displacement change condition meets a preset displacement change condition;

[0042] When the displacement change condition satisfies the preset displacement change condition, collecting a plurality of conductivity data of the target monitoring area;

[0043] Calculating water level depth data of the target monitoring area according to the plurality of conductivity data;

[0044] Calculating the landslide occurrence coefficient of the target monitoring area based on the water level depth data;

[0045] When it is determined that the landslide occurrence coefficient is less than or equal to a preset threshold value, and the prediction result corresponding to the landslide occurrence coefficient is consistent with the displacement change, an early warning prompt is issued.

[0046] Optionally, the calculating the landslide occurrence coefficient of the target monitoring area based on the water level depth data specifically includes:

[0047] Dividing the target monitoring area into a plurality of sliding surfaces;

[0048] Calculating the pore water pressure of each sliding surface based on the water level depth data;

[0049] Based on the pore water pressure, calculating the sliding force of each sliding surface, and calculating the anti-sliding force of each sliding surface;

[0050] Calculating the landslide occurrence coefficient according to the plurality of sliding forces and the plurality of anti-sliding forces;

[0051] The landslide occurrence coefficient of the target monitoring area is calculated based on the plurality of position data and the water level depth data.

[0052] Optionally, based on the pore water pressure, the sliding force of each sliding surface is calculated, and the anti-sliding force of each sliding surface is calculated. Specifically, the anti-sliding force is calculated by the following formula:

[0053] R=c·j+(W·cosα-τ·(Hd)·S)·tanΦ

[0054] Wherein, R is the anti-sliding force, c is the soil cohesion of the sliding surface, j is the length of the sliding surface, W is the gravity of the sliding surface, α is the angle between the sliding surface and the horizontal plane, τ is the unit weight of the groundwater corresponding to the target monitoring area, H is the water level depth data, d is the actual dry soil thickness from the sliding surface to the upper part of the soil, S is the projection area of ​​the orthographic projection of the sliding surface on the horizontal plane, and Φ is the internal friction angle;

[0055] Specifically, the sliding force is calculated by the following formula:

[0056] S=W·sinα

[0057] Wherein, S is the sliding force, W is the gravity of the sliding surface, and α is the angle between the sliding surface and the horizontal plane.

[0058] Optionally, the landslide occurrence coefficient is calculated according to the plurality of sliding forces and the plurality of anti-sliding forces, specifically by the following formula:

[0059]

[0060] Wherein, F is the landslide occurrence coefficient, c i is the soil cohesion of the i-th sliding surface, j i is the length of the i-th sliding surface, W i is the gravity of the i-th sliding surface, α i is the angle between the sliding surface and the horizontal plane, τ is the unit weight of groundwater corresponding to the target monitoring area, d i is the actual dry soil thickness from the i-th sliding surface to the top of the soil, H is the water level depth data, S i is the projection area of ​​the i-th sliding surface on the horizontal plane, and Φ is the internal friction angle.

[0061] Optionally, calculating the water level depth data of the target monitoring area according to the plurality of conductivity data specifically includes:

[0062] Determining measurement depth data of each electroosmosis sensor, wherein the electroosmosis sensor is a sensor corresponding to the electroosmosis monitoring module;

[0063] Acquiring conductivity data sent by each of the electroosmotic sensors;

[0064] Obtaining environmental coefficients of the target monitoring area, wherein the environmental coefficients include soil porosity, soil type coefficient, soil temperature, ion concentration correction coefficient, and aquifer coefficient;

[0065] The water level depth data corresponding to each of the electroosmosis sensors is calculated according to the environmental coefficient, the conductivity data and the measurement depth data.

[0066] Optionally, the water level depth data corresponding to each of the electroosmotic sensors is calculated according to the conductivity data and the measured depth data, specifically by the following formula:

[0067]

[0068] Among them, H is the water level depth data, K s is the soil type coefficient, K a is the aquifer coefficient, n is the soil porosity, ρ is the conductivity data, β is the temperature sensitivity coefficient, T is the soil temperature, T0 is the reference temperature, C ion is the ion concentration correction factor.

[0069] Optionally, determining the displacement change of the target monitoring area based on the plurality of position data specifically includes:

[0070] Collecting a plurality of initial position data of the target monitoring area;

[0071] Correcting each of the initial position data to obtain the position data;

[0072] Acquire the plurality of position data sent by the GNSS displacement monitoring module;

[0073] Based on the plurality of position data, time series analysis is performed to obtain the displacement change situation, which includes the displacement amount, displacement speed and displacement acceleration of the target monitoring area.

[0074] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0075] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0076] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0077] 1. This application realizes a multi-dimensional assessment of landslide risks by integrating multi-source information such as location data and water level depth data. First, the displacement changes are monitored by GNSS to quickly identify potential landslide areas; then, the groundwater level depth is calculated based on the conductivity data to gain an in-depth understanding of the internal hydrological conditions; finally, the landslide occurrence coefficient is calculated, the surface displacement is combined with the groundwater level changes, and the landslide risk is comprehensively analyzed. When these different levels of data consistently point to potential landslide risks, the system can issue warnings more accurately, thereby greatly improving the accuracy of landslide predictions.

[0078] 2. By dividing the target monitoring area into multiple sliding surfaces, the pore water pressure, sliding force and anti-sliding force of each sliding surface are calculated separately, so as to obtain a more accurate landslide occurrence coefficient. This method refines the mechanical analysis of each part of the mountain, takes into account the differences between different sliding surfaces, and helps to comprehensively assess the landslide risk. By integrating the mechanical data of these sliding surfaces, the landslide occurrence coefficient finally calculated can more accurately reflect the landslide risk of the entire area, thereby improving the accuracy and reliability of the early warning.

