Method for monitoring drainage of tailings pond and device therefor
By collecting data using drones and performing 3D modeling and semantic segmentation, abnormal tailings dam drainage can be identified, solving the problem of low efficiency in manual inspections and achieving efficient and accurate monitoring of tailings dam drainage.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA COAL RES INST
- Filing Date
- 2024-09-25
- Publication Date
- 2026-05-01
AI Technical Summary
Current tailings dam drainage monitoring mainly relies on manual inspections, which is inefficient, unreliable, and inaccurate, and cannot effectively monitor drainage anomalies.
Using drones to collect surveying data, and through 3D modeling and semantic segmentation technology, the system identifies monitoring objects in the tailings dam, such as the beach, tailings dam, and drainage ditch, identifies drainage anomalies, and generates drainage monitoring data.
This improved the efficiency and reliability of tailings dam drainage monitoring, reduced manpower input, ensured the accuracy and comprehensiveness of data collection, and enhanced the precision of drainage monitoring.
Smart Images
Figure CN119360014B_ABST
Abstract
Description
Methods and devices for monitoring drainage from tailings ponds Technical Field
[0001] This application relates to the field of coal mine safety monitoring, and in particular to a method and apparatus for monitoring drainage from a tailings dam. Background Technology
[0002] Tailings ponds are numerous and widely distributed, and their scale and dam height are increasing, leading to a rise in safety hazards. Therefore, safety monitoring of tailings ponds is particularly important, especially for wet-discharge tailings ponds, where abnormal drainage requires focused monitoring to identify potential safety risks. Currently, manual inspection is still the primary method for monitoring tailings ponds. However, this method is not only time-consuming and labor-intensive, but also inefficient, with poor reliability and accuracy. Summary of the Invention
[0003] This application provides a method and apparatus for monitoring drainage in tailings ponds, which can be applied to the field of coal mine safety monitoring. It eliminates the need for manual inspections and improves the efficiency, reliability, and accuracy of tailings pond drainage monitoring.
[0004] In a first aspect, embodiments of this application provide a method for monitoring drainage from a tailings dam, the method comprising:
[0005] Acquire mapping data of the target tailings dam collected by drones;
[0006] Based on the mapping data, a three-dimensional model of the target tailings dam is performed to obtain an oblique image model and a three-dimensional model of the target tailings dam.
[0007] Based on the tilted image model and the three-dimensional model, semantic segmentation is performed on the target tailings dam to obtain data information of different monitoring objects of the target tailings dam, wherein the monitoring objects include at least the beach surface, tailings dam and drainage ditch.
[0008] Based on the data information of the monitored object, the monitoring object is used to identify drainage anomalies;
[0009] Based on the identification results of the monitored objects, drainage monitoring data of the target tailings dam is generated.
[0010] In some embodiments, acquiring the mapping data information of the target tailings pond collected by the UAV includes:
[0011] The system acquires the set flight path of the UAV, controls the UAV to survey the target tailings dam according to the flight path, obtains the survey data information of the target tailings dam, and receives the survey data information fed back by the UAV.
[0012] The mapping data includes image data and spatial location data of the target tailings dam from different perspectives.
[0013] In some embodiments, the step of performing three-dimensional modeling of the target tailings dam based on the mapping data information to obtain an oblique image model and a three-dimensional model of the target tailings dam includes:
[0014] The survey data information is reconstructed in three dimensions to generate model-related data of the target tailings dam, wherein the model-related data includes three-dimensional point cloud data, digital orthophoto data and digital elevation data of the target tailings dam;
[0015] Based on the model-related data of the target tailings dam, a three-dimensional model of the target tailings dam is performed to obtain an oblique image model and a three-dimensional model of the target tailings dam.
[0016] In some embodiments, the step of semantically segmenting the target tailings pond based on the tilted image model and the 3D model to obtain data information of different monitoring objects of the target tailings pond includes:
[0017] Based on a pre-trained semantic segmentation neural network, the tilted image model and the 3D model are semantically segmented according to the tailings pond beach, tailings dam and drainage ditch, to obtain the data information of the tailings pond beach, tailings dam and drainage ditch respectively. The data information includes the digital surface model and attribute information of the monitored object.
[0018] In some embodiments, the step of identifying drainage anomalies in the monitored object based on the data information of the monitored object includes:
[0019] Based on the attribute information of the monitored object, the digital surface model of the monitored object is divided into regions to obtain N regions;
[0020] Traverse the N regions, and for the currently traversed region m, obtain the region m+1 that is adjacent to region m;
[0021] Based on the correlation data between region m and region m+1, it is determined whether there is water accumulation or siltation in region m.
