Waste dump slope network monitoring method, computer storage medium and system
By integrating a LiDAR sensor, total station, and GPS receiver into a surveying robot, high-precision, real-time monitoring of spoil heap slopes was achieved, solving the problems of time-consuming, labor-intensive, and low-accuracy existing technologies, and improving the accuracy and safety of slope deformation identification.
Patent Information
- Application Number
- CN202511655320.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-12
- Publication Date
- 2026-01-30
Smart Images
Figure CN121430486A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of surveying and remote sensing deformation monitoring technology, and in particular to a method, computer storage medium and system for monitoring the slope network of a spoil heap. Background Technology
[0002] As composite slopes formed by the accumulation of soil, rock, or waste in open-pit mines, spoil heaps directly impact the production safety of the entire mining area and the protection of the surrounding ecological environment. Deformation of spoil heaps can lead to landslides, a common geological problem in open-pit mines, posing a serious threat to the ecological environment, infrastructure, and safety of residents in the mining area and surrounding areas. Therefore, effective monitoring of slope deformation can provide early warning of potential geological disasters, preventing casualties and property damage.
[0003] The existing methods for monitoring slopes of spoil heaps are manual inspection and geodetic surveying. These methods require the use of theodolites, levels, and total stations to periodically measure monitoring points and compare the positions of these points to determine if the steps have shifted. This method is time-consuming, labor-intensive, and lacks real-time accuracy, making it difficult to meet actual production needs. More importantly, the accuracy of existing slope monitoring methods is low, which is insufficient to meet the high requirements for safety monitoring of spoil heap slopes. Summary of the Invention
[0004] In view of the above problems, this application provides a method and system for network monitoring of spoil heap slopes to achieve high-precision monitoring of spoil heap slopes. The specific solution is as follows:
[0005] The first aspect of this application provides a method for monitoring a spoil heap slope network, which is applied to a measurement robot in a spoil heap slope network monitoring system. The system includes a measurement robot and monitoring point equipment. The method includes:
[0006] Acquire monitoring data from various network monitoring points on the slope of the spoil heap; the monitoring data should include at least lidar point cloud data, total station monitoring data, and GPS monitoring data.
[0007] Time synchronization and spatial alignment of lidar point cloud data, total station monitoring data and GPS monitoring data are performed and then fused to obtain multi-temporal point cloud data.
[0008] Registration processing is performed on multi-temporal point cloud data to obtain a point cloud dataset; the point cloud dataset includes point cloud data of each network monitoring point at different times.
[0009] By comparing the point cloud data of each network monitoring point at different times, the deformation information of each network monitoring point is obtained.
[0010] In one possible implementation, lidar point cloud data, total station monitoring data, and GPS monitoring data are time-synchronized and spatially aligned, and then fused to obtain multi-temporal point cloud data, including:
[0011] Data with the same timestamp from lidar point cloud data, total station monitoring data, and GPS monitoring data are synchronized to obtain the first monitoring data after time synchronization.
[0012] The first monitoring data is transformed to the same reference coordinate system to obtain the second monitoring data after time synchronization and spatial alignment.
[0013] Based on the total station monitoring data and GPS monitoring data in the second monitoring data, the lidar point cloud data under the same reference coordinate system is corrected to obtain multi-temporal point cloud data.
[0014] In one possible implementation, registration processing is performed on multi-temporal point cloud data to obtain a point cloud dataset, including:
[0015] Determine the corresponding 3D models for multi-temporal point cloud data at different times;
[0016] The point cloud dataset is obtained by iteratively processing the nearest point of the corresponding 3D model at different times.
[0017] In one possible implementation, point cloud data from each network monitoring point at different times are compared to obtain deformation information for each network monitoring point, including:
[0018] The nearest neighbor search process is performed on the point cloud data of each network monitoring point at different times to search for the point cloud data that is spatially closest to each point cloud data, thus obtaining the nearest neighbor point of each point cloud data.
[0019] Calculate the three-dimensional Euclidean distance between each point cloud data point and its nearest neighbor to obtain a three-dimensional Euclidean distance set;
[0020] The three-dimensional Euclidean distance set is used as deformation information.
