A mine slope monitoring system based on internet of things intelligent sensor

Abstract

The application discloses a mine slope monitoring system based on an Internet of Things intelligent sensor, and particularly relates to the technical field of mine geological environment monitoring, and is used for solving the problems of multi-source monitoring parameter fragmentation and deep hidden crack positioning lag. First, time sequence baseline data is generated by synchronously collecting displacement, infiltration line and radon concentration to construct a multivariate perception base. Subsequently, dynamic analysis is performed on the main strain rate, hardware-level high-frequency synchronous capture is triggered when approaching instability, multivariate transient features are extracted, and cross-medium abnormality acquisition under low power consumption is realized. Further, the abnormality time sequence lag gradient is analyzed and is reversely projected along displacement to accurately mark the three-dimensional coordinate sequence of underground water-guiding gas cracks. Finally, three-dimensional geological topology intersection discrimination with the uranium mine slag layer boundary is performed, penetration nodes are extracted, grouting intervention instructions are targetedly generated, and a closed-loop system from multi-source variable perception to active ecological physical blocking is constructed, thereby providing scientific support for uranium mine compound disaster prevention and control.

Description

Technical Field

[0001] This invention relates to the field of mine geological environment monitoring technology, specifically a mine slope monitoring system based on Internet of Things (IoT) smart sensors. Background Technology

[0002] The mining and hydrometallurgical processes of uranium and other associated radioactive mines generate large amounts of waste rock piles and tailings ponds. Under the combined effects of long-term natural weathering, rainfall erosion, and internal groundwater infiltration, the slopes of these mines are highly susceptible to structural damage, leading to landslides, debris flows, and other geological disasters. Compared to conventional non-metallic or coal mines, the physical and mechanical instability of slopes in these specialized mines not only causes conventional engineering damage but also triggers large-scale ecological leaks of associated radioactive materials such as uranium-containing wastewater and radon gas through geological fissures. Therefore, utilizing IoT sensing technology for 24 / 7 synchronous monitoring of slope physical deformation parameters and environmental radioactivity parameters is a crucial technical means to prevent complex mine disasters.

[0003] Existing automated monitoring systems have significant operational flaws when dealing with the aforementioned complex scenarios. On the one hand, existing systems typically employ a mode of independent and continuous data collection from various types of sensors at fixed frequencies. This results in extremely high overall power consumption of the equipment under long-term high-frequency operation, making it prone to power exhaustion and communication interruptions under harsh conditions such as continuous rain in the field. On the other hand, existing monitoring mechanisms treat physical and mechanical deformation data and radioactive environmental indicators separately, relying solely on a single sensor to trigger a single-point alarm when a single indicator exceeds the static safety limit. This passive response mode fails to reveal the physical causal relationship between surface deformation and the movement of internal fluids and gases, leading to severe lag in early warning and making it impossible to accurately locate the source and proactively intervene in engineering before physical landslides or large-scale spread of radioactive materials occur. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a mine slope monitoring system based on Internet of Things (IoT) smart sensors, which solves the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a mine slope monitoring system based on IoT smart sensors, comprising the following modules: a collaborative sensing module, used to synchronously collect the three-dimensional displacement of the mine slope surface, the depth of the groundwater infiltration line, and the concentration of radon leaching at the surface according to a steady-state inspection cycle, and analyze the three-dimensional spatial evolution vector corresponding to the surface three-dimensional displacement within adjacent sampling cycles as time-series baseline data; an edge scheduling module, used to construct a deformation rate tensor within a sliding time window based on the time-series baseline data, perform dynamic singular value analysis on the principal strain rate scalar of the deformation rate tensor, and when it is determined that the principal strain rate scalar exhibits a nonlinear step and approaches the critical state of rheological instability, generate a hardware-level concurrent wake-up command, synchronously adjust the sampling frequency of the displacement sensor, the infiltration line sensor, and the radon sensor to a high-frequency transient capture mode, and extract the three-dimensional displacement vector generated in the high-frequency transient capture mode. The system combines the direction of the displacement vector, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration into a multivariate transient feature dataset. The spatiotemporal intersection module extracts the time stamps of the anomalies in the three-dimensional displacement vector direction, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration from the multivariate transient feature dataset. It also analyzes the temporal hysteresis gradient of the cross-medium response, performs inverse spatial projection along the three-dimensional displacement vector direction, and performs kinematic intersection calibration at the depth geometric position mapped by the temporal hysteresis gradient to obtain the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel. The risk intervention module performs three-dimensional geological topological intersection discrimination between the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel and the pre-stored depth coordinates of the historical uranium slag deposit layer. It extracts the three-dimensional spatial coordinate nodes of the underground water-conducting gas fracture channel that penetrate the depth limit of the historical uranium slag deposit layer and generates targeted physical intervention commands covering the specified latitude, longitude, and grouting target area depth.

[0006] Furthermore, the specific process of synchronously collecting three-dimensional displacement of the mine slope surface, groundwater infiltration line depth, and surface radon emission concentration according to the steady-state inspection cycle, and analyzing the three-dimensional spatial evolution vector corresponding to the surface three-dimensional displacement within adjacent sampling cycles as time-series baseline data is as follows: Trigger the built-in hardware clock of the edge computing gateway to send synchronous timing pulses to the displacement sensor, infiltration line sensor, and radon sensor to lock a unified time reference; collect three-dimensional coordinate point data, water pressure head level data, and radon activity volume concentration with a unified time reference according to the steady-state inspection cycle; convert the three-dimensional coordinate point data into surface three-dimensional displacement, convert the water pressure head level data into groundwater infiltration line depth, and convert the radon activity volume concentration into surface radon emission concentration; extract the surface three-dimensional displacement within adjacent sampling cycles, perform spatial vector difference analysis, obtain the three-dimensional spatial evolution vector containing displacement azimuth and absolute displacement scalar, and use the three-dimensional spatial evolution vector as time-series baseline data.

[0007] Furthermore, a deformation rate tensor within a sliding time window is constructed based on the time-series baseline data. Dynamic singular value analysis is performed on the principal strain rate scalar of the deformation rate tensor. When the principal strain rate scalar is determined to exhibit a nonlinear step and approach the critical state of rheological instability, the specific process of generating a hardware-level concurrent wake-up command is as follows: The time-series baseline data is sequentially extracted and pushed into a sliding time window of a preset length. Temporal differentiation is performed on the three-dimensional spatial evolution vector within the sliding time window along the time axis, outputting a deformation rate tensor containing both normal and tangential deformation rates. Matrix feature analysis is then performed on the deformation rate tensor. Value decomposition is performed to extract the maximum principal eigenvalue as the principal strain rate scalar. The second derivative trajectory of the principal strain rate scalar within adjacent time windows is calculated. Dynamic singular value analysis is performed to extract the principal strain rate scalar corresponding to the abrupt extreme point in the second derivative trajectory. The principal strain rate scalar corresponding to the abrupt extreme point is compared with the dynamic adaptive benchmark threshold to determine the rheological instability boundary. When the principal strain rate scalar exceeds the dynamic adaptive benchmark threshold and exhibits a nonlinear step and indicates that it is approaching the critical state of rheological instability, the edge computing gateway interrupt control pin is triggered to generate a hardware-level concurrent wake-up command.

