High-position remote geological disaster experiment system and implementation method

Abstract

The application discloses a high-position remote geological disaster experiment system, which comprises a test system host, an electric control system, a sedimentation tank and a collapse area; the test system host comprises a main frame, a hopper is arranged on the tail upper side of the main frame, the main frame is pushed away from the ground by four groups of multistage telescopic cylinders to realize a 45-degree slope angle, S-shaped tooling, V-shaped tooling, arc-shaped tooling and 90-degree and 45-degree combined tooling are installed on the main frame, and the high-position remote geological disaster experiment system has the following advantages: the system solves the problems of the existing geological disaster model test in terms of terrain reduction, scale effect and data acquisition, provides an experiment system and a method capable of highly restoring a real scene, restores complex terrain conditions, breaks through the limitation of single slope simulation, and reproduces key geometric characteristics such as bending, valley and fluctuation in a real geological condition; and the system realizes high-position remote simulation and large-volume simulation, and meets the needs of potential energy accumulation and remote motion process simulation of a large-height-difference and large-volume disaster body.

Description

Technical Field

[0001] This invention relates to a high-altitude remote geological disaster experimental system and its implementation method, specifically a large-scale high-altitude remote ice-rock debris flow experimental system and its implementation method with variable angle, curvature, and undulation. Background Technology

[0002] Due to complex climate, geological conditions, and human engineering, my country experiences frequent and widespread geological disasters such as landslides, collapses, and debris flows, seriously threatening the lives and property of the people and the safety of engineering construction. The occurrence of geological disasters is a complex nonlinear process influenced by multiple factors, with the following main characteristics: the scale of geological disasters varies across different regions and types, and the impact range and disaster effect increase with the volume of the disaster body, exhibiting a volume effect; the disaster-inducing environment varies, with material composition and geological environmental conditions being key factors influencing the dynamic response characteristics of the disaster body, and topographic effects being prominent; the disaster-causing patterns are diverse, with complex internal and external dynamic mechanisms at different evolutionary stages and numerous dynamic characteristic parameters.

[0003] The study of the dynamic mechanism of geological disaster formation is of great significance to the development of disaster prevention and mitigation work. In order to effectively reproduce the dynamic process of geological disaster formation under actual geological environment conditions, model experiments have become a key means to carry out research on the dynamics of geological disasters.

[0004] While current geological hazard modeling techniques do not reveal complex material compositions, there are few model testing devices capable of accurately recreating the movement and collapse processes of hazard bodies under actual terrain conditions, and of acquiring and analyzing complex dynamic data. From a simulation methodology perspective, existing technologies primarily employ single-slope driven trough physical model experiments. This method has significant limitations in principle: First, the underlying physical model is overly simplified, considering only the slope variable and failing to reproduce key geometric features such as bends, valleys, and base undulations in real terrain, resulting in insufficient similarity between the model and the actual hazard prototype. Second, due to device size limitations, it cannot achieve potential energy accumulation and long-range movement processes for large-volume, high-altitude test materials, making it difficult to reveal the core dynamic mechanisms of high-altitude, long-range hazard events. Third, its data acquisition methods are limited, heavily reliant on image analysis, and lack direct, synchronous measurement of key dynamic parameters such as impact force and acceleration, failing to meet the data requirements for in-depth mechanism research.

[0005] Patent application number 2019113613425 provides an experimental device for simulating the evolution process of a landslide-dammed dam-dam-break flood disaster chain. The landslide dynamic condition simulation system only considers the influence of the slope of the landslide channel on the dynamic process of the disaster body, using jacks and lifting motors to adjust the angle of the landslide channel. A wireless measurement system constructed using motion cameras and computers is used to measure the disaster evolution process.

[0006] Patent application number 2025106532113 discloses a hydraulically driven coal mine landslide speed control device. It employs a hydraulic tilting cylinder to adjust the angle of the material box, overcoming the problems of slow response and inaccurate control found in traditional mechanical devices.

[0007] Problems exist:

[0008] The terrain reconstruction is too simplistic and ignores key geometric features: Existing methods can only simulate ideal two-dimensional straight slopes and cannot reproduce these key terrain effects, resulting in a large deviation between the experimental results and the real situation, and a low degree of similarity principle satisfaction.

[0009] The driving and starting methods fail to reflect the high-position and remote characteristics: the length and height of the existing chute are limited, and it is impossible to provide enough potential energy to the disaster body to simulate the remote motion characteristics;

[0010] The measurement dimension is limited and cannot reveal complex dynamic mechanisms: Existing technologies mainly rely on image post-processing analysis, lacking direct measurement of internal force chains, impact forces, and accelerations during motion. This makes it impossible to deeply analyze core dynamic issues such as the interaction mechanism between debris flow and the substrate, and the interaction between particles.

[0011] Insufficient scale effect fails to reflect large volume characteristics: Geological hazards exhibit a significant volume effect, meaning that the larger the volume, the farther the movement distance and the more complex the dynamic behavior. Existing experimental devices are relatively small in scale, with limited volume of experimental materials, resulting in a significant scale effect.

[0012] Existing technologies can only acquire motion or dynamic data separately, without clarifying the temporal relationship between the two. This leads to a reliance on subjective experience in judging the evolution logic of disasters, ignoring the influence of terrain parameters on dynamic response, and only obtaining scattered data under a single working condition. It cannot answer why different terrains cause dynamic differences. When faced with data anomalies (such as asynchronous changes in flow velocity and impact force), the data must either be directly discarded or it is impossible to distinguish whether it is caused by equipment error or actual working conditions. This results in low reliability of experimental results, and data processing is limited to parameter extraction. It is impossible to dig out the core dynamic mechanism from the data, making it difficult to transform experimental results into practical applications. Summary of the Invention

[0013] The technical problem this invention aims to solve is to address the above-mentioned shortcomings by providing a high-altitude remote geological disaster experimental system and its implementation method. This system overcomes the deficiencies of existing geological disaster model experiments in terms of terrain reconstruction, scale effect, and data acquisition. It provides an experimental system and method that can highly reproduce real-world scenarios, recreating complex terrain conditions, overcoming the limitations of single-slope simulation, and replicating key geometric features such as bends, valleys, and undulations in real geology. It achieves high-altitude remote and large-volume simulation, meeting the needs for potential energy accumulation and remote motion process simulation of large-scale disaster bodies with significant elevation differences. It enriches the dimensions of data acquisition, simultaneously acquiring multiple types of data such as motion morphology, impact force, and acceleration, overcoming the limitations of single-data-dimensional approaches. Furthermore, it establishes a data correlation and mechanism analysis system, clarifying the temporal and spatial correlations of motion and dynamic parameters, supporting research on the dynamic mechanisms of geological disasters.

[0014] To solve the above technical problems, the present invention adopts the following technical solution:

[0015] A high-altitude remote geological disaster experimental system includes a main experimental system, an electrical control system, a sedimentation tank, and a collapse zone;

[0016] The main unit of the test system includes a main frame, and a hopper is arranged on the upper side of the rear end of the main frame. The main frame achieves a 45° slope angle off the ground through the thrust of four sets of multi-stage telescopic cylinders. The front end of the multi-stage telescopic cylinder is equipped with a force sensor, and each telescopic arm connection of the multi-stage telescopic cylinder is equipped with a displacement sensor to achieve synchronous lifting of the four sets of telescopic cylinders, and at the same time display the extension length of each stage of each multi-stage telescopic cylinder.

[0017] The main frame is equipped with S-bend fixtures, V-shaped fixtures, arc-shaped fixtures, and 90-degree and 45-degree combination fixtures. The S-bend fixtures are mainly installed on the side wall of the main frame slide. Different types of S-bend fixtures are combined according to the bending conditions of the channel to be simulated. The V-shaped fixtures are installed at the base of the main frame slide. The arc-shaped fixtures and the 90-degree and 45-degree combination fixtures need to be used simultaneously. The arc-shaped fixtures are installed near the outlet of the main frame slide, with their outermost edge aligned with the inlet of the 90-degree and 45-degree combination fixtures. The 90-degree and 45-degree combination fixtures are installed outside the right side wall of the main frame slide near the outlet.

[0018] Furthermore, the main frame is equipped with a base plate, which is made of hot-rolled steel plates spliced ​​together to form five bending shafts of the same shape, and divides them into six independent overturning surfaces. Two rows of paired luffing cylinders are installed at the bottom of the base plate, and the extension and retraction of the luffing cylinders are controlled to simulate the different overall and local angle changes of the chute base.

[0019] Furthermore, the base plate is fixed to the main frame via a variable-amplitude cylinder at the bottom of the bending shaft. The position of the variable-amplitude cylinder installed at the bottom of the bending shaft is controlled and monitored by a laser displacement sensor and synchronously controlled by a proportional servo valve. By driving the variable-amplitude cylinder under each bending shaft, the angle between the 12 independent plate surfaces can be changed. The main frame can reach a slope angle of up to 45° from the ground. By adjusting the angle between the base plates, test conditions with a maximum slope angle of 60° can be simulated, and the requirements of undulating ramps can also be simulated.

[0020] Furthermore, the electronic control system adopts hydraulic control, and the hydraulic supply of the hydraulic control is divided into three parts: multi-stage telescopic cylinder, 90-degree and 45-degree combined tooling and variable amplitude cylinder, which are supplied by two sets of oil sources respectively; the electronic control system is equipped with visual recording and data capture monitoring functions, and can acquire dynamic data by installing high-speed cameras, three-dimensional impact force sensors and acceleration sensors.

