Multi-source monitoring and early warning method for slope stability of strip mine dump

Abstract

The invention discloses a multi-source monitoring and early warning method for slope stability of a strip mine dump. The method comprises the steps that multi-source monitoring data of a target slope are acquired, and the multi-source monitoring data comprise radar monitoring data, GNSS monitoring data and deep displacement sensor data; a two-dimensional model of the central axis of the maximum deformation area of the target slope is established based on the numerical simulation technology, the model is generated according to slope design parameters and rock stratum physical and mechanical parameters, and the deformation rate and the accumulated displacement early warning threshold value are obtained by simulating the landslide process; and according to the slope surface deformation rate and the deep accumulated displacement, landslide risk early-warning grades are divided, and the surface early-warning grade and the deep early-warning grade are fused to output a final landslide risk early-warning result. The method is comprehensive and accurate in monitoring and early warning of the slope stability of the strip mine dumping site, and is suitable for monitoring and evolution of the slope stability of the strip mine dumping site.

Description

Technical Field

[0001] This invention relates to the field of geological disaster technology, and in particular to a multi-source monitoring and early warning method for the stability of open-pit mine spoil heap slopes. Background Technology

[0002] Open-pit mining is a crucial method for extracting mineral resources. During open-pit mining, a large amount of loose material, consisting of topsoil and slag, is generated. This loose material is transported to specific areas for unified disposal; these areas are generally called spoil heaps. With the increasing scale of open-pit mining production, the scale of spoil heap disposal is also growing, highlighting the growing stability issues of spoil heap slopes. Deformation and landslides at spoil heaps occur frequently. The complexity of the geological conditions and influencing factors of open-pit mine slopes necessitates advanced monitoring methods and equipment for slope early warning. In recent years, the introduction of advanced international technologies has led to significant progress in my country's surveying and mapping science. Various new monitoring methods have emerged, and major open-pit mines are increasingly adopting high-precision, intelligent slope monitoring technologies. Among these, GNSS and slope radar monitoring technologies have been widely used in my country's open-pit mines, especially in slope stability monitoring, where significant progress has been made.

[0003] A GNSS system is a comprehensive system integrating high technologies such as structural analysis and calculation, computer technology, communication technology, network technology, and sensor technology. Its basic principle is to measure the distance between a satellite at a known location and the GNSS receiver, then analyze the data from multiple satellites to obtain the receiver's specific location, which is then transmitted to the server to obtain the three-dimensional coordinate data of the monitoring point. This data is then recorded as a time series based on the deformation information of the slope rock mass. The entire system can provide open-pit mines with slope deformation information such as time, velocity, acceleration, and three-dimensional coordinates, offering advantages such as real-time, global coverage, and continuity.

[0004] Slope radar monitoring technology consists of four main parts: a data acquisition unit, a wireless communication unit, a power supply unit, and a mobile transportation unit. The data acquisition unit obtains slope deformation information by transmitting and receiving electromagnetic waves through radar, processes and analyzes this information, and then transmits it to the service terminal at the monitoring center via the wireless communication unit. Finally, technicians conduct slope stability analysis and safety assessments in open-pit mines. Radar can measure slope stability with sub-millimeter precision, intuitively and quickly identifying areas of abnormal deformation, and specifically collecting and statistically analyzing displacement and velocity changes over time in these areas. It also dynamically tracks and monitors the entire process of slope rock mass deformation, achieving the goal of accurately predicting geological disasters such as spalling and landslides, thus ensuring safe production in open-pit mines.

[0005] However, in practical field applications, both GNSS and radar monitoring have limitations due to their inherent technical characteristics. GNSS requires the deployment of multiple monitoring points on the slope surface, and the placement of these points is somewhat random, making it impossible to ensure that they are precisely positioned on the central axis of the maximum deformation zone. Consequently, the data may not reflect the maximum deformation of the measured area. GNSS monitoring units often rely on solar panels for power, making them unsuitable for steep slopes, and their measurement accuracy is lower than that of radar. Slope radar, deployed on the opposite side of the slope, is more flexible and reliable than GNSS, and its measurement accuracy is also higher. However, it still has a drawback: it cannot perform tracking monitoring of the landslide body. If the monitored slope undergoes significant deformation or displacement within the monitoring period, the area scanned by the slope radar in two consecutive scans may have already experienced subsidence, and the acquired data from the two scans may not be continuous deformation data of the monitored area. Therefore, the slope surface deformation information acquired by slope radar may not be a true reflection of the landslide deformation characteristics. Summary of the Invention

[0006] In view of this, the purpose of this invention is to address the shortcomings of the prior art by providing a multi-source monitoring and early warning method for the stability of open-pit mine spoil heap slopes. The method is simple in steps, novel and reasonable in design, easy to implement, and highly reliable. It provides comprehensive and accurate monitoring and early warning of the stability of open-pit mine spoil heap slopes, is applicable to the stability monitoring and evolution of open-pit mine spoil heap slopes, is highly practical, and is easy to promote and use.

[0007] According to one aspect of the present invention, a multi-source monitoring and early warning method for the stability of open-pit mine spoil heap slopes is provided, the method comprising:

[0008] Acquire multi-source monitoring data of the target slope, including radar monitoring data, GNSS monitoring data, and deep displacement sensor data;

[0009] A two-dimensional model of the central axis of the maximum deformation zone of the target slope is established based on numerical simulation technology. The model is generated according to the slope design parameters and the physical and mechanical parameters of the rock strata. The deformation rate and cumulative displacement warning threshold are obtained by simulating the landslide process.

[0010] Landslide risk warning levels are classified based on the slope surface deformation rate and deep cumulative displacement, and the final landslide risk warning result is output by merging the surface and deep warning levels; the warning level is determined by comparing multi-source monitoring data with warning thresholds;

[0011] The warning threshold is dynamically adjusted based on the cumulative displacement-time curve of the landslide process.

[0012] In the aforementioned technical solution, the pain points of "inaccurate, incomplete, and discontinuous measurement" of single monitoring methods for spoil heap slopes are addressed. The solution proposes a multi-source collaborative approach of "radar-GNSS-deep displacement" and uses numerical simulation to dynamically calibrate the early warning threshold. It forms a closed loop from four stages: data acquisition, model deduction, risk classification, and threshold correction. This approach overcomes the limitations of random GNSS point deployment, power supply constraints on steep slopes, and the inability of radar to track sliding bodies. Furthermore, it incorporates deep slip zone information into the evaluation system, making the monitoring results significantly superior to single-source solutions in terms of breadth, depth, and accuracy. This meets the engineering requirements of "real-time, accurate, and continuous" stability early warning for open-pit mine slopes.

[0013] Data complementarity: By fusing the high-precision surface deformation of radar, the single-point absolute displacement of GNSS, and the sliding information of deep displacement sensors, "surface-deep, point-surface" complementarity is achieved. This not only makes up for the radar's inability to track sliding bodies, but also eliminates the omission of the maximum deformation caused by the random deployment of GNSS points, thus forming full spatial coverage.