[0008] Furthermore, the sampling frequencies of the displacement sensor, wetting line sensor, and radon sensor are synchronously adjusted to a high-frequency transient capture mode. The specific process of extracting the three-dimensional displacement vector direction, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration generated in the high-frequency transient capture mode, and combining them into a multivariate transient feature dataset, is as follows: A hardware-level concurrent wake-up command is sent to the underlying control registers of the displacement sensor, wetting line sensor, and radon sensor, overwriting the sampling period configuration parameters, and synchronously switching the sampling frequencies of the displacement sensor, wetting line sensor, and radon sensor to the high-frequency transient capture mode. The method involves: acquiring transient three-dimensional displacement coordinates, transient water pressure head data, and transient radon activity data in parallel under high-frequency transient capture mode; performing spatial trajectory fitting on the transient three-dimensional displacement coordinates to extract the direction of the three-dimensional displacement vector; extracting the liquid level drop slope on the transient water pressure head data to obtain the transient attenuation of the wetting line water level; and calculating the concentration rise gradient on the transient radon activity data to obtain the spatial transition rate of radon precipitation concentration. The three-dimensional displacement vector direction, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration are aligned by synchronizing the timestamps and packaged into a multivariate transient feature dataset.

[0009] Furthermore, the specific process of extracting the anomaly timestamps of the three-dimensional displacement vector direction, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration from the multivariate transient feature dataset, and analyzing the temporal hysteresis gradient of the cross-media response, is as follows: A temporal extremum retrieval is performed on the multivariate transient feature dataset, and the transient microsecond-level time points corresponding to the initial deflection of the three-dimensional displacement vector direction, the transient attenuation of the wetting line water level crossing the baseline, and the peak value of the spatial transition rate of radon precipitation concentration are extracted as anomaly timestamps; the displacement anomaly timestamps, the wetting line anomaly timestamps, and the radon precipitation anomaly timestamps are aligned with the time axis coordinates. Extract the liquid phase conduction delay phase relative to the displacement anomaly timestamp of the wetting line anomaly, and extract the gas phase escape delay phase relative to the displacement anomaly timestamp of the radon precipitation anomaly. Retrieve the pre-configured slope soil permeability coefficient and gas diffusion coefficient, perform permeability dynamics tensor mapping on the liquid phase conduction delay phase and the slope soil permeability coefficient to obtain the liquid phase penetration hysteresis gradient. Perform diffusion dynamics tensor mapping on the gas phase escape delay phase and the gas diffusion coefficient to obtain the gas phase escape hysteresis gradient. Perform multi-field coupling vector fusion on the liquid phase penetration hysteresis gradient and the gas phase escape hysteresis gradient to encapsulate it into a time-series hysteresis gradient of the cross-medium response.

[0010] Further, the specific process of obtaining the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel by performing inverse spatial projection along the direction of the three-dimensional displacement vector and kinematic intersection calibration at the depth geometric position of the temporal hysteresis gradient mapping is as follows: Extract the three-dimensional coordinates of the geodetic origin corresponding to the surface displacement sensor that generated the anomaly timestamp; construct a three-dimensional inverse tracing spatial ray along the direction of the three-dimensional displacement vector based on the three-dimensional coordinates of the geodetic origin; perform spatial equivalent distance mapping according to the liquid phase penetration hysteresis gradient and the gas phase escape hysteresis gradient along the three-dimensional inverse tracing spatial ray to determine the first spatial projection node of the infiltration line attenuation source and the second spatial projection node of the radon transition source; perform three-dimensional envelope spatial fitting on the three-dimensional coordinates of the geodetic origin, the first spatial projection node and the second spatial projection node to extract the three-dimensional topological spatial connection connecting the three-dimensional coordinates of the geodetic origin, the first spatial projection node and the second spatial projection node; perform equidistant grid discretization processing along the three-dimensional topological spatial connection to output the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel composed of multiple discrete three-dimensional spatial coordinate points.

[0011] Furthermore, the specific process for performing the three-dimensional geological topological intersection discrimination between the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel and the pre-stored historical uranium slag deposit depth coordinates is as follows: A three-dimensional stratigraphic structure layer containing mine geological exploration data is retrieved; the pre-stored historical uranium slag deposit depth coordinates marked in the three-dimensional stratigraphic structure layer are extracted, and a three-dimensional solid bounding box of the uranium slag deposit is generated; the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel is imported into the same global digital twin coordinate system containing the three-dimensional solid bounding box of the uranium slag deposit; each discrete three-dimensional spatial coordinate point in the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel is traversed, and a spatial Boolean intersection operation is performed on the three-dimensional closed boundary between each discrete three-dimensional spatial coordinate point and the three-dimensional solid bounding box of the uranium slag deposit, outputting the three-dimensional geological topological intersection discrimination result representing the spatial membership state inside and outside each discrete three-dimensional spatial coordinate point.

[0012] Further, the specific process of extracting the three-dimensional spatial coordinate nodes of the underground water-conducting gas fracture channel that penetrates the depth limit of the historical uranium slag deposit layer and generating targeted physical intervention commands covering the specified latitude and longitude and the grouting target area depth is as follows: Based on the internal and external spatial affiliation status in the three-dimensional geological topological intersection discrimination results, discrete three-dimensional spatial coordinate points located outside the three-dimensional entity bounding box of the uranium slag deposit layer are screened out, and discrete three-dimensional spatial coordinate points located inside the spatial Boolean intersection area are extracted as the three-dimensional spatial coordinate nodes of the underground water-conducting gas fracture channel; the deepest intrusion point is retrieved for the three-dimensional spatial coordinate nodes of the underground water-conducting gas fracture channel located inside the spatial Boolean intersection area to obtain the limit penetration three-dimensional depth value and the corresponding surface projection two-dimensional latitude and longitude; the limit penetration three-dimensional depth value is calibrated as the grouting target area depth, the surface projection two-dimensional latitude and longitude is mapped to the specified latitude and longitude, the specified latitude and longitude and the grouting target area depth are encapsulated to generate a structured intervention data message, and the structured intervention data message is output as a physical intervention command.

[0013] The present invention has the following beneficial effects: (1) A mine slope monitoring system based on IoT smart sensors extracts the three-dimensional spatial evolution vector corresponding to the three-dimensional displacement of the surface as time-series baseline data through a collaborative sensing module according to the steady-state inspection cycle, and performs dynamic singular value analysis on the principal strain rate scalar using an edge scheduling module. When the system is determined to be approaching the critical state of rheological instability, a hardware-level concurrent wake-up command is generated to synchronously adjust the sampling frequency of the displacement, wetting line, and radon gas sensors to a high-frequency transient capture mode. This mechanism changes the power consumption drawback of blind high-frequency sampling of traditional equipment, realizes the adaptive transition from low-frequency dormancy to high-frequency synchronous capture, and accurately acquires the transient correlation data of physical deformation and radioactive ecological factors at the moment of micro-fracture of rock and soil with extremely low power consumption, significantly improving the survivability and data effectiveness of IoT sensor nodes in complex field conditions.

[0014] (2) A mine slope monitoring system based on IoT smart sensors analyzes the temporal hysteresis gradient of the cross-medium response in a multivariate transient feature dataset through a spatiotemporal intersection module, performs inverse spatial projection along the three-dimensional displacement vector to obtain the three-dimensional spatial coordinate sequence of underground water-conducting gas fracture channels, and uses a risk intervention module to perform three-dimensional geological topological intersection discrimination. This mechanism overcomes the lag defect of existing monitoring methods that can only perform single-point threshold alarms on the ground. By utilizing the time delay and spatial mapping relationship between physical deformation and environmental parameters, it inversely deduces the development channels of invisible fractures inside the underground rock mass. When the fracture cuts through the high-risk historical slag layer, it generates physical intervention instructions in advance that cover the specified latitude and longitude and the depth of the grouting target area, realizing a fundamental shift from passive post-event alarm to proactive pre-emptive treatment.

