A comprehensive monitoring and measuring method for tailings ponds

Abstract

The application belongs to the field of monitoring, and discloses a tailing pond comprehensive monitoring and measuring method, which comprises the following steps: S1, obtaining monitoring parameters of a tailing pond by using a fixed acquisition period through a sensor-based monitoring system; S2, calculating a UAV inspection probability value based on the monitoring parameters; S3, if the inspection probability value is greater than a set probability value threshold, entering S4, otherwise, entering S1; S4, monitoring the tailing pond by using a UAV through multiple means to obtain multiple types of monitoring data; and S5, monitoring the tailing pond based on the monitoring data to obtain a monitoring result. When the UAV inspection probability value is greater than the set probability value threshold, the tailing pond is monitored by using the UAV through multiple means, the monitoring result is more comprehensive, the state of the tailing pond can be more accurately and comprehensively obtained, and the risks existing in the tailing pond can be found in time.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of monitoring, in particular to a tailing pond comprehensive monitoring method. BACKGROUND

[0002] The tailing pond refers to a place formed by dam interception or surrounding land, used for storing tailings or other industrial waste slag discharged after ore selection in metal or non-metal mines. The tailing pond is a man-made debris flow hazard source with high potential energy, and there is a risk of dam break. Once the accident occurs, it is easy to cause serious accidents.

[0003] The traditional tailing pond safety monitoring mainly adopts sensor measurement and manual inspection. The monitoring system monitored by the sensor has the problem of small effective monitoring range because the sensor cannot cover all areas. The manual safety inspection has the problem of not being able to cover comprehensively due to the limitation of the terrain. Therefore, how to comprehensively monitor the tailing pond becomes a technical problem to be solved. SUMMARY

[0004] The purpose of the present application is to disclose a tailing pond comprehensive monitoring method, which solves the problem of how to comprehensively monitor the tailing pond.

[0005] In order to achieve the above purpose, the present application provides the following technical scheme:

[0006] The present application provides a tailing pond comprehensive monitoring method, comprising:

[0007] S1, obtaining the monitoring parameters of the tailing pond by the sensor-based monitoring system with a fixed acquisition period;

[0008] S2, calculating the unmanned aerial vehicle inspection probability value based on the monitoring parameters;

[0009] S3, if the inspection probability value is greater than the set probability value threshold, then entering S4, otherwise, entering S1;

[0010] S4, monitoring the tailing pond by the unmanned aerial vehicle in multiple ways to obtain multiple types of monitoring data;

[0011] S5, monitoring the tailing pond based on the monitoring data to obtain the monitoring result.

[0012] Optionally, the sensor-based monitoring system comprises a phreatic line monitoring module, a deep horizontal displacement monitoring module, a reservoir area water level monitoring module, a rainfall monitoring module, a dry beach length monitoring module and a data center;

[0013] The data center is used for receiving and saving the monitoring parameters sent by the phreatic line monitoring module, the deep horizontal displacement monitoring module, the reservoir area water level monitoring module, the rainfall monitoring module and the dry beach length monitoring module with a fixed acquisition period.

[0014] Optionally, the monitoring parameter sent by the phreatic line monitoring module comprises a phreatic line height of the tailings pond; the monitoring parameter sent by the deep horizontal displacement monitoring module comprises a deep horizontal displacement of the tailings pond; the monitoring parameter sent by the water level monitoring module comprises a water level of the tailings pond; the monitoring parameter sent by the rainfall monitoring module comprises a rainfall of the tailings pond; and the monitoring parameter sent by the dry beach length monitoring module comprises a dry beach length of the tailings pond.