[0021] A method for implementing a high-altitude remote geological disaster experimental system includes the following steps:

[0022] Step 1: Establish a physical model based on reconfigurable terrain;

[0023] Step 2: Adjust the local micro-topography according to the actual situation of the simulated geological disaster terrain. It can be divided into three types of micro-topography: longitudinal undulation of the base, transverse V-shape of the base, and lateral S-shape of the gully.

[0024] Step 3: Synchronous monitoring and data acquisition of multi-source heterogeneous sensing systems. Sensor units are deployed at key locations in the physical model to build a monitoring network, enabling synchronous acquisition and transmission of measurement and data for two monitoring types: high-speed photography and dynamic parameters.

[0025] Step 4: Experiment-driven and process execution. The experiment will be conducted after the physical model and monitoring system are ready.

[0026] Step 5: Fusion processing and analysis of multimodal data;

[0027] Step 6: Model Validation and Iterative Optimization. Compare the dynamic parameters obtained from the experiment with the numerical simulation results or actual field case data to verify the effectiveness of the physical model and the applicability of the similarity law.

[0028] Furthermore, the implementation of step two specifically includes the following steps:

[0029] For the longitudinal undulating type of the base, by controlling the two rows of variable amplitude hydraulic cylinders at the bottom of the five bending shafts, the independent panels are driven to generate different tilt angles according to the undulating shape of the base to be simulated, and these tilt angles are superimposed on the overall slope to generate a continuous undulating base shape.

[0030] For simulating the transverse V-shaped channel of the base, the base plate is adjusted to a straight state, and then the assembled V-shaped fixture is installed on the base with bolts.

[0031] For the lateral S-shaped working condition of the channel, the assembled S-bend fixture is installed along the side wall of the chute using bolts.

[0032] When it is necessary to simulate the outlet of the flow area with different inclination angles, the arc-shaped fixture is installed at the bottom of the chute and connected to the inlet of the 90-degree and 45-degree combined fixture, and the steel plate at the inlet of the 90-degree and 45-degree combined fixture is opened.

[0033] When the angle between the simulated terrain outlet and the ditch is 90 degrees, move the steel plate to make the 90-degree assembly tooling unobstructed. When the angle between the simulated terrain outlet and the ditch is 45 degrees, move the steel plate to make the 45-degree tooling unobstructed for unloading.

[0034] Furthermore, the implementation of step five specifically includes the following steps:

[0035] Step 1: Collect raw data by category, including high-speed photography data and dynamic sensor data. High-speed photography data is a sequence of images captured by multiple high-speed cameras deployed along the debris flow trajectory. Each device is named according to device number, acquisition time, and shooting area. Dynamic sensor data is the raw electrical signal data output by triaxial impact force sensors and acceleration sensors installed at key impact points of the debris flow. It is classified according to sensor number, installation location, and acquisition time.

[0036] Step 2: Single-modal data preprocessing, including signal denoising, outlier handling, and signal standardization.

[0037] Step 3, Single-modal data parsing and parameter extraction;

[0038] Step 4: Spatiotemporal alignment and fusion of multimodal data;

[0039] Step 5, Multimodal data coupling relationship analysis;

[0040] Step 6: Feature parameter integration and database construction;

[0041] Step 7: Visualize the analysis results and output the report.

[0042] Furthermore, the implementation of step 3 specifically includes the following steps:

[0043] Step 3.1, high-speed photography data analysis and kinematic parameter calculation:

[0044] Particle identification and tracking employs particle image velocimetry technology to analyze preprocessed image sequences. In two adjacent frames, a cross-correlation algorithm is used to identify the displacement vector of the debris flow particle group.

[0045] For a single feature particle, particle image velocimetry is used, and its position coordinates (x, y, t) in consecutive frames are tracked by template matching algorithm to construct the motion trajectory of a single particle;

[0046] The calculation of core kinematic parameters includes flow velocity calculation, flow depth calculation, frontal trajectory calculation, and depositional morphology parameters;

[0047] Step 3.2, Analysis of kinetic sensor data and calculation of kinetic parameters:

[0048] Analysis of three-dimensional impact force parameters, including instantaneous peak impact force, impact force time history curve, and impact force impulse:

[0049] Peak instantaneous impact force: Identify the maximum impact force in the X / Y / Z directions in the time series, which is the peak instantaneous impact force, and record the time when the peak occurs;

[0050] Impact force time history curves: Plot impact force time history curves in the X, Y, and Z directions with time as the horizontal axis and impact force as the vertical axis to analyze the variation of impact force over time.

[0051] Impact impulse: The impact impulse is calculated by integrating the impact time history curve, which reflects the total impact of debris flow on the substrate.

[0052] Analysis of instantaneous acceleration peak value: Identify the maximum acceleration values ​​in the X / Y / Z directions, which are the instantaneous acceleration peak values. Combine this with time information to determine the correlation between the acceleration peak value and the impact force peak value.

[0053] Acceleration power spectrum analysis: Fourier transform is used to convert the acceleration time-domain signal into a frequency-domain signal to obtain the acceleration power spectral density curve, and the main vibration frequencies of debris flow motion are analyzed.

[0054] Motion state assessment: The motion state of debris flow is determined by the changing trend of the acceleration signal.

[0055] Furthermore, the implementation of step 4 specifically includes the following steps:

[0056] Step 4.1, Timeline Alignment;

[0057] With the trigger time of the unified timecode generator as time 0, the time axis of the high-speed photography data and the time axis of the dynamic sensor data are calibrated. The time of the high-speed photography data is the frame time t1 calculated based on the frame rate, and the time axis of the dynamic sensor data is the sampling time t2 calculated based on the sampling frequency, ensuring that t1=t2.

[0058] For data with slight time deviations, linear interpolation is used to adjust the time coordinates of the data points to make the time resolution of the two types of data consistent.

[0059] Step 4.2, spatial axis alignment;

[0060] Establish a three-dimensional spatial coordinate system for the test system: with the starting end of the main frame as the origin (0,0,0), the X-axis along the length of the frame, the Y-axis perpendicular to the length of the frame, and the Z-axis perpendicular to the ground;

[0061] The position coordinates in the high-speed photography data are converted into three-dimensional spatial coordinates (X,Y,Z) through camera calibration parameters, which are the spatial coordinates of debris flow movement.

[0062] The installation position of the dynamic sensor is located in a three-dimensional coordinate system to obtain the spatial coordinates (X0, Y0, Z0) of the sensor.

[0063] By associating the spatial coordinates of debris flow motion with the spatial coordinates of dynamic sensors, spatial correlation between motion position and dynamic response is achieved. The specific steps are as follows:

[0064] Spatial intersection judgment: Calculate in real time whether the spatial coordinates (X(t), Y(t), Z(t)) of the debris flow at time t intersect with the effective monitoring range of each sensor. (X0_min, Y0_min, Z0_min) are the coordinates of the minimum effective monitoring range of the dynamic sensor, and (X0_max, Y0_max, Z0_max) are the coordinates of the maximum effective monitoring range of the dynamic sensor. Determine whether (X(t), Y(t), Z(t)) intersects with the effective monitoring range of each dynamic sensor: If X(t)∈[X0_min, X0_max], Y(t)∈[Y0_min, Y0_max], Z(t)∈[Z0_min, Z0_max], then it is determined that the debris flow particles entered the monitoring area of ​​the sensor at time t.

[0065] Time synchronization matching: Combining the synchronized time axis of the unified timecode generator, when debris flow particles enter the monitoring area of ​​a certain sensor, the dynamic data and acceleration of the sensor before and after that moment are extracted synchronously to achieve time stamp alignment of motion position and dynamic response;

[0066] Population association optimization: For a debris flow particle group, calculate the center coordinates of the particle group (X_g(t)=∑X(t) / N,Y_g(t)=∑Y(t) / N,Z_g(t)=∑Z(t) / N), where N is the number of particles. When the center coordinates of the group enter the sensor monitoring range, associate them with the average dynamic data of the sensor during that period to avoid association errors caused by abnormal displacement of a single particle.

[0067] Step 4.3, Multimodal Data Fusion and Association:

[0068] Matrix dimension definition:

[0069] The time dimension is used as rows, with the test start time t=0 of the unified timecode generator as the benchmark, and time nodes are divided according to fixed time intervals, covering the entire cycle of debris flow from start-up, migration to accumulation; the spatial dimension is used as columns, based on the three-dimensional coordinate system of the test system, and spatial positions are divided according to fixed spatial intervals, covering the key areas of the debris flow trajectory; the kinematic dimension is used to reflect the motion state of the debris flow at specific time-space nodes, with the core being the key kinematic parameters of the test; the dynamic dimension is used to reflect the dynamic response of the debris flow at specific time-space nodes, with the core being the key dynamic parameters of the test.

[0070] Time-space node gridding matching:

[0071] Based on the time and space axes, a two-dimensional mesh is constructed. Each mesh cell corresponds to a unique t-X1-Y1 coordinate, representing a specific scenario of debris flow at spatial location (X1, Y1) at a certain time t. The specific operation is as follows:

[0072] Traverse all time nodes t and synchronously traverse all spatial locations (X1, Y1) to form equally discrete grid cells;

[0073] For each grid cell, spatial intersection is used to determine whether the debris flow particles are within the valid range of the spatial location (X1, Y1) at time t: if the particle coordinates (X1(t), Y1(t)) ∈ [X1_min, X1_max] × [Y1_min, Y1_max], then the grid cell is determined to have valid data; if the particles have not reached or have left the location, it is marked as no data.

[0074] Motion-dynamic parameter filling:

[0075] For the time-space grid cells identified as having valid data, the kinematic and dynamic parameters at the corresponding time and location are extracted and filled into the grid cells to form a complete four-dimensional correlation matrix.

