Mine multidirectional landslide early warning processing simulation experiment platform

Abstract

The invention discloses a mine multidirectional landslide early warning processing simulation experiment platform, and belongs to the technical field of mine engineering, the platform comprises three core systems: 1, a side slope physical simulation system, a main body model groove with a transparent observation window, a multi-cause device capable of simulating rainfall, water level and vibration, and a configurable rock-soil body material; 2, a multi-dimensional sensor monitoring network can synchronously monitor the internal, surface and environment information of the slope; and 3, the data acquisition and intelligent analysis system synchronously integrates data through a multi-channel high-speed acquisition instrument, and calculates an instability comprehensive index based on multi-source information fusion to realize multi-stage dynamic early warning. The platform can simulate four types of typical landslide scenes, collaborative observation of internal and external processes is realized through a transparent window, actual mine slope monitoring can be directly guided through a threatened feature database and an early warning threshold value established through experiments, the early warning accuracy and timeliness are remarkably improved, and reliable support is provided for landslide mechanism research and engineering early warning.

Description

Technical Field

[0001] This invention relates to the field of mining engineering technology, and in particular to a simulation experimental platform for multi-directional landslide early warning and treatment in mines. Background Technology

[0002] Slope stability in mines is crucial for ensuring the safe extraction of mineral resources and the safety of personnel. To study landslide mechanisms and develop effective early warning technologies, physical simulation using simulation platforms is an important method. However, existing mine landslide simulation platforms and their early warning methods have significant limitations.

[0003] First, in terms of causation simulation, most existing platforms are limited in function. They can usually only simulate a single type of landslide causation (such as simulating only rainfall or only vibration), making it difficult to reproduce the complex landslide scenarios under the coupled effects of multiple factors in real mining environments (such as rainfall infiltration, groundwater level fluctuations, earthquake or blasting vibrations, engineering excavation, etc.), resulting in deviations between experimental conclusions and actual working conditions.

[0004] Secondly, in terms of monitoring methods, existing technologies suffer from incomplete monitoring dimensions and asynchronous data. Traditional experimental platforms have sparse monitoring points or only focus on surface displacement monitoring, failing to comprehensively capture the evolution of key physical fields within the slope (such as pore water pressure distribution and the incubation process of potential slip surfaces). Furthermore, monitoring data from different sources, such as internal response, surface deformation, and microseismic activity, are often independent of each other, lacking spatiotemporal synchronization, making it difficult to establish a complete correlation from microscopic rupture to macroscopic instability.

[0005] Furthermore, existing early warning models generally suffer from delayed warnings and high false alarm rates. This is mainly because they typically rely on single, static early warning indicators (such as cumulative displacement thresholds), which fail to effectively identify early warning signs of landslides. Such "black box" or overly simplified early warning models are ill-suited to the complex dynamic behavior of slope instability under different geological conditions and triggers, resulting in insufficient reliability in practical applications. Summary of the Invention

[0006] The main objective of this invention is to provide a simulation experimental platform for early warning and treatment of landslides in mines from multiple directions, which can effectively solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A simulation experimental platform for early warning and handling of multi-directional landslides in mines includes:

[0009] The slope physical simulation system includes a main model tank with a transparent observation window, various landslide induction simulation devices, and configurable soil and rock materials;

[0010] A multi-dimensional sensor monitoring network, including sensor arrays for monitoring internal slope response, surface deformation, and environmental and physical field information;

[0011] The data acquisition and intelligent analysis system includes a data acquisition device for synchronously acquiring data from the sensor array and a data analysis module based on multi-source information fusion.

[0012] The data analysis module achieves multi-level early warning by establishing a safety baseline, identifying abnormal deviations, calculating an instability comprehensive index, and dynamic threshold early warning.

[0013] This platform integrates three core components to achieve a closed-loop experimental process from landslide causation simulation and full-process monitoring to intelligent early warning. Its core innovation lies in the organic integration of multiple technologies, which solves the problems of traditional experimental platforms having limited functions and incomplete monitoring.