[0079] 3. By combining the measured depth of the electroosmosis sensor, the conductivity data and the environmental coefficient of the target monitoring area, the water level depth corresponding to each sensor is accurately calculated. This method combines surface and underground environmental factors with the data measured by the sensor to make the calculation of water level depth more accurate. By considering multiple environmental factors such as soil porosity, soil type, temperature, ion concentration, etc., the scheme can more comprehensively reflect the actual hydrological conditions, thereby improving the accuracy and reliability of landslide risk analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a module schematic diagram of a landslide monitoring and early warning system disclosed in an embodiment of the present application;

[0081] Figure 2 This is a schematic diagram of sliding surface division disclosed in an embodiment of the present application;

[0082] Figure 3 It is a flow chart of a landslide monitoring method disclosed in an embodiment of the present application;

[0083] Figure 4It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0084] Explanation of the accompanying drawings: 101, GNSS displacement monitoring module; 102, processing module; 103, judgment module; 104, electroosmosis monitoring module; 105, early warning module; 401, processor; 402, communication bus; 403, user interface; 404, network interface; 405, memory. DETAILED DESCRIPTION

[0085] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0086] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0087] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0088] Landslides are natural disasters caused by geological, meteorological or human factors, where soil, rocks, etc. suddenly slide down the slope. They are sudden and highly destructive. Although landslide location data is a commonly used risk monitoring method, it is difficult to fully assess landslide risks based solely on location data, as landslide risk is affected by multiple factors such as rainfall, groundwater level, soil properties and geological structure, resulting in insufficient monitoring accuracy.

[0089] This embodiment discloses a landslide monitoring and early warning system. Figure 1 The system includes a GNSS displacement monitoring module 101, a processing module 102, an electroosmosis monitoring module 104, a judgment module 103 and an early warning module 105, wherein:

[0090] The GNSS displacement monitoring module 101 is a device specifically used to monitor surface displacement. It measures the precise position of the target point in real time and tracks its tiny movement in space by receiving signals from the Global Navigation Satellite System (GNSS). The GNSS displacement monitoring module 101 can provide high-frequency data updates, so that the monitoring and early warning system can capture the dynamic changes of the target monitoring area in real time. By deploying multiple GNSS displacement monitoring modules 101 in areas where landslides may occur (target monitoring areas), the three-dimensional position changes of ground points can be continuously tracked, thereby identifying the acceleration, deceleration and stationary stages of the target monitoring area.

[0091] The GNSS displacement monitoring module 101 includes at least a GNSS receiver, which is the core component of the entire system and is used to receive signals from multiple GNSS satellites (such as GPS, GLONASS, Galileo, Beidou, etc.). High-precision receivers usually have multi-band receiving capabilities, which can improve positioning accuracy and anti-interference capabilities. It also includes a GNSS antenna for receiving satellite signals and transmitting them to the receiver. The selection and layout of the antenna are crucial to the stability and accuracy of signal reception, and high-gain, low-noise antennas are usually used. The GNSS displacement monitoring module 101 also includes a data acquisition and processing unit, which is responsible for collecting the received GNSS signals and obtaining high-precision position data through special algorithm processing. Common processing methods include real-time kinematic (RTK), precise point positioning (PPP), etc.

[0092] First, it is necessary to determine the potential landslide area, that is, the target monitoring area for subsequent landslide monitoring. This is usually done through historical landslide records, geological surveys, and preliminary on-site surveys to identify landslide-prone areas. For example, by analyzing the topography and geomorphic features of the mountain area, steep slopes, height differences on both sides of the river valley, and areas where the stratum inclination is consistent with the slope direction are all potential landslide hazard areas. By analyzing the terrain, it is possible to identify the terrain conditions that are prone to landslides.

[0093] The GNSS displacement monitoring module 101 is used to collect multiple position data of the target monitoring area. Multiple monitoring points are selected in the target monitoring area to deploy the GNSS displacement monitoring module 101. The monitoring points should be representative and able to reflect the movement characteristics of each part of the target monitoring area. The following positions are usually selected: the head of the landslide, the upper area of ​​the target monitoring area, which is usually the place where the landslide starts. The monitoring points are deployed here to capture the start signal of the landslide early. The middle of the landslide, the middle area of ​​the target monitoring area, is mainly used to monitor the sliding speed and movement of the landslide. The tail of the landslide, the lower area of ​​the target monitoring area, is mainly used to monitor the overall displacement of the landslide and its possible impact on the area below. At the same time, a benchmark point is set up in a stable area outside the target monitoring area as a reference point to ensure the accuracy of the monitoring data. The benchmark point should be selected in an area that is geologically stable and not affected by the landslide.

[0094] The GNSS displacement monitoring module 101 receives signals from multiple satellites and calculates the three-dimensional coordinates (longitude, latitude, and elevation) of each monitoring point in real time to obtain initial position data. The frequency of data collection depends on the risk level of the landslide and the purpose of monitoring. For landslides that may occur quickly, data should be collected at a high frequency (such as once per second or minute); for slower deformations, data can be collected once an hour or a day. The original data usually contains noise and errors. The data is corrected by differential processing (DGNSS) or precise point positioning technology (PPP), and a filtering algorithm is applied to remove abnormal data to obtain position data. By comparing the changes in the three-dimensional coordinates of each monitoring point at different times, the position data of the monitoring point is calculated. The displacement can be decomposed into horizontal displacement (X, Y direction) and vertical displacement (Z direction) to comprehensively analyze the movement trend of the target monitoring area.