[0076] Motion parameter filling: Extract the flow velocity v and flow depth h corresponding to the t-X1-Y1 node from the kinematic parameter database and fill them into the motion dimension field of the matrix;

[0077] Dynamic parameter filling: Extract the triaxial impact force F and acceleration a corresponding to the t-X1-Y1 node from the dynamic parameter database and fill them into the dynamic dimension field of the matrix;

[0078] Data association verification: If multiple sets of similar data exist at a certain time-space node, the average value method is used to fuse the data before filling in the data to avoid individual device errors.

[0079] For key event points, extract multimodal data combinations of those event points to form event data packets.

[0080] Furthermore, the implementation of step 5 specifically includes the following steps:

[0081] Step 5.1, Motion-Dynamic Temporal Coupling Analysis:

[0082] Plot biaxial curves of time-flow velocity-impact force and time-flow depth-acceleration to visually observe the temporal variation of kinematic and dynamic parameters;

[0083] Calculate the correlation coefficient between kinematic parameters and dynamic parameters to quantify the degree of linear association between the two. A correlation coefficient close to 1 indicates a strong positive correlation, close to -1 indicates a strong negative correlation, and close to 0 indicates no significant association.

[0084] Step 5.2, Spatial Location-Dynamic Response Coupled Analysis:

[0085] Plot curves along the debris flow trajectory, showing the relationship between spatial location and peak impact force, and between spatial location and peak acceleration, to analyze the differences in dynamic response at different terrain locations.

[0086] By combining terrain parameters, a correlation model of terrain parameters, motion parameters, and dynamic parameters is established;

[0087] Step 5.3, Anomaly Attribution Analysis:

[0088] If an abnormal situation occurs where the flow velocity increases but the impact force does not increase, analyze the high-speed photography images to determine if debris flow diversion exists.

[0089] If the peak impact force and peak acceleration are not synchronized, analyze whether there is a force transmission delay in conjunction with the sensor installation location;

[0090] Quantifying parameter correlations reveals temporal patterns. By plotting biaxial curves and calculating correlation coefficients, the degree of linear correlation between kinematic and dynamic parameters is clarified, allowing previously scattered parameters to form a quantifiable temporal logic and avoiding subjective judgments about parameter relationships.

[0091] By linking topography and dynamics, clarifying spatial response differences, analyzing the dynamic response differences at different topographic locations along the debris flow trajectory, and establishing a correlation model of topographic parameters, motion parameters, and dynamic parameters.

[0092] The present invention adopts the above technical solution and has the following technical effects compared with the prior art:

[0093] This invention not only improves the hardware structure, but also provides a comprehensive simulation method that can highly reproduce the complex terrain and dynamic processes of real geological disasters. Compared with existing methods and technologies, it has the following advantages:

[0094] The high-altitude remote geological disaster experimental system and its implementation method are based on multiple sets of variable-amplitude hydraulic cylinders to achieve local terrain tilt angle changes, and are equipped with different sized combination fixtures to simulate real terrain conditions. It also features a visualization recording and high-speed camera system, as well as a data capture and monitoring system for three-dimensional impact force and acceleration. By controlling the coordination between different structures through the hydraulic system, the system can invert the dynamic disaster-causing process of large-scale geological disasters under real terrain conditions and acquire dynamic characteristic parameters. This provides support for the research on the dynamic disaster-causing mechanism of geological disasters and offers an effective platform for the development of geological disaster monitoring and early warning equipment, risk assessment technology, and engineering protection technology innovation. It has the following effects:

[0095] 1. By using a dual-oil-source multi-stage telescopic cylinder and an independent multi-stage oil cylinder device, equipped with a high-precision laser displacement sensor, the overall angle change of the ramp and the undulation of the ramp bottom plate 2 were realized. At the same time, with different sizes of tooling, the test parameters of U-shaped, V-shaped, S-shaped and other actual terrain conditions were reproduced, which solved the problem that the existing technology can only meet the control of a single slope variable.

[0096] 2. The main body of the test equipment was built using a large horizontal platform. The total mass of the main frame of the test system, the slope undulating base plate 2, the variable amplitude cylinder and various tooling reached hundreds of tons. The strength of the main structure was verified through numerical simulation, which met the needs of large-volume geological disaster model tests and solved the defects of small and medium-sized model test devices in reproducing the dynamic process of large-scale geological disasters.

[0097] 3. Synchronous Data Acquisition and Analysis Method Using Multiple Heterogeneous Sensors: This method integrates high-speed cameras, triaxial force sensors, and accelerometers to simultaneously acquire three types of data: motion pattern, impact force, and motion acceleration. This generates a comprehensive data package that reveals the complex dynamic disaster mechanisms, solving the problem of single data dimensions in existing methods. Specifically, it brings the following technical benefits:

[0098] Data quality is improved by eliminating interference factors. Through classified collection and preprocessing, issues such as noise, distortion, and outliers in the raw data are resolved. High-speed photography data undergoes noise reduction, distortion correction, and invalid frame removal to ensure the accuracy of motion parameter calculations. Dynamic sensor data undergoes noise reduction, baseline correction, and standardization transformation to make physical quantities such as impact force and acceleration more reliable, laying a high-quality data foundation for subsequent analysis.

[0099] The parameter extraction is comprehensive, covering core features, and enabling the systematic extraction of key kinematic and dynamic parameters. At the kinematic level, parameters such as flow velocity, flow depth, leading edge trajectory, and deposition morphology are obtained, fully reflecting the debris flow motion process; at the dynamic level, the peak impact force, time history curve, impulse, and acceleration-related features are analyzed to accurately capture the dynamic response law, making up for the shortcomings of traditional methods with single data dimensions.

[0100] By deepening data association and constructing four-dimensional cognition, a close connection between time, space, motion, and dynamics is established through spatiotemporal alignment and fusion. This achieves both time synchronization (millisecond-level accuracy) and spatial positioning of different types of data, and forms a structured four-dimensional association matrix, making it clear what kind of dynamic response corresponds to the motion state at a certain moment and location, thus providing support for revealing complex coupling relationships.

[0101] The scientific approach to mechanistic analysis supports in-depth research. Through temporal coupling, spatial correlation, and anomaly attribution analysis, it quantifies the correlation between motion and dynamic parameters, and clarifies the influence of terrain on dynamic response (such as the relationship between curves, slope angles, and impact forces). It can also explain data anomalies (such as the asynchronous flow velocity and impact force), providing direct evidence for the study of geological disaster dynamic mechanisms.

[0102] The results are put into practical use, enabling real-world applications. Standardized characteristic parameters and structured databases facilitate cross-condition retrieval and comparative analysis (such as parameter differences under different slope angles and terrains). Multi-dimensional visualization charts and comprehensive reports present test results intuitively, directly supporting the development of monitoring and early warning equipment, optimization of risk assessment technologies, and innovation of engineering protection solutions. Attached Figure Description

[0103] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0104] Figure 1 This is a schematic diagram of the experimental system platform layout of the present invention;

[0105] Figure 2 This is a schematic diagram of the host computer of the test system of the present invention;

[0106] The markings in the diagram are: 1 - hopper, 2 - bottom plate 2, 3 - S-bend fixture, 4 - V-shaped fixture placement point, 5 - arc fixture, 6 - 90-degree and 45-degree combination fixture, 7 - multi-stage telescopic cylinder, 8 - force sensor, 9 - main frame.

[0107] Figure 3 This is a schematic diagram of the internal base plate and bending shaft of the present invention;

[0108] The markings in the diagram are: 11 - bending shaft, 12 - plate surface, 13 - luffing cylinder.

[0109] Figure 4 This is a schematic diagram of the S-bend tooling of the present invention;

[0110] Figure 5 This is a schematic diagram of the V-shaped tooling of the present invention;

[0111] Figure 6 This is a schematic diagram of the installation of the 90-degree and 45-degree combined tooling of the present invention. Detailed Implementation

[0112] like Figure 1 As shown, a high-altitude remote geological disaster experimental system comprises four parts: the main experimental system, the electrical control system, the sedimentation tank, and the collapse zone.

[0113] like Figure 2 As shown, the main unit of the test system is constructed from a 40-meter large horizontal platform. The main unit includes a main frame 9, which achieves a 45° slope angle off the ground through the thrust of four sets of multi-stage telescopic cylinders. Force sensors 8 are equipped at the front end of the multi-stage telescopic cylinders, and displacement sensors are equipped at the connection points of each telescopic arm of the multi-stage telescopic cylinders to achieve synchronous lifting of the four sets of telescopic cylinders. At the same time, the extension length of each stage of each multi-stage telescopic cylinder is displayed, allowing the user to have a comprehensive grasp of the lifting process.

[0114] like Figure 3 As shown, the main frame 9 has a base plate 2 inside. The base plate 2 is made of 10 sections of hot-rolled steel plates spliced ​​together to form 5 identical bending shafts 11, dividing the base plate into 6 independent overturning plates 12. Two rows of paired luffing cylinders 13 are installed at the bottom of the base plate. By controlling the extension and retraction of the luffing cylinders 13, the different overall and local angle changes of the chute base are simulated. The bending shafts 11 are mainly composed of butt hinges, used to connect two adjacent plates 12. The connection is composed of a coaxial cylinder and a pin. The butt hinge design can effectively connect adjacent plates 12 and realize the bending between adjacent plates 12. The structure is simple and more durable. The base plate 2 is fixed to the main frame 9 by the luffing cylinders at the bottom of the bending shafts 11. The luffing cylinders installed at the bottom of the 5 bending shafts 11 are controlled and monitored by laser displacement sensors and synchronously controlled by proportional servo valves. By driving the 4 luffing cylinders 13 under each bending shaft 11, the angle change between the independent plates 12 is realized. The main frame 9 can reach a slope angle of up to 45° from the ground. By adjusting the angle between the base plates 2, it can simulate test conditions with a slope angle of up to 60°, and can also simulate the requirements of undulating ramps.