[0021] One possible implementation also includes:
[0022] Cluster analysis is performed on the three-dimensional Euclidean distance set, and network monitoring points corresponding to point cloud data whose three-dimensional Euclidean distance exceeds a preset threshold are identified as outliers.
[0023] Early warning information is generated and an alarm is issued based on the point cloud data corresponding to the anomalies.
[0024] The second aspect of this application provides a computer storage medium carrying one or more computer programs, which, when executed by an electronic device, enable the electronic device to implement the spoil heap slope network monitoring method described in the first aspect or any implementation thereof.
[0025] A third aspect of this application provides a network monitoring system for spoil heap slopes, including a surveying robot and monitoring equipment deployed at network monitoring points on spoil heap slopes. The monitoring equipment includes a GPS receiver group and a prism group.
[0026] The surveying robot consists of a sensor array; the sensor array includes a lidar sensor and a total station.
[0027] The surveying robot is used to transmit surveying signals to the monitoring equipment and receive monitoring data from each network monitoring point on the spoil heap slope corresponding to the transmitted signal. The monitoring data includes lidar point cloud data collected by lidar sensors, total station monitoring data determined by total station and prism group, and GPS monitoring data collected by GPS receiver group.
[0028] Time and space synchronization processing and fusion of lidar point cloud data, total station monitoring data and GPS monitoring data are performed to obtain multi-temporal point cloud data.
[0029] Registration processing is performed on multi-temporal point cloud data to obtain a point cloud dataset; the point cloud dataset includes point cloud data of each network monitoring point at different times.
[0030] By comparing the point cloud data of each network monitoring point at different times, the deformation information of each network monitoring point is obtained.
[0031] In one possible implementation, the measuring robot also includes a slope change early warning module;
[0032] If the slope deformation at any network monitoring point exceeds a preset threshold, the slope change early warning module will issue an alarm.
[0033] In one possible implementation, the measuring robot is deployed on the roof of a building opposite the spoil heap slope.
[0034] In one possible implementation, monitoring point devices are evenly distributed on each step of the spoil heap slope at preset intervals.
[0035] Using the above technical solution, the spoil heap slope network monitoring method, computer storage medium, and system provided in this application are applied to a measurement robot in the spoil heap slope network monitoring system. Multiple sensors in the measurement robot collect monitoring data, including at least lidar point cloud data, total station monitoring data, and GPS monitoring data. The monitoring data is time-synchronized, spatially aligned, and fused to obtain highly accurate multi-temporal point cloud data, effectively improving the accuracy of subsequently determined deformation information. Then, the multi-temporal point cloud data is registered to obtain a point cloud dataset. By comparing the point cloud data of each network monitoring point at different times, the deformation information of each network monitoring point is obtained. This application uses multiple heterogeneous sensors to collect and fuse monitoring data of the spoil heap slope, which greatly improves the accuracy of slope deformation area identification compared to the limitations of single sensors in existing technologies. Attached Figure Description
[0036] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.
[0037] Figure 1 A flowchart illustrating a method for monitoring a spoil heap slope network provided in this application;
[0038] Figure 2 Example diagram of slope change area provided in this application;
[0039] Figure 3 Example diagram of the site layout for the measurement robot provided in this application
[0040] Figure 4 Example diagram showing the distribution of monitoring points provided in this application. Detailed Implementation
[0041] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.
[0042] The embodiments of this application will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are equally applicable to similar technical problems.
[0043] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.
[0044] In mining environments, slope stability at spoil heaps is a crucial aspect of ensuring mine safety. Slope collapses and other hazards pose a serious threat to the safety of miners and equipment, especially in high-risk areas such as spoil heaps. Therefore, it is essential to monitor and prevent potential slope hazards in a timely manner.