[0022] In some embodiments, identifying whether there is water accumulation or siltation in region m based on the association data of the neighboring region m+1 of region m includes:
[0023] A three-dimensional coordinate system is established in the digital surface model of the monitored object, and the center point of each area and the position coordinates of the center point are determined;
[0024] Based on the location coordinates of the center point, obtain the slope and direction between the center point of region m and the center point of region m+1;
[0025] Obtain the area of the first region m and the area of the second region m+1;
[0026] Based on the slope and direction, as well as the areas of the first and second regions, it is determined whether there is water accumulation or siltation in region m.
[0027] In some embodiments, identifying whether region m has water accumulation or siltation based on the slope and aspect, and the areas of the first and second regions, includes:
[0028] If the slope is greater than the slope threshold corresponding to the monitored object, the area of the first region is greater than the first area threshold corresponding to the monitored object, the slope direction is in each of the four quadrants, and the area of the second region is greater than the second area threshold corresponding to the monitored object, it is determined that there is water accumulation or siltation in region m.
[0029] In some embodiments, after identifying whether the area m has water accumulation or blockage, the method further includes:
[0030] The location information, region number, and region area of the region are obtained and used as the attribute information of the region.
[0031] The judgment result of the region and the attribute information of the region are associated and stored.
[0032] In some embodiments, after associating and storing the judgment result of the region and the attribute information of the region, the method further includes:
[0033] Based on the location information of the area, areas with water accumulation or siltation are identified using the tilted image model and 3D model of the target tailings dam; and / or,
[0034] Based on the judgment results and attribute information of the waterlogged or clogged area, an alarm message is generated.
[0035] Secondly, embodiments of this application provide a drainage monitoring device for a tailings dam, the device comprising:
[0036] The data acquisition module is used to acquire mapping data of the target tailings dam collected by the UAV;
[0037] The 3D modeling module is used to perform 3D modeling of the target tailings dam based on the survey data, and to obtain the tilted image model and 3D model of the target tailings dam.
[0038] The semantic segmentation module is used to perform semantic segmentation on the target tailings pond based on the tilted image model and the three-dimensional model to obtain data information of different monitoring objects of the target tailings pond, wherein the monitoring objects include at least the beach, tailings dam and drainage ditch.
[0039] The drainage anomaly identification module is used to identify drainage anomalies in the monitored object based on the data information of the monitored object.
[0040] The real-time monitoring module is used to generate drainage monitoring data of the target tailings dam based on the identification results of the monitored object.
[0041] Thirdly, embodiments of this application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the tailings dam drainage monitoring method provided in the first aspect of this disclosure.
[0042] Fourthly, embodiments of this application provide a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the tailings dam drainage monitoring method provided in the first aspect of this disclosure.
[0043] Fifthly, embodiments of this application provide a computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the tailings dam drainage monitoring method as described in the first aspect of this disclosure.
[0044] The technical solution provided in this application has at least the following beneficial effects:
[0045] In this embodiment, the tailings dam's mapping data collected by drones enables the construction of a 3D model, resulting in an oblique image model and a 3D model of the tailings dam. Semantic segmentation of the oblique image model and 3D model yields data on different monitoring objects such as the tailings dam surface, tailings dam, and drainage ditch. Based on this data, drainage anomalies of the monitored objects are identified, providing tailings dam drainage monitoring data. This eliminates reliance on manual inspections, saving manpower and improving the efficiency of tailings dam drainage monitoring. Drone-based tailings dam mapping ensures more accurate and comprehensive collection of raw data, enabling the construction of more accurate oblique image models and 3D models, thereby further improving the reliability and accuracy of tailings dam drainage monitoring. Attached Figure Description
[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.
[0047] Figure 1 is a flowchart illustrating a tailings dam drainage monitoring method provided in an embodiment of this application;
[0048] Figure 2 is a flowchart illustrating another tailings dam drainage monitoring method provided in an embodiment of this application;
[0049] Figure 3 is a schematic diagram of the structure of a tailings dam drainage monitoring device provided in an embodiment of this application;
[0050] Figure 4 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0051] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.
[0052] It should be noted that the tailings dam drainage monitoring method provided in any embodiment of this application can be executed alone, or in combination with possible implementation methods in other embodiments, or in combination with any technical solution in related technologies.
[0053] The drainage monitoring method and apparatus for tailings ponds provided in this application will be described in detail below with reference to the accompanying drawings.
[0054] Figure 1 is a flowchart illustrating a tailings dam drainage monitoring method provided in this application. As shown in Figure 1, the tailings dam drainage monitoring method includes, but is not limited to, the following steps:
[0055] S101: Acquire mapping data of the target tailings dam collected by the drone.
[0056] The tailings dam drainage monitoring method provided in this application embodiment is configured in an electronic device, which can be a terminal device or a server, etc. It can monitor the drainage of the tailings dam based on the mapping data information collected by the UAV, without relying on manual inspection.