[0115] The main frame 9 is mainly constructed of 200×200 square steel frame and 630×180×17 I-beams welded together. It is approximately 40 meters long, 9.2 meters wide, and 9 meters high. The sides are welded with rectangular reinforcing ribs to effectively improve the overall strength. The main frame 9 is the main load-bearing frame of the test system host and a core component for the safety and reliability of the test system host. The main frame 9 is composed of standard steel welded together. The front side is connected to the ground with a large hinged seat. The middle and rear are supported by two pairs of four sets of multi-stage telescopic cylinders 7. Considering the self-weight of the frame and the load of 100 tons of uniformly distributed load at the bottom of the frame, according to the ANSYS structural analysis, the deformation and equivalent stress meet the safety requirements of materials and equipment. The equivalent maximum stress is 81MPa and the maximum deformation is 3.3mm.

[0116] The main frame 9 is equipped with S-bend fixture 3, V-shaped fixture 4, arc-shaped fixture 5, and 90-degree and 45-degree combined fixture 6. The S-bend fixture 3 is mainly installed on the side wall of the chute. Different types of S-bend fixtures 3 are combined according to the required simulating chute bending conditions. Figure 4 As shown, the S-bend fixture 3 includes steel frames consisting of rectangular, cubic, triangular, and trapezoidal platforms that are closed on the sides. When the rectangular and cubic frames are installed, the closed surfaces are parallel to the sides of the chute, while when the triangular and trapezoidal platforms are installed, the closed surfaces are at 30 / 45 / 60 degrees to the sides of the chute. By combining steel frames of different shapes, flow channels of different widths and inlet / outlet inclination rates can be simulated conveniently and quickly.

[0117] like Figure 5 As shown, the V-shaped fixture 4 is mainly installed at the base of the chute. Different types of V-bend fixtures can be combined to simulate the cross-sectional shape of the channel. The V-shaped fixture 4 includes a steel frame with a triangular concave and a wedge-shaped concave closed surface. The concave angles of the triangular concave steel frame are 10 / 20 / 30 degrees, which can simulate flow channels with different degrees of concavity. The angle of the side closed surface of the wedge-shaped concave steel frame corresponds to the concave angle of the triangular concave steel frame. By combining steel frames with different concave shapes, the simulation of different base shapes can be quickly achieved. It should be noted that the base plate 2 needs to be adjusted to a straight state when using the fixture.

[0118] like Figure 6As shown, the arc-shaped fixture 5 and the 90-degree and 45-degree combined fixture 6 need to be used simultaneously. The arc-shaped fixture 5 is installed near the outlet of the chute, with its outermost edge aligned with the inlet of the 90-degree and 45-degree combined fixture. The 90-degree and 45-degree combined fixture is installed outside the right side wall of the chute near the outlet. The arc-shaped fixture 5 is mainly formed by welding multiple sections of high-strength steel plates supported by T-shaped steel on the sides, providing strong impact resistance. Its main function is to connect the chute with the 90-degree and 45-degree combined fixture when simulating ramp transitions at different angles, allowing the sliding material to be unloaded from the 90-degree and 45-degree combined fixture. When a 100-ton impact force is applied in the ramp direction, the ANSYS simulation results show only slight deformation and small stress, with an equivalent maximum stress of 20.6 MPa and a maximum deformation of 0.8 mm. The 90-degree and 45-degree combination fixtures are supported by a universal steel frame. The tilt angle is adjusted by a back support cylinder and overlaps with the main frame 9 using steel plates. These steel plates can be folded back. The 90-degree and 45-degree ramp steel plates share a common baffle design, allowing users to switch between the two ramp angles by changing the baffle positions. The S-curve, V-shaped, and arc-shaped fixtures are mainly fixed to the main frame 9 with bolts for easy installation and disassembly, facilitating the simulation of different working conditions.

[0119] The multi-stage telescopic cylinder 7 adopts the telescopic boom form used in truck cranes, consisting of a six-section boom, telescopic cylinders, and a rope system. Each telescopic boom section is approximately 7 meters long. The main boom is installed on the load-bearing piles at the bottom of the basement, providing primary support for the main frame. When fully extended, the maximum lifting height is approximately 40 meters, and the maximum axial load exceeds 50 tons. Height adjustment is achieved by extending and retracting each section of the telescopic boom sequentially via the telescopic cylinders and the built-in rope system.

[0120] The main frame 9 has a hopper 1 arranged on the upper side of its tail end. The hopper 1 has a maximum volume of 30 cubic meters. The hopper 1 has a reserved baffle. Users can disassemble or install it to change the volume as needed. During the test, when the position where soil needs to be released is reached, the operator only needs to use the wireless remote control on the ground to click the release button. The electric telescopic motors on both sides drive the left and right hook locks to open, and the door of the hopper 1 opens downwards instantly, releasing the soil instantly.

[0121] The electronic control system adopts hydraulic control. The hydraulic supply of the main unit is mainly divided into three parts: multi-stage telescopic cylinder, 90-degree and 45-degree combined tooling and variable amplitude cylinder, which are supplied by two sets of oil sources respectively.

[0122] Equipped with a visual recording and data capture monitoring system, it can acquire dynamic data by installing high-speed cameras, triaxial impact sensors, and accelerometers. The high-speed cameras are installed on the upper crossbeam and along the soil slippage path. The triaxial impact sensors are installed on the back of the steel plate at the bend of the slippage trajectory. The accelerometers are also installed on the back of the steel plate at the bend of the slippage trajectory. This system is used for acquiring dynamic data on high-altitude geological disasters, developing monitoring and early warning equipment, and innovating risk assessment and engineering protection technologies.

[0123] The implementation method of a high-altitude remote geological disaster experimental system includes the following specific steps:

[0124] Step 1: Establishment of a physical model based on reconfigurable terrain: According to the experimental objectives, determine the geometric similarity ratio (such as the Froude similarity criterion) and material similarity of the model, and prepare experimental materials with corresponding particle size distribution and physical properties. Then, adjust the overall terrain of the chutes, and adjust the 40-meter-long main frame 9 to the predetermined slope angle (0-45°) by controlling the synchronous lifting of four sets of multi-stage telescopic cylinders, thus establishing the basic slope field for the experiment.

[0125] Step 2: Adjust the local micro-topography according to the actual situation of the simulated geological disaster terrain. This can be divided into three types: longitudinal undulation of the base, transverse V-shaped base, and lateral S-shaped channel. For the longitudinal undulation of the base, the main control is to operate the two rows of variable-amplitude hydraulic cylinders at the bottom of the five bending shafts 11, driving each independent panel to generate different inclination angles based on the required simulated base undulation shape, which are then superimposed on the overall slope to create a continuous undulating base shape. For the simulation of a transverse V-shaped channel, the base plate 2 needs to be adjusted to a straight state first, and then the assembled V-shaped fixture is installed on the base using bolts. For the lateral S-shaped channel, the assembled S-bend fixture is installed along the sidewall of the chute using bolts. Furthermore, when simulating the outlet of the flow area with different inclination angles, the arc-shaped fixture needs to be installed at the bottom of the chute and connected to the inlet of the 90-degree and 45-degree combined fixtures using bolts, and the steel plates at the inlets of the 90-degree and 45-degree combined fixtures need to be opened. When the angle between the simulated terrain's outlet and the channel is 90 degrees, the steel plate is moved to allow the 90-degree assembly tooling to pass smoothly. When the angle between the simulated terrain's outlet and the channel is 45 degrees, the steel plate is moved to allow the 45-degree assembly tooling to pass smoothly for unloading. By combining the above working conditions, modular and high-precision reproduction of complex 3D terrain can be achieved.

[0126] Step 3: Synchronous Monitoring and Data Acquisition of Multi-Source Heterogeneous Sensing Systems: This mainly involves deploying individual sensors at key locations on the physical model to form a monitoring network. The monitoring primarily consists of two types: high-speed photography and dynamic parameter measurement, with synchronized data acquisition and transmission. High-speed photography involves deploying multiple high-speed cameras (frame rate ≥ 1000fps) along the crossbeams and sliding paths of the model for synchronized triggering and acquisition. The imaging range covers the entire process of debris flow initiation, transport, and accumulation, used for non-contact measurement of subsequent kinematic parameters (such as flow depth, velocity, and leading edge trajectory). Dynamic parameter measurement involves installing triaxial impact force sensors and accelerometers on the back of steel plates at key impact points of the debris flow (such as the outside of curves and abrupt changes in undulations) to directly measure the triaxial impact force and time-history acceleration signals of the flow-base interaction. A unified timecode generator sends a synchronized trigger signal to all monitoring devices, ensuring that the high-speed cameras, triaxial impact force sensors, and accelerometers begin data acquisition at the same time, eliminating time differences between devices (time synchronization accuracy controlled at the millisecond level). The data is collected in real time by a multi-channel data acquisition instrument and transmitted to the server in the control room for storage.

[0127] Step Four: Experiment Driving and Process Execution: The experiment is executed after the physical model and monitoring system are ready. By triggering the electric telescopic motors on both sides of the hopper, the hook locks are released instantaneously, and the 30-cubic-meter hopper door opens rapidly under gravity, achieving the instantaneous and complete release of large-scale experimental material to simulate the initiation conditions of a high-altitude rockfall. The debris flow travels along the pre-set terrain and eventually accumulates in the collapse zone. A light-colored gridded base plate is pre-set in the collapse zone for precise quantification of the morphology, area, thickness distribution, and other termination characteristic parameters of the accumulation body in subsequent image analysis.