[0045] Current technologies generally employ methods such as manual measurement, total station monitoring, and UAV remote sensing. Manual measurement is time-consuming and labor-intensive, has limited coverage, and its accuracy is difficult to guarantee due to human factors. While total station monitoring provides relatively accurate data, its automation level is low and its accuracy is not high. UAV remote sensing can acquire data over a large area, but it is difficult to achieve real-time monitoring and has poor adaptability to adverse weather conditions. Therefore, these existing monitoring methods for spoil heap slopes generally suffer from high costs, operational complexity, insufficient real-time performance, and inadequate accuracy, all of which fail to meet the high requirements for safety monitoring of spoil heap slopes.
[0046] To address the aforementioned issues, this application provides a method and system for network monitoring of spoil heap slopes.
[0047] Optional, see Figure 1 This application provides a schematic flowchart of a method for monitoring the slope network of spoil heaps.
[0048] The aforementioned spoil heap slope network monitoring method is mainly applied to the measurement robot of the spoil heap slope network monitoring system. The spoil heap slope network monitoring system mainly includes the measurement robot and monitoring point equipment. The monitoring robot mainly includes a sensor group, which mainly integrates a lidar sensor and a total station; the monitoring point equipment mainly includes a GPS (Global Positioning System) receiver.
[0049] like Figure 1 As shown, the method for monitoring the slope network of a spoil heap includes the following steps:
[0050] Step 101: Obtain monitoring data from each network monitoring point on the slope of the spoil heap; the monitoring data should include at least lidar point cloud data, total station monitoring data, and GPS monitoring data.
[0051] It should be noted that monitoring points are generally set up in identified or potential landslide areas and arranged along key parts of the boundary line, so as to achieve full coverage of risk areas in the spoil heap slope.
[0052] Specifically, the monitoring points can be laid out according to a grid pattern, with the distance between monitoring points generally controlled within 5 to 15 meters. This distance can be adjusted based on the site topography and risk level. During installation, the monitoring points must be firmly bonded to the slope rock mass to ensure accurate reflection of rock mass displacement. For example, a hole is drilled in the rock mass at the selected location, with a drilling depth of no less than 0.5 meters. A metal rod with a diameter of 20 mm and a length of 0.8 to 1.0 meter is inserted into the hole, and high-strength cement mortar or epoxy resin and other gel materials are used to anchor the metal rod within the hole, ensuring a tight bond between it and the rock mass.
[0053] The main monitoring data acquired by the measurement robot includes lidar point cloud data collected by lidar sensors, total station monitoring data collected by total stations, and GPS monitoring data collected by GPS receivers in the monitoring point equipment.
[0054] Specifically, lidar point cloud data primarily consists of high-resolution 3D topographic surface data of spoil heap slopes, providing the geometric shape of the slopes for monitoring overall slope changes. It can be a 3D dataset containing not only the location information of each point but also rich additional information, including a massive number of 3D coordinate points. These coordinate points can accurately reconstruct the surface morphology of the spoil heap slope, generating a high-precision digital elevation model or 3D reality model. Additional information includes, but is not limited to, echo intensity, echo count, color information, scan time, GPS timestamps, and classification information.
[0055] Total station monitoring data includes the three-dimensional coordinates of the monitoring points and their changes, primarily used to provide precise displacement of the monitoring points. The total station mainly works in conjunction with prisms deployed at the monitoring points to monitor the slopes of spoil heaps. Specifically, the total station emits a laser beam that reaches the prism, which efficiently and accurately reflects it back to the total station. By measuring the phase or propagation time of the light wave, the slope distance, horizontal angle, and vertical angle between the two points are calculated, thereby determining the three-dimensional coordinates of the prism's center point.
[0056] GPS monitoring data includes the absolute geographical location of the monitoring point, such as latitude and longitude. While its accuracy is relatively low, it provides an absolute baseline and is not strictly limited by line-of-sight conditions. GPS monitoring data is primarily collected by GPS receivers deployed at the monitoring points to receive satellite signals and obtain the absolute geographical location of the monitoring point.
[0057] Optionally, the measuring robot uses a lidar sensor to collect lidar point cloud data and a total station to collect total station monitoring data. The measuring robot also receives GPS monitoring data collected by a GPS receiver in the monitoring point equipment.