[0015] Optionally, the unmanned aerial vehicle inspection probability value is calculated based on the monitoring parameter, comprising:

[0016] The unmanned aerial vehicle inspection probability value is calculated by using the following function:

[0017] profins k =w1×infheivar k +w2×depdisvar k +w3×watlevvar k +w4×raifalvar k +w5×bealenvar k

[0018] profins k represents the unmanned aerial vehicle inspection probability value calculated based on the monitoring parameter obtained in the kth acquisition period, infheivar k , depdisvar k , watlevvar k , raifalvar k and bealenvar k respectively represent a phreatic line height parameter, a deep horizontal displacement parameter, a water level parameter, a rainfall parameter and a dry beach length parameter calculated based on the monitoring parameter obtained in the kth acquisition period; w1, w2, w3, w4 and w5 respectively represent weights of the phreatic line height parameter, the deep horizontal displacement parameter, the water level parameter, the rainfall parameter and the dry beach length parameter.

[0019] Optionally, the calculation function of the phreatic line height parameter is:

[0020]

[0021] nor represents a normalized calculation on the variables in the parentheses, infhei k and infhei k-1 respectively represent the phreatic line height of the tailings pond collected in the kth acquisition period and the k-1th acquisition period; bsinfhei represents a preset phreatic line height;

[0022] The calculation function of the deep horizontal displacement parameter is:

[0023]

[0024] depdis k and depdis k-1 respectively represent the deep horizontal displacement of the tailings pond collected in the kth acquisition period and the k-1th acquisition period, and bsdepdis represents the preset deep horizontal displacement;

[0025] The calculation function of the water level parameter is:

[0026]

[0027] watlev k and watlev k-1 respectively represent the water level of the tailings pond collected in the kth acquisition period and the k-1th acquisition period, and bswatlev represents the preset water level;

[0028] The calculation function of the rainfall parameter is:

[0029]

[0030] raifal k and raifal k-1 respectively represent the rainfall of the tailings pond collected in the kth acquisition period and the k-1th acquisition period, and bsraifal represents the preset rainfall;

[0031] The calculation function of the dry beach length parameter is:

[0032]

[0033] bealen k and bealen k-1 respectively represent the dry beach length of the tailings pond collected in the kth acquisition period and the k-1th acquisition period, and bsbealen represents the preset dry beach length.

[0034] Optionally, the tailings pond is monitored by the unmanned aerial vehicle through multiple means to obtain multiple types of monitoring data, including:

[0035] The monitoring data includes visible light images, point cloud data, and infrared images;

[0036] The visible light images, point cloud data, and infrared images of the tailings pond are obtained by the unmanned aerial vehicle, so as to realize the monitoring of the tailings pond through multiple means.

[0037] Optionally, the tailings pond is monitored based on the monitoring data to obtain a monitoring result, including:

[0038] obtaining a DSM of the tailings pond based on the visible light image, and obtaining a first elevation, a first storage capacity and a first dam height of the tailings pond based on the DSM;

[0039] obtaining a DEM of the tailings pond based on the point cloud data, and obtaining a second elevation, a second storage capacity and a second dam height of the tailings pond based on the DEM;

[0040] obtaining a potential landslide danger zone in the tailings pond based on the infrared image;

[0041] calculating a third elevation, a third storage capacity and a third dam height based on the first elevation, the first storage capacity and the first dam height and the second elevation, the second storage capacity and the second dam height;

[0042] calculating a risk coefficient of the tailings pond by comparing the third elevation, the third storage capacity and the third dam height obtained in two adjacent monitoring;

[0043] The monitoring result comprises the potential landslide danger zone in the tailings pond and the risk coefficient.

[0044] Optionally, the calculating of the third elevation, the third storage capacity and the third dam height based on the first elevation, the first storage capacity and the first dam height and the second elevation, the second storage capacity and the second dam height comprises:

[0045] The calculation function of the third elevation is:

[0046]

[0047] elev1, elev2 and elev3 represent the first elevation, the second elevation and the third elevation respectively;

[0048] The calculation function of the third storage capacity is:

[0049]

[0050] stocap1, stocap2 and stocap3 represent the first storage capacity, the second storage capacity and the third storage capacity respectively;

[0051] The calculation function of the third dam height is:

[0052]

[0053] damhei1, damhei2 and damhei3 represent the first dam height, the second dam height and the third dam height respectively.