[0014] Model Priority: The potential slip surface and the central axis of the maximum deformation zone are inverted by numerical simulation. The most dangerous profile is locked in advance by design parameters and physical and mechanical indicators, so that the on-site sensor deployment is changed from "experience-based deployment" to "model-guided deployment", which improves the hit rate of monitoring points and the scientific nature of thresholds.

[0015] Hierarchical fusion: After dividing the surface deformation rate and deep cumulative displacement into independent early warning levels, the final risk level is output by fusion according to weight or rules, avoiding misjudgment by a single indicator, realizing "fast surface - stable deep" coupled decision-making, and reducing false alarms and missed alarms.

[0016] Dynamic threshold: The early warning threshold is corrected online using the cumulative displacement-time curve, so that the early warning system can adaptively adjust with the evolution of the sliding body, taking into account conditions such as rainy season acceleration and blasting disturbance, thereby improving the system's robustness and life cycle.

[0017] Engineering-friendly: The process is simple, the hardware is universal, both radar and GNSS are conventional equipment in open-pit mines, the drilling technology for deep sensors is mature, the numerical simulation software is highly commercialized, the whole is easy to promote in mines, and the operation and maintenance costs are controllable.

[0018] In summary, this multi-source monitoring and early warning method overcomes the inherent limitations of single GNSS or radar in steep slope scenarios at spoil heaps through a four-in-one technical approach of "multi-source complementarity, model prior, hierarchical fusion, and dynamic threshold." It achieves continuous monitoring and intelligent early warning of the entire process and full range of the sliding body surface and deep subsurface with high reliability. The solution is simple, reliable, and highly systematic, meeting current standards while reserving expansion interfaces, and has significant potential for widespread engineering application.

[0019] In some embodiments, acquiring multi-source monitoring data of the target slope includes:

[0020] Radar monitoring is installed on the target slope, and radar monitoring data is recorded; the radar monitoring data is corrected using a correction formula to eliminate errors caused by the illumination angle.

[0021] GNSS monitoring points are set on the surface of the target slope. The GNSS monitoring points are densely arranged along the central axis of the maximum deformation zone. The arrangement ensures that the monitoring range covers the target slope. The GNSS monitoring data records the slope deformation rate.

[0022] A borehole is set at the central axis of the maximum deformation zone of the target slope, and multiple deep displacement sensors are arranged at different locations. The sensors record the cumulative displacement at different coordinate points.

[0023] In the above technical solution, this section, based on the previous multi-source framework, further provides specific methods for the "deployment-correction-recording" of GNSS, radar, and deep sensors: by correcting radar illumination angle errors, "densifying" GNSS points along the central axis of the maximum deformation zone, and layering sensors along the same central axis in deep boreholes, the three types of data achieve coaxial alignment, complementary accuracy, and controlled error in a geometric and physical sense, thereby providing the most reliable and focused raw data for subsequent numerical simulation and risk fusion.

[0024] Radar illumination angle error correction: After introducing the correction formula, the systematic error of the radar under high elevation angle or side-view conditions is explicitly eliminated, the mapping accuracy from the scanning surface to the real deformation surface is significantly improved, the "patterning" and "compression" phenomena caused by the tricky viewing angle of the high and steep slope of the spoil heap are suppressed, and the surface displacement field is not distorted.

[0025] GNSS central axis densification: The monitoring points are changed from random distribution to high-density distribution along the central axis of the maximum deformation zone locked by numerical simulation. This not only directly captures the maximum displacement of potential landslides, but also forms a "displacement gradient profile" through the upstream and downstream point groups of the axis, realizing the joint control of the overall and local deformation of the slope, and making up for the shortcomings of traditional sparse distribution that misses key deformations.

[0026] Coaxial layering of deep boreholes: The boreholes are also located on the central axis, and multiple sensors are arranged at different elevations to form a "deep slip vertical profile". This profile can be accurately registered with the GNSS surface axis and radar deformation field in the same coordinate system, achieving a one-to-one correspondence between the surface and deep space. At the same time, the layered data can be used to calculate the slip surface depth, thickness and evolution rate, providing the most direct evidence for dynamic threshold adjustment.

[0027] Enhanced data consistency: The three methods share the same spatial reference (central axis) and time reference (unified time synchronization), avoiding errors generated later and improving the reliability of real-time early warning.

[0028] The project is feasible: radar correction only requires software algorithm iteration and no additional hardware is needed; GNSS densification can be carried out in the central axis area, and the number and depth of boreholes can be optimized according to simulation results to avoid blind drilling. The overall construction volume and cost are under control and the interference to the site is small.

[0029] By achieving high-precision coaxial deployment and error correction of radar, GNSS, and deep sensors on the key geometric element of the "central axis of the maximum deformation zone," this scheme transforms the originally scattered and heterogeneous multi-source data into a "three-dimensional monitoring profile" that can directly interpret the deformation behavior of the sliding body throughout the entire process. This ensures both the spatial representativeness and physical consistency of the data, while also taking into account the economy and operability of on-site implementation, laying a solid data foundation for the stability monitoring of open-pit mine spoil heap slopes.

[0030] In some embodiments, a two-dimensional model of the central axis of the maximum deformation zone of the target slope is established based on numerical simulation technology, including:

[0031] Obtain the design parameters of the target slope, including step height, step slope angle, and flat plate width;

[0032] Obtain the physical and mechanical parameters of each rock layer on the target slope, including the internal friction angle, cohesion, and unit weight;

[0033] Based on the design parameters and physical and mechanical parameters, a two-dimensional model of the slope section with the central axis of the maximum deformation zone of the target slope is generated by computer simulation software. The model is used to simulate the landslide process.

[0034] The above technical solution takes "acquiring design parameters + physical and mechanical parameters → generating a two-dimensional cross-sectional model of the central axis of the maximum deformation zone" as its core, directly transforming conventional mine data into a calculable and evolvable landslide simulation platform. This provides a unified geometric and mechanical benchmark for subsequent radar-GNSS-deep data fusion, enabling monitoring deployment, threshold setting, and risk classification to be based on verifiable physical models, thereby improving the scientific nature and pertinence of early warning from the source.

[0035] The parameters are reliable: design parameters such as step height, slope angle, and flat plate width are directly taken from the mine construction drawings and annual stripping plan. The internal friction angle, cohesion, and unit weight of the rock strata are given by on-site sampling or historical survey reports, which avoids model distortion caused by experience estimation and ensures that the simulation results are isomorphic to the real slope.

[0036] The most dangerous section is locked by the central axis: the central axis of the maximum deformation zone is used as the two-dimensional model section, which is not only spatially aligned with the radar scanning center, GNSS densified axis and deep borehole axis deployed on site, but also naturally corresponds to the main sliding direction of the potential slip surface, realizing a one-to-one mapping between "simulation and monitoring", reducing the amount of calculation of the three-dimensional model without losing key information.

[0037] In some embodiments, obtaining deformation rate and cumulative displacement early warning thresholds by simulating a landslide process includes:

[0038] The landslide process of the slope is simulated by a two-dimensional model of the key section, and the cumulative displacement-time curve is obtained. The curve is used to determine the average rate of the uniform deformation stage of the landslide.

[0039] The slope surface sliding deformation rate under different slope tangent angles is calculated by the key tangent angle tangent value multiple method. The tangent angle includes multiple preset angles. The deformation rate is used as the early warning threshold for the deformation stage rate of surface monitoring.