[0014] Preferably, the multiple landslide causation simulation device includes a precision rainfall system, a water level control system, and a vibration simulation system. The precision rainfall system includes multiple independently controlled spray nozzles, simulating different intensities of rainfall by adjusting water pressure and switching combinations. The water level control system achieves precise control of the groundwater level through feedback from a liquid level sensor. The vibration simulation system can input real seismic waves to simulate seismic disturbances. Through these three independent causation simulation devices and their precise control methods, this platform can reproduce the individual or coupled effects of multiple disaster-causing factors in the real environment, overcoming the technical limitation of existing experimental devices that can only simulate a single causation factor.

[0015] Preferably, the sensors used to monitor the internal response of the slope include miniature pore water pressure sensors arranged in a grid along the slope's height and depth, miniature earth pressure cells deployed near potential slip surfaces, and inclinometers and miniature tiltmeters used to monitor deep soil deformation and the location of potential slip surfaces. By deploying multi-type sensor arrays at key locations within the slope, precise capture of pore water pressure evolution, stress state changes, and the formation process of potential slip surfaces is achieved, overcoming the limitations of traditional experiments that can only observe surface phenomena.

[0016] Preferably, the sensors used to monitor surface deformation include high-precision displacement sensors, a distributed fiber optic sensor network arranged in a grid on the slope surface, and a high-speed industrial camera that acquires full-field displacement and deformation data of the slope surface by aligning with the transparent observation window and using digital image correlation analysis. By combining contact and non-contact measurement technologies, a surface deformation monitoring system from point to surface is constructed. In particular, the digital image correlation analysis method enables the visualized measurement and quantitative analysis of full-field deformation of the slope surface.

[0017] Preferably, the sensors used to monitor environmental and physical field information include a microseismic / acoustic emission sensor array deployed around the model trench to capture elastic wave signals from micro-fractures within the soil and rock mass, and a laser scanner for generating a high-precision three-dimensional point cloud model to quantify changes in landform morphology. By combining acoustic signal capture with three-dimensional morphological scanning technology, cross-scale monitoring from internal micro-fracture activity to external morphological changes is achieved, providing multi-dimensional data support for studying the entire process of landslide formation.

[0018] Preferably, the data analysis module calculates the instability composite index by weighted fusion of pore water pressure deviation, microseismic event rate deviation, displacement rate deviation, and internal strain deviation. The calculation formula is as follows:

[0019] I=α1·ΔP / ΔP0+α2·ΔE / ΔE0+α3·ΔD / ΔD0+α4·ΔS / ΔS0

[0020] Where I represents the instability comprehensive index, ΔP / ΔP0 represents the pore water pressure deviation, ΔE / ΔE0 represents the microseismic event rate deviation, ΔD / ΔD0 represents the displacement rate deviation, ΔS / ΔS0 represents the internal strain deviation, and α1 to α4 are weighting coefficients. This instability comprehensive index algorithm comprehensively considers the coordinated changes of multiple physical field parameters such as hydrology, mechanics, and deformation, and establishes a dynamic early warning model based on multi-source information fusion, significantly improving the accuracy and reliability of early warning.

[0021] Preferably, the platform enables coordinated observation and spatiotemporal correlation analysis of the internal seepage process, potential slip surface formation process, surface displacement field evolution, and crack development of the slope through the transparent observation window. The design of the transparent observation window allows for simultaneous observation and correlation analysis of the internal seepage process, slip surface formation mechanism, and surface deformation characteristics, realizing the visualization study of the landslide "black box" process.

[0022] Preferably, the platform can activate the various landslide trigger simulation devices individually or in combination to simulate four typical landslide scenarios: rainfall-induced, sudden water level drop, vibration-induced, and engineering activity-induced. By flexibly combining various trigger simulation devices, the platform can systematically study the disaster-causing mechanisms of different types of landslides, providing an experimental basis for establishing a comprehensive landslide classification and early warning theoretical system.

[0023] Preferably, the data acquisition device is a multi-channel high-speed data acquisition instrument, which realizes synchronous acquisition of data from all sensors and ensures the temporal consistency of data from different monitoring methods. The use of synchronous trigger acquisition technology ensures the temporal consistency of monitoring data from different sources and of different types, providing key technical support for the fusion analysis and collaborative early warning of multi-source information.

[0024] Preferably, the precursor feature database and early warning thresholds established through the platform can be applied to actual mine slope monitoring and early warning. By comparing the early warning thresholds obtained from experiments with field monitoring data, accurate landslide early warning can be achieved. The "experiment-field" linkage mechanism established by this platform enables experimental research results to directly guide the monitoring and early warning of actual mine slopes, forming a complete technology chain from theoretical research to engineering application.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] This invention constructs a comprehensive experimental platform integrating multi-causal simulation, multi-dimensional monitoring, and multi-information fusion early warning. By combining a transparent model tank with an internal sensor array, it achieves coordinated observation of the entire process of slope deformation induced by multiple factors such as rainfall, sudden water level drop, and vibration, from internal seepage and slip surface formation to surface deformation. Based on the instability comprehensive index established by multi-source data fusion, it realizes progressive dynamic early warning, effectively overcoming the limitations of traditional methods such as delayed early warning and high false alarm rate. It significantly improves the accuracy and timeliness of landslide early warning, providing reliable technical support for the research and monitoring and early warning of mine slope disaster mechanisms. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the structural composition of the present invention;

[0028] Figure 2 This is a schematic diagram of the experimental process of the present invention. Detailed Implementation

[0029] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0030] I. Experimental Objective

[0031] like Figures 1-2 As shown, this embodiment uses the geological conditions and common disaster-causing factors (rainfall, groundwater level fluctuations, blasting vibrations) of an actual mine slope to verify the platform's ability to monitor the entire process of slope evolution from internal seepage to surface instability. It establishes a landslide precursor feature database and dynamic early warning threshold suitable for the mine, and ultimately provides experimental support for on-site slope monitoring and early warning.

[0032] II. Experimental Platform Setup

[0033] (I) Configuration of Slope Physical Simulation System

[0034] Main model tank: It adopts a steel structure frame + tempered glass transparent observation window design, with dimensions of 3.0m long × 0.8m wide × 1.5m high; the transparent window is made of 15mm thick ultra-clear tempered glass with a light transmittance of ≥92%, ensuring clear observation of the internal seepage trajectory and the formation process of the slip surface. A millimeter-level coordinate grid is attached to the outside of the window to facilitate the location of internal cracks and deformation.

[0035] Soil and rock mass material configuration: Based on the actual soil and rock mass composition of this iron mine slope (crushed stone + medium sand + silty clay = 6:3:1), the physical parameters were converted according to the scale ratio: compaction density 1.8 g / cm³. 3 (A layered compaction method is adopted, with each layer of soil 20cm thick and compacted 3 times with a small road roller to ensure consistent compaction of each layer.) The natural moisture content is 12% (adjusted by spray watering and drying, and calibrated in real time with a moisture meter). To simulate the potential slip surface of the slope (the mud interlayer that exists in the actual mine), a 5mm thick mica sheet (friction coefficient 0.25, consistent with the mechanical properties of the actual mud interlayer) is laid at a height of 0.6m. The length of the slip surface covers 80% of the width of the model groove (2.4m).

[0036] Landslide induced factor simulation device:

[0037] Precision Rainfall System: Twelve independently controlled high-pressure spray nozzles are installed on the top of the model tank, with a nozzle spacing of 20cm, covering the entire slope model surface; equipped with a water pressure regulating pump and flow meter, it can simulate three types of rainfall intensity in the mine: light rain (0.5-1.0mm / h, water pressure 0.1MPa), moderate rain (2.0-3.0mm / h, water pressure 0.2MPa), and heavy rain (4.0-5.0mm / h, water pressure 0.4MPa). The rainfall duration can be set via a timer (continuously adjustable from 0-24h).

[0038] Water level control system: A groundwater level simulation chamber is set at the bottom of the model tank, with two submersible liquid level sensors. The sensors provide real-time feedback of water level data, which in turn links the water inlet pump and the drainage valve to achieve precise adjustment of the groundwater level within the range of 0.2-0.8m, with an adjustment rate of 0.05m / h (simulating the scenario of groundwater level rising during the rainy season and water level dropping sharply during the dry season in the mine).

[0039] Vibration simulation system: An electromagnetic vibration table is used, which is rigidly connected to the bottom of the model tank; the actual blasting vibration wave of the mine is input (through the blasting acceleration time history curve collected on site, the peak acceleration after scaling is 0.05-0.1g, and the duration is 10-30s) to simulate the disturbance of the slope to the daily blasting operation in the mine.

[0040] (II) Deployment of Multi-Dimensional Sensor Monitoring Network

[0041] Internal response monitoring sensors:

[0042] Miniature pore water pressure sensor: arranged in a "2 rows × 4 columns" grid: the first row is 0.3m deep (shallow slope), the second row is 0.6m deep (near potential slip surface), with a horizontal spacing of 0.5m, for a total of 8 sensors; the sensor probe is wrapped with permeable stone (0.1mm pore size) to avoid clogging by soil particles, and the wire is led out along the side wall of the model trench to connect to the data acquisition instrument.

[0043] Miniature earth pressure cells: Two cells are placed above and below the potential slip surface (0.1m away from the slip surface) to monitor stress changes at the slip surface; the surface of the pressure cell is in close contact with the soil and rock mass to avoid gaps that could lead to measurement errors.

[0044] Inclinometer and miniature inclinometer: An anti-interference inclinometer tube (50mm in diameter) is buried at a depth of 1.2m in the middle of the model slope (0.4m from the trench wall). It is equipped with a digital inclinometer and collects inclination data at a depth of 0.1m to calculate the displacement of the deep soil. At the same time, two miniature inclinometers are set up at the top and middle of the slope to help monitor the overall tilt trend of the slope.

[0045] Surface deformation monitoring sensor:

[0046] High-precision displacement sensors: One sensor is installed at the top of the slope (0.2m from the shoulder), the middle of the slope (0.8m high), and the foot of the slope (0.3m from the bottom). The sensor transmitter is aimed at the reflective target (20mm in diameter) on the slope surface to collect surface point displacement data in real time.

[0047] Distributed optical fiber sensing network: Single-mode optical fiber is used and the network is laid out in a grid of "3 horizontal lines + 2 vertical lines". The horizontal optical fibers are laid at slope heights of 0.4m, 0.8m and 1.2m, and the vertical optical fibers are laid at slope lengths of 0.5m and 2.0m. Distributed optical fiber strain gauges are connected to both ends of the optical fibers, and the surface deformation distribution is calculated by the change of optical fiber strain.

[0048] High-speed industrial cameras: Two industrial cameras are installed 1.5m in front of the transparent window of the model trough. The focal length of the lens is 25mm, and the shooting range covers the entire slope surface. Using the principle of digital image correlation (DIC), the displacement of the slope surface is calculated (accuracy ±0.05mm) by comparing the grayscale changes of pixels in the images at different times. The crack propagation trajectory and deformation concentration areas can be presented intuitively.

[0049] Environmental and physical field information sensors:

[0050] Microseismic / acoustic emission sensor array: Six piezoelectric microseismic sensors are deployed around the model trench (0.3m from the trench wall). The sensors are attached to the trench wall by magnetic bases and capture elastic wave signals generated by micro-fractures inside the rock and soil in real time (sampling frequency 1000Hz) to identify early fracture activity of potential slip surfaces.

[0051] Laser scanner: A 3D laser scanner is set up 2.0m to the side of the model trench and scans the slope surface once every 30 minutes to generate a high-precision 3D point cloud model. By comparing the point cloud data at different times, the changes in surface morphology (such as crack width and settlement) are quantified. The scanned data is exported in PLY format for easy subsequent processing.

[0052] (III) Data Acquisition and Intelligent Analysis System Debugging

[0053] Data acquisition device: A multi-channel high-speed data acquisition instrument is selected. The output signals of all sensors (pore water pressure, soil pressure, displacement, micro-vibration, fiber optic, etc.) are connected to the acquisition instrument through shielded cables. The sampling frequency is set as follows: 50Hz for internal monitoring sensors, 10Hz for surface monitoring sensors, and 1000Hz for micro-vibration sensors. The synchronous trigger function built into the acquisition instrument ensures that the timestamp error of all data is ≤1ms, avoiding analysis deviations caused by data asynchrony.

[0054] Data Analysis Module Configuration: A data analysis platform is built based on LabVIEW software to achieve three core functions:

[0055] Safety baseline establishment: Before the experiment, the slope model was monitored for stability for 2 hours. The "pore water pressure (average 5 kPa), microseismic event rate (average 0.5 times / h), displacement rate (average 0.001 mm / h), and internal strain (average 0.0001)" during this period were collected as the safety baseline and stored as the benchmark dataset.

[0056] Abnormal deviation identification: Set deviation thresholds (e.g., pore water pressure deviating from the baseline by ±30%, displacement rate deviating from the baseline by ±50%). When the monitored data exceeds the threshold, the system automatically marks it as "abnormal data" and highlights the abnormal time period and the corresponding sensor location.

[0057] Instability Comprehensive Index Calculation and Early Warning: The algorithm is configured according to the formula in claim 6, and the weight coefficients are calibrated based on the historical instability data of the mine slope: α1 (pore water pressure deviation) = 0.3, α2 (micro-seismic event rate deviation) = 0.2, α3 (displacement rate deviation) = 0.3, α4 (internal strain deviation) = 0.2; three preset early warning thresholds are set: Level 1 warning I = 0.5 (slight slope abnormality, monitoring needs to be strengthened), Level 2 warning I = 0.7 (moderate slope risk, patrol should be initiated), and Level 3 warning I = 0.9 (slope is about to become unstable, emergency response should be triggered).

[0058] III. Experimental Steps and Procedures

[0059] (I) Experiment 1: Simulation Experiment of Single Rainfall Inducing Factor

[0060] Preparation phase: After confirming the compaction degree of the slope model and the normal connection of the sensors, start the data acquisition instrument and conduct 1 hour of stability monitoring to verify that the safety baseline data (pore water pressure 5.1 kPa, micro-seismic event rate 0.48 times / h) meet the expectations and there is no abnormal data.

[0061] Trigger simulation: The precision rainfall system is activated, and the "moderate rain" is set (rainfall intensity 3mm / h, water pressure 0.2MPa) for continuous rainfall for 4 hours; at the same time, the groundwater level is stabilized at 0.4m through the water level control system to avoid water level fluctuations interfering with the rainfall-induced effect.

[0062] Monitoring process:

[0063] One hour after the rainfall: The soil and rock at the top of the slope began to moisten through the transparent window, and the seepage traces extended downward along the pores (the seepage path was marked with red dye, and the red area can be seen extending from the top of the slope to a depth of 0.6m); the pore water pressure sensor data rose to 6.8kPa (ΔP / ΔP0=0.34), and the displacement rate stabilized at 0.0012mm / h (no abnormal deviation).

[0064] 2.5 hours after rainfall: a tiny crack (0.1 mm wide, located at a length of 1.2-1.8 m in the model trench via coordinate grid) appeared at the potential slip surface (0.6 m depth); the microseismic event rate rose to 1.2 times / h (ΔE / ΔE0 = 1.4), and the system marked it as "microseismic anomaly"; the instability comprehensive index I = 0.3×0.4 + 0.2×1.4 + 0.3×0.2 + 0.2×0.1 = 0.12 + 0.28 + 0.06 + 0.02 = 0.48 (approaching the first-level warning).

[0065] Four hours after rainfall: a transverse crack (0.5m long, 0.3mm wide) appeared in the middle of the slope surface. The laser scanner point cloud comparison showed a settlement of 0.2mm at the top of the slope; the displacement rate increased to 0.0025mm / h (ΔD / ΔD0=1.5), and the pore water pressure reached 8.5kPa (ΔP / ΔP0=0.7); the instability comprehensive index I=0.3×0.7+0.2×1.6+0.3×1.5+0.2×0.4=0.21+0.32+0.45+0.08=1.06 (triggering a level 3 warning). Rainfall was immediately stopped, and the "rainfall intensity-duration-deformation" data at this time was recorded as a precursor to landslides.

[0066] (II) Experiment 2: Simulation Experiment of Rainfall + Vibration Coupling Induced Factors

[0067] Preparation phase: The slope model after Experiment 1 was re-trimmed (the surface cracked area of ​​the soil and rock was removed, the newly configured soil and rock material was added, and it was compacted and left to stand for 2 hours), and the safety baseline was recalibrated (pore water pressure 4.9 kPa, displacement rate 0.0009 mm / h).

[0068] Trigger Simulation:

[0069] Step 1: Turn on the rainfall system and set it to "light rain mode" (rainfall intensity 1mm / h, water pressure 0.1MPa). Continue rainfall for 2 hours to keep the slope soil and rock moist (pore water pressure rises to 6.0kPa).

[0070] Step 2: Keep the rainfall constant, start the vibration simulation system, input the blasting vibration wave of the mine (peak acceleration 0.08g, duration 20s, frequency 8Hz), trigger the vibration once every 1 hour, for a total of 3 times.

[0071] Monitoring process:

[0072] After the first vibration: the microseismic sensor captured two obvious micro-fracture signals (event rate increased to 1.0 times / h, ΔE / ΔE0=1.0); the transparent window showed that the width of the potential slip surface crack increased from 0.1mm to 0.2mm, and the distributed optical fiber showed that the strain in the slope increased to 0.0003 (ΔS / ΔS0=2.0); the instability comprehensive index I=0.3×0.22+0.2×1.0+0.3×0.33+0.2×2.0=0.066+0.2+0.099+0.4=0.765 (triggered a level 2 warning).

[0073] After the third vibration: the transverse cracks on the surface expanded to 1.2m in length and 0.5mm in width; inclinometer data showed a displacement of 0.8mm at a depth of 0.6m (signs of deep soil sliding); the comprehensive instability index I = 0.3×0.8 + 0.2×1.8 + 0.3×1.8 + 0.2×2.5 = 0.24 + 0.36 + 0.54 + 0.5 = 1.64 (far exceeding the level three warning), and a localized landslide occurred in the middle area of ​​the slope model (the landslide volume was approximately 0.02m). 3 Stop the experiment and record the instability critical parameters under the coupling of "rainfall + vibration".

[0074] The key data from the two experiments were compiled into a database, with core entries including:

[0075]

[0076] This embodiment successfully simulated two typical landslide scenarios using the platform: single rainfall and rainfall + vibration coupling. It achieved coordinated monitoring of internal seepage, slip surface formation, and surface deformation of the slope. The established instability comprehensive index algorithm and early warning threshold accurately identified slope risks in field applications, proving that the platform has a closed-loop capability of "experimental simulation - data analysis - field guidance" and can effectively support the research on the mechanism of mine slope landslides and the optimization of early warning technology.

[0077] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A simulation experimental platform for early warning and treatment of multi-directional landslides in mines, characterized in that: include: The slope physical simulation system includes a main model tank with a transparent observation window, various landslide induction simulation devices, and configurable soil and rock materials; A multi-dimensional sensor monitoring network, including sensor arrays for monitoring internal slope response, surface deformation, and environmental and physical field information; The data acquisition and intelligent analysis system includes a data acquisition device for synchronously acquiring data from the sensor array and a data analysis module based on multi-source information fusion. The data analysis module achieves multi-level early warning by establishing a safety baseline, identifying abnormal deviations, calculating an instability comprehensive index, and dynamic threshold early warning.