[0057] In some embodiments, the flight path of the drone can be acquired, and the drone can be controlled to survey the target tailings dam according to the flight path to obtain the survey data information of the target tailings dam, and the survey data information fed back by the drone can be received.
[0058] In some embodiments, the mapping data may include, but is not limited to, image data and spatial location data of the target tailings dam from different perspectives.
[0059] In some embodiments, a monitoring task can be generated based on a pre-set monitoring time and flight path of the UAV, and then sent to the UAV. The UAV can then map the target tailings dam based on the monitoring time and flight path, obtaining mapping data information of the target tailings dam. Furthermore, mapping data information fed back by the UAV can be received.
[0060] S102, Based on the surveying and mapping data, perform three-dimensional modeling of the target tailings dam to obtain the tilted image model and three-dimensional model of the target tailings dam.
[0061] In some embodiments, model-related data for 3D modeling can be obtained based on surveying data information. Optionally, 3D reconstruction can be performed on the surveying data information to generate model-related data corresponding to the target tailings dam.
[0062] In some embodiments, the model-related data of the target tailings dam may include, but are not limited to, the three-dimensional point cloud data, digital orthophoto data, and digital elevation data of the target tailings dam.
[0063] Furthermore, based on the model-related data of the target tailings dam, a 3D model of the target tailings dam is performed, resulting in an oblique image model and a 3D model of the target tailings dam. In other words, based on 3D point cloud data, digital orthophoto data, a 3D model of the target tailings dam is performed, resulting in an oblique image model of the target tailings dam. Further, a 3D model of the target tailings dam is generated based on the oblique image model of the target tailings dam. It can be understood that, guided by the oblique image model of the target tailings dam, a 3D model of the target tailings dam matching the corresponding model-related data can be generated.
[0064] In some embodiments, the oblique image model of the target tailings dam can be a digital orthophoto map (DOM). Optionally, it is a planar map with map geometric accuracy and image features generated by cropping scanned digital aerial photographs or remote sensing images (monochrome or color) based on three-dimensional point cloud data, digital orthophoto data, and digital elevation data, using a digital elevation model (DEM) to perform pixel-by-pixel radiometric correction, differential correction, and mosaicking.
[0065] In some embodiments, the three-dimensional model of the target tailings dam can be a digital surface model (DSM). The three-dimensional point cloud data, digital orthophoto data and digital elevation data can be preprocessed, and operations such as stereo pair matching, LiDAR point cloud processing or photogrammetry and remote sensing integration can be performed on the preprocessed data to generate the DSM.
[0066] S103. Based on the oblique image model and the three-dimensional model, perform semantic segmentation on the target tailings dam to obtain data information on different monitoring objects of the target tailings dam, wherein the monitoring objects include at least the beach, tailings dam and drainage ditch.
[0067] In some embodiments, semantic segmentation of the target tailings pond is performed based on a pre-trained semantic segmentation neural network to obtain data information of different monitoring objects of the target tailings pond. That is, the tilted image model and three-dimensional model of the target tailings pond are input into the semantic segmentation model, and the semantic segmentation neural network performs semantic segmentation to obtain data information of different monitoring objects of the target tailings pond.
[0068] In some embodiments, the different monitoring objects of the target tailings dam may include at least the tailings dam surface, tailings dam, and drainage ditch. That is, data information of the tailings dam surface, tailings dam, and drainage ditch can be obtained.
[0069] In some embodiments, the data information of the monitored object may include the DSM model corresponding to the monitored object and the attribute information of the monitored object segmented from the three-dimensional model of the target tailings dam. That is, the DSM model and attribute information of the tailings beach, the DSM model and attribute information of the tailings dam, and the DSM model and attribute information of the drainage ditch can be segmented from the three-dimensional model of the target tailings dam.
[0070] In some embodiments, the attribute information of the monitored object may include, but is not limited to, information such as the length, width, and / or area of the monitored object.
[0071] S104, Identify drainage anomalies of the monitored object based on the data information of the monitored object.
[0072] In some embodiments, the monitored object can be divided into regions based on the data information of the monitored object, resulting in multiple regions. These regions can then be traversed to identify drainage anomalies in each region and obtain the identification results for each region.
[0073] In some embodiments, relevant information for each region can be obtained, such as the slope, aspect, and area of the region, and further, the drainage of the region can be determined based on the above information.
[0074] In some embodiments, drainage anomalies may include, but are not limited to, water accumulation and siltation. For example, it may be possible to identify whether there are water-filled pits on the beach surface, whether there are water-filled pits on the slope of the tailings dam, and whether there is siltation in the drainage ditch.
[0075] S105, Based on the identification results of the monitored objects, generate drainage monitoring data for the target tailings dam.