[0040] The cumulative deep displacement of different coordinate points of the two-dimensional model during the landslide process at the tangent angle is obtained, and the cumulative displacement is used as the displacement early warning threshold for the deformation stage of deep displacement monitoring of the slope.

[0041] In the above technical solution, the core is "two-dimensional model landslide full process simulation". First, the cumulative displacement-time curve is output to lock the average rate of the uniform speed segment. Then, the surface rate threshold is back-calculated using the key tangent angle tangent value multiple method. At the same time, the cumulative displacement of each coordinate point in the deep part under the same angle is extracted as the displacement threshold. Thus, the abstract stability coefficient is transformed into a "rate-displacement" dual index early warning threshold that can be directly applied to radar, GNSS and deep sensors.

[0042] Standardization of the average rate in the uniform deformation phase: The average rate of the landslide is automatically identified by the cumulative displacement-time curve and calculated. This rate has a clear physical meaning and is statistically stable. It can be used as the first-level benchmark for the "surface monitoring rate threshold" to avoid the arbitrariness caused by manual setting.

[0043] The key tangent angle multiple method can be flexibly extended: based on the uniform speed segment rate, it can be amplified step by step according to the tangent value multiple of different preset tangent angles to quickly generate multi-level surface rate thresholds, which not only correspond to the "yellow-orange-red" early warning classification commonly used in mines, but also take into account the differentiated needs of different slope shapes and different lithologies.

[0044] Deep threshold spatialization: Under the same tangent angle conditions, the deep cumulative displacement of each coordinate point in the two-dimensional model is directly read to form a threshold table with one-to-one correspondence between "coordinates and displacements". This table is precisely matched with the positions of deep sensors deployed in layers along the central axis on site, realizing direct threshold projection of "model point = sensor point" without the need for interpolation or secondary conversion.

[0045] By employing a dual-path parallel calculation of "cumulative displacement-time curve → average velocity of uniform segment → multiple of key tangent angle → surface velocity threshold" and "cumulative displacement of deep coordinate points at the same angle → deep displacement threshold", this section transforms the results of two-dimensional numerical simulation into a rate-displacement early warning threshold system that can be managed hierarchically and dynamically refreshed. This ensures both the scientific rigor of the thresholds and the ease of use for multi-source monitoring in the field, providing a quantitative standard for open-pit mine spoil heap slopes that is a complete closed loop of "model-monitoring-early warning".

[0046] In some embodiments, landslide risk warning levels are classified based on the slope surface deformation rate and deep cumulative displacement, including:

[0047] The deformation rate monitored by radar and the deformation rate monitored by GNSS are obtained, and the maximum value of the two is taken as the surface deformation rate warning value. According to the preset deformation stage rate warning threshold, the surface deformation rate warning value is divided into multiple warning levels, which include multiple levels from low to high.

[0048] The cumulative displacement monitored by the deep displacement sensor is obtained. Based on the preset displacement warning threshold for the deformation stage, the deep cumulative displacement is divided into multiple warning levels, which include multiple levels from low to high. The highest warning level among the deep cumulative displacement monitoring points is taken as the final deep cumulative displacement warning level.

[0049] The above technical solution proposes a dual-channel hierarchical logic of "taking the maximum value for the surface and the highest level for the deep part": first, the surface deformation rate is obtained in parallel by radar and GNSS and the maximum value of the two is taken; then, the surface warning level is divided by comparing it with the rate threshold output by the simulation; at the same time, all deep displacement sensor readings are compared with the displacement threshold, and the level of the most dangerous point is taken as the deep warning level. Thus, the most unfavorable working conditions are used as the standard to ensure that no local anomaly is missed, forming a simple, unified, and relatively safe multi-level risk scale.

[0050] The surface rate is "double-insured": it adopts both the high-precision surface rate of radar and the absolute single-point rate of GNSS, and takes the maximum value of the two. It utilizes the dense radar network to capture local changes while retaining the drift-free advantage of the long baseline of GNSS, thus avoiding missed alarms caused by blind spots due to a single method.

[0051] The principle of depth rating is to rate all deep sensors one by one and take the highest level to ensure that any sudden slippage at any borehole or depth can trigger the corresponding warning. This is consistent with the landslide mechanism of "partial penetration of the slip surface followed by overall instability", and has a high degree of safety redundancy.

[0052] By using the algorithm of "maximizing the surface rate and taking the highest deep displacement", multi-source heterogeneous data can be quickly converged into two independent and comparable multi-level early warning levels. This not only leverages the complementary advantages of various monitoring methods, but also ensures that no early warning is missed by using the most unfavorable operating conditions as the bottom line.

[0053] In some embodiments, the final landslide risk warning result is output by fusing surface and deep warning levels, including:

[0054] The surface deformation rate warning level and the deep cumulative displacement warning level are compared, and the comparison is based on a preset fusion rule; the rule is as follows:

[0055] If the surface warning level and the deep warning level are the same, the common level shall be adopted as the final warning level;

[0056] If the difference between the surface warning level and the deep warning level is 1, then the higher warning level shall be used as the final warning level.

[0057] If the difference between the surface warning level and the deep warning level is 2, then the midpoint between the two will be used as the final warning level.

[0058] If the difference between the surface warning level and the deep warning level is 3, then the level that is reduced by one level from the higher warning level shall be used as the final warning level.

[0059] In the above technical solution, the surface rate level and deep displacement level are compressed into a single final warning level by using the "difference-mapping" fusion rule. The rule retains the safety orientation of "choosing the higher level" and introduces a moderate compromise through difference subdivision to avoid excessive warning caused by simply "taking the maximum", thereby achieving a balance between risk sensitivity and on-site operability.

[0060] Safety redundancy and false alarm suppression are equally important: when the difference is ≤1, a higher level is directly adopted to ensure that the slip surface can be quickly upgraded once any subsystem on the surface or deep is abruptly changed; when the difference is ≥2, an intermediate value is introduced or the processing is downgraded to effectively filter out single-source anomalies caused by short-term disturbances such as blasting vibration and local rainfall, and reduce the false alarm rate.

[0061] Alignment with mine management practices: The level of the rule output still falls within the original "yellow-orange-red" or I-IV level framework, without the need for additional revision of emergency plans; the dispatch center can directly initiate corresponding shutdown, evacuation, and reinforcement measures based on the final level, with the shortest decision-making chain.

[0062] Transparent parameters and easy to optimize: If it is necessary to tighten or loosen the safety margin later, only the mapping table needs to be adjusted without changing the hardware and software architecture, so as to adapt to the differentiated needs of different mines and different climate zones.

[0063] By using a four-level fusion rule of "difference-mapping", the two independent early warning levels of surface and deep areas are seamlessly integrated into a single final level. This not only inherits the safety baseline of the "highest level" principle, but also uses the difference subdivision strategy to suppress excessive alarms. The algorithm is lightweight, the rules are transparent, and the management is friendly, providing a final path for landslide risk determination for open-pit mine spoil heap slopes.