[0095] Furthermore, the processing module 102 is used to determine the displacement change of the target monitoring area based on the multiple position data.

[0096] Specifically, the processing module 102 is an independent computing unit, such as a processor or a microcontroller. It can perform specific tasks or calculations and is usually composed of multiple submodules (such as an arithmetic logic unit, a register, a control unit, etc.). A typical example of the hardware processing module 102 is a CPU (central processing unit), which is the core of a computer and is used to execute instructions and process data.

[0097] The GNSS displacement monitoring module 101 calculates the three-dimensional coordinates (longitude, latitude, and elevation) of the monitoring point in real time by receiving signals from multiple GNSS satellites. These coordinate data reflect the precise position of the monitoring point in space. The collected position data is usually stored in the form of a time series, and the coordinate point at each moment includes information in three dimensions: X (longitude), Y (latitude), and Z (elevation), and this part of the data is transmitted to the processing module 102.

[0098] The processing module 102 performs time series analysis on the three-dimensional coordinate data of each monitoring point at different times. Assuming that at two consecutive moments, the coordinates of a monitoring point are (X1, Y1, Z1) and (X2, Y2, Z2), the processing module 102 obtains the total displacement of the monitoring point by calculating the modulus of the displacement vector, which is specifically calculated by the following formula:

[0099]

[0100] Among them, the direction of the displacement vector can be used to determine the movement trend of the target monitoring area (such as sliding down, lateral movement, etc.). In a monitoring area, multiple GNSS monitoring points are usually deployed. The processing module 102 can calculate the displacement change of the entire target monitoring area by synthesizing the displacement data of all monitoring points. At the same time, the processing module 102 constructs a displacement field of the monitoring area to display the displacement direction and size of each monitoring point. Through the analysis of the displacement field, the overall movement trend of the target monitoring area can be intuitively seen. In addition, based on the displacement data of each monitoring point, the deformation mode of the monitoring area is identified, such as overall sliding, rotation, expansion, etc. It should be noted that calculating the displacement of the landslide body is only a conventional technical means in the relevant technical field, so it will not be further described here.

[0101] Furthermore, the processing module 102 calculates the displacement velocity of the monitoring point, calculates the average velocity of the monitoring point through the displacement change at consecutive time points, and calculates the acceleration through the rate of change of the velocity. The change of displacement acceleration often indicates the acceleration or deceleration of the landslide, and is an important early warning indicator for the occurrence of landslides.

[0102] Furthermore, the judgment module 103 is used to judge whether the displacement change meets the preset displacement change conditions, which usually include thresholds of displacement, displacement velocity and acceleration, and these thresholds need to be set according to historical data, geological characteristics and engineering experience. When the displacement, velocity or acceleration of the monitoring point reaches or exceeds the preset threshold, the judgment module 103 will identify that the area may have a landslide risk.

[0103] The judgment module 103 is usually a functional module dedicated to making decisions and judgments. The judgment module 103 includes an artificial intelligence system, an automated control system, a data analysis platform, etc. The main function of the judgment module 103 is to make logical judgments or inferences based on the input data, rules or models, and output corresponding decisions or results.

[0104] The judgment module 103 compares the monitored displacement change data with the preset threshold in real time. If the data of one or several monitoring points exceeds the preset threshold, the system will determine that there is a landslide risk in the area. In addition to single-point judgment, regional judgment can also be made based on the comprehensive displacement data of the entire monitoring area to identify the scope and severity of the landslide risk.

[0105] Furthermore, the electroosmosis monitoring module 104 is used to collect multiple conductivity data of the target monitoring area when the displacement change condition meets the preset displacement change condition.

[0106] Specifically, changes in groundwater have an important impact on the development and stability of landslides. The dynamics of groundwater can not only directly affect the physical properties of the landslide body, but also can cause the occurrence of landslides by affecting the stress distribution in the landslide body. When the groundwater level rises, the pore water pressure in the landslide body will increase. The increase in pore water pressure will reduce the effective stress between soil particles (i.e., the true contact pressure between soil particles), thereby reducing the shear strength of the soil, making the landslide body more likely to slide.

[0107] Electroosmosis is a technique that measures the movement of water and changes in water content in soil by applying an electric field. It is particularly suitable for monitoring groundwater levels and water changes. Under the action of the electric field, water molecules in the soil will move along the direction of the electric field. This movement of water molecules will cause changes in soil conductivity. By measuring changes in conductivity, changes in groundwater levels can be indirectly inferred. Electrodes are arranged in the target monitoring area or potential landslide area to form an electric field. By applying voltage between the electrodes, the movement and changes of water in the soil are monitored.

[0108] In a possible implementation, the processing module 102 is also used to determine the measurement depth data of each electroosmosis sensor, which is a sensor corresponding to the electroosmosis monitoring module 104; the processing module 102 is also used to obtain the conductivity data sent by each electroosmosis sensor; the processing module 102 is also used to obtain the environmental coefficient of the target monitoring area, which includes soil porosity, soil type coefficient, soil temperature, ion concentration correction coefficient and aquifer coefficient; the processing module 102 is also used to calculate the water level depth data corresponding to each electroosmosis sensor based on the environmental coefficient, conductivity data and measurement depth data.