[0076] Once the identification results of the monitored objects are obtained, drainage monitoring data for the target tailings dam can be generated based on these results. It is understood that drainage monitoring data may include, but is not limited to: whether there are water-filled pits in multiple areas of the tailings dam surface, whether there are water-filled pits on the slope in multiple areas of the tailings dam, and whether there is siltation in multiple areas of the drainage ditch.
[0077] In this embodiment, the tailings dam's mapping data collected by drones enables the construction of a 3D model, resulting in an oblique image model and a 3D model of the tailings dam. Semantic segmentation of the oblique image model and 3D model yields data on different monitoring objects such as the tailings dam surface, tailings dam, and drainage ditch. Based on this data, drainage anomalies of the monitored objects are identified, providing tailings dam drainage monitoring data. This eliminates reliance on manual inspections, saving manpower and improving the efficiency of tailings dam drainage monitoring. Drone-based tailings dam mapping ensures more accurate and comprehensive collection of raw data, enabling the construction of more accurate oblique image models and 3D models, thereby further improving the reliability and accuracy of tailings dam drainage monitoring.
[0078] Figure 2 is a flowchart illustrating another tailings dam drainage monitoring method provided in this application. As shown in Figure 2, the tailings dam drainage monitoring method includes, but is not limited to, the following steps:
[0079] S201, acquire the set flight path of the UAV, and control the UAV to survey the target tailings dam according to the flight path, and obtain the survey data information of the target tailings dam.
[0080] For optional implementations of step S201, please refer to the optional implementations of step S101 in Figure 1 and other related parts in the embodiments involved in Figure 1, which will not be repeated here.
[0081] S202, perform three-dimensional reconstruction of the survey data to generate model-related data of the target tailings dam. The model-related data includes three-dimensional point cloud data, digital orthophoto data and digital elevation data of the target tailings dam.
[0082] S203. Based on the relevant data of the target tailings dam model, perform three-dimensional modeling of the target tailings dam to obtain the tilted image model and three-dimensional model of the target tailings dam.
[0083] For optional implementations of steps S202 to S203, please refer to the optional implementation of step S102 in Figure 1 and other related parts in the embodiments involved in Figure 1, which will not be repeated here.
[0084] S204. Based on the oblique image model and the three-dimensional model, perform semantic segmentation on the target tailings dam to obtain data information on different monitoring objects of the target tailings dam, wherein the monitoring objects include at least the beach, tailings dam and drainage ditch.
[0085] For optional implementations of step S204, please refer to the optional implementations of step S103 in Figure 1 and other related parts in the embodiments involved in Figure 1, which will not be repeated here.
[0086] S205, Identify drainage anomalies of the monitored object based on the data information of the monitored object.
[0087] In some embodiments, based on the attribute information of the monitored object, the digital surface model of the monitored object is divided into regions, resulting in N regions, where N is a natural number. Optionally, the plane can be divided into N regions with equal areas according to a set area. Optionally, the plane can be divided into N regions with equal areas according to a set length.
[0088] Furthermore, the N regions are traversed. For the currently traversed region m, the adjacent region m+1 is obtained. Based on the correlation data between region m and region m+1, it is determined whether there is water accumulation or siltation in region m.
[0089] In some embodiments, a three-dimensional coordinate system is established in the digital surface model of the monitored object, and the center point and its coordinates are determined for each region. Based on the coordinates of the center point, the slope and aspect between the center point of region m and the center point of region m+1 are obtained. Further, the area of a first region m and the area of a second region m+1 are obtained. After obtaining the slope and aspect, and based on the areas of the first and second regions, it can be determined whether there is water accumulation or siltation in region m. It is understood that m is a natural number and less than or equal to N.
[0090] In some embodiments, if the slope is greater than the slope threshold corresponding to the monitored object, the area of the first region is greater than the first area threshold corresponding to the monitored object, the slope direction is in one of the four quadrants, and the area of the second region is greater than the second area threshold corresponding to the monitored object, it is determined that there is water accumulation or siltation in region m.
[0091] In some embodiments, the process of monitoring whether there are puddles on the beach surface may include the following steps:
[0092] For the digital surface model of the beach, the plane is divided into n1 regions with equal areas according to the set area.
[0093] A three-dimensional coordinate system is established in the digital surface model of the beach, and the center point of each area is selected as the calculation point to calculate the position coordinates of each calculation point.
[0094] The slope and aspect between two adjacent calculation points are calculated using trigonometric functions; the slope between the m-th calculation point and the (m+1)-th calculation point is represented by I(m,m+1), and the aspect between the m-th calculation point and the (m+1)-th calculation point is represented by F(m,m+1).