[0064] In some embodiments, the early warning threshold is dynamically adjusted based on the cumulative displacement-time curve of the landslide process, including:

[0065] Analyze the uniform and acceleration phases of the cumulative displacement-time curve to determine the landslide deformation trend;

[0066] Based on the deformation trend, the deformation rate during the uniform deformation stage of the landslide is dynamically adjusted, and the adjustment is used to optimize the surface deformation rate early warning threshold.

[0067] Based on the deformation trend, the deep cumulative displacement of the two-dimensional model is updated, and the update is used to optimize the displacement early warning threshold during the deformation stage.

[0068] Based on the adjusted warning thresholds, the surface and deep warning levels are recalculated, and the optimized final landslide risk warning level is output.

[0069] In the above technical solution, deformation trends are captured by online identification of uniform speed segment and acceleration segment, and the surface rate threshold and deep displacement threshold are iteratively refreshed accordingly. The warning level is then recalculated, forming a closed-loop link of "monitoring-identification-threshold adjustment-reassessment". This enables the early warning system to adaptively evolve with the actual evolution of the slope and always maintain sensitivity and accuracy to potential landslides.

[0070] Real-time trend recognition: By using curve slope abrupt change detection or sliding window regression, threshold updates can be triggered as soon as deformation enters the acceleration phase.

[0071] Threshold refresh bidirectional coupling: The surface rate threshold and the deep displacement threshold both originate from the same curve, but correspond to the average rate and cumulative displacement of the uniform segment, respectively. After synchronous update, they still maintain physical consistency.

[0072] Online inversion of model parameters: The latest monitoring data is fed back into the two-dimensional model to achieve digital twin synchronization of "slope-model" and ensure that subsequent threshold updates are always based on the latest geological conditions.

[0073] By using the cumulative displacement-time curve as a real-time feedback source, an adaptive closed loop of "curve analysis-threshold refresh-level recalculation" is constructed, which enables the early warning threshold to change dynamically with the slope deformation stage. This allows for maintaining a reasonable margin during the uniform deformation stage and tightening the threshold in a timely manner during the acceleration stage, ensuring that the open-pit mine spoil heap slope monitoring and early warning system is always "learning and evolving online," significantly improving the reliability and economy of long-term operation.

[0074] According to another aspect of the present invention, a multi-source monitoring and early warning device for the stability of open-pit mine spoil heap slopes is provided, comprising:

[0075] The acquisition module is used to acquire multi-source monitoring data of the target slope, including radar monitoring data, GNSS monitoring data and deep displacement sensor data.

[0076] The numerical simulation module is used to establish a two-dimensional model of the central axis of the maximum deformation zone of the target slope based on numerical simulation technology. The model is generated according to the slope design parameters and the physical and mechanical parameters of the rock strata. The deformation rate and cumulative displacement warning threshold are obtained by simulating the landslide process.

[0077] The early warning module is used to classify landslide risk warning levels based on the slope surface deformation rate and deep cumulative displacement, and to merge the surface and deep warning levels to output the final landslide risk warning result; the warning level is determined by comparing multi-source monitoring data with the warning threshold.

[0078] The adjustment module is used to dynamically adjust the early warning threshold based on the cumulative displacement-time curve of the landslide process.

[0079] In order to better utilize the above method, this application proposes a multi-source monitoring and early warning device for the stability of open-pit mine spoil heap slopes. Each module corresponds to a step of the above method, and its specific principle has been described above and will not be repeated here.

[0080] According to another aspect of the present invention, a multi-source monitoring and early warning device for the slope stability of an open-pit mine spoil heap is provided, characterized in that it comprises:

[0081] At least one processor and a memory communicatively connected to said at least one processor;

[0082] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described above.

[0083] In the above technical solution, to better operate and process the method, the method is stored in memory, and the stored method is executed by a processor. It should be noted that the principle and effect of each step have been described above and will not be elaborated upon here.

[0084] According to another aspect of the present invention, a computer-readable storage medium is provided storing a computer program, characterized in that the computer program implements the above-described method when executed by a processor.

[0085] In the above technical solution, to better operate and use the method, the method is stored in a computer-readable storage medium and implemented using a processor. It should be noted that the principle and effect of each step have been described above and will not be elaborated upon here. Attached Figure Description

[0086] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0087] Figure 1 This is a flowchart illustrating one embodiment of the multi-source monitoring and early warning method for slope stability of open-pit mine spoil heaps according to the present invention;

[0088] Figure 2 This is a flowchart illustrating a second embodiment of the multi-source monitoring and early warning method for slope stability of open-pit mine spoil heaps according to the present invention.

[0089] Figure 3 This is a cross-sectional view of the centerline of the maximum deformation zone in the second embodiment of the multi-source monitoring and early warning method for the stability of open-pit mine spoil heap slopes of the present invention;

[0090] Figure 4 This is a sensor location diagram of the second embodiment of the multi-source monitoring and early warning method for slope stability of open-pit mine spoil heaps of the present invention;

[0091] Figure 5 This is a model diagram of the spoil heap slope in the second embodiment of the multi-source monitoring and early warning method for the stability of open-pit mine spoil heap slopes according to the present invention;

[0092] Figure 6 This is a landslide St curve diagram from the second embodiment of the multi-source monitoring and early warning method for slope stability of open-pit mine spoil heaps of the present invention.

[0093] Figure 7 This is a schematic diagram of an embodiment of a multi-source monitoring and early warning device for slope stability of an open-pit mine spoil heap according to the present invention. Detailed Implementation

[0094] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0095] This invention provides a multi-source monitoring and early warning method for the stability of open-pit mine spoil heap slopes. The method is simple in steps, novel and reasonable in design, easy to implement, and highly reliable. It provides comprehensive and accurate monitoring and early warning of the stability of open-pit mine spoil heap slopes, is applicable to the stability monitoring and evolution of open-pit mine spoil heap slopes, is highly practical, and is easy to promote and use.

[0096] Example 1

[0097] Please see Figure 1 A multi-source monitoring and early warning method for slope stability of open-pit mine spoil heaps, the method comprising:

[0098] S1. Acquire multi-source monitoring data of the target slope, including radar monitoring data, GNSS monitoring data and deep displacement sensor data;

[0099] In this embodiment, S1, acquiring multi-source monitoring data of the target slope includes:

[0100] S11. Set up radar monitoring on the target slope and record the radar monitoring data; the radar monitoring data is corrected using a correction formula to eliminate errors caused by the illumination angle;

[0101] For example, radar monitoring is set up on the target slope, radar monitoring data is recorded, and the central axis of the maximum deformation zone of the slope is found based on the slope deformation data. The slope deformation rate measured by the radar is recorded as v. r The slope deformation rate v measured by radar r The following formula is used to calculate and eliminate data errors caused by the radar illumination angle. The rate after error elimination is denoted as v. a .

[0102]

[0103] In the formula, θ is the slope angle of the step; α is the angle between the actual displacement direction of the monitoring unit and the horizontal plane; β is the elevation angle of the radar wave; This is the horizontal angle of the radar wave (0° when it is orthogonal).

[0104] S12. Set up GNSS monitoring points on the surface of the target slope. The GNSS monitoring points are densely arranged at the central axis of the maximum deformation zone. The arrangement ensures that the monitoring range covers the target slope. The GNSS monitoring data records the slope deformation rate.