[0109] Specifically, the electroosmotic monitoring module 104 includes a plurality of electroosmotic sensors, which are sensors based on the electroosmotic effect for measuring the electric field and flow rate in a fluid. The electroosmotic effect is a phenomenon that when an electric field is applied, charged particles (such as ions) in a fluid move along the direction of the electric field, thereby causing the movement of the entire fluid. An electric field is applied to the soil in the target monitoring area, usually through electrodes at both ends, and under the action of the electric field, the charged particles in the soil fluid begin to move. Due to the movement of these particles, they will drive the surrounding liquid to move together, generating fluid flow. The electroosmotic sensor senses changes in the environment by detecting the flow rate, flow direction or other flow-related parameters of the fluid. These parameters can be measured by different detection methods, such as changes in conductivity, etc. Finally, the electroosmotic sensor converts the measured data into an electrical signal output for further analysis of the water level depth in the soil.

[0110] When installing the electroosmotic sensor, the installation depth of each sensor is recorded. The sensor installation depth can be obtained by the depth scale used during installation or by the depth measurement function inside the sensor. The installation depth data of each sensor is input into the processing module 102 as part of the subsequent calculation. The electroosmotic sensor collects conductivity data in real time and sends the data to the processing module 102 via wired communication.

[0111] Calculating the water depth is a more complex process to more accurately reflect the actual situation. The relationship between water depth and conductivity may be affected by many factors, such as soil type, aquifer structure, porosity, temperature changes, ion concentration, etc. The principle of the water depth calculation formula is to use the conductivity data measured by the electroosmosis sensor, combined with environmental factors such as soil porosity, soil type, temperature, ion concentration and aquifer characteristics, to infer the depth of the groundwater level. Conductivity reflects the changes in moisture content and ion concentration in the soil, and these environmental factors will affect the conductivity of water. Therefore, the water depth at the location of the sensor can be accurately calculated through the corrected formula. The following more complex formula is used to calculate the water depth:

[0112]

[0113] Among them, H is the water level depth data, K s is the soil type coefficient, K a is the aquifer coefficient, n is the soil porosity, ρ is the conductivity data, β is the temperature sensitivity coefficient, T is the soil temperature, T0 is the reference temperature, C ion is the ion concentration correction factor.

[0114] In the water level depth data calculation formula, the soil type coefficient reflects the influence of different soil types such as clay, sandy soil, etc. on the conductivity and water level depth. According to the geological survey or geological report of the area, the processing module 102 will preset this coefficient. If the soil type changes (such as seasonal changes), it needs to be recalculated or adjusted. Soil type coefficient K s The coefficient set according to different soil types is usually determined by experiments or experience. For example, for clay, K s may be larger, while for sandy soil, K s Probably smaller.

[0115] The aquifer coefficient takes into account the properties of the aquifer, such as the thickness and geological structure of the aquifer, and is also usually obtained through geological exploration data. The processing module 102 will set this parameter during initialization and update it according to new exploration data when necessary. Soil porosity reflects the pore structure of the soil, affects the permeability and conductivity of water, and usually takes values ​​between 0 and 1, reflecting the volume ratio of pores in the soil. The water level of soil with high porosity may change rapidly, while soil with low porosity is relatively stable. It is usually obtained through field surveys or laboratory analysis. The processing module 102 needs to update this data regularly to reflect changes in soil conditions. The ion concentration correction coefficient reflects the effect of ion concentration in water on conductivity. It is set through water quality analysis or historical data and input into the processing module 102. If the ion concentration in the environment changes, this coefficient needs to be adjusted.

[0116] In the water level depth data calculation formula, 1+β(T-T0) is used to calculate the temperature correction factor, because temperature has a significant effect on the conductivity of water. When the temperature rises, the movement of water molecules intensifies, resulting in an increase in conductivity, so it is necessary to correct it according to the actual temperature. The soil temperature is measured in real time by a temperature sensor and sent to the processing module 102. The soil temperature has a significant effect on the conductivity and needs to be updated in real time. The temperature sensitivity coefficient is a parameter used to describe the sensitivity of conductivity to temperature changes. It indicates the percentage of conductivity change for every 1°C change in temperature. The reference temperature is the standard temperature referenced in conductivity measurement or calculation. 25°C is usually selected as the reference temperature because 25°C is widely regarded as the standard condition for conductivity measurement in hydrological and environmental monitoring. The reference temperature is used to determine the starting point of conductivity change with temperature to ensure the consistency of temperature correction. All temperature corrections are based on the reference temperature, and the correction factor describes the degree of deviation from this reference temperature.

[0117] In a possible implementation, the processing module 102 is also used to divide the target monitoring area into multiple sliding surfaces; the processing module 102 is also used to calculate the pore water pressure of each sliding surface based on the water level depth data; the processing module 102 is also used to calculate the sliding force of each sliding surface based on the pore water pressure, and calculate the anti-sliding force of each sliding surface; the processing module 102 is also used to calculate the landslide occurrence coefficient based on multiple sliding forces and multiple anti-sliding forces.

[0118] Specifically, the limit equilibrium method is one of the rock and soil stability analysis methods. It usually calculates the stability coefficient of the block based on the ratio of the shear force of the block along the failure surface to the shear force of the block along the failure surface acting on the potential failure surface in the rock and soil, so as to determine whether a landslide may occur. The core idea is to divide the landslide body into several potential sliding surfaces, analyze the mechanical equilibrium conditions on the sliding surfaces, and determine whether the landslide body is in a critical state.