[0058] In summary, by integrating multiple heterogeneous sensors—LiDAR sensor, total station, and GPS—the measurement robot in this application can autonomously collect high-precision data on the slope of the spoil heap, effectively solving the limitations of single sensors in terms of accuracy, coverage area, and real-time performance in existing technologies.
[0059] Step 102: Perform time synchronization and spatial alignment processing on the lidar point cloud data, total station monitoring data, and GPS monitoring data, and then fuse them to obtain multi-temporal point cloud data.
[0060] First, the lidar point cloud data, total station monitoring data, and GPS monitoring data are preprocessed to ensure data quality and consistency.
[0061] Specifically, noise reduction processing is performed on the lidar point cloud data. This can be achieved by using a progressively encrypted triangular mesh point cloud filtering algorithm to remove noise, thereby ensuring the accuracy of the lidar point cloud data.
[0062] The formula for progressive encryption triangular mesh point cloud filtering is as follows:
[0063]
[0064] in, These are the filtered points. It is a point within the neighborhood. It represents the number of points within the neighborhood.
[0065] The preprocessing of total station monitoring data and GPS monitoring data mainly involves detecting and removing abnormal data.
[0066] Next, the preprocessed lidar point cloud data, total station monitoring data, and GPS monitoring data are sequentially synchronized in time and aligned in space.
[0067] Optionally, the data with the same timestamp among the lidar point cloud data, total station monitoring data, and GPS monitoring data are synchronized to obtain the first monitoring data after time synchronization. Then, the first monitoring data is converted to the same reference coordinate system to obtain the second monitoring data after time synchronization and spatial alignment.
[0068] Specifically, the lidar point cloud data, total station monitoring data, and GPS monitoring data collected at different times are aligned and synchronized in time and space to ensure that the monitoring data collected by these heterogeneous sensors can be analyzed and fused within the same spatiotemporal framework. Time synchronization primarily involves using timestamps to synchronize the lidar point cloud data, total station monitoring data, and GPS monitoring data, ensuring data correspondence at the same moment. Spatial alignment involves transforming the coordinates of the total station monitoring data and GPS monitoring data into a coordinate system consistent with the lidar point cloud data, converting them from their respective coordinate systems to a Cartesian coordinate system. Kalman filters can also be used to reduce noise interference. This coordinate transformation corrects the spatial position of the monitoring data, ensuring that the coordinates of the monitoring points collected by GPS and the total station match the coordinate system of the point cloud data collected by the lidar sensors.
[0069] Next, the total station monitoring data and GPS monitoring data from the second monitoring data are used to correct the lidar point cloud data under the same reference coordinate system to obtain multi-temporal point cloud data.
[0070] Subsequently, total station monitoring data and GPS data were used to correct the lidar point cloud data, resulting in more accurate multi-temporal point cloud data.
[0071] The above process can also be viewed as a data fusion process. In this process, GPS monitoring data can serve as an overall benchmark and for gross error detection, while total station data provides millimeter-level high-precision deformation details. The two types of data are used together to correct the lidar point cloud data, resulting in more accurate multi-temporal point cloud data.
[0072] It should be noted that the above fusion process can be performed in the data processing module of the measurement robot.
[0073] Step 103: Perform registration processing on the multi-temporal point cloud data to obtain a point cloud dataset; the point cloud dataset includes point cloud data of each network monitoring point at different times.
[0074] It should be noted that the high-precision registration of multi-temporal point cloud data in this application is a prerequisite for ensuring accurate detection of minor slope deformations. The registration process mainly involves precisely aligning point cloud data collected at different times to the same coordinate system. Subsequent comparisons, such as subtraction, will ensure that the resulting changes truly represent the slope deformation and avoid errors caused by different scanning positions or angles. Unregistered point cloud data can lead to huge false change signals, obscuring the true deformation information.
[0075] Optionally, determine the corresponding 3D models for point cloud data collected at different times in the multi-temporal point cloud data, and process these 3D models using the ICP (Iterative Closest Point) algorithm. Specifically, this algorithm is used for matching and stitching multi-temporal point clouds to ensure high-precision matching of point clouds at different times, resulting in a point cloud dataset including data from each network monitoring point at different times. The formula for the ICP algorithm can be as follows:
[0076] ;
[0077] in, For rotation matrix, It is a translation vector. For the current electric cloud, For reference point cloud.