[0128] Step 5: Fusion processing and analysis of multimodal data, including the following steps:

[0129] Step 1, raw data classification and collection:

[0130] High-speed photography data: Collect image sequences captured by multiple high-speed cameras (frame rate ≥ 1000fps) deployed along the debris flow trajectory (starting zone, flow zone, accumulation zone). Each device is named according to device number-acquisition time-shooting area, and the storage format is lossless image format (such as TIFF) to retain the original resolution to ensure the accuracy of subsequent motion parameter calculations.

[0131] Dynamic sensor data: Collect raw electrical signal data from triaxial impact force sensors (force signals in the X / Y / Z directions) and acceleration sensors (acceleration signals in the X / Y / Z directions) installed at key impact points of debris flow (outer steel plate of bends, back of steel plate at abrupt changes in undulation). Classify the data by sensor number, installation location, and acquisition time. Store the data in binary or CSV format and retain the original sampling frequency.

[0132] Data association and tagging: Establish test condition tags in the storage system (e.g., terrain type: S-curve + V-shaped base; slope angle: 30°; test material: graded crushed stone) to associate and tag high-speed photography data and dynamic sensor data under the same test condition, which facilitates subsequent retrieval and analysis by test condition.

[0133] Step 2, Single-modal data preprocessing:

[0134] Step 2.1, High-speed photography data preprocessing:

[0135] Image denoising and enhancement employs a Gaussian filtering algorithm to remove random noise from the image sequence. The filter kernel size is adjusted according to the noise intensity, typically 3×3 or 5×5. For scenarios with low contrast between debris flow and background, histogram equalization or adaptive threshold segmentation algorithms are used to enhance the boundary features between debris flow particles and the substrate (steel plate, tooling), ensuring the accuracy of subsequent particle recognition.

[0136] Image distortion correction is performed based on the intrinsic parameters (focal length, pixel size, principal point coordinates) and extrinsic parameters (installation position, shooting angle) of the high-speed camera. The Zhang Zhengyou calibration method is used to correct the distortion of the image sequence of each camera, eliminating the influence of lens fisheye distortion and perspective distortion on motion parameter calculation. For the overlapping area captured by multiple cameras, the SIFT algorithm is used to match feature points to realize image stitching and construct a complete trajectory view of debris flow motion.

[0137] Invalid frame removal: Automatically identify and remove invalid image frames in the early stage of acquisition (before the debris flow enters the shooting area) and the late stage (after the debris flow has completely accumulated); For blurry frames caused by equipment shaking or sudden changes in light, use the variance method to evaluate image sharpness and filter and remove them, retaining the valid motion frame sequence.

[0138] Step 2.2, Preprocessing of kinetic sensor data:

[0139] Signal denoising involves using low-pass filtering to remove high-frequency noise from the original sensor signal, such as equipment vibration and electromagnetic interference. The cutoff frequency is determined based on the sensor sampling frequency and signal characteristics, and is typically 1 / 10 to 1 / 5 of the sampling frequency. Baseline drift (such as sensor zero drift) in impact force and acceleration signals is corrected using a moving average method to ensure signal baseline stability.

[0140] Outlier handling is based on the 3σ criterion, which defines outliers as values ​​that exceed the mean ± 3 standard deviations under a normal distribution. Abnormal peak values ​​in sensor data (such as transient interference signals caused by equipment collisions) are identified. If the number of outliers is small, linear interpolation is used to complete the outliers. If the outliers are continuous and short, adjacent valid data segments are used for smooth transition to avoid affecting subsequent dynamic parameter calculations.

[0141] Signal standardization involves converting the original electrical signal (unit: mV) into a physical quantity signal (impact force unit: kN; acceleration unit: m / s²) based on sensor calibration coefficients (e.g., impact force sensor: 1mV corresponds to 0.1kN; acceleration sensor: 1mV corresponds to 0.1m / s²). For multiple sets of sensor data at the same installation location (e.g., two triaxial impact force sensors at the same bend), the average value method is used for data fusion to reduce individual device errors.

[0142] Preprocessing the data allows the scattered raw data to be used directly, transforming unstructured and semi-structured raw data such as high-speed photography image sequences and sensor electrical signals into standardized physical quantity parameters (such as m / s, kN, m / s²), eliminating data format barriers.

[0143] Distortion correction and invalid frame removal for kinematic data, as well as outlier handling and multi-sensor fusion for dynamic data, effectively reduce the impact of equipment errors and environmental interference. Standardized parameter calculation procedures (such as flow velocity formulas and impact impulse integral methods) ensure data consistency and comparability, making the analytical results more convincing.

[0144] Step 3, Single-modal data parsing and parameter extraction;

[0145] Step 3.1, high-speed photography data analysis and kinematic parameter calculation:

[0146] Particle identification and tracking employs particle image velocimetry technology to analyze preprocessed image sequences. In two adjacent frames, a cross-correlation algorithm is used to identify the displacement vector of the debris flow particle group.

[0147] For a single feature particle (such as a particle with a large particle size and high recognizability), particle image velocimetry technology is used, and its position coordinates (x, y, t) in continuous frames are tracked by template matching algorithm to construct the motion trajectory of a single particle.

[0148] Calculation of core kinematic parameters, including flow velocity, flow depth, frontal trajectory, and depositional morphology parameters:

[0149] Velocity calculation: Based on the particle displacement vector and time interval, calculate the overall velocity (average velocity) and local velocity (such as the instantaneous velocity at bends and steep slopes) of the debris flow. The formula is: velocity v = Δx / Δt, where Δx is the displacement vector and Δt is the time interval.

[0150] Flow depth calculation: In a cross-sectional image perpendicular to the direction of debris flow movement, the boundary between the base and the bottom of the debris flow and the top of the debris flow and the air is identified by an edge detection algorithm. The vertical distance between the two boundaries is the flow depth.

[0151] Forward trajectory calculation: Track the position coordinates of the leading edge particles of the debris flow, fit the forward trajectory curve, and calculate the time for the forward to reach each monitoring point (such as the time to reach the bend and the time to reach the accumulation zone).

[0152] Accumulation morphology parameters: After the debris flow stops accumulating, the contour of the accumulation body is extracted by image segmentation algorithm, and the accumulation length, accumulation width, accumulation height and accumulation angle are calculated.

[0153] Step 3.2, Analysis of kinetic sensor data and calculation of kinetic parameters:

[0154] Analysis of three-dimensional impact force parameters, including instantaneous peak impact force, impact force time history curve, and impact force impulse:

[0155] Instantaneous impact peak: Identify the maximum impact force in the X / Y / Z directions in the time series, which is the instantaneous impact peak, and record the time when the peak occurs (e.g., the time when debris flow impacts the steel plate of the curve).

[0156] Impact force time history curves: Plot impact force time history curves in the X, Y, and Z directions with time as the horizontal axis and impact force as the vertical axis to analyze the variation of impact force with time (such as impact rise time, fall time, and duration).

[0157] Impact impulse: The impact impulse (unit: kN·s) is calculated by integrating the impact time history curve (the integration interval is the impact duration), which reflects the total impact of debris flow on the substrate.

[0158] Instantaneous acceleration peak value analysis: Identify the maximum acceleration values ​​in the X / Y / Z directions, which are the instantaneous acceleration peak values. Combine this with time information to determine the correlation between the acceleration peak value and the impact force peak value (e.g., whether the acceleration peak value and the impact force peak value occur synchronously).

[0159] Acceleration power spectrum analysis: Fourier transform is used to convert the acceleration time domain signal into a frequency domain signal to obtain the acceleration power spectral density curve, and the main vibration frequencies of debris flow motion (such as high-frequency vibration caused by particle collision and low-frequency vibration caused by overall motion) are analyzed.

[0160] Motion state judgment: The motion state of the debris flow is judged by the trend of the acceleration signal (e.g., when the acceleration is positive, the debris flow accelerates; when the acceleration is negative, the debris flow decelerates; when the acceleration is zero, the debris flow moves at a constant speed).

[0161] The technical effects of single-modal data analysis and parameter extraction are as follows: Kinematic parameter analysis: It completely restores the entire process of debris flow motion. Through particle identification, trajectory tracking and core parameter calculation, it obtains key data such as flow velocity, flow depth, front trajectory and accumulation morphology, and comprehensively depicts the motion law of debris flow from initiation, migration to accumulation. The non-contact measurement method avoids interference with debris flow motion and ensures the authenticity of motion parameters. At the same time, it quantifies local motion differences that are difficult to capture by traditional methods (such as the difference in flow velocity between curves and straight sections).

[0162] Dynamic parameter analysis: Precisely capturing core characteristics of the dynamic response, this method analyzes parameters such as peak impact force, time history curves, peak impulse and acceleration, and vibration frequency, directly quantifying core dynamic behaviors such as the interaction between debris flow and the substrate, and interparticle collisions. It achieves a multi-dimensional description of the dynamic response, clearly defining instantaneous peak characteristics, understanding their time-varying patterns, and identifying vibration sources through power spectrum analysis, providing precise data support for mechanism research.

[0163] Step 4, Spatiotemporal alignment and fusion of multimodal data:

[0164] Step 4.1, Timeline Alignment;

[0165] With the trigger time of the unified timecode generator as time 0, the time axis of the high-speed photography data and the time axis of the dynamic sensor data are calibrated. The time of the high-speed photography data is the frame time t1 calculated based on the frame rate, and the time axis of the dynamic sensor data is the sampling time t2 calculated based on the sampling frequency, ensuring that t1=t2.

[0166] For data with slight time deviations (such as time differences caused by frame rate fluctuations in high-speed cameras), linear interpolation is used to adjust the time coordinates of the data points to make the time resolution of the two types of data consistent (e.g., uniformly adjusted to 1ms / data point).