[0105] For example, GNSS monitoring points are set up on the surface of the target slope, with a relatively dense concentration of GNSS monitoring points within the zone of maximum deformation. A GNSS monitoring line is then laid out along the central axis of the zone of maximum deformation, ensuring that the GNSS monitoring range completely covers the target slope. The slope deformation rate measured by GNSS is denoted as v. b .

[0106] S13. A borehole is set at the centerline of the maximum deformation zone of the target slope, and multiple deep displacement sensors are arranged at different locations. The sensors record the cumulative displacement at different coordinate points.

[0107] For example, boreholes are drilled at the central axis of the maximum deformation zone of the target slope, and several deep displacement sensors are arranged at different locations. The cumulative displacement measured by the sensors is recorded as follows:

[0108] M(x1,y1),M(x2,y2),M(x3,y3),…,M(x n ,y n ).

[0109] S2. A two-dimensional model of the central axis of the maximum deformation zone of the target slope is established based on numerical simulation technology. The model is generated according to the slope design parameters and the physical and mechanical parameters of the rock strata. The deformation rate and cumulative displacement warning threshold are obtained by simulating the landslide process.

[0110] In this embodiment, S2, establishing a two-dimensional model of the central axis of the maximum deformation zone of the target slope based on numerical simulation technology, includes:

[0111] S21. Obtain the design parameters of the target slope, including the step height, step slope angle and flat plate width;

[0112] S22. Obtain the physical and mechanical parameters of each rock layer of the target slope, including the internal friction angle, cohesion and unit weight;

[0113] S23. Based on the design parameters and physical and mechanical parameters, a two-dimensional model of the slope section with the central axis of the maximum deformation zone of the target slope is generated by computer simulation software. The model is used to simulate the landslide process.

[0114] For example, the design parameters of the target spoil heap slope and the physical and mechanical parameters of each layer of the target spoil heap slope are obtained, and a two-dimensional model of the slope section at the central axis of the maximum deformation zone of the target slope is established. The slope design parameters include the step height, step slope angle, and flat plate width; the physical and mechanical parameters of each rock layer include the internal friction angle, cohesion, and unit weight. The specific establishment process is based on existing technology, and the simulation software used in subsequent examples is PFC2D 5.0 ​​software from Itasco, which will not be described in detail here.

[0115] In this embodiment, S2, obtaining the deformation rate and cumulative displacement early warning threshold by simulating the landslide process, includes:

[0116] S24. Simulate the landslide process of the slope using a two-dimensional model of the key section and obtain the cumulative displacement-time curve. The curve is used to determine the average rate of the uniform deformation stage of the landslide.

[0117] S25. Calculate the slope surface sliding deformation rate under different slope tangent angles using the key tangent angle tangent value multiple method. The tangent angle includes multiple preset angles, and the deformation rate is used as the deformation stage rate early warning threshold for surface monitoring.

[0118] S26. Obtain the cumulative deep displacement of different coordinate points of the two-dimensional model at the tangent angle during the landslide process. The cumulative displacement is used as the displacement early warning threshold for the deformation stage of deep displacement monitoring of the slope.

[0119] For example, the landslide process is simulated using a two-dimensional model of the key section to obtain the St curve of the landslide process, i.e., the cumulative displacement-time curve, and the deformation rate v under the uniform deformation stage of the landslide is determined by the St curve. s The slope surface sliding deformation rates v1, v2, v3, and v4 at slope tangent angles γ1 = 45°, γ2 = 60°, γ3 = 70°, and γ4 = 80° were calculated using the key tangent angle multiple method and used as early warning values ​​for the deformation stage of slope surface monitoring.

[0120] v i =v s ·tanγ i

[0121] In the formula, v i This is the early warning threshold for slope deformation rate, in mm·d. -1 ;v s The average rate of uniform deformation of the target slope is expressed in mm·d. -1 ;γ i This is the key tangent angle for slope deformation.

[0122] Obtain different coordinates (x, y) of several two-dimensional models during the landslide process. i ,y i The cumulative deep displacement S1(x) of the slope at points γ1, γ2, γ3, and γ4 is... i ,y i ),S2(x i ,y i ),S3(x i ,y i ),S4(x i ,y i This serves as an early warning value for the deformation stage in deep slope displacement monitoring.

[0123] S3. Based on the slope surface deformation rate and deep cumulative displacement, landslide risk warning levels are divided and the surface and deep warning levels are merged to output the final landslide risk warning result; the warning level is determined by comparing multi-source monitoring data with the warning threshold.

[0124] In this embodiment, S3, landslide risk warning levels are classified based on the slope surface deformation rate and deep cumulative displacement, including:

[0125] S31. Obtain the deformation rate monitored by radar and the deformation rate monitored by GNSS, and take the maximum value of the two as the surface deformation rate warning value; according to the preset deformation stage rate warning threshold, divide the surface deformation rate warning value into multiple warning levels, the warning level includes multiple levels from low to high.

[0126] S32. Obtain the cumulative displacement monitored by the deep displacement sensor, and divide the deep cumulative displacement into multiple warning levels according to the preset deformation stage displacement warning threshold. The warning levels include multiple levels from low to high. Take the highest warning level among the deep cumulative displacement monitoring points as the final deep cumulative displacement warning level.

[0127] For example, landslide risk warning levels for spoil heaps are classified based on the slope surface deformation rate. The slope deformation rate v measured by radar is obtained. a and the slope deformation rate v measured by GNSS b Take max(v) a ,v b As a warning value, v1 <max(v a ,v b The interval ≤ v2 is divided into 4 warning levels; v2 <max(v a ,v b The interval ≤ v3 is divided into 3 warning levels; v3 <max(v a ,v b The interval ≤ v4 is divided into Level 2 warnings; v4 is... <max(v a ,v b The interval is divided into Level 1 warning.

[0128] The landslide risk warning level of the spoil heap is determined based on the cumulative deep displacement of the slope. The slope displacements measured by deep displacement sensor radar are obtained as follows: M(x1,y1), M(x2,y2), M(x3,y3), ..., M(x... i ,y i ), take S1(x i ,y i ),S2(x i ,y i ),S3(x i ,y i ),S4(x i ,y i ) as the warning value, S1(x) i ,y i ) <M(x i ,y i )≤S2(xi ,y i The interval of S2(x) is divided into 4 levels of early warning; i ,y i ) <M(x i ,y i )≤S3(x i ,y i The interval of S3(x) is divided into 3 levels of early warning; i ,y i ) <M(x i ,y i )≤S4(x i ,y i The interval of S4(x) is divided into two levels of early warning; i ,y i ) <M(x i ,y i The interval is divided into Level 1 warnings, and the highest warning level among the cumulative displacement monitoring points is taken as the final deep cumulative displacement warning level.

[0129] In this embodiment, S3, the final landslide risk warning result is output by fusing surface and deep warning levels, including: comparing the surface deformation rate warning level and the deep cumulative displacement warning level, the comparison being based on a preset fusion rule; the rule is as follows:

[0130] If the surface warning level and the deep warning level are the same, the common level is used as the final warning level; if the difference between the surface warning level and the deep warning level is 1, the higher warning level is used as the final warning level; if the difference between the surface warning level and the deep warning level is 2, the midpoint between the two is used as the final warning level; if the difference between the surface warning level and the deep warning level is 3, the higher warning level is reduced by one level as the final warning level.