[0078] In addition to using the ICP algorithm for point cloud data registration, other registration methods such as the normal distribution transformation algorithm and feature point-based registration methods can also be used.
[0079] Step 104: Compare the point cloud data of each network monitoring point at different times to obtain the deformation information of each network monitoring point.
[0080] It should be noted that this step is mainly used to extract the slope change area, which is mainly detected by the nearest neighbor search algorithm in the slope of the spoil heap.
[0081] Optionally, the nearest neighbor search process is performed on the point cloud data of each network monitoring point at different times to search for the point that is spatially closest to each point cloud data, obtain the nearest neighbor of each point cloud data, calculate the three-dimensional Euclidean distance between each point cloud data and its nearest neighbor, obtain the three-dimensional Euclidean distance set, and use the three-dimensional Euclidean distance set as deformation information.
[0082] The above process primarily quantifies slope deformation by calculating the spatial distance between corresponding points in point clouds at two adjacent time points. Point cloud data from two precisely registered time points are selected. The point cloud data from the more distant time point is chosen as the baseline point cloud, and the point cloud data from the more recent time point is chosen as the comparison point cloud. A spatial index is constructed for the baseline point cloud, and a nearest neighbor search is performed. Each point in the comparison point cloud is traversed, and the point closest to it in three-dimensional space is quickly found in the baseline point cloud. This nearest neighbor is the corresponding point group between the comparison point cloud and its nearest neighbor. The three-dimensional Euclidean distance between each corresponding point group is calculated, resulting in a three-dimensional Euclidean distance set, which represents the deformation information.
[0083] Understandably, nearest neighbor search algorithms can detect regions of change in data from two different time phases. For example, see... Figure 2 Example diagram of slope change area provided in this application. Figure 2 As shown in the figure, the marked areas are the areas with significant slope displacement or deformation after calculating the change distance of the point cloud.
[0084] For example, the formula for calculating the change distance in the nearest neighbor search algorithm can be as follows:
[0085] ;
[0086] in, This represents the minimum distance between point clouds; if the distance exceeds a certain threshold, the slope is considered to have changed.
[0087] In addition, the method for monitoring the slope of spoil heaps in this application, after obtaining deformation information, also includes the following steps:
[0088] Step 201: Perform cluster analysis on the three-dimensional Euclidean distance set, and identify the monitoring points corresponding to the point cloud data whose three-dimensional Euclidean distance exceeds the preset threshold as outliers.
[0089] Step 202: Generate early warning information and issue an alarm based on the point cloud data corresponding to the anomaly points.
[0090] Specifically, based on engineering safety standards and historical data, a reasonable three-dimensional Euclidean distance threshold is pre-set. This threshold represents the upper limit of the allowable normal deformation, and anything exceeding it is considered abnormal.
[0091] Then, spatial clustering analysis is performed on each three-dimensional Euclidean distance in the three-dimensional Euclidean distance set to form anomaly region clusters. The monitoring points corresponding to each point cloud data in the anomaly region cluster are identified as anomaly points. Early warning information is generated and alarms are issued based on the information of the anomaly points.
[0092] In summary, the spoil heap slope network monitoring method provided in this application is applied to a measurement robot within a spoil heap slope network monitoring system. The robot uses multiple sensors to collect monitoring data, including at least lidar point cloud data, total station monitoring data, and GPS monitoring data. This data is then time-synchronized, spatially aligned, and fused to obtain highly accurate multi-temporal point cloud data, effectively improving the accuracy of subsequent deformation information determination. The multi-temporal point cloud data is then registered to obtain a point cloud dataset. By comparing the point cloud data of each network monitoring point at different times, the deformation information of each network monitoring point is obtained. This application employs multiple sensors to collect and fuse monitoring data from spoil heap slopes, significantly improving the accuracy of slope deformation area identification compared to the limitations of single sensors in existing technologies.