[0015] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0016] Figure 1 This is a flowchart of a mine slope monitoring system based on Internet of Things (IoT) smart sensors according to the present invention. Figure 2 Flowchart for triggering the edge scheduling module; Figure 3 Flowchart for spatiotemporal intersection and 3D coordinate generation; Figure 4 Generate flowcharts for risk intervention instructions. Detailed Implementation

[0017] This application provides a mine slope monitoring system based on Internet of Things (IoT) smart sensors. This system addresses the problems of unreasonable power consumption distribution in existing mine slope monitoring equipment and the inability to accurately locate the source of internal cracks in the early stages of a disaster due to the isolation of multi-source heterogeneous monitoring data. The overall concept of the solution in this application is as follows: By leveraging the computing power of IoT edge computing nodes to perform time-series analysis on three-dimensional spatial evolution vectors, when a nonlinear step instability precursor is detected in the physical and mechanical deformation, a low-level hardware interrupt triggers multiple types of heterogeneous sensors to synchronously enter a high-frequency transient capture mode, acquiring multi-parameter synchronous anomaly data of physical displacement, groundwater level, and surface radioactive gas. Subsequently, based on the time lag difference between solid deformation, liquid water conduction, and gas diffusion, combined with the spatial direction of the displacement vector, a reverse three-dimensional geometric intersection calculation is performed to deduce the specific three-dimensional coordinate sequence of interconnected fractures inside the slope. Finally, the calculated three-dimensional coordinates of the internal fractures are topologically compared with the pre-stored spatial data of high-risk slag deposits in mines to accurately output the latitude, longitude, and depth parameters for guiding on-site grouting.

[0018] Please see Figure 1This invention provides a technical solution: a mine slope monitoring system based on IoT smart sensors, comprising the following modules: a collaborative sensing module, used to synchronously collect the three-dimensional displacement of the mine slope surface, the depth of the groundwater infiltration line, and the concentration of radon leaching on the surface according to a steady-state inspection cycle, and analyze the three-dimensional spatial evolution vector corresponding to the three-dimensional displacement of the surface within adjacent sampling cycles as time-series baseline data; an edge scheduling module, used to construct a deformation rate tensor within a sliding time window based on the time-series baseline data, perform dynamic singular value analysis on the principal strain rate scalar of the deformation rate tensor, and when it is determined that the principal strain rate scalar exhibits a nonlinear step and approaches the critical state of rheological instability, generate a hardware-level concurrent wake-up command, synchronously adjust the sampling frequency of the displacement sensor, the infiltration line sensor, and the radon gas sensor to a high-frequency transient capture mode, and extract the three-dimensional displacement vector direction, infiltration line depth, and radon leaching concentration generated in the high-frequency transient capture mode. The transient attenuation of the wetting line water level and the spatial transition rate of radon precipitation concentration are combined into a multivariate transient feature dataset. The spatiotemporal intersection module is used to extract the anomaly timestamps of the three-dimensional displacement vector direction, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration from the multivariate transient feature dataset, and to analyze the temporal hysteresis gradient of the cross-medium response. Inverse spatial projection is performed along the direction of the three-dimensional displacement vector, and kinematic intersection calibration is performed at the depth geometric position of the temporal hysteresis gradient mapping to obtain the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel. The risk intervention module is used to perform three-dimensional geological topological intersection discrimination between the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel and the pre-stored depth coordinates of the historical uranium slag deposit layer, extract the three-dimensional spatial coordinate nodes of the underground water-conducting gas fracture channel that penetrate the depth limit of the historical uranium slag deposit layer, and generate targeted physical intervention instructions covering the specified latitude and longitude and the depth of the grouting target area.

[0019] In this implementation plan, the collaborative sensing module is used to synchronously measure the three-dimensional displacement reflecting mechanical deformation, the infiltration line depth reflecting groundwater changes, and the radon release concentration reflecting radioactive ecological risks at fixed time intervals when the mine slope is in a normal safe state. It generates a three-dimensional spatial evolution vector by calculating the displacement change between two adjacent measurements, which serves as the basis data sequence for subsequent judgments. The steady-state inspection cycle refers to the default low-frequency dormancy and wake-up sampling interval under conditions of no geological anomalies. The three-dimensional spatial evolution vector represents the direction and absolute distance of the actual movement of surface measuring points in three-dimensional space within a specific time period. The time-series baseline data refers to the basic reference dataset arranged chronologically and reflecting the underlying physical, mechanical, and environmental conditions of the slope. The technical role of this module is to construct a multivariate basic measurement base covering solid soil and rock, liquid groundwater, and gaseous radioactive materials, fully meeting the classification attribute requirements for measuring two or more variables, and providing a reliable initial physical environment state benchmark for subsequent analysis of slope internal deformation and special fluid migration evolution.

[0020] The edge scheduling module receives the aforementioned basic data sequence, constructs a tensor matrix containing normal and tangential deformation rates, extracts the maximum eigenvalue for anomaly mutation point calculation, and immediately triggers the underlying hardware control circuit to force all different types of heterogeneous sensors to simultaneously enter high-frequency acquisition mode, thereby acquiring a composite variable dataset such as displacement direction, water level drop, and radon gas surge at the moment of anomaly occurrence. The deformation rate tensor within the sliding time window refers to the matrix of deformation rate changes of the soil and rock mass in various dimensions of three-dimensional space over a fixed time period. Dynamic singular value analysis refers to identifying extreme mutation points in the time series data representing micro-fractures in the soil and rock mass through mathematical eigenvalue decomposition. The rheological instability critical state refers to the ultimate physical and mechanical boundary condition when the slope rock mass is about to transition from a slow deformation stage to an accelerated landslide failure stage. The technical role of this module is to break the fragmented state of independent measurements in traditional multivariate monitoring systems, and to establish an adaptive measurement and control mechanism based on the hardware-level synchronous high-frequency linkage of multiple types of special sensors triggered by tiny precursors of physical deformation. This effectively solves the problems of excessive power consumption caused by long-term high-frequency measurements of field monitoring equipment and easy downtime and disconnection in extreme weather. At the same time, it ensures that the system never misses the synchronous data of multi-media mutation characteristics at the moment of micro-fracture.

[0021] The spatiotemporal intersection module is used to extract specific microsecond-level time points corresponding to anomalies in solid displacement, liquid water drop, and gaseous radon gas in multivariate transient feature datasets. It calculates the time difference of abrupt signal generation in different physical media and converts it into a vertical distance gradient. Then, it extends the displacement movement from the surface into the underground interior in reverse, and performs spatial coordinate calibration at the depth position calculated by the corresponding distance gradient. Finally, it connects the points to reconstruct the three-dimensional spatial coordinate sequence of the invisible underground water-conducting gas fracture channels inside the slope. Among them, the temporal hysteresis gradient of the cross-media response refers to the depth physical distance scale converted from the time difference of the abnormal signals captured by different types of sensors due to the natural difference in physical transmission speed between rock mass fracture conduction, groundwater liquid phase seepage, and radon gas phase diffusion. The reverse spatial projection refers to the geometric mapping process of finding the fracture source path in the deep slope along the opposite spatial geometric direction of the surface rock mass sliding. The kinematic intersection calibration refers to the spatial calibration action of accurately locating the hidden internal fracture nodes in the three-dimensional coordinate system by combining the depth scale converted by time delay and the displacement direction guidance. The technical role of this module is to make full use of the cross-medium transmission characteristics of multi-source heterogeneous physical parameters on the time axis, accurately convert the time dimension delay difference into spatial dimension geometric depth data, and realize the core innovative function of inversely and non-contactly inferring the location of underground hidden fissures through joint measurement of multiple variables on the ground surface.