[0119] Assuming that there are one or more potential sliding surfaces within the target monitoring area, refer to Figure 2 , the target monitoring area is divided into several strips along the vertical direction of the sliding surface. These sliding surfaces may be planes, arc surfaces or complex curved surfaces, and the interaction forces between the sliding surfaces are negligible. The target monitoring area usually has complex geometric shapes and non-uniform physical properties. By dividing the target monitoring area into multiple strips, each strip can be considered as a relatively simple shape (usually a rectangle or trapezoid), which makes the calculation simpler and more accurate.

[0120] The sliding force (such as gravity, groundwater pressure) and anti-sliding force (such as soil strength, friction) on the sliding surface are calculated to establish the equilibrium equation. Then the safety factor F is calculated. The safety factor is the core indicator of the limit equilibrium analysis and is defined as the ratio of the anti-sliding force to the sliding force. F>1 indicates that the target monitoring area is stable, while F<1 indicates that the target monitoring area may be unstable.

[0121] However, in the prior art, the influence of groundwater is often ignored when analyzing the stability of rock and soil by the limit equilibrium method. This application introduces the pore water pressure factor caused by the groundwater level in the calculation, which can make the landslide stability analysis more accurate. Pore water pressure is an important factor affecting the stability of landslides. It will reduce the effective stress of the soil, thereby affecting the stability of the landslide body.

[0122] Specifically, for each sliding surface, the pore water pressure u reduces the effective stress of the soil, thereby reducing the sliding resistance. The effective stress σ′ is the total normal stress σ minus the pore water pressure u, as follows:

[0123] σ′=σ-u

[0124] The pore water pressure u can be estimated based on the water level depth data H corresponding to the sliding surface and the actual dry soil thickness from the sliding surface to the top of the soil. The calculation formula is as follows:

[0125] u=τ·(Hd)

[0126] Among them, u is the pore water pressure, τ is the unit weight of groundwater corresponding to the target monitoring area, H is the water level depth data, and d is the actual dry soil thickness from the sliding surface to the upper part of the soil.

[0127] For example, the groundwater level is 2m from the sliding surface, the thickness of the dry soil layer from the sliding surface to the top of the soil is 1m, and the unit weight of water is usually 9.81kN / m 3 , the projection area of ​​the sliding surface on the horizontal plane is 1m 2 ,but:

[0128] u=τ·(Hd)·S=9.81kN / m 3 ·(2m-1m)·1m 2 =9.81kN / m 2 =9.81kN.

[0129] The total normal stress σ is usually generated by the gravity of the target monitoring area, that is:

[0130] σ=W·cosα

[0131] Then, for each sliding surface, the anti-slip force is calculated by the following formula:

[0132] R=c·j+(W·cosα-τ·(Hd)·S)·tanΦ

[0133] Among them, R is the anti-sliding force, c is the soil cohesion of the sliding surface, j is the length of the sliding surface, W is the gravity of the sliding surface, α is the angle between the sliding surface and the horizontal plane, τ is the unit weight of the groundwater corresponding to the target monitoring area, H is the water level depth data, d is the actual dry soil layer thickness from the sliding surface to the upper part of the soil, S is the projection area of ​​the sliding surface on the horizontal plane, and Φ is the internal friction angle.

[0134] Anti-slip force is the force that prevents sliding when the target monitoring area slides along the sliding surface. It is mainly composed of soil cohesion and friction. The calculation of anti-slip force takes into account the soil strength and effective normal stress on the sliding surface. Among them, soil cohesion refers to the intrinsic bonding force between particles of soil due to intermolecular force and chemical bonding under the action of no shear stress. It can effectively resist shear failure. Soil cohesion is a constant, usually given by laboratory test results of soil.

[0135] Friction is the force that resists sliding due to friction between soil particles on the sliding surface. Friction is related to the effective normal stress on the sliding surface. The effective normal stress is the total normal stress acting on the sliding surface minus the pore water pressure. The pore water pressure reduces the effective stress and thus the friction. The calculation of friction is based on the effective normal stress and the internal friction angle of the soil, i.e. σ′·tanΦ. The total resistance to sliding can therefore be considered as the sum of the contribution of cohesion to the resistance to sliding and the contribution of friction to the resistance to sliding.

[0136] For example, assume that the parameters of the sliding surface are as follows: sliding surface length j = 10m, the angle between the sliding surface and the horizontal plane α = 30°, the soil cohesion of the sliding surface c = 20kPa, the internal friction angle Φ = 25°, the gravity of the sliding surface W = 1000kN, the water level depth data is h = 6m, the actual dry soil thickness from the sliding surface to the top of the soil is h = 1m, and the projection area of ​​the sliding surface on the horizontal plane is S = 1m 2 ,but:

[0137] Pore ​​water pressure u=τ·(Hd)=9.81kN / m 3 ·(6-1)m·1m 2 =49.05kN.

[0138] Effective normal force σ′=W·cosα-u=1000kN·cos30°-49.05kN=816.95kN.

[0139] Anti-slip force R = c·j + σ′·tanΦ = 20kPa·10m + 816.95kN·tan25° = 580.5kN.

[0140] Furthermore, the sliding force is the force that pushes the target monitoring area to slide along the sliding surface, which is mainly composed of the gravity component of the target monitoring area and possible external loads. Gravity is the main factor that pushes the target monitoring area to slide. The gravity of the target monitoring area can be decomposed into the sliding force component along the sliding surface and the normal force component perpendicular to the sliding surface. The sliding force is the component of the gravity of the target monitoring area in the direction of the sliding surface, which can be calculated using the following formula:

[0141] S=W·sinα

[0142] Among them, S is the sliding force, W is the gravity of the sliding surface, and α is the angle between the sliding surface and the horizontal plane.

[0143] In addition to gravity, the target monitoring area may also be subject to other external loads that can also generate sliding forces. The calculation of external loads depends on the specific load type and location, and can be combined with the gravity component to calculate the total sliding force.

[0144] Furthermore, the processing module 102 calculates the landslide occurrence coefficient of the target monitoring area according to the anti-sliding force and sliding force of all sliding surfaces. The landslide occurrence coefficient is an important indicator for measuring the stability of the target monitoring area, which represents the ratio between the anti-sliding force and the sliding force. It is specifically calculated by the following formula:

[0145]

[0146] Where F is the landslide occurrence coefficient, c i is the soil cohesion of the i-th sliding surface, j i is the length of the i-th sliding surface, W i is the gravity of the i-th sliding surface, α i is the angle between the sliding surface and the horizontal plane, τ is the unit weight of groundwater corresponding to the target monitoring area, d i is the actual dry soil thickness from the i-th sliding surface to the top of the soil, H is the water level depth data, S i is the projection area of ​​the i-th sliding surface on the horizontal plane, and Φ is the internal friction angle.

[0147] Since the target monitoring area is divided into multiple sliding surfaces, each part of the target monitoring area can be gradually integrated and accumulated by dividing the multiple sliding surfaces, so as to more accurately calculate the overall anti-sliding force and sliding force of the target monitoring area, and then obtain a more accurate landslide occurrence coefficient. Therefore, it is necessary to sum the sliding force and anti-sliding force of each sliding surface and then calculate the landslide occurrence coefficient.

[0148] Furthermore, the early warning module 105 is used to issue an early warning prompt when it is determined that the landslide occurrence coefficient is less than or equal to a preset threshold value, and the prediction result corresponding to the landslide occurrence coefficient is consistent with the displacement change.

[0149] When the landslide occurrence coefficient is greater than 1, the anti-slip force is greater than the sliding force, which means that the target monitoring area is relatively stable and not prone to sliding. The larger the landslide occurrence coefficient, the more stable the target monitoring area. When the landslide occurrence coefficient is equal to 1, the anti-slip force is equal to the sliding force, the target monitoring area is in a critical state, and the possibility of sliding is greater. When the landslide occurrence coefficient is less than 1, the sliding force is greater than the anti-slip force, the target monitoring area is unstable, and sliding or landslide is very likely to occur. By presetting a threshold in the monitoring and early warning system in advance, the preset threshold can be equal to 1, or it can be less than 1 or greater than 1, and it needs to be set according to actual needs. When the early warning module 105 determines by comparison that the landslide occurrence coefficient is greater than the preset threshold, it indicates that the probability of landslide occurrence is low. On the contrary, when the landslide occurrence coefficient is less than or equal to the preset threshold, it indicates that the probability of landslide occurrence is high.

[0150] Based on the displacement transformation of the target monitoring area collected and calculated by the GNSS displacement monitoring module 101 and the processing module 102, that is, based on the displacement data of all monitoring points being synthesized, the displacement change of the entire target monitoring area calculated. Further, the early warning module 105 judges the state of the current landslide body by analyzing the relationship between the landslide occurrence coefficient and the displacement monitoring data. If the landslide occurrence coefficient is higher (i.e., the landslide body is more stable), but the displacement change data show that the landslide body is significantly displaced, or if the landslide occurrence coefficient is lower (i.e., the landslide body is unstable), but the displacement change data show that the landslide body is significantly displaced, then it is necessary to verify the data or adopt other measurement methods. On the contrary, if the landslide occurrence coefficient is lower (i.e., the landslide body is unstable), and the displacement change data also show that the landslide body has moved significantly, the consistency between the two shows that the landslide risk is higher.

[0151] When the landslide occurrence coefficient is less than the preset threshold value and the predicted result is consistent with the actual displacement change, the early warning module 105 will be triggered when the above two conditions are met. The early warning module 105 generates a signal, which can alert relevant personnel through sound alarms, lights, text messages or other means. Or send early warning information to on-site staff, local management departments and relevant decision makers, detailing the current status and potential risks of the landslide body.

[0152] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0153] This embodiment also discloses a landslide monitoring method, which is applied to a monitoring and early warning system. Figure 3 The method comprises the following steps S110-S170:

[0154] S110, collecting multiple location data of the target monitoring area.

[0155] S120, determining the displacement change of the target monitoring area based on the multiple position data.

[0156] S130, determining whether the displacement change condition satisfies a preset displacement change condition.

[0157] S140, when the displacement change condition meets the preset displacement change condition, collecting multiple conductivity data of the target monitoring area.

[0158] S150, calculating water level depth data of the target monitoring area according to the plurality of conductivity data.

[0159] S160, calculating the landslide occurrence coefficient of the target monitoring area based on the water level depth data.

[0160] S170, when it is determined that the landslide occurrence coefficient is less than or equal to the preset threshold value, and the prediction result corresponding to the landslide occurrence coefficient is consistent with the displacement change, an early warning prompt is issued.

[0161] In a possible implementation, based on the water level depth data, calculating the landslide occurrence coefficient of the target monitoring area specifically includes:

[0162] The target monitoring area is divided into multiple sliding surfaces.

[0163] Based on the water level depth data, the pore water pressure of each sliding surface is calculated.

[0164] Based on the pore water pressure, the sliding force of each sliding surface is calculated, and the anti-sliding force of each sliding surface is calculated.

[0165] The landslide occurrence coefficient is calculated based on multiple sliding forces and multiple anti-sliding forces.

[0166] Based on multiple location data and water level depth data, the landslide occurrence coefficient of the target monitoring area is calculated.

[0167] In a possible implementation, based on the pore water pressure, the sliding force of each sliding surface is calculated, and the anti-sliding force of each sliding surface is calculated. Specifically, the anti-sliding force is calculated by the following formula:

[0168] R=c·j+(W·cosα-τ·(Hd)·S)·tanΦ

[0169] Among them, R is the anti-sliding force, c is the soil cohesion of the sliding surface, j is the length of the sliding surface, W is the gravity of the sliding surface, α is the angle between the sliding surface and the horizontal plane, τ is the unit weight of the groundwater corresponding to the target monitoring area, H is the water level depth data, d is the actual dry soil layer thickness from the sliding surface to the upper part of the soil, S is the projection area of ​​the sliding surface on the horizontal plane, and Φ is the internal friction angle.

[0170] The sliding force is calculated by the following formula:

[0171] S=W·sinα

[0172] Among them, S is the sliding force, W is the gravity of the sliding surface, and α is the angle between the sliding surface and the horizontal plane.

[0173] In a possible implementation, the landslide occurrence coefficient is calculated according to multiple sliding forces and multiple anti-sliding forces, specifically by the following formula:

[0174]

[0175] Where F is the landslide occurrence coefficient, c i is the soil cohesion of the i-th sliding surface, j i is the length of the i-th sliding surface, W i is the gravity of the i-th sliding surface, α i is the angle between the sliding surface and the horizontal plane, τ is the unit weight of groundwater corresponding to the target monitoring area, d i is the actual dry soil thickness from the i-th sliding surface to the top of the soil, H is the water level depth data, S i is the projection area of ​​the i-th sliding surface on the horizontal plane, and Φ is the internal friction angle.

[0176] In a possible implementation, calculating the water level depth data of the target monitoring area according to the plurality of conductivity data specifically includes:

[0177] Determine the measurement depth data of each electroosmosis sensor, where the electroosmosis sensor is the sensor corresponding to the electroosmosis monitoring module 104.

[0178] Obtain conductivity data sent by each electroosmotic sensor.

[0179] Obtain the environmental coefficients of the target monitoring area, which include soil porosity, soil type coefficient, soil temperature, ion concentration correction coefficient and aquifer coefficient.

[0180] The water level depth data corresponding to each electroosmosis sensor is calculated based on the environmental coefficient, conductivity data and measurement depth data.

[0181] In a possible implementation, the water level depth data corresponding to each electroosmotic sensor is calculated according to the conductivity data and the measured depth data, specifically by the following formula:

[0182]

[0183] Among them, H is the water level depth data, K s is the soil type coefficient, K a is the aquifer coefficient, n is the soil porosity, ρ is the conductivity data, β is the temperature sensitivity coefficient, T is the soil temperature, T0 is the reference temperature, C ion is the ion concentration correction factor.

[0184] In a possible implementation, based on multiple position data, determining the displacement change of the target monitoring area specifically includes:

[0185] Collect multiple initial position data of the target monitoring area.

[0186] Each initial position data is corrected to obtain position data.

[0187] Acquire multiple location data sent by the GNSS displacement monitoring module 101.

[0188] Based on multiple position data, time series analysis is performed to obtain displacement changes, which include displacement amount, displacement speed and displacement acceleration of the target monitoring area.

[0189] This embodiment also discloses an electronic device, referring to Figure 4 The electronic device may include: at least one processor 401 , at least one communication bus 402 , a user interface 403 , a network interface 404 , and at least one memory 405 .

[0190] The communication bus 402 is used to realize the connection and communication between these components.

[0191] The user interface 403 may include a display screen (Display) and a camera (Camera), and the optional user interface 403 may also include a standard wired interface and a wireless interface.

[0192] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0193] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the processor 401 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 401 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 401, and it can be implemented by a single chip.

[0194] Among them, the memory 405 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 405 may also be optionally at least one storage device located away from the aforementioned processor 401. The memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface 403 module, and an application program for a landslide monitoring method.

[0195] exist Figure 4 In the electronic device shown, the user interface 403 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 401 can be used to call an application program for a landslide monitoring method stored in the memory 405. When executed by one or more processors 401, the electronic device executes one or more methods in the above embodiments.

[0196] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0197] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0198] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0199] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0200] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0201] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory 405 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory 405 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0202] The present application also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores instructions. When executed by one or more processors 401, the electronic device executes one or more of the methods described in the above embodiments.

[0203] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A landslide monitoring and early warning system, characterized in that: The system comprises a GNSS displacement monitoring module (101), a processing module (102), a judgment module (103), an electroosmosis monitoring module (104) and an early warning module (104), wherein: The GNSS displacement monitoring module (101) is used to collect multiple position data of the target monitoring area; The processing module (102) is used to determine the displacement change of the target monitoring area based on the plurality of position data; The judging module (103) is used to judge whether the displacement change condition satisfies a preset displacement change condition; The electroosmosis monitoring module (104) is used to collect a plurality of conductivity data of the target monitoring area when the displacement change condition satisfies the preset displacement change condition; The processing module (102) is further used to calculate the water level depth data of the target monitoring area according to the plurality of conductivity data; The processing module (102) is further used to calculate the landslide occurrence coefficient of the target monitoring area based on the water level depth data; The early warning module (104) is used to issue an early warning prompt when it is determined that the landslide occurrence coefficient is less than or equal to a preset threshold value and the prediction result corresponding to the landslide occurrence coefficient is consistent with the displacement change.

2. A landslide monitoring and early warning system according to claim 1, characterized in that: The processing module (102) is further used to divide the target monitoring area into a plurality of sliding surfaces; The processing module (102) is further used to calculate the pore water pressure of each sliding surface based on the water level depth data; The processing module (102) is further used to calculate the sliding force of each sliding surface and the anti-sliding force of each sliding surface based on the pore water pressure; The processing module (102) is further used to calculate the landslide occurrence coefficient according to the plurality of sliding forces and the plurality of anti-sliding forces; The processing module (102) is further used to calculate the landslide occurrence coefficient of the target monitoring area based on the plurality of position data and the water level depth data.

3. A landslide monitoring and early warning system according to claim 2, characterized in that: The processing module (102) is further used to calculate the sliding force of each sliding surface and the anti-sliding force of each sliding surface based on the pore water pressure, and the anti-sliding force is specifically calculated by the following formula: R=c·j+(W·cosα-τ·(Hd)·S)·tanΦ Wherein, R is the anti-sliding force, c is the soil cohesion of the sliding surface, j is the length of the sliding surface, W is the gravity of the sliding surface, α is the angle between the sliding surface and the horizontal plane, τ is the unit weight of the groundwater corresponding to the target monitoring area, H is the water level depth data, d is the actual dry soil thickness from the sliding surface to the upper part of the soil, S is the projection area of ​​the orthographic projection of the sliding surface on the horizontal plane, and Φ is the internal friction angle; The processing module (102) is further configured to calculate the sliding force specifically through the following formula: S=W·sinα Wherein, S is the sliding force, W is the gravity of the sliding surface, and α is the angle between the sliding surface and the horizontal plane.

4. A landslide monitoring and early warning system according to claim 2, characterized in that: The processing module (102) is further used to calculate the landslide occurrence coefficient according to the plurality of sliding forces and the plurality of anti-sliding forces, specifically by the following formula: Wherein, F is the landslide occurrence coefficient, c i is the soil cohesion of the i-th sliding surface, j i is the length of the i-th sliding surface, W i is the gravity of the i-th sliding surface, α i is the angle between the sliding surface and the horizontal plane, τ is the unit weight of groundwater corresponding to the target monitoring area, d i is the actual dry soil thickness from the i-th sliding surface to the top of the soil, H is the water level depth data, S i is the projection area of ​​the i-th sliding surface on the horizontal plane, and Φ is the internal friction angle.

5. A landslide monitoring and early warning system according to claim 1, characterized in that: The processing module (102) is further used to determine the measurement depth data of each electroosmosis sensor, wherein the electroosmosis sensor is a sensor corresponding to the electroosmosis monitoring module (104); The processing module (102) is also used to obtain the conductivity data sent by each of the electroosmotic sensors; The processing module (102) is further used to obtain the environmental coefficient of the target monitoring area, wherein the environmental coefficient includes soil porosity, soil type coefficient, soil temperature, ion concentration correction coefficient and aquifer coefficient; The processing module (102) is also used to calculate the water level depth data corresponding to each of the electroosmosis sensors based on the environmental coefficient, the conductivity data and the measured depth data.

6. A landslide monitoring and early warning system according to claim 5, characterized in that: The processing module (102) is further used to calculate the water level depth data corresponding to each of the electroosmotic sensors according to the conductivity data and the measured depth data, specifically by the following formula: Among them, H is the water level depth data, K s is the soil type coefficient, K a is the aquifer coefficient, n is the soil porosity, ρ is the conductivity data, β is the temperature sensitivity coefficient, T is the soil temperature, T0 is the reference temperature, C ion is the ion concentration correction factor.

7. A landslide monitoring and early warning system according to claim 1, characterized in that: The GNSS displacement monitoring module (101) is further used to collect a plurality of initial position data of the target monitoring area; The GNSS displacement monitoring module (101) is further used to correct each of the initial position data to obtain the position data; The processing module (102) is further used to obtain the plurality of position data sent by the GNSS displacement monitoring module (101); The processing module (102) is further used to perform time series analysis based on the plurality of position data to obtain the displacement change situation, wherein the displacement change situation includes the displacement amount, displacement speed and displacement acceleration of the target monitoring area.

8. A landslide monitoring method, characterized in that: The method is applied to a monitoring and early warning system, and the method comprises: Collect multiple location data of the target monitoring area; Based on the plurality of position data, determining a displacement change of the target monitoring area; Determining whether the displacement change condition meets a preset displacement change condition; When the displacement change condition satisfies the preset displacement change condition, collecting a plurality of conductivity data of the target monitoring area; Calculating water level depth data of the target monitoring area according to the plurality of conductivity data; Calculating the landslide occurrence coefficient of the target monitoring area based on the water level depth data; When it is determined that the landslide occurrence coefficient is less than or equal to a preset threshold value, and the prediction result corresponding to the landslide occurrence coefficient is consistent with the displacement change, an early warning prompt is issued.

9. An electronic device, characterized in that: The electronic device comprises a processor (401), a communication bus (402), a memory (405), a user interface (403), a network interface (404) and a memory (405), wherein the memory (405) is used to store instructions, the user interface (403) and the network interface (404) are both used to communicate with other devices, and the processor (401) is used to execute the instructions stored in the memory (405) so that the electronic device executes the method according to claim 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to claim 8 is performed.

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