[0093] This application also provides a computer storage medium that carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any of the spoil heap slope network monitoring methods provided in this application.
[0094] This application also provides a spoil heap slope network monitoring system. The spoil heap slope network monitoring system mainly includes a surveying robot and monitoring equipment deployed at network monitoring points on the spoil heap slope.
[0095] The surveying robot provided in this application integrates a lidar sensor and a total station. Specifically, the surveying robot can be obtained by integrating a lidar sensor and GPS with a commercially available high-precision automatic tracking total station. It systematically integrates the lidar sensor and GPS, and the surveying robot also includes a data fusion processing module and an early warning module.
[0096] The core component of the surveying robot is the sensor array, which mainly includes a lidar sensor and a total station. The lidar sensor and total station are deployed together at the site, which can be the rooftop of a building opposite the spoil heap slope. Specifically, a forced centering disc and bricks are poured with cement, and after complete hardening, the surveying robot is placed on top for site setup. For an example, see [link to example]. Figure 3 This application provides an example diagram of the deployment site for a surveying robot. The diagram shows a surveying robot deployed on the roof of a building opposite the slope of a spoil heap, using a cement-cast forced centering disc and bricks for fixation.
[0097] It should be noted that the monitoring point prisms used in conjunction with the total station to monitor the spoil heap slope are deployed at the monitoring points. The purpose of deploying the prisms is primarily to provide the total station with a stable, highly reflective, and accurate target for aiming. The prisms are installed on the pre-embedded metal rods at the monitoring points on the slope, and they must be firmly aligned with the direction of the total station. A completely unobstructed straight line of sight must be maintained between the total station and the prisms.
[0098] The system also includes monitoring equipment, including a GPS receiver and a prism array. The GPS receiver is deployed at the monitoring points to receive satellite signals and obtain the absolute geographical location of the monitoring points. The prism array mainly serves as a cooperative target for the total station and can also act as a reflective marker. It is also deployed at the monitoring points. The total station mainly uses the prisms to accurately measure the angle and distance of the monitoring points.
[0099] In summary, this spoil heap slope network monitoring system integrates transmitting / scanning sensors (LiDAR sensors, total stations) and signal receiving / reflecting targets (GPS receivers, prisms) to achieve real-time monitoring of spoil heap slopes.
[0100] The monitoring process for spoil heap slopes using the spoil heap slope network monitoring system is as follows:
[0101] The surveying robot is used to transmit measurement signals to the monitoring equipment and receive monitoring data from various network monitoring points on the spoil heap slope corresponding to the transmitted signals: lidar point cloud data collected by lidar sensors, total station monitoring data determined by the total station and prism group, and GPS monitoring data collected by the GPS receiver group.
[0102] Time and space synchronization processing is performed on lidar point cloud data, total station monitoring data and GPS monitoring data and then fused to obtain multi-temporal point cloud data. Registration processing is performed on the multi-temporal point cloud data to obtain a point cloud dataset including point cloud data of each network monitoring point at different times. By comparing the point cloud data of each network monitoring point at different times, the deformation information of each network monitoring point is obtained.
[0103] The above process can occur in the data processing module of the measurement robot. This module mainly performs change monitoring and fusion analysis. Based on multi-temporal point cloud data and real-time monitoring data that have been synchronized in time and aligned in space, it jointly analyzes slope changes to determine the displacement or deformation of the slope. It aligns the displacement of the monitoring points accurately measured by the robot with the details in the global point cloud, thereby improving the accuracy of the multi-temporal point cloud data. This fusion can optimize the accuracy of the slope change area, especially performing well in the detection of changes in vertical settlement and local slope displacement. At the same time, based on the multi-temporal point cloud data and combined with real-time monitoring data, it identifies areas of significant slope changes. For areas of significant change, especially areas of height change, based on the high-precision Z-value provided by the measurement robot, a more reliable slope change model can be generated, and these changes can be further classified to mark different change patterns such as settlement and slippage.