[0022] The risk intervention module is used to compare the spatial coordinate sequence of underground water- and gas-conducting fracture channels derived from the preliminary steps with the spatial coordinate range of historical high-risk uranium slag deposits pre-entered in the system's digital twin database. This comparison identifies the coordinates of fracture nodes that have extended and cut through high-risk slag areas, and then generates targeted engineering intervention commands containing specific geographical latitude and longitude coordinates and the required grouting depth, which are then output externally. The three-dimensional geological topological intersection judgment refers to using spatial Boolean operations within the same three-dimensional global digital coordinate system to determine whether the three-dimensional connection of fracture channels composed of discrete coordinate lattices enters or passes through the three-dimensional closed entity bounding box representing the historical slag deposit. Penetration of the historical uranium slag deposit depth limit refers to the direct exposure of radioactive contaminants originally under a safe geological cover to newly formed water- and gas-conducting channels due to the development of newly formed, concealed fractures. The technical role of this module is to transform the purely digital three-dimensional geometric model data obtained from multivariate collaborative monitoring and spatiotemporal calculation into physical engineering parameters that can directly guide on-site construction operations in high-risk uranium mines. It accurately determines the dual grouting reinforcement target area for preventing physical landslide instability and curbing the ecological leakage of radioactive materials, and truly completes the technical closed loop from multidimensional risk perception and diagnosis to proactive pre-emptive physical intervention in ecological disasters.

[0023] Specifically, the process of synchronously collecting three-dimensional displacement of the mine slope surface, groundwater infiltration line depth, and surface radon emission concentration according to the steady-state inspection cycle, and analyzing the three-dimensional spatial evolution vector corresponding to the surface three-dimensional displacement within adjacent sampling cycles as time-series baseline data is as follows: Trigger the built-in hardware clock of the edge computing gateway to send synchronous timing pulses to the displacement sensor, infiltration line sensor, and radon sensor to lock a unified time reference; collect three-dimensional coordinate point data, water pressure head level data, and radon activity volume concentration with a unified time reference according to the steady-state inspection cycle; convert the three-dimensional coordinate point data into surface three-dimensional displacement, convert the water pressure head level data into groundwater infiltration line depth, and convert the radon activity volume concentration into surface radon emission concentration; extract the surface three-dimensional displacement within adjacent sampling cycles, perform spatial vector difference analysis to obtain a three-dimensional spatial evolution vector containing displacement azimuth and absolute displacement scalar, and use the three-dimensional spatial evolution vector as time-series baseline data.

[0024] In this implementation scheme, the underlying operating logic of the collaborative sensing module is to ensure absolute temporal alignment of multi-source heterogeneous data through hardware-level timing synchronization, and to complete the accurate conversion from raw sensor signals to geological and engineering physical quantities. The synchronous timing pulse refers to the high-precision physical electrical signal sent by the edge computing gateway to each heterogeneous sensor node, used to eliminate time errors caused by crystal oscillator drift or communication protocol delays in different sensors, forcing all devices to lock the sampling action at the same physical moment. Hydraulic head level data and radon activity volume concentration refer to the raw environmental sensing parameters directly acquired by the front-end detection components without engineering conversion. Spatial vector difference analysis refers to extracting the absolute three-dimensional coordinate data of the same slope monitoring point in two adjacent measurement cycles, using kinematic geometry laws to perform spatial coordinate subtraction calculations, thereby obtaining the movement trajectory characteristics of the monitoring point in the real three-dimensional physical space. The displacement orientation angle and absolute displacement scalar accurately characterize the specific three-dimensional spatial orientation and actual physical sliding distance of the slope's soil and rock mass undergoing micro-sliding failure, respectively. The technical significance of this step lies in completely solving the problem of misalignment and fragmentation of three independent parameters—solid displacement, liquid groundwater, and gaseous radioactive material—on the time axis in a multivariable integrated measurement system from the bottom layer of IoT hardware. This meets the stringent requirements of high-risk mining environments for the synchronization of multi-source heterogeneous data. At the same time, it transforms the discrete raw monitoring point data into a three-dimensional spatial evolution vector with a strictly unified timestamp and a clear physical spatial direction. This lays an extremely rigorous foundation of underlying data and timing reference for the system to accurately capture rheological instability precursors and accurately calculate the cross-medium conduction delay time difference.

[0025] Please see Figure 2 Specifically, a deformation rate tensor within a sliding time window is constructed based on the time-series baseline data. Dynamic singular value analysis is performed on the principal strain rate scalar of the deformation rate tensor. When the principal strain rate scalar is determined to exhibit a nonlinear step and approach the critical state of rheological instability, the specific process of generating a hardware-level concurrent wake-up command is as follows: The time-series baseline data is sequentially extracted and pushed into a sliding time window of a preset length. Temporal differentiation is performed on the three-dimensional spatial evolution vector within the sliding time window along the time axis, outputting a deformation rate tensor containing both normal and tangential deformation rates. Matrix feature analysis is then performed on the deformation rate tensor. Value decomposition is performed to extract the maximum principal eigenvalue as the principal strain rate scalar. The second derivative trajectory of the principal strain rate scalar within adjacent time windows is calculated. Dynamic singular value analysis is performed to extract the principal strain rate scalar corresponding to the abrupt extreme point in the second derivative trajectory. The principal strain rate scalar corresponding to the abrupt extreme point is compared with the dynamic adaptive benchmark threshold to determine the rheological instability boundary. When the principal strain rate scalar exceeds the dynamic adaptive benchmark threshold and exhibits a nonlinear step and indicates that it is approaching the critical state of rheological instability, the edge computing gateway interrupt control pin is triggered to generate a hardware-level concurrent wake-up command.

[0026] In this implementation scheme, the core of the edge scheduling module lies in accurately capturing the critical precursors of the evolution from microscopic fracturing to macroscopic instability within the soil and rock mass through temporal dynamic analysis. The system first inputs continuously acquired discrete three-dimensional spatial evolution vector sequences into a preset-width sliding time window. By continuously differentiating along the time dimension, the relative deformation velocities in each dimension of the three-dimensional space are obtained, thereby constructing a deformation rate tensor that reflects the stress-induced deformation tension state. Subsequently, eigenvalue decomposition is performed on this tensor matrix to extract the largest principal eigenvalue representing the dominant failure direction as the principal strain rate scalar. To filter out conventional fluctuations caused by equipment errors or environmental noise, the system further solves for the second derivative trajectory of the principal strain rate scalar on the time axis. The second derivative physically represents the acceleration of deformation. Performing dynamic singular value analysis on the second derivative trajectory essentially uses catastrophe theory to find the mathematical inflection point where the deformation acceleration changes from gradual to a sharp increase, extracting the value corresponding to this inflection point as the principal strain rate scalar corresponding to the catastrophe extreme point. When performing rheological instability boundary discrimination, the system adopts a dynamic adaptive benchmark threshold determination method based on historical environmental background, and its calculation formula is as follows: ;In the formula, Dynamic adaptive baseline threshold; : The arithmetic mean of the principal strain rates of the mine slope during the historical steady-state safe period; Geological structure sensitivity penalty factor; The standard deviation of the principal strain rate scalar of the mine slope during its historical steady-state safety period. The purpose of the above determination method is to enable the judgment boundary to adaptively float according to the mine's own early geological stability conditions, avoiding false alarms and false negatives caused by fixed empirical parameters. The principal strain rate scalar corresponding to the extreme value of the abrupt change is logically compared with the calculated threshold. Once the threshold is exceeded and the value shows a step increase, it indicates that the rock mass has passed the elastic deformation stage and entered the critical acceleration state approaching rheological instability and failure. At this time, in order to ensure the immediacy of the response, the system directly triggers the interrupt control pin of the edge computing gateway at the underlying hardware level, instantly blocking the conventional sleep polling process and broadcasting a wake-up command to the downstream heterogeneous sensor network in a delay-free manner.