[0095] The following rules 1, 2, and 3 are used to determine the m-th region:
[0096] Rule 1:
[0097] I(m,m+1)>I T And S m >S T1 Among them, I T S is the first slope threshold. m Let S be the area of the m-th region. T1 The first area threshold;
[0098] Rule 2:
[0099] F(m,m+1) is distributed in four quadrants {(0°,90°),(90°,180°),(180°,270°),(270°,360°)};
[0100] Rule 3:
[0101] The area S of the adjacent regions of the m-th region nei Satisfy S nei >S T2 Among them, S T2 This is the second area threshold;
[0102] If the m-th region simultaneously satisfies rules 1, 2, and 3, then the m-th region is determined to have a puddle formed on a beach.
[0103] In some embodiments, the monitoring process for the presence of water accumulation pits on the slope of tailings dams may include the following steps:
[0104] For the digital surface model of the tailings dam, the plane is divided into n2 regions with equal areas according to the set area.
[0105] A three-dimensional coordinate system is established in the digital surface model of the tailings dam. The center point of each region is selected as the calculation point, and the position coordinates of each calculation point are calculated.
[0106] The slope and aspect between two adjacent calculation points are calculated using trigonometric functions. The slope between the m1th calculation point and the (m1+1)th calculation point is represented by I(m1,m1+1), and the aspect between the m1th calculation point and the (m1+1)th calculation point is represented by F(m1,m1+1).
[0107] The following rules 1', 2', and 3' are used to determine the m1-th region:
[0108] Rule 1':
[0109] I(m1,m1+1)>I T1 And S m1 >S T3 Among them, I T1 S is the second slope threshold. m1 Let S be the area of the m1-th region. T3 The third area threshold;
[0110] Rule 2':
[0111] F(m1,m1+1) is distributed in four quadrants {(0°,90°),(90°,180°),(180°,270°),(270°,360°)};
[0112] Rule 3':
[0113] The area S of the adjacent regions of the m1th region nei1 Satisfy S nei1 >S T4 Among them, S T4 This is the fourth area threshold;
[0114] If the m1th region simultaneously satisfies rules 1', 2', and 3', then the m1th region is determined to have a tailings dam slope area water pit.
[0115] In some embodiments, the process of monitoring whether a drainage ditch is clogged may include the following steps:
[0116] Based on the drainage ditch data of the target tailings dam obtained in step S4, the digital surface model of the drainage ditch is divided into n3 regions on the plane according to the set area.
[0117] A three-dimensional coordinate system is established in the digital surface model of the drainage ditch. The center point of each area is selected as the calculation point, and the position coordinates of each calculation point are calculated.
[0118] The slope and aspect between two adjacent calculation points are calculated using trigonometric functions; the slope between the m2th calculation point and the (m2+1)th calculation point is represented by I(m2,m2+1), and the aspect between the m2th calculation point and the (m2+1)th calculation point is represented by F(m2,m2+1).
[0119] The following rules 1, 2, and 3 are used to determine the m2th region:
[0120] Rule 1:
[0121] I(m2,m2+1)>I T2 And S m2 >S T5 Among them, I T2 S is the third slope threshold. m2 Let S be the area of the m2th region. T5 The fifth area threshold;
[0122] Rule 2”:
[0123] F(m1,m1+1) is distributed in two quadrants {(0°,90°),(180°,270°)} or {(90°,180°),(270°,360°)};
[0124] Rule 3:
[0125] The area S of the adjacent regions of the m2th region nei2 Satisfy S nei2 >S T6 Among them, S T6 The sixth area threshold;
[0126] If the m2th region simultaneously satisfies Rule 1, Rule 2, and Rule 3, then the m2th region is determined to have a blocked drainage ditch.
[0127] S206. Based on the identification results of the monitored objects, generate drainage monitoring data for the target tailings dam.
[0128] Once the identification results of the monitored objects are obtained, drainage monitoring data for the target tailings dam can be generated based on these results. It is understood that drainage monitoring data may include, but is not limited to: whether there are water-filled pits in multiple areas of the tailings dam surface, whether there are water-filled pits on the slope in multiple areas of the tailings dam, and whether there is siltation in multiple areas of the drainage ditch.
[0129] In some embodiments, the location information, region number, and region area of a region are obtained as the region's attribute information. Furthermore, the region identification results and the region's attribute information are associated and stored.
[0130] In some embodiments, areas with water accumulation or siltation are identified in the tilted image model and 3D model of the target tailings dam based on the location information of the area; and / or,
[0131] Based on the identification results and attribute information in areas with water accumulation or siltation, alarm information is generated.
[0132] In this embodiment, the tailings dam's mapping data collected by drones enables the construction of a 3D model, resulting in an oblique image model and a 3D model of the tailings dam. Semantic segmentation of the oblique image model and 3D model yields data on different monitoring objects such as the tailings dam surface, tailings dam, and drainage ditch. Based on this data, drainage anomalies of the monitored objects are identified, providing tailings dam drainage monitoring data. This eliminates reliance on manual inspections, saving manpower and improving the efficiency of tailings dam drainage monitoring. Drone-based tailings dam mapping ensures more accurate and comprehensive collection of raw data, enabling the construction of more accurate oblique image models and 3D models, thereby further improving the reliability and accuracy of tailings dam drainage monitoring.