[0104] In summary, the work of a surveying robot mainly includes active detection, control and data aggregation, and data processing. Specifically, active detection involves using its integrated total station and lidar sensors to actively transmit measurement signals to measure targets (prisms and monitoring points) on the slope, generating monitoring data. Control and data aggregation involves controlling the entire monitoring process and receiving GPS monitoring data transmitted from GPS monitoring points. Data processing can involve data fusion, analysis, and early warning judgment on a local machine or a backend server. Therefore, the surveying robot serves as the core data generator for data acquisition and analysis, and also acts as the brain controlling the entire system.
[0105] In addition, the measurement robot in the spoil heap slope network monitoring system provided in this application also includes a slope change early warning module. If the slope deformation at any monitoring point in the deformation information exceeds a preset threshold, an early warning is triggered, and the slope change early warning module issues a real-time alarm.
[0106] It should also be noted that before setting up monitoring points, benchmark points need to be established first. These benchmark points provide an absolutely unchanging positional and coordinate reference system for the entire spoil heap slope network monitoring system. Specifically, benchmark points are placed on stable ground or slopes outside the landslide body, ensuring that their positions themselves will not move or deform. At least three benchmark points should be established, arranged in an equilateral triangle with 120° intervals around the measuring robot. The three benchmark points must be mutually visible, and their angle with the measuring robot should not exceed 45°. The top of each benchmark point should be made into a pillar approximately 10cm high.
[0107] In the spoil heap slope network monitoring system, monitoring points are evenly distributed on each step of the spoil heap slope at preset intervals.
[0108] Specifically, 60 prisms were evenly distributed across each layer of the spoil heap slope. First, a surveying robot was used to determine the exact location and height of the prisms. Small red flags were placed at each installation location, and the height visible to the robot was marked. Then, steel wire was driven into the monitoring points, raising the height by approximately 60 centimeters. The prisms were reinforced by embedding three ground anchors to tighten the wires. This process resolved the problems of low prism placement preventing total station scanning and prism swaying causing inaccurate positioning. Field tests showed that under 5-6 level winds, the prisms remained largely stationary and were still detectable by the total station. Ultimately, approximately 60 monitoring points were placed on the spoil heap slope, distributed as follows: 1 point in the first layer, 2 points in the second layer, 3 points in the third layer, 8 points in the fourth layer, 8 points in the fifth layer, 11 points in the sixth layer, 10 points in the seventh layer, 9 points in the eighth layer, and 8 points in the ninth layer. See this application for details. Figure 4 This is an example diagram showing the distribution of monitoring points provided in this application.
[0109] It should be noted that the spoil heap slope network monitoring system provided in this application constructs an integrated monitoring network, which connects total station, lidar sensor and GPS and other sensors to the network to realize the real-time acquisition and transmission of multi-source monitoring information. The data is fused and intelligently analyzed on the networked platform to realize online assessment and early warning of spoil heap slope stability.
[0110] In summary, the spoil heap slope network monitoring system provided in this application includes a measurement robot and its integrated sensor group, data processing module, and slope change early warning module, as well as a monitoring equipment group. Through automated measurement and data processing algorithms, this system can accurately monitor and track changes in spoil heap slopes, autonomously complete high-precision data acquisition and real-time monitoring over a large area, reduce reliance on manual operation, and lower labor and resource costs. Furthermore, the intelligent analysis of multi-temporal point cloud data in the data processing module can autonomously identify slope change areas, effectively avoiding subjective errors in human operation. The entire system adopts unmanned robot monitoring, reducing the need for personnel to enter dangerous areas, thereby improving the safety of the monitoring process, especially when there is a potential risk of slope collapse in the spoil heap.
[0111] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the accompanying drawings of the device embodiments provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.
[0112] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods of the various embodiments of this application.
[0113] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.
[0114] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).