[0131] S4. Dynamically adjust the early warning threshold based on the cumulative displacement-time curve of the landslide process.

[0132] In this embodiment, S4, dynamically adjusting the early warning threshold based on the cumulative displacement-time curve of the landslide process, includes:

[0133] S41. Analyze the uniform and acceleration phases of the cumulative displacement-time curve to determine the landslide deformation trend;

[0134] S42. Based on the deformation trend, dynamically adjust the deformation rate during the uniform deformation stage of the landslide, wherein the adjustment is used to optimize the surface deformation rate warning threshold.

[0135] S43. Based on the deformation trend, update the deep cumulative displacement of the two-dimensional model, wherein the update is used to optimize the displacement early warning threshold during the deformation stage.

[0136] S44. Based on the adjusted warning thresholds, recalculate the surface and deep warning levels, and output the optimized final landslide risk warning level.

[0137] For example, if the slope deformation does not change significantly with the warning level, the deformation rate during the uniform deformation stage of the landslide should be dynamically adjusted according to the actual trend of the St curve and the slope deformation development time to improve the warning effect. This step mainly addresses false alarms caused by excessively high numerical simulation thresholds and demonstrates the flexibility of this method in adjusting alarm thresholds. Those skilled in the art can adjust the model according to actual needs, which will not be elaborated here.

[0138] Example 2

[0139] This embodiment is based on the method described in one of the embodiments, and is illustrated with a specific example:

[0140] like Figure 2 As shown, the present invention provides a multi-source monitoring and early warning method for slope stability of open-pit mine spoil heaps based on numerical simulation, comprising the following steps:

[0141] Step 1: Set up radar monitoring on the target slope, record the radar monitoring data, and find the central axis of the maximum deformation zone of the slope based on the slope deformation data. Record the slope deformation rate measured by the radar as v. r The slope deformation rate v measured by radar r The following formula is used to calculate and eliminate data errors caused by the radar illumination angle. The rate after error elimination is denoted as v. a .

[0142]

[0143] In the formula, θ is the slope angle of the step; α is the angle between the actual displacement direction of the monitoring unit and the horizontal plane; β is the elevation angle of the radar wave; This is the horizontal angle of the radar wave (0° when it is orthogonal).

[0144] This embodiment focuses on multi-source early warning monitoring of slope stability in an open-pit coal mine in Inner Mongolia Autonomous Region. A typical spoil heap slope is selected as the research object, and radar is set up at the slope to monitor slope deformation data.

[0145] Step 2: Set up GNSS monitoring points on the target slope surface, with a relatively dense concentration of GNSS monitoring points within the maximum deformation zone. Arrange a GNSS monitoring line along the central axis of the maximum deformation zone to ensure the GNSS monitoring range completely covers the target slope. Record the slope deformation rate measured by GNSS as v. b The cross-sectional view of the centerline of the maximum deformation zone is shown below. Figure 3 As shown

[0146] Step 3: Drill holes at the centerline of the maximum deformation zone of the target slope, and deploy several deep displacement sensors at different locations. Record the cumulative displacement measured by the sensors as follows:

[0147] M(x1,y1),M(x2,y2),M(x3,y3),…,M(x n ,y n ).

[0148] In this step, according to Figure 4 Six deep displacement sensors are arranged as shown, with coordinates set at (336,53), (391,53), (541,80), (596,80), (732,116), and (787,116). The borehole depth is determined based on the actual conditions of the spoil heap. Monitoring instruments are installed inside the boreholes. After the earth pressure gauges are installed, soil needs to be backfilled into the boreholes until they are level with the ground surface.

[0149] Step 4: Obtain the design parameters of the target spoil heap slope and the physical and mechanical parameters of each layer of the target spoil heap slope, and establish a two-dimensional model of the slope section at the central axis of the maximum deformation zone of the target slope. The slope design parameters include step height, step slope angle, and flat plate width; the physical and mechanical parameters of each rock layer include internal friction angle, cohesion, and unit weight.

[0150] In this step, the slope profile of the target area is obtained from the open-pit coal mine design document, the spoil heap plan, and the geological and topographic map. The calculated slope profile consists of an upper layer of spoil and a lower base layer. The base layer contains a weak layer with the roof of coal seam 21 as its bottom surface. The slope toe of the spoil heap is 26°–32°, and the spoil heap height is 12–24m. The calculated spoil heap profile is as follows: Figure 3 As shown in Table 1, the physical and mechanical properties of the soil and rock mass are shown in Table 1.

[0151] Table 1 Geophysical parameters

[0152] Rock mass name Angle of internal friction (°) Cohesion (kPa) Bulk density (kN / m3) 21 Coal 28.5 172.5 12.8 fine sandstone 23.5 86.9 20.0 Waste disposal 24 5 19.1

[0153] This embodiment uses PFC. 2D To establish the slope profile model, this simulation first involves layering the slope in CAD, and then... 2D The software imports the data as a geometry graphic, then generates the wall structure based on the geometry graphic. Finally, it uses ball distribute to generate particles inside the wall and assigns values ​​to the particles according to real-world geophysical parameters, completing the model creation. The excavation slope model is shown below. Figure 5 As shown, the model is 1210m long and 256m high, generating approximately 72,000 particles.

[0154] Step 5: Simulate the landslide process using a two-dimensional model of the key section to obtain the St curve (cumulative displacement-time curve) of the landslide process, and determine the deformation rate v under the uniform deformation stage of the landslide using the St curve. s The slope tangent angles γ1 = 45° and γ2 = were calculated using the key tangent angle multiple method.

[0155] The slope surface sliding deformation rates v1, v2, v3, and v4 at 60°, γ3 = 70°, and γ4 = 80° are used as early warning values ​​for the deformation stage of slope surface monitoring.

[0156] v i =v s ·tanγ i

[0157] In the formula, v i This is the early warning threshold for slope deformation rate, in mm·d. -1 ;v s The average rate of uniform deformation of the target slope is expressed in mm·d. -1 ;γ i This is the key tangent angle for slope deformation.

[0158] In this step, the model uses a strength reduction method to reduce the tensile strength (cb_tensile), shear strength (cb_shear), and friction coefficient (fric) of the micro-parameters, bringing the slope to the critical failure state. The slope deformation rate is recorded, and the St curve is plotted as follows. Figure 6 As shown. The deformation rate v during the uniform deformation stage of the landslide can be calculated. s =7.46mm·d -1 v1 = 7.46 mm·d -1 v2 = 12.92 mm·d -1 v3 = 20.49 mm·d -1 v4 = 42.31 mm·d -1

[0159] Step Six: Obtain different coordinates (x, y) of several two-dimensional models during the landslide process in Step Five. i ,y i The cumulative deep displacement of the slope at points γ1, γ2, γ3, and γ4

[0160] S1(x i ,y i ),S2(x i ,y i ),S3(x i ,y i ),S4(x i ,y iThis serves as an early warning value for the deformation stage in deep slope displacement monitoring.