[0054] Optionally, the calculating of the risk coefficient of the tailings pond by comparing the third elevation, the third storage capacity and the third dam height obtained in two adjacent monitoring comprises:

[0055] The risk coefficient is calculated by using the following function:

[0056]

[0057] risk represents a risk coefficient, elev 3,i and elev 3,i-1 respectively represent the third elevation obtained by the i-th and the i-1-th monitoring, elev std is the maximum value of the elevation of the tailings pond, stocap 3,i and stocap 3,i-1 respectively represent the third storage capacity obtained by the i-th and the i-1-th monitoring, stocap std is the maximum value of the storage capacity of the tailings pond, damhei 3,i and damhei 3,i-1 respectively represent the third dam height obtained by the i-th and the i-1-th monitoring, damhei std represents the maximum value of the dam height of the tailings pond, λ1, λ2 and λ3 respectively represent the elevation weight, the storage capacity weight and the dam height weight.

[0058] Beneficial effects:

[0059] Compared with the prior art, the unmanned aerial vehicle is used to realize the monitoring of the tailings pond by multiple means when the unmanned aerial vehicle inspection probability value calculated by the monitoring parameters obtained by the monitoring system is greater than the set probability value threshold on the basis of the existing sensor-based monitoring system. The monitoring by the unmanned aerial vehicle can cover most of the surface area of the tailings pond, so that the monitoring result of the present application is more comprehensive, thereby the state of the tailings pond can be more accurately and comprehensively obtained, and the risks existing in the tailings pond can be found in time. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed for the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0061] Figure 1 is a schematic diagram of a tailings pond comprehensive monitoring method of the present application.

[0062] Figure 2 is a schematic diagram of the interface for establishing a new project of the present application. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of the present application.

[0064] As shown in an embodiment, Figure 1 The present application provides a tailing pond comprehensive monitoring method, comprising:

[0065] S1, obtaining monitoring parameters of the tailing pond by a sensor-based monitoring system with a fixed acquisition period;

[0066] S2, calculating a UAV inspection probability value based on the monitoring parameters;

[0067] S3, if the inspection probability value is greater than a set probability value threshold, entering S4, otherwise, entering S1;

[0068] S4, monitoring the tailing pond by a UAV with multiple means to obtain multiple types of monitoring data;

[0069] S5, monitoring the tailing pond based on the monitoring data to obtain a monitoring result.

[0070] When the UAV inspection probability value calculated based on the monitoring parameters obtained by the monitoring system is greater than the set probability value threshold, the present application realizes monitoring the tailing pond by a UAV with multiple means. The monitoring by the UAV can cover most of the surface area of the tailing pond, so the monitoring result of the present application is more comprehensive, thereby the state of the tailing pond can be more accurately and comprehensively obtained, which is conducive to timely discovering the risks existing in the tailing pond.

[0071] In addition, the present application does not directly utilize the UAV to periodically monitor the tailing pond, because the service life of the UAV is limited and it cannot continuously monitor the tailing pond for a long time like the sensor-based monitoring system. The present application only utilizes the UAV for further monitoring when the UAV inspection probability value is greater than the set probability value threshold, thereby obtaining more accurate comprehensive monitoring results, timely discovering the risks existing in the tailing pond, and prolonging the service life of the UAV.

[0072] Optionally, the sensor-based monitoring system comprises a phreatic line monitoring module, a deep horizontal displacement monitoring module, a reservoir area water level monitoring module, a rainfall monitoring module, a dry beach length monitoring module and a data center.

[0073] The data center is configured to receive and store monitoring parameters sent by the phreatic line monitoring module, the deep horizontal displacement monitoring module, the water level monitoring module, the rainfall monitoring module and the dry beach length monitoring module at a fixed acquisition period.

[0074] Optionally, the monitoring parameter sent by the phreatic line monitoring module comprises a phreatic line height of the tailings pond; the monitoring parameter sent by the deep horizontal displacement monitoring module comprises a deep horizontal displacement of the tailings pond; the monitoring parameter sent by the water level monitoring module comprises a water level of the tailings pond; the monitoring parameter sent by the rainfall monitoring module comprises a rainfall of the tailings pond; and the monitoring parameter sent by the dry beach length monitoring module comprises a dry beach length of the tailings pond.

[0075] Specifically, when monitoring the phreatic line of the tailings pond, a cross section is selected on a cross section that is representative and can control main seepage conditions, and a plurality of monitoring profiles are established in the dam body in combination with the deep horizontal displacement monitoring cross section.

[0076] In one embodiment, when monitoring the phreatic line of the tailings pond, the equipment of the measuring point is buried at a depth of 10 m.

[0077] Specifically, when monitoring the deep horizontal displacement of the tailings pond, one monitoring vertical line is arranged on a maximum dam height cross section of the tailings dam, and the monitoring vertical line is arranged to form a longitudinal monitoring cross section to monitor the displacement in the dam body.

[0078] Specifically, when monitoring the water level of the tailings pond, a water level sensor and a water level scale are installed near a drainage well in the tailings pond to monitor the water level of the tailings pond in real time.

[0079] Specifically, when monitoring the dry beach length, the dry beach length can be automatically generated according to the beach top elevation and the water level meter reading.

[0080] Specifically, when monitoring the rainfall, the acquisition period can be used as the monitoring time length, and the rainfall in the acquisition period is obtained through the rainfall monitoring equipment.

[0081] Optionally, the data center is further configured to issue a warning to a manager of the tailings pond when any of the following conditions is monitored:

[0082] Condition one: the rainfall is greater than a set rainfall threshold;

[0083] Condition two: a change amount of the dry beach length obtained in two adjacent acquisition periods is greater than a set dry beach length change threshold;

[0084] Condition three: the water level of the tailings pond is greater than a set water level threshold;

[0085] Condition four: a change amount of the phreatic line height obtained in two adjacent acquisition periods is greater than a set phreatic line height change threshold.

[0086] Case five: the change amount of deep horizontal displacement obtained by two adjacent acquisition periods is greater than the set deep horizontal displacement change threshold.

[0087] Optionally, the unmanned aerial vehicle inspection probability value is calculated based on the monitoring parameters, and the calculation comprises:

[0088] The unmanned aerial vehicle inspection probability value is calculated by using the following function:

[0089] profins k = w1 x infheivar k + w2 x depdisvar k + w3 x watlevvar k + w4 x raifalvar k + w5 x bealenvar k

[0090] profins k represents the unmanned aerial vehicle inspection probability value calculated based on the monitoring parameters obtained in the kth acquisition period, infheivar k , depdisvar k , watlevvar k , raifalvar k and bealenvar k respectively represent the phreatic line height parameter, the deep horizontal displacement parameter, the water level parameter, the rainfall parameter and the dry beach length parameter calculated based on the monitoring parameters obtained in the kth acquisition period; w1, w2, w3, w4 and w5 respectively represent the weights of the phreatic line height parameter, the deep horizontal displacement parameter, the water level parameter, the rainfall parameter and the dry beach length parameter.

[0091] Specifically, by weighting and summing the change conditions of various types of monitoring parameters, it can be more accurately judged whether the state of the tailing pond has changed a lot. When the change is large, that is, the unmanned aerial vehicle inspection probability value is greater than the set probability value threshold, the present application uses the unmanned aerial vehicle to further monitor. Thus, the state of the tailing pond is confirmed twice, which is conducive to more accurately judging the risks existing in the tailing pond.

[0092] Optionally, the weights of the phreatic line height parameter, the deep horizontal displacement parameter, the water level parameter, the rainfall parameter and the dry beach length parameter are all 0.2.

[0093] Optionally, the set probability value threshold is 0.05.

[0094] Optionally, the calculation function of the phreatic line height parameter is:

[0095]

[0096] nor represents a normalized calculation of the variables in the parentheses, infhei k and infhei k-1 respectively represent the phreatic line height of the tailings pond collected in the kth acquisition period and the (k-1)th acquisition period, and bsinfhei represents a preset phreatic line height;

[0097] The calculation function of the deep horizontal displacement parameter is:

[0098]

[0099] depdis k and depdis k-1 respectively represent the deep horizontal displacement of the tailings pond collected in the kth acquisition period and the (k-1)th acquisition period, and bsdepdis represents a preset deep horizontal displacement;

[0100] The calculation function of the water level parameter is:

[0101]

[0102] watlev k and watlev k-1 respectively represent the water level of the tailings pond collected in the kth acquisition period and the (k-1)th acquisition period, and bswatlev represents a preset water level;

[0103] The calculation function of the rainfall parameter is:

[0104]

[0105] raifal k and raifal k-1 respectively represent the rainfall of the tailings pond collected in the kth acquisition period and the (k-1)th acquisition period, and bsraifal represents a preset rainfall;

[0106] The calculation function of the dry beach length parameter is:

[0107]

[0108] bealen k and bealen k-1 respectively represent the dry beach length of the tailings pond collected in the kth acquisition period and the (k-1)th acquisition period, and bsbealen represents a preset dry beach length.

[0109] Specifically, the preset phreatic line height is a maximum value of the phreatic line height of the tailings pond in a specified time range, for example, in the last 5 years. The preset deep horizontal displacement is a maximum value of the deep horizontal displacement of the tailings pond in the specified time range. The preset water level is a maximum value of the water level of the tailings pond in the specified time range. The preset rainfall is a maximum value of the rainfall of the tailings pond in the specified time range. The preset dry beach length is a maximum value of the dry beach length of the tailings pond in the specified time range.

[0110] In addition, by normalizing each type of parameter respectively, the influence of different value ranges on the weighting result can be avoided, so that the unmanned aerial vehicle inspection probability value can better represent the necessity of using the unmanned aerial vehicle for secondary determination.

[0111] Optionally, the tailings pond is monitored by the unmanned aerial vehicle in multiple manners to obtain multiple types of monitoring data, including:

[0112] The monitoring data includes visible light images, point cloud data, and infrared images.

[0113] The visible light images, the point cloud data, and the infrared images of the tailings pond are respectively acquired by the unmanned aerial vehicle, so as to realize the monitoring of the tailings pond in multiple manners.

[0114] In an embodiment, the process of acquiring the visible light images of the tailings pond by the unmanned aerial vehicle includes:

[0115] The unmanned aerial vehicle is controlled to fly along the flight route to obtain the visible light images of multiple regions of the tailings pond.

[0116] Further, the DJI Wenli M300RTK is used to carry the visible light camera to acquire the visible light images.

[0117] In an embodiment, the point cloud data of the tailings pond is acquired by the unmanned aerial vehicle, including:

[0118] The DJI M300 RTK is used to carry the Chan Si L1 lens to collect the spatial information of the tailings pond. First, the flight route is planned, and only the radar beam needs to cover all the interested regions, so the overlap rate is set to 40%, the flight height is 80m away from the photographed surface, the ground effect flight function is turned on, so that the unmanned aerial vehicle can change the flight height according to the terrain, to avoid the problem that the overlap rate between adjacent flight routes is not enough due to large height difference; the multi-echo mode is turned on, so that the radar can penetrate the vegetation and obtain the ground echo, thereby eliminating the influence of vegetation in the later processing and improving the accuracy of the results.

[0119] The specific operation process is as follows:

[0120] The control points are marked by red and yellow squares with a side length of 1 m. The control points are arranged along the side of the tailings pond, and the coordinates of the control points are measured by using the Sino Global Positioning System RTK.

[0121] (1) Sensor parameter setting: mainly includes radar sensor parameter setting and image parameter setting.

[0122] (2) Start collecting: after the project starts, the sensor and the unmanned aerial vehicle need to be kept in a static state for 5 minutes, the purpose is to ensure that more static ephemeris are obtained for later adjustment processing.

[0123] (3) "8" flight and inertial navigation initialization: the purpose of the "8" flight is to make the inertial navigation enter the stable state faster, and the formal data collection can be started after the "8" flight is finished.

[0124] (4) Start collecting: before the "8" flight is about to end, click "start collecting" to start collecting laser radar point cloud data and image data. The cross-shaped flight path can better generate a three-dimensional model. The heading overlap of the unmanned aerial vehicle data collection is 80%, the lateral overlap is 80%, the flight height is 120 m, the GSD is 1.5 cm / pixel, which meets the requirements of high-precision photogrammetry.

[0125] (5) Stop collecting: after the flight is completed, the unmanned aerial vehicle lands, the power of the unmanned aerial vehicle and the remote controller is turned off, and the data collection is completed.

[0126] In one embodiment, the infrared image of the tailings pond is obtained by using the unmanned aerial vehicle, comprising:

[0127] In the process of visible light shooting, infrared image shooting is also carried out.

[0128] Optionally, the tailings pond is monitored based on the monitoring data to obtain a monitoring result, comprising:

[0129] The DSM of the tailings pond is obtained based on the visible light image, the first elevation, the first reservoir capacity and the first dam height of the tailings pond are obtained based on the DSM;

[0130] The DEM of the tailings pond is obtained based on the point cloud data, the second elevation, the second reservoir capacity and the second dam height of the tailings pond are obtained based on the DEM;

[0131] The potential landslide danger zone in the tailings pond is obtained based on the infrared image;

[0132] The third elevation, the third reservoir capacity and the third dam height are calculated based on the first elevation, the first reservoir capacity and the first dam height and the second elevation, the second reservoir capacity and the second dam height;

[0133] The risk coefficient of the tailing pond is calculated by comparing the third elevation, the third storage capacity and the third dam height obtained by two adjacent monitoring;

[0134] The monitoring result includes a potential landslide danger zone in the tailing pond and the risk coefficient.

[0135] Optionally, the DSM of the tailing pond is acquired based on the visible light image, and the DSM acquisition method comprises the following steps:

[0136] The visible light image is quickly filtered to obtain a filtered visible light image, and the DSM of the tailing pond is generated by using the filtered visible light image.

[0137] Specifically, the noise in the image can be reduced by filtering, which is beneficial to obtaining a more accurate DSM subsequently.

[0138] Optionally, the visible light image is quickly filtered to obtain a filtered visible light image, and the filtering method comprises the following steps:

[0139] P represents the visible light image;

[0140] An image GRYP corresponding to P is acquired;

[0141] The image GRYP is partitioned, and the image GRYP is divided into a plurality of square sub-images with a side length of Q;

[0142] The filtering priority of each sub-image is calculated respectively;

[0143] Based on the order from high to low of the filtering priority, each sub-image is filtered in GRYP in turn, and a filtered visible light image is obtained.

[0144] The filtering priority enables the present application to filter the sub-image with a greater influence on the surrounding sub-images in advance, so that the pixel points of the surrounding sub-images can be filtered based on more accurate gray information of the neighborhood, and a more accurate filtering result is obtained.

[0145] Optionally, the calculation function of the filtering priority is as follows:

[0146]

[0147] filprival z represents the filtering priority of the sub-image z, graycef z represents the variance of the gray value of the pixel points in z, magray represents the median value of the gray value of the pixel points in GRYP, num z represents the total number of the sub-images adjacent to z, δ1 represents the gray difference weight, and δ2 represents the position weight.

[0148] The filtering priority value is obtained by comprehensively weighting the variance of the gray value of the pixel point and the total number of adjacent sub-images. The greater the variance, the more the contour information contained in z, and the greater the number of adjacent sub-images, the greater the influence on the surrounding z sub-images. Therefore, when filtering the pixel points in the sub-image with a large filtering priority value, more correct filtering results can be accumulated for the subsequent filtering process to a greater extent, so that the subsequent filtering can be based on more accurate neighborhood gray information to obtain more accurate filtering results.

[0149] For sub-images in the edge region of the GRYP, the number of other sub-images adjacent thereto is relatively small. Therefore, if the values of the contour information are the same, the filtering order of these sub-images will be later. Because the influence of these sub-images on the surrounding sub-images is not as great as that of the sub-images with 8 edges adjacent to the edges of other sub-images.

[0150] Optionally, the values of δ1 and δ2 are 0.6 and 0.4, respectively.

[0151] Optionally, based on the order from high to low of the filtering priority values, each sub-image in the GRYP is sequentially filtered to obtain a filtered visible light image, including:

[0152] Saving all the sub-images to a set S;

[0153] First filtering:

[0154] Taking the sub-image with the largest filtering priority value from the set S, and deleting the taken sub-image from the set S;

[0155] Filtering the taken sub-image in the GRYP to obtain an image GRYP1;

[0156] dth filtering:

[0157] Taking the sub-image with the largest filtering priority value from the set S, and deleting the taken sub-image from the set S;

[0158] Filtering the taken sub-image in the GRYP d-1 to obtain an image GRYP d ;

[0159] If d is less than or equal to the total number D of sub-images, the next filtering is continued, otherwise, the GRYP d is taken as the filtered visible light image.

[0160] The existing filtering calculation is usually based on the original gray image, i.e. GRYP, and the previous filtering result is not utilized, obviously, the result obtained by such filtering is not accurate enough. Because filtering of all pixel points is based on the original image with noise content. The present application is different, the filtering priority of each sub-image is calculated respectively, then the sub-image with large filtering priority is filtered preferentially, so that the noise content in the image as the basis of filtering gradually reduces along with the filtering process, i.e. the effective degree of the information of the pixel points in the neighborhood as the basis of filtering is higher and higher, so that the previous filtering result can affect the subsequent filtering process, and a more accurate filtering result is obtained.

[0161] In an embodiment, generating a DSM of the tailings pond by using the filtered visible light image comprises:

[0162] 1) Obtaining original data, including the filtered visible light image, GPS / IMU data, and control point data. Checking whether the numbering of the GPS / IMU data and the filtered visible light image correspond, and eliminating the filtered visible light image with unqualified quality.

[0163] 2) Establishing a new project and importing relevant data, such as the filtered visible light image, latitude and longitude information, attitude angle information, camera information, etc. as shown in the following table. Figure 2

[0164] 3) Obtaining dense point cloud of the survey area. According to the input data, aerial triangulation is performed to obtain the exterior orientation elements of each image and the three-dimensional coordinates of the connection points, wherein the connection points constitute a sparse point cloud.

[0165] 4) Automatically generating a DSM, image correction and splicing. The accuracy of the DSM generated by the computer directly without using ground control points is not accurate enough, but it is sufficient for the correction and splicing of aerial images. The aerial images are orthorectified according to the interior and exterior orientation elements of the photos obtained by aerial triangulation and the DSM of the survey area. The images after correction not only change the projection mode, but also have corresponding geographic coordinates and other information. Then, the images are inlaid to splice all the images into a complete image.

[0166] In an embodiment, obtaining the first elevation, the first reservoir capacity and the first dam height of the tailings pond based on the DSM comprises:

[0167] By dividing the DSM into regions, the average elevation of the dry beach area of the reservoir in the DSM is obtained, and the average elevation is taken as the first elevation. The reservoir area in the DSM is obtained, and the first reservoir capacity of the reservoir area is calculated by the DSM. The dam body area in the DSM is obtained, and the average height of the dam body is calculated by the DSM to obtain the first dam height. ​

[0168] Specifically, the process of obtaining the second elevation, the second storage capacity and the second dam height based on the DEM is the same as the above calculation process, except that the calculation is performed in the DEM.

[0169] In an embodiment, the DEM of the tailings pond based on the point cloud data comprises:

[0170] The initial acquisition data is derived from the L1 sensor, imported into DJI ZhiTu, the point cloud precision optimization function is opened, the point cloud data is exported after reconstruction, and after point cloud classification processing by special point cloud processing software, different types of point cloud files such as ground, vegetation and water body can be obtained. Then, the ground point extraction is performed, and the DEM can be generated by the ground points.

[0171] In an embodiment, the potential landslide hazard area in the tailings pond is obtained based on the infrared image, comprising:

[0172] Landslide is a sliding phenomenon of slope rock-soil along a through-going fracture surface. Therefore, the formation of landslide must have a weak surface that can produce sliding. Due to the existence of the weak sliding surface, the rocks on both sides of the sliding surface are inevitably discontinuous in space, and the sliding surface often contains a lot of water, which leads to a significant difference in thermodynamic properties from the surrounding rocks. Therefore, according to the above principle, the thermal imaging technology can be used to detect the tailings dam slope and obtain temperature field data. By analyzing the characteristics of the temperature field, the geological structure, water-bearing zone and rock-soil weak surface that affect the formation of landslide can be identified and interpreted, so as to determine the landslide hazard area and lay a foundation for further displacement observation.

[0173] Optionally, the third elevation, the third storage capacity and the third dam height are calculated based on the first elevation, the first storage capacity and the first dam height and the second elevation, the second storage capacity and the second dam height, comprising:

[0174] The calculation function of the third elevation is:

[0175]

[0176] elev1, elev2 and elev3 represent the first elevation, the second elevation and the third elevation, respectively;

[0177] The calculation function of the third storage capacity is:

[0178]

[0179] stocap1, stocap and stocap3 represent the first storage capacity, the second storage capacity and the third storage capacity, respectively;

[0180] The calculation function of the third dam height is:

[0181]

[0182] damhei1, damhei2, and damhei3 represent the first dam height, the second dam height, and the third dam height respectively.

[0183] Specifically, the elevation, the storage capacity, and the dam height are obtained by calculating the average of the values calculated by the two models, and compared with the measurement based on a single model, the accuracy of the measurement result of the two models is higher.

[0184] Optionally, the risk coefficient of the tailings pond is calculated by comparing the third elevation, the third storage capacity, and the third dam height obtained by the adjacent two times of monitoring, including:

[0185] The risk coefficient is calculated by using the following function:

[0186]

[0187] risk represents the risk coefficient, elev 3,i and elev 3,i-1 represent the third elevation obtained by the i-th and the i-1-th monitoring respectively, elev std is the maximum value of the elevation of the tailings pond, stocap 3,i and stocap 3,i-1 represent the third storage capacity obtained by the i-th and the i-1-th monitoring respectively, stocap std is the maximum value of the storage capacity of the tailings pond, damhei 3,i and damhei 3,i-1 represent the third dam height obtained by the i-th and the i-1-th monitoring respectively, damhei std represents the maximum value of the dam height of the tailings pond, λ1, λ2, and λ3 represent the elevation weight, the storage capacity weight, and the dam height weight respectively.

[0188] Specifically, the risk coefficient is calculated by comprehensively weighting the change amount of the elevation, the change amount of the storage capacity, and the change amount of the dam height, the greater the change amount of the elevation, the greater the reduction range of the storage capacity, and the greater the reduction range of the dam height, the greater the risk. Therefore, the application can calculate the risk coefficient from multiple aspects, and then combine the potential landslide area, so that the residents in the influence range of the potential landslide area can be warned when the risk coefficient is greater than a certain value.

[0189] