Landslide mechanism-data dual-drive early warning system based on deformation work theory

Abstract

The invention relates to the technical field of geological disaster early warning, in particular to a landslide mechanism-data dual-drive early warning system based on a deformation work theory. According to the method, a mechanism driving model is constructed, and a geomechanical mechanism is quantified into a computable energy reference; the data acquisition module acquires real-time data to construct a data driving model, the energy dynamic calculation module analyzes data such as deformation power, and the data acquisition module and the energy dynamic calculation module cooperate to provide a more scientific and comprehensive basis for early warning; graded early warning signals are obtained through numerical analysis and matching, the landslide state can be judged more accurately, and early warnings of different grades are sent out in time; according to the method, corresponding emergency response operations such as data acquisition frequency adjustment, standby sensor starting, intelligent retaining wall hydraulic support system triggering and evacuation prompt playing are triggered according to the graded early warning signals, visual monitoring is performed according to the ratio of deformation power to damage power, and landslide risk management and control are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster early warning, and in particular to a landslide mechanism-data dual-driven early warning system based on deformation work theory. Background Art

[0002] Landslides, a common geological disaster, occur frequently around the world, causing severe damage to human life and property, infrastructure, and the ecological environment. Landslides are particularly prevalent in mountainous and hilly areas, as well as in areas with frequent construction activities.

[0003] However, traditional landslide early warning methods based on geomechanical mechanisms rely primarily on analyzing the geological conditions and developing mechanical models. By investigating and studying factors such as the landslide's geological structure, rock and soil properties, and slope, combined with mechanical principles, the stability coefficient of the landslide is calculated to determine the likelihood of a landslide. However, this approach has significant limitations. First, the development of geomechanical models requires a large amount of geological exploration data, which is costly and time-consuming to acquire. In practical applications, the complexity and uncertainty of geological conditions make it difficult to accurately obtain all necessary parameters, resulting in significant deviations between model calculations and actual conditions. Second, geomechanical models are typically based on static assumptions and fail to account for the various dynamic factors of the landslide, such as the impact of external disturbances such as rainfall and earthquakes on landslide stability. Therefore, relying solely on geomechanical mechanisms for landslide early warning often fails to capture early signs of a landslide in a timely and accurate manner, resulting in suboptimal early warning results.

[0004] In response to the above problems, it is necessary to propose a landslide mechanism-data dual-driven early warning system based on deformation work theory. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems existing in the background technology and to propose a landslide mechanism-data dual-driven early warning system based on deformation work theory.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A landslide mechanism-data dual-driven early warning system based on deformation work theory includes a geological parameter inversion module, a data acquisition module, an energy dynamic calculation module, a collaborative early warning decision module and a hierarchical execution response module.

[0008] The geological parameter inversion module accesses the drilling data and geological survey data in the geological exploration report, determines the sliding surface parameters and strength indicators through numerical simulation, and constructs a mechanism-driven model for landslide early warning.

[0009] Obtain the borehole profile and the soil layer data contained in the geological exploration report, and build a 3D geometric model of the sliding surface based on the slice method. The specific process is as follows:

[0010] Get the total area of ​​the equivalent sliding surface, divide the sliding surface into strips, obtain a preset number of sliding strips, number the sliding strips sequentially, and pass the constraints:

[0011] The sum of the products of the segmented area, sliding surface weight and equivalent inclination angle of each strip shall not exceed the total area of ​​the equivalent sliding surface.

[0012] Constrain the segment area of ​​each bar.

[0013] As a preferred method of the present invention, geological survey data of each sliding surface block is obtained, including soil layer type, bulk density, cohesion, thickness, and internal friction angle. By simulating the ultimate failure state of each sliding surface block, a mechanism-driven model for landslide early warning is constructed. The specific process is as follows:

[0014] The rigid-plastic model is used to solve the ultimate failure state of each strip in the sliding surface:

[0015] The failure power is calculated by integrating the product of the velocity discontinuity vector and the normal force of each sliding surface over the total area of ​​the equivalent sliding surface. The velocity discontinuity vector is obtained through plastic zone simulation, and the normal force is derived from the cohesion, internal friction angle, soil bulk density, and equivalent thickness of the sliding surface.

[0016] The data acquisition module accesses the IoT edge gateway to obtain the real-time displacement data and velocity field data of the landslide deep collected by the fiber optic deep displacement meter, and collects metadata for the construction of a data-driven model for landslide early warning.

[0017] The displacement data inside the soil is obtained by using a fiber optic deep displacement meter to reconstruct the velocity field. The specific process is as follows:

[0018] As a preferred embodiment of the present invention, a preset number of measuring points are arranged in the landslide, the measuring points are numbered sequentially, and a vertical detection hole is arranged at each measuring point.

[0019] Each test hole is divided into a preset number of cylindrical units along the depth direction. Fiber optic deep displacement meters installed at the upper and lower ends of each cylindrical unit collect soil displacement data, including the upper and lower endpoint displacements of the cylindrical unit. The upper and lower endpoint unit velocities of each cylindrical unit are calculated by calculating the upper and lower endpoint displacements.

[0020] As a preferred embodiment of the present invention, the unit displacement velocity of each cylindrical unit is obtained by calculating the average value of the upper end unit velocity and the lower end unit velocity of each cylindrical unit;

[0021] The unit angular velocity is obtained by calculating the quotient of the difference between the upper end unit velocity and the lower end unit velocity of each cylindrical unit and the length of the cylindrical unit.

[0022] The unit movement speed and unit angular velocity of each cylindrical unit in each detection hole are obtained and sent to the energy dynamic calculation module.

[0023] The energy dynamic calculation module builds a data-driven model for landslide early warning based on real-time displacement data and velocity field data in the slope depth, analyzes the deformation power data and deformation power mutation data containing landslide early warning characteristics, and calculates the kinetic energy change rate through a segmented kinetic energy accumulation model.

[0024] The data-driven model for early warning features is constructed as follows:

[0025] The mass and moment of inertia of each cylindrical unit are calculated by the average mass of the soil layer in the detection hole, the preset detection hole radius and the length of the cylindrical unit;

[0026] The unit movement speed and unit angular velocity of each cylindrical unit are obtained, and the translational kinetic energy part is calculated by the unit movement speed and the mass of the cylindrical unit; the rotational kinetic energy part is calculated by the unit angular velocity and moment of inertia.

[0027] As a preferred embodiment of the present invention, the first derivative of the dominant term of the kinetic energy change rate with time is calculated and recorded as the kinetic energy change rate of the soil in the detection hole.

[0028] The collaborative early warning decision module integrates and compares the data-driven and mechanism-driven models of landslide early warning. By integrating the mechanism model with real-time data, the mechanism model and data model of deformation power mutation characteristics are numerically analyzed and matched to generate graded early warning signals.

[0029] The historical maximum values ​​of the destructive power, the deformation power of the landslide soil, and the kinetic energy change rate of the soil in the detection hole are obtained, and the early warning index is obtained by calculating the weighted sum of the ratio of the destructive power to the deformation power and the historical maximum value of the kinetic energy change rate.

[0030] As a preferred embodiment of the present invention, numerical analysis and signal matching are carried out based on the specific values ​​of the warning index, the first derivative of the unit angular velocity with time, the unit movement speed, and the kinetic energy change rate of the soil in the detection hole to obtain a graded warning signal. The specific process is as follows:

[0031] If it is identified that the warning index is less than the first preset threshold value of the warning index and the first derivative of the unit angular velocity with time is less than the first preset threshold value of the unit angular velocity, a signal SIG_0 is output;

[0032] If it is identified that the warning index is greater than or equal to the first preset threshold value of the warning index and less than the second preset threshold value of the warning index, or there is a cylindrical unit moving at a speed greater than 5 mm per minute, a signal SIG_1 is output;

[0033] If it is identified that the warning index is greater than or equal to the second preset threshold value of the warning index and less than the third preset threshold value of the warning index, and the kinetic energy change rate of the soil in the detection hole is greater than 10, then a signal SIG_2 is output;

[0034] If it is identified that the warning index is greater than or equal to the third preset threshold of the warning index, and the first-order derivative of the unit angular velocity with time is greater than 1, a signal SIG_3 is output.

[0035] Among them, the third preset threshold of the warning index is greater than the second preset threshold and greater than the first preset threshold.

[0036] The hierarchical execution response module triggers corresponding emergency response operations according to the hierarchical warning signals, and performs visual monitoring of the ratio of deformation power to destructive power.

[0037] If signal SIG_0 is identified, it is determined that the sliding body is in normal condition and there is no landslide risk. The normal monitoring state is maintained and the data collection frequency is kept at the default value, i.e., 10 minutes / time.

[0038] If signal SIG_1 is detected, it is determined that there is abnormal data in the sliding state, the data collection frequency is increased to 1 minute / time, and the backup sensor is activated; a work order is automatically dispatched to the inspection personnel, requiring them to complete the slope crack inspection within 2 hours;

[0039] If signal SIG_2 is detected, the sliding body is determined to be in a landslide risk state. The intelligent retaining wall hydraulic support system is triggered, increasing the hydraulic system pressure to 1.5 times the preset value. A work order is automatically issued to the inspection personnel, requiring them to complete the slope crack inspection within 2 hours.

[0040] If signal SIG_3 is detected, it is determined that a landslide is about to occur; the broadcast system is activated to play the evacuation prompt in a loop.

[0041] As a preferred embodiment of the present invention, the ratio of deformation power to destruction power is visually monitored. The deformation power and the ratio of deformation power to destruction power at each moment are recorded, and a deformation power-time graph and a power ratio-time graph are generated. Portions of the deformation power-time graph and the power ratio-time graph that exceed a preset threshold are highlighted.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. The present invention constructs a mechanism-driven model through a geological parameter inversion module, quantifying geomechanical mechanisms into a calculable energy benchmark. The data acquisition module acquires real-time data to construct a data-driven model, and the energy dynamic calculation module analyzes data such as deformation power. The two work together to provide a more scientific and comprehensive basis for early warning.

[0044] 2. The present invention performs data fusion and comparative analysis on the mechanism-driven model and the data-driven model, and obtains graded warning signals through numerical analysis and matching, which can more accurately judge the landslide status and issue warnings of different levels in a timely manner;

[0045] 3. The present invention triggers corresponding emergency response operations based on graded warning signals, such as adjusting data collection frequency, activating backup sensors, triggering the intelligent retaining wall hydraulic support system, playing evacuation prompts, etc., and visually monitors the ratio of deformation power to destructive power, thereby strengthening landslide risk management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings:

[0047] Figure 1 is a system block diagram of the present invention;

[0048] Figure 2 This is a schematic diagram of the sliding body deformation power theory proposed in the embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of the arrangement of detection holes proposed in an embodiment of the present invention;

[0050] Figure 4 This is a deformation power-time diagram proposed in an embodiment of the present invention;

[0051] Figure 5 This is a power ratio-time diagram proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] See also Figure 1 As shown, a landslide mechanism-data dual-driven early warning system based on deformation work theory includes a geological parameter inversion module, a data acquisition module, an energy dynamic calculation module, a collaborative early warning decision module and a hierarchical execution response module.

[0054] The geological parameter inversion module accesses the drilling data and geological survey data in the geological exploration report, determines the sliding surface parameters and strength indicators through numerical simulation, and constructs a mechanism-driven model for landslide early warning.

[0055] Obtain the borehole profile and the soil layer data contained in the geological exploration report, and build a 3D geometric model of the sliding surface based on the slice method. The specific process is as follows:

[0056] Obtain the total area SD* of the equivalent sliding surface, divide the sliding surface into strips, obtain a preset number of sliding strips, and sequentially number the sliding strips with the number symbol i, i = 1, 2, ..., n; n is the total number of sliding strips, and pass the constraint condition:

[0057]

[0058] Constrain the segmented area Ai of each strip, where αi is the sliding surface weight of the i-th sliding strip, and cosθi is the equivalent inclination angle of the i-th sliding strip;

[0059] It should be noted that the essence of the constraint condition is the mathematical expression of the law of conservation of sliding surface area projection, which aims to prevent area distortion during the strip division process: ensure that the sum of the weighted projected areas of all strips does not exceed the total area of ​​the sliding surface, maintain three-dimensional geometric continuity, and project the inclined sliding surface onto the horizontal plane to eliminate the area magnification effect caused by the slope.

[0060] Furthermore, geological survey data for each sliding surface is obtained, including soil type, bulk density, cohesion, thickness, and internal friction angle. By simulating the ultimate failure state of each sliding surface, a mechanism-driven model for landslide early warning is constructed. The specific process is as follows:

[0061] The rigid-plastic model is used to solve the ultimate failure state of each strip i in the sliding surface:

[0062]

[0063] Calculate the destructive power P f , where ki is the influence coefficient of block i, [V t * ] is the preset velocity discontinuity vector, obtained through FLAC3D plastic zone simulation;

[0064] where ci is the cohesion of sliding surface block i, φi is the internal friction angle of sliding surface block i, γi is the soil bulk density of sliding surface block i, and hi is the equivalent thickness of sliding surface block i.

[0065] It should be noted that the damage power is a core index in the geological parameter inversion module representing the energy rate required for the sliding body to reach the limit damage state. The essence is the minimum power required for the overall shear failure of the sliding body along the potential sliding surface, which is determined by the sliding surface geometry, rock shear strength parameters and plastic deformation velocity field. This index quantifies the geomechanical mechanism into a calculable energy benchmark, providing theoretical support for subsequent collaborative warning by integrating real-time monitoring data.

[0066] It should be further noted that please refer to Figure 2 The sliding body deformation power theory is a core theory for landslide time prediction developed based on the rigid-plastic model of plastic mechanics. The core of the theory is to achieve dynamic division of the landslide deformation stage and accurate prediction of the time of catastrophic sliding by quantifying the energy dissipation characteristics in the deformation process of the sliding body. The theory assumes that the sliding body is an incompressible rigid-plastic body that satisfies the volume invariance condition, and considers the gradual failure process of the landslide as a mechanical process of the gradual development of the plastic zone of the sliding zone to full penetration. Two key indicators are defined, including deformation power and damage power. The deformation power refers to the power done by the volume force (such as gravity) on the displacement velocity field in the real-time deformation of the sliding body, reflecting the energy dissipation rate of the current deformation stage. The damage power refers to the limit power when the sliding body reaches the limit damage (sliding surface fully penetrates and overall sliding), which is determined by numerical simulation or mechanical derivation.

[0067] The specific values of the cohesion, internal friction angle and soil bulk density are obtained through natural language processing, data extraction and field analysis of the geological survey report. When it cannot be obtained through the geological survey report, the specific values are determined through the following parameter preset value correspondence table:

[0068]

[0069] The data acquisition module accesses the Internet of Things edge gateway to obtain the real-time displacement data and velocity field data of the landslide deep part collected by the fiber optic deep displacement meter, and collects metadata for the construction of the data-driven model of landslide warning.

[0070] The displacement data inside the soil body is obtained through the fiber optic deep displacement meter, and the velocity field is reconstructed. The specific process is as follows:

[0071] Please refer to Figure 2 A preset number m of measuring points are arranged in the landslide, and the measuring points are sequentially numbered, with the number symbol j, j = 1, 2, …, m; a vertical detection hole is arranged at each measuring point.

[0072] Each detection hole j is divided into a preset number K of cylindrical units along the depth direction, and the soil displacement data collected by the fiber optic deep displacement meter installed at the upper end and lower end of each cylindrical unit is obtained, including the upper end point displacement δi and the lower endpoint displacement δ i+1 , where k is the sequential number of the cylindrical element, k = 1, 2, ..., K;

[0073] By default formula:

[0074] Calculate the upper endpoint element velocity v1k and the lower endpoint element velocity v2k of each cylindrical element k.

[0075] Furthermore, by pre-setting the formula

[0076] Calculate the unit movement speed v of each cylindrical unit k ck and unit angular velocity ω k , where h k is the length of the cylindrical unit k, that is, the height difference from the upper end to the lower end.

[0077] The unit movement velocity vck and unit angular velocity ωk of each cylindrical unit k in each detection hole j are obtained and sent to the energy dynamic calculation module.

[0078] The energy dynamic calculation module builds a data-driven model for landslide early warning based on real-time displacement data and velocity field data in the slope depth, analyzes the deformation power data and deformation power mutation data containing landslide early warning characteristics, and calculates the kinetic energy change rate through a segmented kinetic energy accumulation model.

[0079] Calculate the kinetic energy of each cylindrical unit k in each detection hole j, including the translational kinetic energy part and the rotational kinetic energy part. The formula is:

[0080]

[0081] where m k is the mass of the cylindrical unit k, ρ is the average mass of the soil layer in the detection hole, I k is the moment of inertia, A is the cross-sectional area of ​​unit k, l k is the length of unit k. is the translational kinetic energy, It is the rotational kinetic energy part.

[0082] Furthermore, the deformation power is calculated based on the sum of the kinetic energy change rate of the sliding body and the internal dissipated power. For the landslide kinetic energy change process dominated by the quasi-static deformation process, the deformation power calculation is simplified to the calculation of the dominant term of the kinetic energy change rate. The specific process is:

[0083] The data-driven model for early warning features is constructed as follows:

[0084] Get the unit movement speed v of each cylindrical unit k ck and unit angular velocity ωk , through the preset formula

[0085] Calculate the dominant term Pd of the kinetic energy change rate and record it as the deformation power of the landslide soil. Where m is the total number of detection holes, m k is the mass of the cylindrical unit k, where r is the preset detection hole radius, ρ is the average mass of the soil layer in the detection hole, and I k is the moment of inertia.

[0086] Furthermore, the first-order derivative of the dominant term Pd of the rate of change of kinetic energy with time t is calculated. Recorded as the rate of change of kinetic energy of the soil in the test hole.

[0087] It should be noted that deformation power is a core indicator in the energy dynamics calculation module that characterizes the energy conversion rate during the real-time deformation of landslide soil. Its calculation relies on the reconstruction of deep, real-time displacement data and kinetic energy analysis. Deformation power is essentially the time-dependent rate of change of kinetic energy generated by the displacement and rotation of the deep landslide soil. By integrating real-time monitoring data from fiber optic displacement meters, soil motion is decomposed into translational and rotational components for energy quantification. This indicator, serving as the core output of the data-driven model, directly reflects the severity of landslide deformation.

[0088] The collaborative early warning decision module integrates and compares the data-driven and mechanism-driven models of landslide early warning. By integrating the mechanism model with real-time data, the mechanism model and data model of deformation power mutation characteristics are numerically analyzed and matched to generate graded early warning signals.

[0089] Get the destructive power P f , deformation power P of landslide soil d and the kinetic energy change rate of the soil in the detection hole The historical maximum By default formula:

[0090] Calculate the warning index WI, where α and β are preset influencing factors, and their default values ​​are 0.7 and 0.3 respectively.

[0091] Furthermore, according to the warning index WI and the unit angular velocity ω k First-order derivative over time Unit movement speed v ck and the kinetic energy change rate of the soil in the detection hole The specific value of the numerical analysis and signal matching is carried out to obtain the graded warning signal. The specific process is as follows:

[0092] If the warning index WI is less than 0.3 and the first-order derivative of the unit angular velocity with time is Then the output signal SIG_0;

[0093] If the warning index is 0.3≤WI<0.5 or there is a cylindrical unit k, the unit movement speed v ck If it is greater than 5 mm / min, the signal SIG_1 is output;

[0094] If the warning index is 0.5≤WI<0.7 and the kinetic energy change rate of the soil in the detection hole is If it is greater than 10, the output signal SIG_2 is output;

[0095] If the warning index WI is ≥ 0.7 and the first derivative of the unit angular velocity with time is If it is greater than 1, the signal SIG_3 is output.

[0096] The hierarchical execution response module triggers corresponding emergency response operations according to the hierarchical warning signals, and performs visual monitoring of the ratio of deformation power to destructive power.

[0097] If signal SIG_0 is identified, it is determined that the sliding body is in normal condition and there is no landslide risk. The normal monitoring state is maintained and the data collection frequency is kept at the default value, i.e., 10 minutes / time.

[0098] If signal SIG_1 is detected, it is determined that there is abnormal data in the sliding state, the data collection frequency is increased to 1 minute / time, and the backup sensor is activated; a work order is automatically dispatched to the inspection personnel, requiring them to complete the slope crack inspection within 2 hours;

[0099] If signal SIG_2 is detected, the sliding body is determined to be in a landslide risk state. The intelligent retaining wall hydraulic support system is triggered, increasing the hydraulic system pressure to 1.5 times the preset value. A work order is automatically issued to the inspection personnel, requiring them to complete the slope crack inspection within 2 hours.

[0100] If signal SIG_3 is detected, it is determined that a landslide is about to occur; the broadcast system is activated to play the evacuation prompt in a loop.

[0101] See also Figure 3 and Figure 4 As shown, the ratio of deformation power to destruction power is visually monitored, and the deformation power P at each moment is recorded. d , and the power ratio of deformation power to destruction power Generate deformed power-time and power ratio-time graphs. Highlight the parts of the deformed power-time and power ratio-time graphs that exceed the preset threshold.