[0027] Specifically, the sampling frequencies of the displacement sensor, wetting line sensor, and radon sensor are synchronously adjusted to a high-frequency transient capture mode. The process of extracting the three-dimensional displacement vector direction, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration generated in this high-frequency transient capture mode, and combining them into a multivariate transient feature dataset, is as follows: A hardware-level concurrent wake-up command is sent to the underlying control registers of the displacement sensor, wetting line sensor, and radon sensor, overwriting the sampling period configuration parameters, and synchronously switching the sampling frequencies of the displacement sensor, wetting line sensor, and radon sensor to the high-frequency transient capture mode. The method involves: acquiring transient three-dimensional displacement coordinates, transient water pressure head data, and transient radon activity data in parallel under high-frequency transient capture mode; performing spatial trajectory fitting on the transient three-dimensional displacement coordinates to extract the direction of the three-dimensional displacement vector; extracting the liquid level drop slope on the transient water pressure head data to obtain the transient attenuation of the wetting line water level; and calculating the concentration rise gradient on the transient radon activity data to obtain the spatial transition rate of radon precipitation concentration. The three-dimensional displacement vector direction, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration are aligned by synchronizing the timestamps and packaged into a multivariate transient feature dataset.

[0028] In this implementation scheme, to ensure strict physical synchronization of multi-source heterogeneous parameters at the moment of micro-fracture, the system avoids the conventional software application layer command delay and directly writes the hardware-level concurrent wake-up command into the underlying control registers of the displacement sensor, wetting line sensor, and radon sensor, forcibly overwriting the original low-frequency sampling period configuration parameters of the equipment, causing all front-end multi-parameter sensing nodes to instantly switch into microsecond-level high-frequency transient capture mode. After entering this mode, the system reads in parallel the original signals of transient correlation anomalies of physical medium deformation, groundwater runoff fluctuations, and radioactive gas diffusion and overflow. For the captured transient data, the system performs unified feature dimensionality reduction and mutation rate extraction operations. First, it performs least squares fitting of spatial trajectory for the spatial coordinate sequence, filters out scatter point offsets, and extracts the three-dimensional displacement vector direction representing the true continuous sliding direction of the soil and rock block. For water pressure head data and radon activity data, the system extracts transient response feature parameters of liquid phase drop and gas phase transition, the calculation process of which is expressed by the following formula: ; ;In the formula, : Transient attenuation of water level along the wetting line; The initial water pressure head depth reference value at the moment the high-frequency transient capture mode is triggered; : The depth of the drop trough water pressure head detected in high-frequency transient capture mode; : The transient time interval step size between adjacent high-frequency sampling actions in the high-frequency transient capture mode; : Spatial transition rate of radon precipitation concentration; Peak transient radon activity volume concentration captured in high-frequency transient capture mode; The high-frequency transient capture mode triggers the surface background radon activity volume concentration. The aforementioned feature extraction operation transforms the decrease in absolute depth of liquid water level and the increase in absolute value of gas concentration into a relative evolution rate parameter reflecting the intensity of transient changes in the heterogeneous physical environment, achieving uniformity across different physical dimensions in the evaluation dimension. Finally, the system uses the underlying microsecond-level synchronization timestamp as the unique data key to strictly align and encapsulate the three-dimensional displacement vector direction, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration within the same tiny physical time slice in the memory frame structure, generating a multivariate transient feature dataset. This fundamentally ensures the absolute temporal consistency of the composite monitoring data, providing uncontaminated, high-fidelity original anomaly feature samples for subsequent accurate calculation of the cross-medium conduction time difference of underground hidden interconnected fractures.

[0029] Please see Figure 3 Specifically, the process of extracting the anomaly timestamps of the three-dimensional displacement vector direction, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration from the multivariate transient feature dataset, and analyzing the temporal hysteresis gradient of the cross-media response, is as follows: A temporal extremum retrieval is performed on the multivariate transient feature dataset, and the transient microsecond-level time points corresponding to the initial deflection of the three-dimensional displacement vector direction, the transient attenuation of the wetting line water level crossing the baseline, and the peak value of the spatial transition rate of radon precipitation concentration are extracted as anomaly timestamps; time axis coordinate alignment is performed on the displacement anomaly timestamp, the wetting line anomaly timestamp, and the radon precipitation anomaly timestamp. Extract the liquid phase conduction delay phase relative to the displacement anomaly timestamp of the wetting line anomaly, and extract the gas phase escape delay phase relative to the displacement anomaly timestamp of the radon precipitation anomaly. Retrieve the pre-configured slope soil permeability coefficient and gas diffusion coefficient, perform permeability dynamics tensor mapping on the liquid phase conduction delay phase and the slope soil permeability coefficient to obtain the liquid phase penetration hysteresis gradient. Perform diffusion dynamics tensor mapping on the gas phase escape delay phase and the gas diffusion coefficient to obtain the gas phase escape hysteresis gradient. Perform multi-field coupling vector fusion on the liquid phase penetration hysteresis gradient and the gas phase escape hysteresis gradient to encapsulate it into a time-series hysteresis gradient of the cross-medium response.

[0030] In this implementation scheme, the core logic of the spatiotemporal intersection module lies in utilizing the natural differences in the conduction speed of different physical media in geological structures to transform the response delay in the time dimension into physical distance in the spatial dimension. When micro-fractures occur on a mine slope, the deformation and displacement of the solid rock and soil mass occur instantaneously, while the leakage of liquid groundwater and the overflow of gaseous radioactive radon along the fractures caused by the fractures require a certain physical conduction time. The system first addresses on an extremely short microsecond-level time axis, precisely locking the instant when the three-dimensional displacement vector initially deflects as the zero point of mechanical failure. Subsequently, it retrieves the time when the transient attenuation of the wetting line water level crosses the background baseline and the time when the spatial transition rate of radon precipitation concentration reaches its peak. These three time points physically represent the precise physical moments of fracture generation, groundwater beginning to converge and leak along the fractures, and high-concentration radon gas breaking through the overburden layer to reach the surface. By performing difference calculations through time axis coordinate alignment, the obtained liquid phase conduction delay phase and gas phase escape delay phase essentially characterize the time cost required for fluids and gases to travel from the underground fracture source to the surface sensor. To accurately map the aforementioned time delay to the geological depth gradient, the system performs permeability dynamics tensor mapping and diffusion dynamics tensor mapping. The internal cross-medium calculation process is expressed by the following formula: ; ;In the formula, Liquid phase penetration hysteresis gradient; : Slope rock and soil permeability dynamics tensor; :Time stamp of the immersion line change; : Displacement anomaly timestamp; : Initial void ratio adjustment coefficient of mine rock mass; : Vapor phase escape hysteresis gradient; :Radioactive gas diffusion dynamics tensor; : Radon precipitation anomaly timestamp; Cross-medium dissipation compensation factor. The initial porosity adjustment coefficient of the mine rock mass is obtained by extracting the ratio of initial pore volume to solid skeleton volume from on-site core sampling, and then linearly interpolating it in conjunction with the effective stress variation amplitude during historical high-water periods. The system multiplies the acquired time difference values ​​by pre-configured soil-rock permeability tensors and gas diffusion tensors, and introduces the porosity and dissipation compensation factor to correct errors caused by heterogeneous media. This operation quantifies the abstract time delay into specific physical path length vectors of fluid and gas transport. Finally, multi-field coupled vector fusion is performed to package the tensors containing liquid and gas transport length information, forming a temporal hysteresis gradient of the cross-medium response with clear three-dimensional geometric indication, providing a precise depth scale for subsequent location of underground fracture sources.

[0031] Specifically, the process of obtaining the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel by performing inverse spatial projection along the direction of the three-dimensional displacement vector and kinematic intersection calibration at the depth geometric position of the temporal hysteresis gradient mapping is as follows: Extract the three-dimensional coordinates of the geodetic origin corresponding to the surface displacement sensor that generated the anomaly timestamp; construct a three-dimensional inverse tracing spatial ray along the direction of the three-dimensional displacement vector based on the three-dimensional coordinates of the geodetic origin; perform spatial equivalent distance mapping according to the liquid phase penetration hysteresis gradient and the gas phase escape hysteresis gradient along the three-dimensional inverse tracing spatial ray to determine the first spatial projection node of the infiltration line attenuation source and the second spatial projection node of the radon transition source; perform three-dimensional envelope spatial fitting on the three-dimensional coordinates of the geodetic origin, the first spatial projection node, and the second spatial projection node to extract the three-dimensional topological spatial connection connecting the three-dimensional coordinates of the geodetic origin, the first spatial projection node, and the second spatial projection node; perform equidistant grid discretization processing along the three-dimensional topological spatial connection to output the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel composed of multiple discrete three-dimensional spatial coordinate points.

[0032] In this implementation scheme, after obtaining the hysteresis depth scale, the system further performs reverse analysis and extrapolation from surface observation data to the underground hidden space. Since the sliding direction of a landslide is usually closely geometrically collinear or coplanar and oppositely correlated with the development and expansion direction of the internal controlling fractures, the system directly uses the three-dimensional coordinates of the geodetic origin where the displacement sensor where the surface deformation occurs as the starting point, and projects a virtual three-dimensional reverse tracing ray into the interior of the slope along the opposite direction of the calculated three-dimensional surface displacement vector. This ray represents the most likely physical development axis of the fracture and sliding of the soil and rock mass inside the slope. Along this reverse tracing ray, the system performs node positioning based on the previously obtained liquid and gas phase gradients. The specific spatial equivalent distance mapping calculation logic is expressed by the following formula: ; ;In the formula, : The three-dimensional coordinate matrix of the first spatial projection node of the wetting line attenuation source; : Three-dimensional coordinate matrix of the geodetic origin; The direction vector of the normalized three-dimensional displacement vector; : The spatial norm of the liquid phase penetration hysteresis gradient; Correction coefficient for the tortuosity of fracture space development; : The three-dimensional coordinate matrix of the second spatial projection node of the radon transition source; : The spatial norm of the gas phase escape hysteresis gradient; The gas-phase microfracture seepage path compensation coefficient is used. The fracture spatial development tortuosity correction coefficient is determined by extracting the ratio of the total unfolded length of the actual fractures in historical landslide profiles of the mine to the straight-line distance between the beginning and end of the fracture, and then smoothing it through multiple empirical data fittings. The system obtains scalar distances by calculating norms, extrapolates along the inverse direction vector, and incorporates tortuosity corrections in the calculation to recreate the actual non-linear geological fracture situation. This accurately anchors the first spatial projection node in three-dimensional space where groundwater leakage begins, and the second spatial projection node where associated radon gas begins to accumulate and overflow. After obtaining these three key physical nodes, the system uses a three-dimensional envelope spatial fitting algorithm to smoothly connect the surface origin with the two hidden underground sources, generating a complete three-dimensional topological spatial connection representing the fracture orientation. Finally, the system performs equidistant grid discretization along this continuous connection, dividing it into discrete three-dimensional spatial coordinate sequence point sets. This transforms the complex internal fracture channels of underground geological hazards into a standard digital spatial coordinate stream that can be directly read by subsequent engineering analysis and grouting equipment.

[0033] Please see Figure 4 Specifically, the process of performing the three-dimensional geological topological intersection discrimination between the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel and the depth coordinates of the pre-stored historical uranium slag deposit layer is as follows: A three-dimensional stratigraphic structure layer containing mine geological exploration data is retrieved; the depth coordinates of the pre-stored historical uranium slag deposit layer marked in the three-dimensional stratigraphic structure layer are extracted, and a three-dimensional solid bounding box of the uranium slag deposit layer is generated; the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel is imported into the same global digital twin coordinate system containing the three-dimensional solid bounding box of the uranium slag deposit layer; each discrete three-dimensional spatial coordinate point in the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel is traversed, and a spatial Boolean intersection operation is performed on each discrete three-dimensional spatial coordinate point and the three-dimensional closed boundary of the three-dimensional solid bounding box of the uranium slag deposit layer, outputting the three-dimensional geological topological intersection discrimination result representing the spatial membership state inside and outside each discrete three-dimensional spatial coordinate point.

[0034] In this implementation plan, the core objective of the risk intervention module is to map the abstract, multivariate, cross-medium evolution path onto the actual three-dimensional geological space of the mine, thereby accurately determining whether physical fractures have triggered a substantial radioactive ecological leakage crisis. Retrieving the three-dimensional stratigraphic structure layer containing mine geological exploration data is primarily to obtain the actual distribution information of soil and rock strata within the slope. Extracting the labeled, pre-stored historical uranium slag deposit depth coordinates and generating a three-dimensional bounding box for the uranium slag deposit layer delineates a three-dimensional warning and isolation zone for high-risk radioactive contamination sources in digital space. The global digital twin coordinate system refers to the unified aggregation of surface environmental sensor locations, groundwater level monitoring nodes, and geological exploration base maps into a single three-dimensional digital mapping space with an absolute geographical reference standard. After importing the derived fracture channel coordinates into this twin space, the system needs to perform rigorous three-dimensional geological topological intersection discrimination to determine whether the fracture has cut through the contamination source warning zone. The system traverses discrete points on the fracture channel and uses mathematical and geometric algorithms to determine the positional relationship between these spatial points and the bounding box boundary. The system employs spatial geometric inequality rules to construct a topological discriminant function that characterizes the spatial membership state of each discrete three-dimensional coordinate point, both inside and outside the space. Its computational logic is expressed by the following formula: ;In the formula, : The internal and external spatial membership state index of the m-th discrete three-dimensional spatial coordinate point; Enumeration identifiers of projected coordinate axes in a three-dimensional Cartesian coordinate system; The actual coordinate components of the m-th discrete three-dimensional space coordinate point in the d-axis direction; The lower limit of the physical boundary of the three-dimensional solid bounding box of the uranium slag deposit in the d-axis direction; The upper limit of the physical boundary of the three-dimensional solid bounding box of the uranium slag deposit in the d-axis direction; Step smoothing activation function. The step smoothing activation function in the above formula is determined as follows: when the geometric distance decision term within the parentheses is greater than zero, the function outputs a value of one, indicating that the current crack coordinate point is inside the radioactive slag boundary; when the decision term is less than or equal to zero, the function outputs a value of zero, indicating that the current coordinate point is outside the boundary or within a safe soil layer. The technical significance of this calculation step lies in its use of rigorous Boolean intersection logic to accurately quantify, point-by-point, how much of the physical cracks, deduced from surface displacement and groundwater seepage, has intruded into high-ecological-risk areas. This successfully achieves spatial cross-verification of the evolution from mechanical deformation disasters to radioactive pollution disasters. The output judgment results provide irrefutable digital legal basis for determining whether emergency engineering intervention is necessary.

[0035] Specifically, the process of extracting the three-dimensional spatial coordinate nodes of the underground water-conducting gas fracture channel that penetrates the depth limit of the historical uranium slag deposit layer and generating targeted physical intervention commands covering the specified latitude and longitude and the grouting target area depth is as follows: Based on the internal and external spatial affiliation status in the three-dimensional geological topological intersection discrimination results, discrete three-dimensional spatial coordinate points located outside the three-dimensional entity bounding box of the uranium slag deposit layer are screened out, and discrete three-dimensional spatial coordinate points located inside the spatial Boolean intersection area are extracted as the three-dimensional spatial coordinate nodes of the underground water-conducting gas fracture channel; the deepest intrusion point is retrieved for the three-dimensional spatial coordinate nodes of the underground water-conducting gas fracture channel located inside the spatial Boolean intersection area to obtain the limit penetration three-dimensional depth value and the corresponding surface projection two-dimensional latitude and longitude; the limit penetration three-dimensional depth value is calibrated as the grouting target area depth, the surface projection two-dimensional latitude and longitude is mapped to the specified latitude and longitude, the specified latitude and longitude and the grouting target area depth are encapsulated to generate a structured intervention data message, and the structured intervention data message is output as a physical intervention command.

[0036] In this implementation plan, the system performs targeted filtering and engineering parameter mapping based on the aforementioned acquired membership status. Its core lies in directly converting the digital-dimensional hazard assessment results into action commands that can be recognized by on-site construction equipment. The system directly filters out safe coordinate points with a status index of zero based on the internal and external spatial membership status, retaining hazard coordinate points with a status index of one as the three-dimensional spatial coordinate nodes of the underground water-conducting gas fracture channels that truly require intervention. The deepest intrusion point retrieval refers to using a sorting algorithm to find the spatial extreme point that penetrates the slag layer the deepest in the vertical direction among these nodes located within high-risk areas. Obtaining the ultimate penetration depth value and the corresponding two-dimensional latitude and longitude of the surface projection is to accurately locate the core source of radioactive gas and fluid escape. To ensure that the final generated physical intervention command not only seals existing cracks but also effectively blocks the potential downward development trend of cracks within the rock and soil mass, the system introduces a dynamic compensation mechanism to calculate the final grouting target area depth. The specific calculation process is expressed by the following formula: ;In the formula, The final grouting target depth required for physical intervention instructions; The total number of coordinate points included in the three-dimensional spatial coordinate sequence of underground water-conducting gas fracture channels; : Absolute elevation of the mine in situ corresponding to the two-dimensional latitude and longitude coordinates of the land surface projection; : The actual elevation coordinates of the m-th discrete three-dimensional spatial coordinate point in the direction perpendicular to the Z-axis; Safety margin for puncture protection calculated based on historical landslide kinetic energy; The spherical diffusion radius of pre-mixed cement grout in uranium slag with a specific porosity is defined. A method for determining the anti-breakdown safety margin coefficient is disclosed, which involves extracting the average rock mass sliding potential energy data from the past five years of unstable slope failures in mines with similar geological structures, and then normalizing and multiplying this data with the current real-time hydrostatic pressure monitoring extreme value of the slope. The above calculation steps are used to extract the absolute depth of the deepest crack intrusion relative to the surface, and then add an additional safety margin for inertial failure of geological landslides and the physical radius of the grout's own diffusion characteristics, thereby calculating an extremely safe and practical borehole grouting depth parameter. Finally, the system encapsulates this depth value and two-dimensional latitude and longitude into a structured intervention data message according to a preset industrial communication protocol standard. This message can be directly sent to the control terminal of the automated drilling and grouting integrated machine, realizing unmanned, full-link closed-loop control from multivariate IoT front-end perception, cross-media spatial extrapolation of rock and soil fracture, to the final radiation-proof ecological barrier engineering operation.

[0037] In summary, this application has at least the following effects: A mine slope monitoring system based on IoT smart sensors breaks through the physical limitations of traditional monitoring equipment, such as high power consumption during normal operation and the disconnect between multi-source heterogeneous parameters. By performing dynamic analysis on the three-dimensional spatial evolution vector at the edge, it triggers hardware-level high-frequency synchronous linkage sampling of solid phase deformation, liquid phase groundwater leakage, and gas phase radioactive radon gas overflow at the moment of accurately capturing the precursory signs of rock mass mechanical deformation and instability. This achieves the acquisition of cross-medium transient characteristic data with a unified microsecond-level time reference at extremely low power consumption. Based on this, the system cleverly utilizes the natural time delay differences in the transmission of different physical media in complex geological structures, combined with spatial inverse projection and kinematic intersection calibration. This technology accurately transforms the transient time difference of multiple variables into a three-dimensional depth scale for underground hidden water-conducting gas fracture channels, enabling non-contact tracking that can reverse-engineer deep geological fracture paths solely based on surface multi-source sensing. Finally, by performing rigorous three-dimensional geological topological intersection discrimination between the derived dynamic fracture channels and the pre-stored three-dimensional bounding box of high-risk uranium slag, the ultimate penetration node that cuts through the pollution source warning zone is precisely identified. Furthermore, diffusion dynamic parameters are superimposed to generate targeted pre-emptive physical intervention commands with clear latitude, longitude, and safe grouting depth. This truly achieves a leapfrog technological upgrade from passive single-point surface alarm to proactive, precise engineering blocking of deep radioactive ecological disasters.

[0038] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A mine slope monitoring system based on Internet of Things (IoT) smart sensors, characterized in that, include: The collaborative sensing module is used to synchronously collect the three-dimensional displacement of the mine slope surface, the depth of the groundwater infiltration line and the concentration of radon leaching on the surface according to the steady-state inspection cycle, and analyze the three-dimensional spatial evolution vector corresponding to the three-dimensional displacement of the surface in adjacent sampling cycles as time-series baseline data. The edge scheduling module is used to construct a deformation rate tensor within a sliding time window based on the time-series baseline data. It performs dynamic singular value analysis on the principal strain rate scalar of the deformation rate tensor. When it is determined that the principal strain rate scalar exhibits a nonlinear step and is close to the critical state of rheological instability, it generates a hardware-level concurrent wake-up command to synchronously adjust the sampling frequency of the displacement sensor, wetting line sensor, and radon gas sensor to a high-frequency transient capture mode. It extracts the three-dimensional displacement vector direction, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration generated in the high-frequency transient capture mode and combines them into a multivariate transient feature dataset. The spatiotemporal intersection module is used to extract the anomaly timestamps of the three-dimensional displacement vector direction, the transient attenuation of the water level at the wetting line, and the spatial transition rate of the radon precipitation concentration from the multivariate transient feature dataset. It also analyzes the temporal hysteresis gradient of the cross-media response, performs inverse spatial projection along the three-dimensional displacement vector direction, and performs kinematic intersection calibration at the deep geometric position of the temporal hysteresis gradient mapping to obtain the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel. The risk intervention module is used to perform three-dimensional geological topological intersection discrimination between the three-dimensional spatial coordinate sequence of underground water-conducting gas fracture channels and the pre-stored depth coordinates of historical uranium slag deposits, extract the three-dimensional spatial coordinate nodes of underground water-conducting gas fracture channels that penetrate the depth limit of historical uranium slag deposits, and generate targeted physical intervention commands covering specified latitude and longitude and grouting target area depth.

2. The mine slope monitoring system based on IoT smart sensors according to claim 1, characterized in that, The specific process of synchronously collecting three-dimensional displacement of the mine slope surface, groundwater infiltration line depth, and surface radon precipitation concentration according to the steady-state inspection cycle, and analyzing the three-dimensional spatial evolution vector corresponding to the surface three-dimensional displacement within adjacent sampling cycles as time-series baseline data is as follows: The built-in hardware clock of the edge computing gateway is triggered, and a synchronization pulse is sent to the displacement sensor, the immersion line sensor and the radon gas sensor to lock a unified time reference. Collect three-dimensional coordinate point data, water pressure head level data, and radon activity volume concentration with a unified time reference according to the steady-state inspection cycle; The three-dimensional coordinate point data is converted into the surface three-dimensional displacement, the water pressure head liquid level data is converted into the groundwater infiltration line depth, and the radon activity volume concentration is converted into the surface radon release concentration. The three-dimensional displacement of the surface within adjacent sampling periods is extracted, and spatial vector difference analysis is performed to obtain the three-dimensional spatial evolution vector containing displacement orientation angle and absolute displacement scalar. The three-dimensional spatial evolution vector is used as the time-series baseline data.

3. The mine slope monitoring system based on IoT smart sensors according to claim 1, characterized in that, Based on the time-series baseline data, a deformation rate tensor within a sliding time window is constructed. Dynamic singular value analysis is performed on the principal strain rate scalar of the deformation rate tensor. When it is determined that the principal strain rate scalar exhibits a nonlinear step and approaches the critical state of rheological instability, the specific process of generating a hardware-level concurrent wake-up command is as follows: The temporal baseline data is extracted sequentially and pushed into a sliding time window of a preset length. The temporal derivative of the three-dimensional spatial evolution vector within the sliding time window is calculated along the time axis, and the deformation rate tensor containing the normal deformation rate and the tangential deformation rate is output. Perform matrix eigenvalue decomposition on the deformation rate tensor, extract the largest principal eigenvalue as the principal strain rate scalar, calculate the second derivative trajectory of the principal strain rate scalar within adjacent time windows, perform dynamic singular value analysis, and extract the principal strain rate scalar corresponding to the abrupt extreme point in the second derivative trajectory. The principal strain rate scalar corresponding to the extreme point of abrupt change is compared with the dynamic adaptive benchmark threshold to determine the rheological instability boundary. When the principal strain rate scalar exceeds the dynamic adaptive benchmark threshold and exhibits a nonlinear step and indicates that it is approaching the critical state of rheological instability, the edge computing gateway interrupt control pin is triggered to generate a hardware-level concurrent wake-up command.

4. A mine slope monitoring system based on an Internet of Things (IoT) smart sensor according to claim 3, characterized in that, The specific process of simultaneously adjusting the sampling frequencies of the displacement sensor, wetting line sensor, and radon gas sensor to high-frequency transient capture mode, and extracting the three-dimensional displacement vector direction, transient attenuation of the wetting line water level, and spatial transition rate of radon precipitation concentration generated in the high-frequency transient capture mode to form a multivariate transient feature dataset is as follows: A hardware-level concurrent wake-up command is sent to the underlying control registers of the displacement sensor, immersion line sensor, and radon sensor to overwrite the sampling period configuration parameters and synchronously switch the sampling frequency of the displacement sensor, immersion line sensor, and radon sensor to the high-frequency transient capture mode. In the high-frequency transient capture mode, transient three-dimensional displacement coordinates, transient water pressure head data and transient radon activity data are acquired in parallel. Spatial trajectory fitting is performed on the transient three-dimensional displacement coordinates to extract the direction of the three-dimensional displacement vector. Liquid level drop slope is extracted from the transient water pressure head data to obtain the transient attenuation of the wetting line water level. Concentration rise gradient calculation is performed on the transient radon activity data to obtain the spatial transition rate of radon precipitation concentration. By aligning the three-dimensional displacement vector orientation, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration using synchronized timestamps, the data are encapsulated and packaged into a multivariate transient feature dataset.

5. A mine slope monitoring system based on Internet of Things (IoT) smart sensors according to claim 1, characterized in that, The specific process of extracting the anomaly timestamps of the three-dimensional displacement vector direction, the transient attenuation of the wetting line water level, and the spatial transition rate of radon precipitation concentration from the multivariate transient feature dataset, and analyzing the temporal hysteresis gradient of the cross-medium response, is as follows: Temporal extreme value retrieval was performed on the multivariate transient feature dataset, and transient microsecond-level time points corresponding to the initial deflection of the three-dimensional displacement vector, the transient attenuation of the wetting line water level crossing the background baseline, and the peak value of the spatial transition rate of radon precipitation concentration were extracted as anomaly timestamps. Time axis coordinate alignment is performed on the displacement anomaly timestamp, wetting line anomaly timestamp, and radon precipitation anomaly timestamp. The liquid phase conduction delay phase of the wetting line anomaly timestamp relative to the displacement anomaly timestamp is extracted, and the gas phase escape delay phase of the radon precipitation anomaly timestamp relative to the displacement anomaly timestamp is extracted. Retrieve the pre-configured permeability coefficient and gas diffusion coefficient of the slope rock and soil, perform permeability dynamics tensor mapping between the liquid phase conduction delay phase and the permeability coefficient of the slope rock and soil, and obtain the liquid phase penetration hysteresis gradient. The gas phase escape delay phase and the gas diffusion coefficient are subjected to diffusion dynamics tensor mapping to obtain the gas phase escape hysteresis gradient. The liquid phase penetration hysteresis gradient and the gas phase escape hysteresis gradient are fused by multi-field coupling vector and encapsulated into a time-series hysteresis gradient of the cross-medium response.

6. A mine slope monitoring system based on an Internet of Things (IoT) smart sensor according to claim 5, characterized in that, The specific process of obtaining the three-dimensional spatial coordinate sequence of underground water-conducting gas fracture channels by performing inverse spatial projection along the direction of the three-dimensional displacement vector and kinematic intersection calibration at the depth geometric position of the temporally hysteretic gradient mapping is as follows: Extract the three-dimensional coordinates of the geodetic origin of the surface displacement sensor corresponding to the time stamp of the anomaly, and construct a three-dimensional reverse tracking space ray based on the three-dimensional coordinates of the geodetic origin along the direction of the three-dimensional displacement vector; Tracing the spatial ray in three dimensions, the spatial equivalent distance mapping is performed according to the liquid phase penetration hysteresis gradient and the gas phase escape hysteresis gradient respectively to determine the first spatial projection node of the wetting line attenuation source and the second spatial projection node of the radon transition source. Perform three-dimensional envelope spatial fitting on the three-dimensional coordinates of the geodetic origin, the first spatial projection node, and the second spatial projection node, and extract the three-dimensional topological spatial connection connecting the three-dimensional coordinates of the geodetic origin, the first spatial projection node, and the second spatial projection node. The equidistant grid discretization process is performed along the three-dimensional topological space connection, and the output is a three-dimensional spatial coordinate sequence of underground water-conducting gas fracture channels composed of multiple discrete three-dimensional spatial coordinate points.

7. A mine slope monitoring system based on Internet of Things (IoT) smart sensors according to claim 1, characterized in that, The specific process for determining the three-dimensional geological topological intersection between the three-dimensional spatial coordinate sequence of underground water-conducting gas fracture channels and the depth coordinates of pre-stored historical uranium slag deposits is as follows: Retrieve the three-dimensional stratigraphic structure layer containing mine geological exploration data, extract the depth coordinates of the pre-stored historical uranium slag deposit layer marked in the three-dimensional stratigraphic structure layer, and generate a three-dimensional solid bounding box of the uranium slag deposit layer. Import the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel into the same global digital twin coordinate system containing the three-dimensional solid bounding box of the uranium slag deposit layer; Traverse each discrete three-dimensional spatial coordinate point in the three-dimensional spatial coordinate sequence of the underground water-conducting gas fracture channel, perform spatial Boolean intersection operation on the three-dimensional closed boundary of each discrete three-dimensional spatial coordinate point and the three-dimensional solid bounding box of the uranium slag deposit layer, and output the three-dimensional geological topological intersection discrimination result that represents the spatial affiliation state of each discrete three-dimensional spatial coordinate point.

8. A mine slope monitoring system based on an Internet of Things (IoT) smart sensor according to claim 7, characterized in that, The specific process of extracting the three-dimensional spatial coordinate nodes of the underground water-conducting gas fracture channel that penetrates the depth limit of the historical uranium slag deposit layer, and generating targeted physical intervention commands covering the specified latitude, longitude, and grouting target area depth is as follows: Based on the internal and external spatial affiliation status in the three-dimensional geological topological intersection discrimination results, discrete three-dimensional spatial coordinate points located outside the three-dimensional entity bounding box of the uranium slag deposit layer are screened out, and discrete three-dimensional spatial coordinate points located inside the spatial Boolean intersection region are extracted as three-dimensional spatial coordinate nodes of the underground water-conducting gas fracture channel. Perform the deepest intrusion point search on the three-dimensional spatial coordinate nodes of the underground water-conducting gas fracture channel located within the spatial Boolean intersection area to obtain the ultimate penetration three-dimensional depth value and the corresponding surface projection two-dimensional latitude and longitude. The ultimate penetration 3D depth value is calibrated as the grouting target area depth, the surface projection 2D latitude and longitude is mapped to the specified latitude and longitude, the specified latitude and longitude and the grouting target area depth are encapsulated to generate a structured intervention data message, and the structured intervention data message is output as a physical intervention command.

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