[0161] In this step, the cumulative displacement values ​​of six corresponding coordinate points under v1, v2, v3, and v4 are read, with S1(336,53) = 2.56mm, S2(336,53) = 4.28mm, S3(336,53) = 7.13mm, and S4(336,53) = 14.24mm; S1(391,53) = 2.14mm, S2(391,53) = 3.87mm, S3(391,53) = 6.78mm, and S4(391,53) = 12.98mm; S1(541,80) = 1.35mm, S2(541,80) = 2.78mm, S3(541,80) = 4.87mm, and S4(541,80) = 1. 0.12mm; S1(596,80)=1.13mm, S2(596,80)=2.25mm, S3(596,80)=3.94mm, S4(596,80)=8.82mm; S1(732,116)=0.89mm, S2(732,116)=1.72mm, S3(732,116)=2.86mm, S4(732,116)=7.52mm; S1(787,116)=0.78mm, S2(787,116)=1.68mm, S3(787,116)=2.57mm, S4(787,116)=7.12mm, are used as early warning values ​​for the deformation stage of deep slope displacement monitoring.

[0162] Step 7: Classify the landslide risk warning level of the spoil heap based on the slope surface deformation rate. Obtain the slope deformation rate v measured by radar. a and the slope deformation rate v measured by GNSS b Take max(v) a ,v b As a warning value, v1 <max(v a ,v b The interval ≤ v2 is divided into 4 warning levels; v2 <max(v a ,v b The interval ≤ v3 is divided into 3 warning levels; v3 <max(v a ,v b The interval ≤ v4 is divided into Level 2 warnings; v4 is... <max(v a ,v b The interval is divided into Level 1 warning.

[0163] In this step, 7.46 mm·d can be obtained. -1 <max(v a ,v b )≤12.92mm·d-1 The interval is divided into 4 warning levels; 12.92 mm·d -1 <max(v a ,v b )≤20.49mm·d -1 The interval is divided into 3 warning levels; 20.49 mm·d -1 <max(v a ,v b )≤42.31mm·d -1 The interval is divided into two warning levels; 42.31 mm·d -1 <max(v a ,v b The interval is divided into Level 1 warning.

[0164] Step 8: Classify the landslide risk warning level of the spoil heap based on the cumulative deep displacement of the slope. Obtain the slope displacements measured by deep displacement sensor radar: M(x1,y1), M(x2,y2), M(x3,y3), ..., M(x... i ,y i Take S1(x) i ,y i ),S2(x i ,y i ),S3(x i ,y i ),S4(x i ,y i ) as the warning value, S1(x) i ,y i ) <M(x i ,y i )≤S2(x i ,y i The interval of S2(x) is divided into 4 levels of early warning; i ,y i ) <M(x i ,y i )≤S3(x i ,y i The interval of S3(x) is divided into 3 levels of early warning; i ,y i ) <M(x i ,y i )≤S4(x i ,y i The interval of S4(x) is divided into two levels of early warning; i ,y i ) <M(x i ,y i The interval is divided into Level 1 warnings, and the highest warning level among the cumulative displacement monitoring points is taken as the final deep cumulative displacement warning level.

[0165] In this step, S1(x1,y1)=2.56mm, S2(x1,y1)=4.28mm, S3(x1,y1)=7.13mm, S4(x1,y1)=14.24mm; S1(x2,y2)=2.14mm, S2(x2,y2)=3.87m m, S3(x2,y2)=6.78mm, S4(x2,y2)=12.98mm; S1(x3,y3)=1.35mm, S2(x3,y3)=2.78mm, S3(x3,y3)=4.87mm, S4(x3,y3)=10.12mm ; S1(x4,y4)=1.13mm, S2(x4,y4)=2.25mm, S3(x4,y4)=3.94mm, S4(x4,y4)=8.82mm; S1(x5,y5)=0.89mm, S2(x5,y5)=1.72mm, S3 (x5,y5)=2.86mm, S4(x5,y5)=7.52mm; S1(x6,y6)=0.78mm, S2(x6,y6)=1.68mm, S3(x6,y6)=2.57mm, S4(x6,y6)=7.12mm, according to S1(x i ,y i ),S2(x i ,y i ),S3(x i ,y i ),S4(x i ,y i The warning levels are divided, and the highest warning level among the cumulative displacement monitoring points is taken as the final deep cumulative displacement warning level.

[0166] Step Nine: In real time, combine the landslide risk warning level of the spoil heap determined in Step Seven based on the slope surface deformation rate with the landslide risk warning level of the spoil heap determined in Step Eight based on the cumulative displacement of the deep slope, to determine the final output landslide risk warning level of the spoil heap. The specific method is as follows:

[0167] 1. When the warning level based on the surface deformation rate is the same as the warning level based on the deep cumulative displacement, the common level shall be taken as the final output landslide risk warning for the spoil heap.

[0168] 2. When the difference between the warning level based on the surface deformation rate and the warning level based on the deep cumulative displacement is 1, the higher warning level is taken as the final output landslide risk warning for the spoil heap.

[0169] 3. When the difference between the warning level based on the surface deformation rate and the warning level based on the deep cumulative displacement is 2, the midpoint between the two shall be taken as the final output of the landslide risk warning for the spoil heap.

[0170] 4. When the difference between the warning level based on the surface deformation rate and the warning level based on the deep cumulative displacement is 3, the level after reducing the higher warning level by 1 is taken as the final output landslide risk warning level of the spoil heap; the specific classification method in this step is shown in Table 2 below.

[0171] Table 2 Grading Method

[0172]

[0173]

[0174] Step 11: If the slope deformation does not change significantly with the warning level, then the slope deformation development time should be dynamically adjusted based on the actual situation and the trend of the St curve. s To improve the effectiveness of early warning.

[0175] Example 3

[0176] Please see Figure 7 A multi-source monitoring and early warning device for slope stability of open-pit mine spoil heaps, comprising:

[0177] The acquisition module is used to acquire multi-source monitoring data of the target slope, including radar monitoring data, GNSS monitoring data and deep displacement sensor data.

[0178] The numerical simulation module is used to establish a two-dimensional model of the central axis of the maximum deformation zone of the target slope based on numerical simulation technology. The model is generated according to the slope design parameters and the physical and mechanical parameters of the rock strata. The deformation rate and cumulative displacement warning threshold are obtained by simulating the landslide process.

[0179] The early warning module is used to classify landslide risk warning levels based on the slope surface deformation rate and deep cumulative displacement, and to merge the surface and deep warning levels to output the final landslide risk warning result; the warning level is determined by comparing multi-source monitoring data with the warning threshold.

[0180] The adjustment module is used to dynamically adjust the early warning threshold based on the cumulative displacement-time curve of the landslide process.

[0181] In order to better utilize the method described in one of the embodiments, this application proposes a multi-source monitoring and early warning device for the stability of open-pit mine spoil heap slopes. Each module corresponds to each step of the above method, and its specific principle has been described above and will not be repeated here.

[0182] Example 4

[0183] A multi-source monitoring and early warning device for slope stability of open-pit mine spoil heaps includes:

[0184] At least one processor and a memory communicatively connected to said at least one processor;

[0185] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described in one of the embodiments.