[0167] Step 4.2, spatial axis alignment;

[0168] Establish a three-dimensional spatial coordinate system for the test system: with the starting end of the main frame as the origin (0,0,0), the X-axis (direction of motion) along the length of the frame, the Y-axis (lateral direction) perpendicular to the length of the frame, and the Z-axis (height direction) perpendicular to the ground.

[0169] The position coordinates (image pixel coordinates) in the high-speed photography data are converted into three-dimensional spatial coordinates (X,Y,Z) through camera calibration parameters, which are the spatial coordinates of debris flow movement;

[0170] The installation location of the dynamic sensor (such as the outer steel plate of the curve) is located in a three-dimensional coordinate system to obtain the spatial coordinates (X0, Y0, Z0) of the sensor.

[0171] By associating the spatial coordinates of debris flow motion with the spatial coordinates of sensors, spatial correlation between motion position and dynamic response is achieved. The specific steps are as follows:

[0172] Spatial intersection judgment: Calculate in real time whether the spatial coordinates (X(t), Y(t), Z(t)) of the debris flow at time t intersect with the effective monitoring range of each sensor. (X0_min, Y0_min, Z0_min) are the coordinates of the minimum effective monitoring range of the sensor, and (X0_max, Y0_max, Z0_max) are the coordinates of the maximum effective monitoring range of the sensor. Determine whether (X(t), Y(t), Z(t)) intersects with the effective monitoring range of each sensor: If X(t)∈[X0_min, X0_max], Y(t)∈[Y0_min, Y0_max], Z(t)∈[Z0_min, Z0_max], then it is determined that the debris flow particles entered the monitoring area of ​​the sensor at time t.

[0173] Time synchronization matching: Combining the synchronous time axis of the unified time code generator (t=0 is the test start time), when debris flow particles enter the monitoring area of ​​a certain sensor, the dynamic data and acceleration of the sensor before and after that moment are extracted synchronously to achieve time stamp alignment of motion position and dynamic response;

[0174] Population association optimization: For a debris flow particle group, calculate the center coordinates of the particle group (X_g(t)=∑X(t) / N,Y_g(t)=∑Y(t) / N,Z_g(t)=∑Z(t) / N), where N is the number of particles. When the center coordinates of the group enter the sensor monitoring range, associate them with the average dynamic data of the sensor during that period to avoid association errors caused by abnormal displacement of a single particle.

[0175] Step 4.3, Multimodal Data Fusion and Association:

[0176] Matrix dimension definition:

[0177] The time dimension is represented as rows, with the test start time t=0 of the unified timecode generator as the baseline, and time nodes are divided according to fixed time intervals (e.g., 1ms / step), covering the entire cycle of debris flow from start-up, migration to accumulation; the spatial dimension is represented as columns, based on the three-dimensional coordinate system of the test system, and spatial positions are divided according to fixed spatial intervals (e.g., 1m / segment), covering the key areas of the debris flow trajectory; the motion dimension is used to reflect the motion state of the debris flow at specific time-space nodes, with the core being the key kinematic parameters of the test; the dynamic dimension is used to reflect the dynamic response of the debris flow at specific time-space nodes, with the core being the key dynamic parameters of the test.

[0178] Time-space node gridding matching:

[0179] Based on the time axis (rows) and spatial axis (columns), a two-dimensional grid (i.e., a time-space node matrix) is constructed. Each grid cell corresponds to a unique t-X1-Y1 coordinate, representing a specific scenario of debris flow at spatial location (X1, Y1) at a certain time t. The specific operation is as follows:

[0180] Traverse all time points t (from t=0 to the end of the experiment), and synchronously traverse all spatial locations (X1, Y1) (covering the entire motion trajectory) to form equally discrete grid cells;

[0181] For each grid cell, spatial intersection is used to determine whether the debris flow particles (or the center of the particle swarm) are within the valid range of the spatial location (X1, Y1) at time t: if the particle coordinates (X1(t), Y1(t)) ∈ [X1_min, X1_max] × [Y1_min, Y1_max], then the grid cell is determined to have valid data; if the particles have not reached or have left the location, it is marked as no data.

[0182] Motion-dynamic parameter filling:

[0183] For the time-space grid cells identified as having valid data, the kinematic and dynamic parameters at the corresponding time and location are extracted and filled into the grid cells to form a complete four-dimensional correlation matrix.

[0184] Motion parameter filling: Extract the flow velocity v and flow depth h corresponding to the t-X1-Y1 node from the kinematic parameter database parsed in step 3.1, and fill them into the motion dimension field of the matrix;

[0185] Dynamic parameter filling: Extract the triaxial impact force F and acceleration a corresponding to the t-X1-Y1 node from the dynamic parameter database analyzed in step 3.2, and fill them into the dynamic dimension field of the matrix;

[0186] Data association verification: If multiple sets of similar data exist at a certain time-space node (such as two accelerometers at the same location), the average value method is used to fuse the data before filling in the data to avoid individual device errors.

[0187] For key event points (such as debris flow impacting a bend or debris flow accelerating downhill), multimodal data of the event point is extracted and combined to form an event data package.

[0188] The technical effects of spatiotemporal alignment and fusion of multimodal data are as follows:

[0189] Precise time synchronization eliminates data time difference interference. By calibrating time 0 using a unified timecode generator and adjusting the data time coordinates using linear interpolation, the time resolution of high-speed photography data (frame time) and sensor data (sampling time) is unified (e.g., 1ms / data point), achieving millisecond-level time synchronization accuracy. This avoids misalignment of motion state and dynamic response time caused by differences in device frame rate and sampling frequency, ensuring precise matching of motion at a given moment with corresponding dynamics in subsequent analysis.

[0190] The integrated spatial positioning establishes a motion-dynamic spatial correlation. Based on the three-dimensional coordinate system of the test system, the pixel coordinates of high-speed photography are converted into three-dimensional spatial coordinates. At the same time, the spatial position of the sensor installation is calibrated to realize the spatial correspondence between the movement position of the debris flow and the monitoring position of the sensor. Through spatial intersection judgment and particle group center coordinate optimization, the motion of the debris flow at a certain spatial position and the dynamic response at that position are accurately correlated, which solves the problem of spatial disconnect between motion and dynamic data in traditional methods.

[0191] By structuring data associations and building a foundation for four-dimensional analysis, a four-dimensional association matrix of time, space, motion, and dynamics is used to structurally integrate dispersed kinematic parameters (flow velocity, flow depth) and dynamic parameters (impact force, acceleration) according to time and location, forming standardized data units. Each data unit can directly correspond to the motion state and dynamic response at a specific time and place, providing clear structured data support for subsequent coupling relationship analysis and key event extraction, significantly reducing the complexity of the analysis.

[0192] Maximizing data value and enhancing the depth of mechanism analysis, spatiotemporal alignment and fusion transform multimodal data from individually effective to synergistically value-added: it allows for tracing the temporal changes in motion and dynamic parameters via the time axis (such as the sequential relationship between flow velocity increase and impact force enhancement), and also allows for comparing the differences in dynamic response at different terrain locations via the spatial axis (such as the difference in impact force between curves and straight sections). At the same time, it avoids the one-sidedness of single-modal data, making the analysis results more comprehensive and more in line with the dynamic characteristics of real disasters.

[0193] Step 5, Multimodal data coupling relationship analysis;

[0194] Step 5.1, Motion-Dynamic Temporal Coupling Analysis:

[0195] Plot biaxial curves of time-flow velocity-impact force and time-flow depth-acceleration to visually observe the temporal variation of kinematic and dynamic parameters: for example, analyze whether the peak flow velocity occurs before the peak impact force (if the increase in flow velocity leads to an increase in impact, then the peak flow velocity will occur first).

[0196] Calculate the correlation coefficient between kinematic parameters and dynamic parameters (such as the Pearson correlation coefficient between flow velocity and impact force) to quantify the degree of linear correlation between the two. A correlation coefficient close to 1 indicates a strong positive correlation, close to -1 indicates a strong negative correlation, and close to 0 indicates no significant correlation.

[0197] Step 5.2, Spatial Location-Dynamic Response Coupled Analysis:

[0198] Plot the curves of spatial location-peak impact force and spatial location-peak acceleration along the debris flow trajectory (X-axis direction) to analyze the differences in dynamic response at different terrain locations: for example, compare the peak impact force of straight sections and curved sections (usually the impact force of curved sections is greater), and the peak acceleration of steep slope sections and gentle slope sections (the acceleration of steep slope sections is greater).

[0199] By combining topographic parameters (such as slope angle and radius of curvature), establish a correlation model of topographic parameters, motion parameters, and dynamic parameters: for example, analyze the increase in flow velocity and impact force for every 5° increase in slope angle; analyze whether the peak impact force is greater when the radius of curvature of the S-bend tool is smaller.

[0200] Step 5.3, Anomaly Attribution Analysis:

[0201] If an abnormal situation occurs where the flow velocity increases but the impact force does not increase, analyze the high-speed photography images to determine if there is debris flow diversion (such as a V-shaped base causing the debris flow to disperse to both sides, increasing the impact area but decreasing the impact force per unit area).

[0202] If the peak impact force and the peak acceleration are out of sync, analyze the sensor installation location to determine if there is a force transmission delay (e.g., deformation of the steel plate causes the impact force signal to lag behind the acceleration signal).

[0203] Quantifying parameter correlations reveals temporal patterns. By plotting biaxial curves and calculating correlation coefficients, the linear correlation between kinematic parameters (flow velocity, flow depth) and dynamic parameters (impact force, acceleration) is clarified, allowing the originally scattered parameters to form a quantifiable temporal logic and avoiding subjective judgments about parameter relationships.