2. The simulation experimental platform for early warning and treatment of multi-directional landslides in mines according to claim 1, characterized in that: The multiple landslide induced factor simulation device includes a precision rainfall system, a water level control system, and a vibration simulation system. The precision rainfall system includes multiple independently controlled spray heads, which simulate different rainfall intensities by adjusting water pressure and switching combinations. The water level control system achieves precise control of the groundwater level through feedback from a liquid level sensor. The vibration simulation system can input real seismic waves to simulate seismic disturbances.

3. The simulation experimental platform for early warning and treatment of multi-directional landslides in mines according to claim 1, characterized in that: Sensors used to monitor the internal response of slopes include miniature pore water pressure sensors arranged in a grid along the slope height and depth, miniature earth pressure cells placed near potential slip surfaces, and inclinometers and miniature tiltmeters used to monitor deep soil deformation and the location of potential slip surfaces.

4. The simulation experimental platform for early warning and treatment of multi-directional landslides in mines according to claim 1, characterized in that: The sensors used to monitor surface deformation include high-precision displacement sensors, a distributed fiber optic sensing network arranged in a grid on the slope surface, and a high-speed industrial camera that acquires full-field displacement and deformation data of the slope surface by aligning with the transparent observation window based on digital image correlation analysis.

5. The simulation experimental platform for early warning and treatment of multi-directional landslides in mines according to claim 1, characterized in that: Sensors used to monitor environmental and physical field information include a microseismic / acoustic emission sensor array deployed around the model trench to capture elastic wave signals from micro-fractures within the soil and rock mass, and a laser scanner for generating high-precision three-dimensional point cloud models to quantify changes in landform.

6. The simulation experimental platform for early warning and treatment of multi-directional landslides in mines according to claim 1, characterized in that: The data analysis module calculates the instability composite index by weighting and fusing pore water pressure deviation, microseismic event rate deviation, displacement rate deviation, and internal strain deviation. The calculation formula is as follows: I=α1·ΔP / ΔP0+α2·ΔE / ΔE0+α3·ΔD / ΔD0+α4·ΔS / ΔS0 Where I is the instability comprehensive index, ΔP / ΔP0 is the pore water pressure deviation, ΔE / ΔE0 is the microseismic event rate deviation, ΔD / ΔD0 is the displacement rate deviation, ΔS / ΔS0 is the internal strain deviation, and α1~α4 are weighting coefficients.

7. The simulation experimental platform for early warning and treatment of multi-directional landslides in mines according to claim 1, characterized in that: The platform enables coordinated observation and spatiotemporal correlation analysis of the internal seepage process of the slope, the formation process of the potential slip surface and the evolution of the surface displacement field, and the development of cracks through the transparent observation window.

8. The simulation experimental platform for early warning and treatment of multi-directional landslides in mines according to claim 1, characterized in that: The platform can activate the various landslide induced cause simulation devices individually or in combination to simulate four typical landslide scenarios: rainfall-induced, sudden drop in water level, vibration-induced, and engineering activity-induced.

9. The simulation experimental platform for early warning and treatment of multi-directional landslides in mines according to claim 1, characterized in that: The data acquisition device is a multi-channel high-speed data acquisition instrument, which realizes the synchronous acquisition of data from all sensors and ensures the time consistency of data from different monitoring methods.

10. The simulation experimental platform for early warning and treatment of multi-directional landslides in mines according to claim 1, characterized in that: The precursor feature database and early warning thresholds established through the platform can be applied to actual mine slope monitoring and early warning. By comparing the early warning thresholds obtained from experiments with on-site monitoring data, accurate early warning of landslides can be achieved.

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