Claims
1. A method of monitoring a network of slopes of a dump, characterized in that, The application relates to a measuring robot applied to a dump slope network monitoring system, the system comprising a measuring robot and monitoring point equipment, and the method comprises the following steps: Monitoring data of each network monitoring point on a dump slope is acquired; the monitoring data at least comprises laser radar point cloud data, total station monitoring data and GPS monitoring data; Time synchronization and space alignment processing and fusion are performed on the laser radar point cloud data, the total station monitoring data and the GPS monitoring data, and multi-time phase point cloud data is obtained; Registration processing is performed on the multi-time phase point cloud data, and a point cloud data set is obtained; the point cloud data set comprises point cloud data of the network monitoring points at different time; The point cloud data of the network monitoring points at different time is compared, and deformation information of the network monitoring points is obtained.
2. The method of claim 1, wherein, The time synchronization and space alignment processing and fusion of the laser radar point cloud data, the total station monitoring data and the GPS monitoring data to obtain the multi-time phase point cloud data comprises the following steps: Synchronization processing is performed on data with the same time stamp in the laser radar point cloud data, the total station monitoring data and the GPS monitoring data, and first monitoring data after time synchronization is obtained; The first monitoring data is converted to the same reference coordinate, and second monitoring data after time synchronization and space alignment is obtained; The laser radar point cloud data in the same reference coordinate system is corrected according to the total station monitoring data and the GPS monitoring data in the second monitoring data, and the multi-time phase point cloud data is obtained.
3. The method of claim 1, wherein, The registration processing of the multi-time phase point cloud data to obtain the point cloud data set comprises the following steps: Corresponding three-dimensional models at different time of the multi-time phase point cloud data are determined; Iterative nearest point processing is performed on the corresponding three-dimensional models at different time, and the point cloud data set is obtained.
4. The method of claim 1, wherein, The comparison of the point cloud data of the network monitoring points at different time to obtain the deformation information of the network monitoring points comprises the following steps: Nearest neighbor search processing is performed on the point cloud data of the network monitoring points at different time, the nearest points in space of each point cloud data are searched, and the nearest neighbor points of each point cloud data are obtained; Three-dimensional Euclidean distances between each point cloud data and the nearest neighbor points thereof are calculated, and a three-dimensional Euclidean distance set is obtained; The three-dimensional Euclidean distance set is taken as the deformation information.
5. The method of claim 1, wherein, Further comprising: Cluster analysis is performed on the three-dimensional Euclidean distance set, and the network monitoring point corresponding to the point cloud data with a three-dimensional Euclidean distance exceeding a preset threshold value is determined as an abnormal point; Warning information is generated according to the point cloud data corresponding to the abnormal point, and an alarm is given.
6. A computer storage medium, characterized in that The storage medium carries one or more computer programs, and when the one or more computer programs are executed by an electronic device, the electronic device can implement the dump slope network monitoring method according to any one of claims 1 to 6.
7. A system for monitoring a network of slopes of a dump, characterized in that The measuring robot comprises a sensor group; the sensor group comprises a laser radar sensor and a total station; The measuring robot comprises a sensor group; the sensor group comprises a laser radar sensor and a total station; The measuring robot is configured to emit a measuring signal to the monitoring device, and receive monitoring data of each network monitoring point on the dump slope corresponding to the emitted signal; the monitoring data includes laser radar point cloud data collected by the laser radar sensor, total station monitoring data determined by the total station and the prism group, and GPS monitoring data collected by the GPS receiver group; The laser radar point cloud data, the total station monitoring data, and the GPS monitoring data are subjected to time synchronization and space synchronization processing and fusion to obtain multi-time phase point cloud data; The multi-time phase point cloud data is subjected to registration processing to obtain a point cloud data set; the point cloud data set includes point cloud data of the network monitoring points at different time instants; The point cloud data of the network monitoring points at different time instants are compared to obtain deformation information of each network monitoring point.
8. The mine bench network monitoring system of claim 5, wherein, The measuring robot further includes a slope change early warning module; In a case where a slope deformation amount of any network monitoring point in the deformation information exceeds a preset threshold, the slope change early warning module issues an alarm.
9. The mine bench network monitoring system of claim 1, wherein, The measuring robot is arranged on a roof of a building opposite the dump slope.
10. The mine bench network monitoring system of claim 1, wherein, The monitoring point devices are uniformly arranged on each step of the dump slope at a preset interval.