[0204] By linking topography and dynamics, clarifying spatial response differences, and analyzing the dynamic response differences at different topographic locations (straight / curved, steep / gentle, V-shaped / S-shaped base) along the debris flow trajectory, a correlation model of topographic parameters, motion parameters, and dynamic parameters is established.

[0205] The conclusions of the coupling relationship analysis (such as a 30% increase in flow velocity at bends leading to a 50% increase in impact force) directly support the deciphering of core dynamic mechanisms such as debris flow-substrate interaction and interparticle collisions. At the same time, they provide quantitative basis for the development of monitoring and early warning equipment (such as setting impact force early warning values ​​based on flow velocity thresholds) and engineering protection optimization (such as strengthening impact-resistant design at bends), so that experimental data can be truly transformed into practical technical support.

[0206] Step 6: Feature parameter integration and database construction;

[0207] Step 6.1, Extraction and Standardization of Core Feature Parameters:

[0208] The core feature parameters required for experimental analysis were extracted from the fused data and categorized as follows:

[0209] Operating parameters: terrain type (e.g., S-curve + V-shaped base), slope angle (°), test material (e.g., graded crushed stone), material volume (m³);

[0210] Kinematic parameters: mean velocity (m / s), maximum velocity (m / s), mean depth (m), frontal movement distance (m), angle of accumulation (°);

[0211] Dynamic characteristic parameters: peak triaxial impact force (kN), average impact force (kN), impact impulse (kN·s), peak triaxial acceleration (m / s²), and dominant vibration frequency (Hz);

[0212] Standardize the characteristic parameters: for example, normalize the flow velocity according to the flow velocity at a slope angle of 30° to facilitate comparison between different working conditions.

[0213] Step 6.2, Experimental database construction;

[0214] A database for simulating geological disaster dynamics experiments can be established using relational databases (such as MySQL) or non-relational databases (such as MongoDB).

[0215] The database table structure design includes: experimental condition table (stores condition parameters), kinematic parameter table (stores kinematic characteristic parameters), dynamic parameter table (stores dynamic characteristic parameters), and raw data table (associated with and stores the preprocessed raw data path).

[0216] Each test sample is assigned a unique test number, which enables data association between tables, facilitating subsequent retrieval (such as retrieving all characteristic parameters under a slope angle of 30°+S-curve terrain) and cross-condition comparative analysis (such as comparing the difference in peak impact force under different slope angles).

[0217] Step 7: Visualize the analysis results and output the report;

[0218] Step 7.1, Multi-dimensional Visualization:

[0219] Spatiotemporal dynamic visualization: Use MATLAB or Python (Matplotlib library) to plot the time history overlay of debris flow trajectory and impact force, and mark the peak impact force at each point on the trajectory curve; use Unity or Blender to build a 3D animation to simulate the debris flow motion process and dynamic response (such as the deformation of the steel plate during impact and the real-time changes of sensor data).

[0220] Visualization of parameter comparison: Bar charts are used to compare the peak impact force and peak velocity under different working conditions; line charts are used to analyze the changing trends of velocity and impact force over time under the same working condition; and heat maps are used to display the velocity distribution in the debris flow region (red indicates high velocity region, and blue indicates low velocity region).

[0221] Step 7.2, Generation of Comprehensive Analysis Report:

[0222] The report includes: an overview of the test conditions, data processing flow (preprocessing methods, fusion methods), single-modal parameter analysis results, conclusions on multimodal coupling relationships (such as the increase in slope angle leading to an increase in flow velocity, which in turn significantly increases the impact force), and analysis and suggestions for abnormal phenomena (such as suggesting optimization of the concave angle of the V-shaped tooling to reduce dynamic data fluctuations caused by debris flow diversion).

[0223] The report includes key visualization charts (such as impact force time history curves, motion trajectory diagrams, and parameter comparison diagrams) and database search paths, providing data support for subsequent research on the dynamic mechanisms of geological disasters (such as the mechanism of debris flow-base interaction) and the development of monitoring and early warning equipment (such as early warning algorithms based on impact force thresholds).

[0224] Step Six: Model Validation and Iterative Optimization: Compare the dynamic parameters (such as distance and velocity) obtained from the experiment with the results of numerical simulation (such as discrete element DEM simulation) or actual field case data to verify the effectiveness of the physical model and the applicability of the similarity law. Based on the comparison results, adjust the similarity ratio, material parameters, or terrain configuration of the physical model, and conduct iterative experiments until the model can reliably reflect the core dynamic characteristics of the prototype disaster.

[0225] The description of this invention is given for illustrative and descriptive purposes only and is not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention and to enable those skilled in the art to understand the invention and design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A high-altitude, remote geological disaster experimental system, characterized in that: This includes the main testing system, electrical control system, sedimentation tank, and dispersion zone; The main unit of the test system includes a main frame (9), and a hopper (1) is arranged on the upper side of the tail end of the main frame (9). The main frame (9) achieves a 45° slope angle off the ground through the thrust of four sets of multi-stage telescopic cylinders (7). The front end of the multi-stage telescopic cylinder is equipped with a force sensor (8), and each telescopic arm connection of the multi-stage telescopic cylinder is equipped with a displacement sensor to achieve synchronous lifting of the four sets of telescopic cylinders, and at the same time display the extension length of each stage of each multi-stage telescopic cylinder. The main frame (9) is equipped with S-bend fixture (3), V-shaped fixture (4), arc fixture (5) and 90-degree and 45-degree combination fixture (6). The S-bend fixture (3) is mainly installed on the side wall of the slide of the main frame (9). Different types of S-bend fixtures (3) are combined according to the bending situation of the channel to be simulated. The V-shaped fixture (4) is installed on the base of the slide of the main frame (9). The arc fixture (5) and the 90-degree and 45-degree combination fixture (6) need to be used at the same time. The arc fixture (5) is installed on the slide of the main frame (9) near the outlet, and its outermost side is aligned with the inlet of the 90-degree and 45-degree combination fixture. The 90-degree and 45-degree combination fixture is installed on the right side wall of the slide of the main frame (9) near the outlet. The main frame (9) has a base plate (2) inside. The base plate (2) is made of hot-rolled steel plate with convex and concave splicing to form 5 bending shafts (11) of the same shape, and divides 6 independent overturning plate surfaces (12). Two rows of paired variable amplitude cylinders (13) are installed at the bottom. By controlling the extension and retraction of the variable amplitude cylinders (13), the different overall and local angle transformation requirements of the chute base are simulated. The base plate (2) is fixed to the main frame (9) by the variable amplitude cylinder at the bottom of the bending shaft (11). The variable amplitude cylinder installed at the bottom of the bending shaft (11) is controlled and monitored by a laser displacement sensor and synchronously controlled by a proportional servo valve. By driving the variable amplitude cylinder (13) under each bending shaft (11), the angle between the independent plate surfaces (12) is changed. The main frame (9) reaches a slope angle of 45° from the ground. The test conditions with a maximum slope angle of 60° are simulated by adjusting the angle between the base plates (2), and the undulating slope requirements are simulated. The electronic control system adopts hydraulic control. The hydraulic supply of the hydraulic control is divided into three parts: multi-stage telescopic cylinder, 90-degree and 45-degree combined tooling and variable amplitude cylinder, which are supplied by two sets of oil sources respectively. The electronic control system is equipped with visual recording and data capture monitoring functions. It acquires dynamic data by installing high-speed cameras, three-dimensional impact force sensors and acceleration sensors.

2. An implementation method of a high-position remote geological disaster experiment system, characterized in that: The implementation method is applied to the high-altitude remote geological disaster experimental system as described in claim 1, and includes the following steps: Step 1: Establish a physical model based on reconfigurable terrain; Step 2: Adjust the local micro-topography according to the actual situation of the simulated geological disaster terrain, which is divided into three types of micro-topography: longitudinal undulation of the base, transverse V-shape of the base, and lateral S-shape of the gully. Step 3: Synchronous monitoring and data acquisition of multi-source heterogeneous sensing systems. Sensor units are deployed at key locations in the physical model to build a monitoring network, enabling synchronous acquisition and transmission of measurement and data for two monitoring types: high-speed photography and dynamic parameters. Step 4: Experiment-driven and process execution. The experiment will be conducted after the physical model and monitoring system are ready. Step 5: Fusion processing and analysis of multimodal data; Step 6: Model Validation and Iterative Optimization. Compare the dynamic parameters obtained from the experiment with the numerical simulation results or actual field case data to verify the effectiveness of the physical model and the applicability of the similarity law.

3. The implementation method of a high-altitude remote geological disaster experimental system as described in claim 2, characterized in that: The implementation of step two specifically includes the following steps: For the longitudinal undulating type of the base, by controlling the two rows of variable amplitude oil cylinders at the bottom of the five bending shafts (11), the independent panels are driven to generate different tilt angles to superimpose on the overall slope to generate a continuous undulating base shape according to the required simulated base undulation shape. For the simulation of the transverse V-shaped channel of the base, the base plate (2) is adjusted to a straight state, and then the assembled V-shaped tooling is installed on the base with bolts; For the lateral S-shaped working condition of the channel, the assembled S-bend fixture is installed along the side wall of the chute using bolts. When it is necessary to simulate the outlet of the flow area with different inclination angles, the arc-shaped fixture is installed at the bottom of the chute and connected to the inlet of the 90-degree and 45-degree combined fixture, and the steel plate at the inlet of the 90-degree and 45-degree combined fixture is opened. When the angle between the simulated terrain outlet and the ditch is 90 degrees, move the steel plate to make the 90-degree assembly tooling unobstructed. When the angle between the simulated terrain outlet and the ditch is 45 degrees, move the steel plate to make the 45-degree tooling unobstructed for unloading.