[0102] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0103] It should also be understood that the terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and the claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations;

[0104] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A landslide mechanism-data dual-driven early warning system based on deformation work theory, including a geological parameter inversion module, a data acquisition module, an energy dynamic calculation module, and a collaborative early warning decision module, characterized by: The geological parameter inversion module accesses the borehole data and geological survey data in the geological exploration report; determines the sliding surface parameters and strength indicators through numerical simulation, and constructs a mechanism-driven model for landslide early warning; obtains the borehole profile and the soil layer data contained in the geological exploration report, establishes a three-dimensional geometric model of the sliding surface based on the strip method, and uses the rigid-plastic model to solve the ultimate failure state of each strip in the sliding surface; The data acquisition module accesses the IoT edge gateway to obtain real-time landslide displacement data and velocity field data collected by the fiber-optic deep displacement meter, and collects metadata for the construction of a data-driven model for landslide early warning. The displacement data inside the soil is obtained by using a fiber optic deep displacement meter to reconstruct the velocity field; The energy dynamic calculation module builds a data-driven model for landslide early warning based on real-time displacement data and velocity field data at the slope depth. It analyzes deformation power data and deformation power mutation data, which contain landslide early warning characteristics, and calculates the kinetic energy change rate using a segmented kinetic energy accumulation model. The collaborative early warning decision module performs data fusion and comparative analysis on the data-driven model and mechanism-driven model of landslide early warning; it integrates the mechanism model and real-time data, and obtains graded early warning signals through numerical analysis and matching of the mechanism model and data model of deformation power mutation characteristics.

2. The landslide mechanism-data dual-driven early warning system based on deformation work theory according to claim 1 is characterized in that: Also includes a hierarchical execution response module: The hierarchical execution response module triggers corresponding emergency response operations according to the hierarchical warning signals, and performs visual monitoring of the ratio of deformation power to destructive power.

3. The landslide mechanism-data dual-driven early warning system based on deformation work theory according to claim 1 is characterized in that: The specific process of establishing the three-dimensional geometric model of the sliding surface based on the strip method is as follows: Obtain the borehole profile and the soil layer data contained in the geological exploration report, and build a 3D geometric model of the sliding surface based on the slice method. The specific process is as follows: Get the total area of ​​the equivalent sliding surface, divide the sliding surface into strips, obtain a preset number of sliding strips, number the sliding strips sequentially, and pass the constraints: The sum of the product of the segmented area, sliding surface weight and equivalent inclination angle of each strip shall not exceed the total area of ​​the equivalent sliding surface, which constrains the segmented area of ​​each strip.

4. The landslide mechanism-data dual-driven early warning system based on deformation work theory according to claim 1 is characterized in that: The specific process of solving the ultimate failure state of each strip in the sliding surface using the rigid-plastic model is as follows: Obtain geological survey data for each sliding surface, including soil type, bulk density, cohesion, thickness, and internal friction angle; construct a mechanism-driven model for landslide early warning by simulating the ultimate failure state of each sliding surface; The rigid-plastic model is used to solve the ultimate failure state of each strip in the sliding surface: The destructive power is calculated by integrating the product of the velocity discontinuity vector and the normal force of each sliding surface over the total area of ​​the equivalent sliding surface. The velocity discontinuity vector is obtained through plastic zone simulation. The normal force is obtained from the cohesion, internal friction angle, soil bulk density and equivalent thickness of the sliding surface.

5. The landslide mechanism-data dual-driven early warning system based on deformation work theory according to claim 1 is characterized in that: The specific process of velocity field reconstruction is as follows: The displacement data inside the soil is obtained by using a fiber optic deep displacement meter to reconstruct the velocity field. The specific process is as follows: Arrange a preset number of measuring points in the landslide, number them sequentially, and arrange vertical detection holes at each measuring point; Each detection hole is divided into a preset number of cylindrical units along the depth direction, and soil displacement data collected by optical fiber deep displacement meters installed at the upper and lower ends of each cylindrical unit are obtained, including the upper end point displacement and the lower end point displacement of the cylindrical unit; the upper end point unit velocity and the lower end point unit velocity of each cylindrical unit are obtained by calculating the upper end point displacement and the lower end point displacement of the cylindrical unit; The unit displacement velocity of each cylindrical unit is obtained by calculating the average value of the upper end unit velocity and the lower end unit velocity of each cylindrical unit; The unit angular velocity is obtained by calculating the quotient of the difference between the upper end unit velocity and the lower end unit velocity of each cylindrical unit and the length of the cylindrical unit; The unit movement speed and unit angular velocity of each cylindrical unit in each detection hole are obtained and sent to the energy dynamic calculation module.

6. The landslide mechanism-data dual-driven early warning system based on deformation work theory according to claim 1 is characterized in that: The specific process of building a data-driven model for early warning features is as follows: The mass and moment of inertia of each cylindrical unit are calculated by the average mass of the soil layer in the detection hole, the preset detection hole radius and the length of the cylindrical unit; Obtain the unit movement speed and unit angular velocity of each cylindrical unit, calculate the translational kinetic energy part by the unit movement speed and the mass of the cylindrical unit; calculate the rotational kinetic energy part by the unit angular velocity and moment of inertia; Calculate the first derivative of the dominant term of the kinetic energy change rate with time, and record it as the kinetic energy change rate of the soil in the test hole.

7. The landslide mechanism-data dual-driven early warning system based on deformation work theory according to claim 1 is characterized in that: The specific process of data fusion of the data-driven model and mechanism-driven model for landslide early warning is as follows: The historical maximum values ​​of the destructive power, deformation power of the landslide soil, and the kinetic energy change rate of the soil in the detection hole are obtained, and the early warning index is obtained by calculating the weighted sum of the ratio of the destructive power to the deformation power and the historical maximum value of the kinetic energy change rate. Numerical analysis and signal matching are carried out based on the specific values ​​of the warning index, the first-order derivative of the unit angular velocity with time, the unit movement speed and the kinetic energy change rate of the soil in the detection hole to obtain a graded warning signal.

8. The landslide mechanism-data dual-driven early warning system based on deformation work theory according to claim 7 is characterized in that: The specific process of numerical analysis and signal matching is as follows: If it is identified that the warning index is less than the first preset threshold value of the warning index and the first derivative of the unit angular velocity with time is less than the first preset threshold value of the unit angular velocity, a signal SIG_0 is output; If it is identified that the warning index is greater than or equal to the first preset threshold value of the warning index and less than the second preset threshold value of the warning index, or there is a cylindrical unit moving at a speed greater than 5 mm per minute, then a signal SI G_1 is output; If it is identified that the warning index is greater than or equal to the second preset threshold value of the warning index and less than the third preset threshold value of the warning index, and the kinetic energy change rate of the soil in the detection hole is greater than 10, then a signal SIG_2 is output; If it is identified that the warning index is greater than or equal to the third preset threshold value of the warning index, and the first-order derivative of the unit angular velocity with time is greater than 1, a signal SIG_3 is output; Among them, the third preset threshold of the warning index is greater than the second preset threshold and greater than the first preset threshold.

9. The landslide mechanism-data dual-driven early warning system based on deformation work theory according to claim 2 is characterized in that: The specific process of triggering corresponding emergency response operations according to graded warning signals is as follows: If signal SIG_0 is identified, it is determined that the sliding body is in normal condition and there is no landslide risk. The routine monitoring state is maintained and the data collection frequency is kept at the default value, i.e., 10 minutes / time. If signal SIG_1 is detected, it is determined that there is abnormal data in the sliding state, the data collection frequency is increased to 1 minute / time, and the backup sensor is activated; a work order is automatically dispatched to the inspection personnel, requiring them to complete the slope crack inspection within 2 hours; If signal SIG_2 is detected, the sliding body is determined to be in a landslide risk state. The intelligent retaining wall hydraulic support system is triggered, increasing the hydraulic system pressure to 1.5 times the preset value. A work order is automatically issued to the inspection personnel, requiring them to complete the slope crack inspection within 2 hours. If signal SIG_3 is detected, it is determined that a landslide is about to occur; the broadcast system is activated to play the evacuation prompt in a loop.

10. The landslide mechanism-data dual-driven early warning system based on deformation work theory according to claim 2, characterized in that: The specific process of visual monitoring of the ratio of deformation power to destruction power is as follows: The deformation power at each moment and the power ratio of the deformation power to the destruction power are recorded to generate a deformation power-time graph and a power ratio-time graph; the portions of the deformation power-time graph and the power ratio-time graph that exceed a preset threshold are highlighted.

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