[0133] In the embodiments provided above, the methods provided in the embodiments of this application are described from the perspectives of the first communication device and the second communication device, respectively.
[0134] This disclosure also proposes an apparatus for implementing any of the above methods. For example, an apparatus is proposed that includes units or modules for implementing the steps performed in any of the above methods. To implement the functions of the methods provided in the embodiments of this application, the apparatus may include hardware structures and software modules, implementing the above functions in the form of hardware structures, software modules, or a combination of hardware structures and software modules. One of the above functions may be executed in the form of hardware structures, software modules, or a combination of hardware structures and software modules.
[0135] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions, and the processor calls the instructions stored in the memory to implement any of the above methods or to achieve the functions of the units or modules in the above device.
[0136] Figure 3 is a schematic diagram of the structure of a tailings dam drainage monitoring device provided in an embodiment of this application. As shown in Figure 3, the tailings dam drainage monitoring device 300 includes: a data acquisition module 31, a three-dimensional modeling module 32, a semantic segmentation module 33, a drainage anomaly identification module 34, and a real-time monitoring module 35.
[0137] In some embodiments, the data acquisition module 31 is used to acquire the mapping data information of the target tailings pond collected by the UAV;
[0138] The 3D modeling module 32 is used to perform 3D modeling of the target tailings dam based on the survey data, and obtain the tilted image model and 3D model of the target tailings dam.
[0139] The semantic segmentation module 33 is used to perform semantic segmentation on the target tailings pond based on the tilted image model and the three-dimensional model to obtain data information of different monitoring objects of the target tailings pond, wherein the monitoring objects include at least the beach, tailings dam and drainage ditch.
[0140] The drainage anomaly identification module 34 is used to identify drainage anomalies in the monitored object based on the data information of the monitored object.
[0141] The real-time monitoring module 35 is used to generate drainage monitoring data of the target tailings dam based on the identification results of the monitored object.
[0142] In some embodiments, the data acquisition module 31 is further configured to: acquire the set flight path of the UAV, control the UAV to perform mapping of the target tailings pond according to the flight path, obtain the mapping data information of the target tailings pond, and receive the mapping data information fed back by the UAV.
[0143] The mapping data includes image data and spatial location data of the target tailings dam from different perspectives.
[0144] In some embodiments, the tailings dam drainage monitoring device 300 further includes a data generation module 36.
[0145] The data generation module 36 is used to perform three-dimensional reconstruction on the surveying data information to generate corresponding tailings dam model data, wherein the tailings dam model data includes three-dimensional point cloud data, digital orthophoto data and digital elevation data of the tailings dam.
[0146] The 3D modeling module 32 is used to perform 3D modeling of the target tailings dam based on the model-related data of the target tailings dam, and obtain the tilted image model and 3D model of the target tailings dam.
[0147] In some embodiments, the semantic segmentation module 33 is further used to perform semantic segmentation on the tilted image model and the three-dimensional model according to the tailings pond beach, tailings dam and drainage ditch, to obtain digital surface models of the tailings pond beach, tailings dam and drainage ditch respectively.
[0148] In some embodiments, the drainage anomaly identification module 34 is further configured to:
[0149] The digital surface model of the monitored object is divided into N regions; the N regions are traversed, and for the currently traversed region m, the adjacent region m+1 is obtained; based on the correlation data between region m and region m+1, it is identified whether there is water accumulation or siltation in region m.
[0150] In some embodiments, the drainage anomaly identification module 34 is further configured to:
[0151] A three-dimensional coordinate system is established in the digital surface model of the monitored object, and the center point of each area and the position coordinates of the center point are determined; based on the position coordinates of the center point, the slope and direction between the center point of area m and the center point of area m+1 are obtained; the area of the first area of area m and the area of the second area of area m+1 are obtained; based on the slope and direction, as well as the area of the first area and the area of the second area, it is identified whether there is water accumulation or siltation in area m.
[0152] In some embodiments, the drainage anomaly identification module 34 is further configured to:
[0153] If the slope is greater than the slope threshold corresponding to the monitored object, the area of the first region is greater than the first area threshold corresponding to the monitored object, the slope direction is in each of the four quadrants, and the area of the second region is greater than the second area threshold corresponding to the monitored object, it is determined that there is water accumulation or siltation in region m.
[0154] In some embodiments, the real-time monitoring module 35 is further configured to:
[0155] The location information, region number, and region area of the region are obtained as the attribute information of the region; the judgment result of the region and the attribute information of the region are associated and stored.
[0156] In some embodiments, the real-time monitoring module 35 is further configured to:
[0157] Based on the location information of the area, areas with water accumulation or siltation are identified in the tilted image model and 3D model of the target tailings dam; and / or, alarm information is generated based on the judgment results and attribute information of the areas with water accumulation or siltation.
[0158] In some embodiments, the data acquisition module 31, the data generation module 36, the 3D modeling module 32, the semantic segmentation module 33, the drainage anomaly identification module 34, and the real-time monitoring module 35 are connected in series.
[0159] In this embodiment, the tailings dam's mapping data collected by drones enables the construction of a 3D model, resulting in an oblique image model and a 3D model of the tailings dam. Semantic segmentation of the oblique image model and 3D model yields data on different monitoring objects such as the tailings dam surface, tailings dam, and drainage ditch. Based on this data, drainage anomalies of the monitored objects are identified, providing tailings dam drainage monitoring data. This eliminates reliance on manual inspections, saving manpower and improving the efficiency of tailings dam drainage monitoring. Drone-based tailings dam mapping ensures more accurate and comprehensive collection of raw data, enabling the construction of more accurate oblique image models and 3D models, thereby further improving the reliability and accuracy of tailings dam drainage monitoring.
[0160] Figure 4 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. As shown in Figure 4, the electronic device 400 may include: a transceiver 41, a processor 42, and a memory 43.
[0161] Processor 42 executes computer execution instructions stored in memory, causing processor 42 to perform the scheme in the above embodiments. Processor 42 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0162] The memory 43 is connected to the processor 42 via the system bus and completes communication between them. The memory 43 is used to store computer program instructions.
[0163] Transceiver 41 can be used to obtain the task to be run and its configuration information.
[0164] The system bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.
[0165] The electronic device provided in this disclosure can be the terminal device, computer, or server described in the above embodiments.
[0166] This disclosure also provides a chip for executing instructions, which is used to implement the technical solution of the tailings dam drainage monitoring method in the above embodiments.
[0167] This disclosure also provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the technical solution of the tailings dam drainage monitoring method described in the above embodiments.
[0168] This disclosure also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the tailings dam drainage monitoring method in the above embodiments.
[0169] Those skilled in the art will also understand that the various illustrative logical blocks and steps listed in the embodiments of this application can be implemented by electronic hardware, computer software, or a combination of both. Whether such functionality is implemented through hardware or software depends on the specific application and the overall system design requirements. Those skilled in the art can implement the described functionality using various methods for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of this application.
[0170] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer programs. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program can be transferred from one website, computer, server, or data center to another 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 can be any available medium accessible to a computer or a data storage device such as a server 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., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).
[0171] Those skilled in the art will understand that the various numerical designations such as "first," "second," etc., involved in this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application, nor do they indicate the order of sequence.
[0172] At least one in this application can also be described as one or more, and multiple can be two, three, four or more, and this application does not impose any limitation. In the embodiments of this application, for a technical feature, the technical features in that technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", and there is no order or size among the technical features described by "first", "second", "third", "A", "B", "C" and "D".
[0173] The correspondences shown in the tables of this application can be configured or predefined. The values of the information in each table are merely examples and can be configured to other values; this application is not limited to these values. When configuring the correspondences between information and parameters, it is not necessarily required to configure all the correspondences shown in each table. For example, the correspondences shown in some rows of the tables in this application may not be configured. Furthermore, appropriate modifications and adjustments can be made based on the above tables, such as splitting, merging, etc. The names of the parameters shown in the headings of the above tables can also use other names that the communication device can understand, and the values or representations of the parameters can also be other values or representations that the communication device can understand. In the implementation of the above tables, other data structures can also be used, such as arrays, queues, containers, stacks, linear lists, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables, or hash tables, etc.
[0174] The term "predefined" in this application can be understood as definition, pre-defined, stored, pre-stored, pre-negotiated, pre-configured, solidified, or pre-burned.
[0175] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0176] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0177] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for monitoring drainage from a tailings dam, comprising the following steps: acquiring mapping data of a target tailings dam collected by a drone; performing a three-dimensional modeling of the target tailings dam based on the mapping data to obtain an oblique image model and a three-dimensional model of the target tailings dam, wherein, The oblique image model of the target tailings dam includes a digital oblique image map, and the three-dimensional model of the target tailings dam includes a digital surface model. Based on the oblique image model and the three-dimensional model, semantic segmentation is performed on the target tailings dam to obtain data information of different monitoring objects. This includes: based on a pre-trained semantic segmentation neural network, semantic segmentation is performed on the oblique image model and the three-dimensional model according to the tailings dam's beach area, tailings dam, and drainage ditch to obtain data information for each of the tailings dam area, tailings dam, and drainage ditch. The data information includes the digital surface model and attribute information of the monitoring objects. The monitored objects include at least the beach surface, tailings dam, and drainage ditch. The process of identifying drainage anomalies based on the data information of the monitored objects includes: dividing the digital surface model of the monitored objects into N regions based on their attribute information; traversing the N regions, and for the currently traversed region m, obtaining the adjacent region m+1; identifying whether region m has water accumulation or siltation based on the correlation data between region m and region m+1; and generating drainage monitoring data for the target tailings dam based on the identification results of the monitored objects.
2. The method according to claim 1, characterized in that, The process of acquiring the mapping data information of the target tailings dam collected by the UAV includes: acquiring the set flight path of the UAV, controlling the UAV to map the target tailings dam according to the flight path, obtaining the mapping data information of the target tailings dam, and receiving the mapping data information fed back by the UAV; wherein, the mapping data information includes image data and spatial location data of the target tailings dam from different perspectives.
3. The method according to claim 1, characterized in that, The step of performing three-dimensional modeling of the target tailings dam based on the surveying data information to obtain an oblique image model and a three-dimensional model of the target tailings dam includes: performing three-dimensional reconstruction of the surveying data information to generate model-related data of the target tailings dam, wherein the model-related data includes three-dimensional point cloud data, digital orthophoto data, and digital elevation data of the target tailings dam; and performing three-dimensional modeling of the target tailings dam based on the model-related data of the target tailings dam to obtain an oblique image model and a three-dimensional model of the target tailings dam.
4. The method according to claim 1, characterized in that, The method of identifying whether there is water accumulation or siltation in region m based on the correlation data of the adjacent region m+1 of region m includes: establishing a three-dimensional coordinate system in the digital surface model of the monitored object and determining the center point of each region and the position coordinates of the center point; obtaining the slope and aspect between the center point of region m and the center point of region m+1 based on the position coordinates of the center point; obtaining the area of a first region of region m and the area of a second region of region m+1; and identifying whether there is water accumulation or siltation in region m based on the slope and aspect, as well as the area of the first region and the area of the second region.
5. The method according to claim 4, characterized in that, The step of identifying whether area m has water accumulation or siltation based on the slope and slope direction, as well as the area of the first area and the area of the second area, includes: if the slope is greater than the slope threshold corresponding to the monitored object, the area of the first area is greater than the first area threshold corresponding to the monitored object, the slope direction is in each of the four quadrants, and the area of the second area is greater than the second area threshold corresponding to the monitored object, then it is determined that area m has water accumulation or siltation.
6. The method according to claim 1, characterized in that, After identifying whether the area m has water accumulation or blockage, the method further includes: obtaining the location information, area number, and area of the area as the attribute information of the area; and associating and storing the judgment result of the area and the attribute information of the area.
7. The method according to claim 1, characterized in that, After associating and storing the judgment result of the area and the attribute information of the area, the method further includes: identifying areas with water accumulation or siltation in the tilted image model and three-dimensional model of the target tailings dam according to the location information of the area; and / or generating alarm information based on the judgment result and attribute information of the water accumulation or siltation area.
8. A tailings dam water accumulation and drainage monitoring device, characterized in that, The device includes: a data acquisition module for acquiring mapping data of the target tailings dam collected by a drone; a 3D modeling module for performing 3D modeling of the target tailings dam based on the mapping data, obtaining an oblique image model and a 3D model of the target tailings dam, wherein the oblique image model of the target tailings dam includes a digital oblique image map, and the 3D model of the target tailings dam includes a digital surface model; and a semantic segmentation module for performing semantic segmentation of the target tailings dam based on the oblique image model and the 3D model, obtaining data information of different monitoring objects of the target tailings dam, including: performing semantic segmentation of the oblique image model and the 3D model according to the tailings dam's reservoir beach, tailings dam, and drainage ditch based on a pre-trained semantic segmentation neural network, obtaining data information of the tailings dam's reservoir beach, tailings dam, and drainage ditch. The monitoring module includes data information for the dam and drainage ditch, comprising a digital surface model and attribute information of the monitored object, wherein the monitored object includes at least a beach, a tailings dam, and a drainage ditch; a drainage anomaly identification module, used to identify drainage anomalies of the monitored object based on the data information of the monitored object, including: dividing the digital surface model of the monitored object into regions based on the attribute information of the monitored object, obtaining N regions; traversing the N regions, and for the currently traversed region m, obtaining the adjacent region m+1; identifying whether there is water accumulation or siltation in region m based on the correlation data between region m and region m+1; and a real-time monitoring module, used to generate drainage monitoring data of the target tailings dam based on the identification results of the monitored object.
Citation Information
Patent Citations
Tailing pond monitoring method and device based on sensor and three-dimensional model
CN114413853A
Environment detection method for mining
CN118247662A