[0186] In the above technical solution, in order to better operate and process the method described in one of the embodiments, the method is stored in a memory, and the stored method is executed by a processor. It should be noted that the principle and effect of each step have been described above and will not be elaborated further here.

[0187] Example 5

[0188] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in one of the embodiments.

[0189] In the above technical solution, to better operate and use the method, the method is stored in a computer-readable storage medium, and a processor is used to implement the method described in one of the embodiments. It should be noted that the principle and effect of each step have been described above and will not be elaborated further here.

[0190] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A multi-source monitoring and early warning method for open-pit mine dump slope stability, characterized in that, The method comprises: obtaining multi-source monitoring data of the target slope, the multi-source monitoring data comprising radar monitoring data, GNSS monitoring data and deep displacement sensor data; establishing a two-dimensional model of the central axis of the maximum deformation zone of the target slope based on numerical simulation technology, the model being generated according to the design parameters of the slope and the physical and mechanical parameters of the rock strata, and the deformation rate and the cumulative displacement early warning threshold being obtained by simulating the landslide process; dividing the landslide risk early warning levels according to the surface deformation rate of the slope and the deep cumulative displacement and fusing the surface and deep early warning levels to output the final landslide risk early warning result; the early warning levels being determined by comparing the multi-source monitoring data with the early warning threshold; dynamically adjusting the early warning threshold according to the cumulative displacement-time curve of the landslide process.

2. The multi-source monitoring early warning method for the stability of the slope of the waste dump of the open-pit mine according to claim 1, characterized in that: the multi-source monitoring data of the target slope comprises: setting up radar monitoring on the target slope to record the radar monitoring data; the radar monitoring data eliminating the error caused by the irradiation angle through a correction formula; setting up GNSS monitoring points on the surface of the target slope, the GNSS monitoring points being densely arranged at the central axis of the maximum deformation zone, the arrangement ensuring that the monitoring range covers the target slope, and the GNSS monitoring data recording the deformation rate of the slope; setting up a borehole at the central axis of the maximum deformation zone of the target slope and arranging a plurality of deep displacement sensors at different positions, the sensors recording the cumulative displacement amount of different coordinate points.

3. The multi-source monitoring early warning method for the stability of the slope of the waste dump of the open-pit mine according to claim 1, characterized in that: the two-dimensional model of the central axis of the maximum deformation zone of the target slope is established based on numerical simulation technology, comprising: obtaining the design parameters of the target slope, the design parameters comprising the step height, the step slope angle and the flat disc width; obtaining the physical and mechanical parameters of each rock stratum of the target slope, the physical and mechanical parameters comprising the internal friction angle, the cohesion and the unit weight; generating the two-dimensional model of the slope section of the central axis of the maximum deformation zone of the target slope through a computer simulation software according to the design parameters and the physical and mechanical parameters, the model being used to simulate the landslide process.

4. The multi-source monitoring early warning method for the stability of the slope of the waste dump of the open-pit mine according to claim 1, characterized in that: the deformation rate and the cumulative displacement early warning threshold are obtained by simulating the landslide process, comprising: simulating the landslide process of the slope through the two-dimensional model of the key section to obtain the cumulative displacement-time curve, the curve being used to determine the average rate of the uniform deformation stage of the landslide; calculating the surface sliding deformation rate of the slope under different slope tangent angles through the tangent value multiple method of the key tangent angle, the tangent angles comprising a plurality of preset angles, and the deformation rate being used as the deformation stage rate early warning threshold of the surface monitoring; obtaining the deep cumulative displacement of the two-dimensional model at different coordinate points at the tangent angle during the landslide process, the cumulative displacement being used as the deformation stage displacement early warning threshold of the deep displacement monitoring of the slope.

5. The multi-source monitoring early warning method for the stability of the slope of the waste dump of the open-pit mine according to claim 4, characterized in that: the landslide risk early warning levels are divided according to the surface deformation rate of the slope and the deep cumulative displacement, comprising: obtaining the surface deformation rate monitored by the radar and the surface deformation rate monitored by the GNSS, taking the maximum of the two as the surface deformation rate early warning value; according to the pre-set deformation stage rate early warning threshold, the surface deformation rate early warning value is divided into multiple early warning levels, and the early warning levels include multiple levels from low to high; obtaining the cumulative displacement monitored by the deep displacement sensor, and according to the pre-set deformation stage displacement early warning threshold, the deep cumulative displacement is divided into multiple early warning levels, and the early warning levels include multiple levels from low to high; taking the highest early warning level of the deep cumulative displacement monitoring point as the final deep cumulative displacement early warning level.

6. The open-pit dump slope stability multi-source monitoring and early warning method according to claim 1, characterized in that, fusing the surface and deep early warning levels to output the final landslide risk early warning result, including: comparing the surface deformation rate early warning level and the deep cumulative displacement early warning level, and the comparison is based on a pre-set fusion rule; the rule is as follows: if the surface early warning level and the deep early warning level are the same, the common level is used as the final early warning level; if the difference between the surface early warning level and the deep early warning level is 1, the higher early warning level is used as the final early warning level; if the difference between the surface early warning level and the deep early warning level is 2, the intermediate value of the two is used as the final early warning level; if the difference between the surface early warning level and the deep early warning level is 3, the higher early warning level is reduced by one level and used as the final early warning level.

7. The open-pit dump slope stability multi-source monitoring and early warning method according to claim 1, characterized in that, adjusting the early warning threshold dynamically according to the cumulative displacement-time curve of the landslide process, including: analyzing the uniform speed stage and the acceleration stage of the cumulative displacement-time curve to determine the landslide deformation trend; according to the deformation trend, dynamically adjusting the deformation rate of the uniform deformation stage of the landslide, which is used to optimize the surface deformation rate early warning threshold; according to the deformation trend, updating the deep cumulative displacement of the two-dimensional model, which is used to optimize the deformation stage displacement early warning threshold; according to the adjusted early warning threshold, recalculating the surface and deep early warning levels, and outputting the optimized final landslide risk early warning level.

8. A multi-source monitoring and early warning device for open-pit mine dump slope stability, characterized in that, The method according to any one of claims 1-7, including: an acquisition module for acquiring multi-source monitoring data of the target slope, the multi-source monitoring data including radar monitoring data, GNSS monitoring data and deep displacement sensor data; a numerical simulation module for establishing a two-dimensional model of the axis of the maximum deformation zone of the target slope based on numerical simulation technology, the model being generated according to the slope design parameters and the rock layer physical and mechanical parameters, and the deformation rate and cumulative displacement early warning threshold being obtained by simulating the landslide process; an early warning module for dividing the landslide risk early warning level according to the surface deformation rate and the deep cumulative displacement, and fusing the surface and deep early warning levels to output the final landslide risk early warning result; the early warning level is determined by comparing the multi-source monitoring data with the early warning threshold; an adjustment module for dynamically adjusting the early warning threshold according to the cumulative displacement-time curve of the landslide process.

9. A multi-source monitoring and early warning device for open-pit mine dump slope stability, characterized in that, including: at least one processor and a memory connected in communication with the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A computer readable storage medium storing a computer program, characterized in that, The computer program, when executed by the processor, implements the method of any one of claims 1 to 7.

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