4. The implementation method of a high-altitude remote geological disaster experimental system as described in claim 2, characterized in that: The implementation of step five specifically includes the following steps: Step 1: Collect raw data by category, including high-speed photography data and dynamic sensor data. High-speed photography data is a sequence of images captured by multiple high-speed cameras deployed along the debris flow trajectory. Each device is named according to device number, acquisition time, and shooting area. Dynamic sensor data is the raw electrical signal data output by triaxial impact force sensors and acceleration sensors installed at key impact points of the debris flow. It is classified according to sensor number, installation location, and acquisition time. Step 2: Single-modal data preprocessing, including signal denoising, outlier handling, and signal standardization. Step 3, Single-modal data parsing and parameter extraction; Step 4: Spatiotemporal alignment and fusion of multimodal data; Step 5: Multimodal data coupling relationship analysis; Step 6: Feature parameter integration and database construction; Step 7: Visualize the analysis results and output the report.

5. The implementation method of a high-altitude remote geological disaster experimental system as described in claim 4, characterized in that: The implementation of step 3 specifically includes the following steps: Step 3.1, high-speed photography data analysis and kinematic parameter calculation: Particle identification and tracking employs particle image velocimetry technology to analyze preprocessed image sequences. In two adjacent frames, a cross-correlation algorithm is used to identify the displacement vector of the debris flow particle group. For a single feature particle, particle image velocimetry is used, and its position coordinates (x, y, t) in consecutive frames are tracked by template matching algorithm to construct the motion trajectory of a single particle; The calculation of core kinematic parameters includes flow velocity calculation, flow depth calculation, frontal trajectory calculation, and depositional morphology parameters; Step 3.2, Analysis of kinetic sensor data and calculation of kinetic parameters: Analysis of triaxial impact force parameters, including peak instantaneous impact force, impact force time history curve, and impact force impulse: Peak instantaneous impact force: Identify the maximum impact force in the X / Y / Z directions in the time series, which is the peak instantaneous impact force, and record the time when the peak occurs; Impact force time history curves: Plot the impact force time history curves in the X, Y, and Z directions with time as the horizontal axis and impact force as the vertical axis to analyze the variation of impact force over time. Impact impulse: The impact impulse is calculated by integrating the impact time history curve, which reflects the total impact of debris flow on the substrate. Analysis of instantaneous acceleration peak value: Identify the maximum acceleration values ​​in the X / Y / Z directions, which are the instantaneous acceleration peak values. Combine this with time information to determine the correlation between the acceleration peak value and the impact force peak value. Acceleration power spectrum analysis: Fourier transform is used to convert the acceleration time-domain signal into a frequency-domain signal to obtain the acceleration power spectral density curve, and the main vibration frequencies of debris flow motion are analyzed. Motion state assessment: The motion state of debris flow is determined by the changing trend of the acceleration signal.

6. The method for implementing a high-altitude remote geological disaster experimental system as described in claim 4, characterized in that: The implementation of step 4 specifically includes the following steps: Step 4.1, Timeline Alignment; With the trigger time of the unified timecode generator as time 0, the time axis of the high-speed photography data and the time axis of the dynamic sensor data are calibrated. The time of the high-speed photography data is the frame time t1 calculated based on the frame rate, and the time axis of the dynamic sensor data is the sampling time t2 calculated based on the sampling frequency, ensuring that t1=t2. For data with slight time deviations, linear interpolation is used to adjust the time coordinates of the data points to make the time resolution of the two types of data consistent. Step 4.2, spatial axis alignment; Establish a three-dimensional spatial coordinate system for the test system: with the starting end of the main frame as the origin (0,0,0), the X-axis along the length of the frame, the Y-axis perpendicular to the length of the frame, and the Z-axis perpendicular to the ground; The position coordinates in the high-speed photography data are converted into three-dimensional spatial coordinates (X,Y,Z) through camera calibration parameters, which are the spatial coordinates of debris flow movement. The installation position of the dynamic sensor is located in a three-dimensional coordinate system to obtain the spatial coordinates (X0, Y0, Z0) of the sensor. By associating the spatial coordinates of debris flow motion with the spatial coordinates of dynamic sensors, spatial correlation between motion position and dynamic response is achieved. The specific steps are as follows: Spatial intersection judgment: Calculate in real time whether the spatial coordinates (X(t), Y(t), Z(t)) of the debris flow at time t intersect with the effective monitoring range of each sensor. (X0_min, Y0_min, Z0_min) are the coordinates of the minimum effective monitoring range of the dynamic sensor, and (X0_max, Y0_max, Z0_max) are the coordinates of the maximum effective monitoring range of the dynamic sensor. Determine whether (X(t), Y(t), Z(t)) intersects with the effective monitoring range of each dynamic sensor: If X(t)∈[X0_min, X0_max], Y(t)∈[Y0_min, Y0_max], Z(t)∈[Z0_min, Z0_max], then it is determined that the debris flow particles entered the monitoring area of ​​the sensor at time t. Time synchronization matching: Combining the synchronized time axis of the unified timecode generator, when debris flow particles enter the monitoring area of ​​a certain sensor, the dynamic data and acceleration of the sensor before and after time t are extracted synchronously to achieve time stamp alignment of motion position and dynamic response; Group correlation optimization: For a group of debris flow particles, calculate the center coordinates of the particle group (X_g(t)=∑X(t) / N,Y_g(t)=∑Y(t) / N,Z_g(t)=∑Z(t) / N), where N is the number of particles. When the center coordinates of the group enter the sensor monitoring range, correlate the average dynamic data of the sensor during the correlation period to avoid correlation errors caused by abnormal displacement of a single particle. Step 4.3, Multimodal Data Fusion and Association: Matrix dimension definition: The time dimension is used as rows, with the test start time t=0 of the unified timecode generator as the benchmark, and time nodes are divided according to fixed time intervals, covering the entire cycle of debris flow from start-up, migration to accumulation; the spatial dimension is used as columns, based on the three-dimensional coordinate system of the test system, and spatial positions are divided according to fixed spatial intervals, covering the key areas of the debris flow trajectory; the kinematic dimension is used to reflect the motion state of the debris flow at specific time-space nodes, with the core being the key kinematic parameters of the test; the dynamic dimension is used to reflect the dynamic response of the debris flow at specific time-space nodes, with the core being the key dynamic parameters of the test. Time-space node mesh matching: Based on the time and space axes, a two-dimensional mesh is constructed. Each mesh cell corresponds to a unique t-X1-Y1 coordinate, representing a specific scenario of debris flow at spatial location (X1, Y1) at a certain time t. The specific operation is as follows: Traverse all time nodes t and synchronously traverse all spatial locations (X1, Y1) to form equally discrete grid cells; For each grid cell, spatial intersection is used to determine whether the debris flow particles are within the valid range of spatial position (X1, Y1) at time t: if the particle coordinates (X1(t), Y1(t)) ∈ [X1_min, X1_max] × [Y1_min, Y1_max], then the grid cell is determined to have valid data; if the particles have not reached or have left the spatial position (X1, Y1), then it is marked as no data. Motion-dynamic parameter filling: For the time-space grid cells identified as containing valid data, the kinematic and dynamic parameters at the corresponding time and location are extracted and filled into the grid cells to form a complete four-dimensional correlation matrix. Motion parameter filling: Extract the flow velocity v and flow depth h corresponding to the t-X1-Y1 node from the kinematic parameter database and fill them into the motion dimension field of the matrix; Dynamic parameter filling: Extract the triaxial impact force F and acceleration a corresponding to the t-X1-Y1 node from the dynamic parameter database and fill them into the dynamic dimension field of the matrix; Data association verification: If multiple sets of similar data exist at a certain time-space node, the average value method is used to fuse the data before filling in the data to avoid individual device errors. For key event points, multimodal data of the key event points are extracted and combined to form event data packets.

7. The method for implementing a high-altitude remote geological disaster experimental system as described in claim 4, characterized in that: The implementation of step 5 specifically includes the following steps: Step 5.1, Motion-Dynamic Temporal Coupling Analysis: Plot biaxial curves of time-flow velocity-impact force and time-flow depth-acceleration to visually observe the temporal variation of kinematic and dynamic parameters; Calculate the correlation coefficient between kinematic parameters and dynamic parameters to quantify the degree of linear association between the two. A correlation coefficient close to 1 indicates a strong positive correlation, close to -1 indicates a strong negative correlation, and close to 0 indicates no significant association. Step 5.2, Spatial Location-Dynamic Response Coupled Analysis: Plot curves along the debris flow trajectory, showing the relationship between spatial location and peak impact force, and between spatial location and peak acceleration, to analyze the differences in dynamic response at different terrain locations. By combining terrain parameters, a correlation model of terrain parameters, motion parameters, and dynamic parameters is established; Step 5.3, Anomaly Attribution Analysis: If an abnormal situation occurs where the flow velocity increases but the impact force does not increase, analyze the high-speed photography images to determine if debris flow diversion exists. If the peak impact force and peak acceleration are not synchronized, analyze whether there is a force transmission delay in conjunction with the sensor installation location; Quantifying parameter correlations reveals temporal patterns. By plotting biaxial curves and calculating correlation coefficients, the linear correlation between kinematic and dynamic parameters is clarified, allowing previously scattered parameters to form a quantifiable temporal logic and avoiding subjective judgments about parameter relationships. By linking topography and dynamics, clarifying spatial response differences, analyzing the dynamic response differences at different topographic locations along the debris flow trajectory, and establishing a correlation model of topographic parameters, motion parameters, and dynamic parameters.

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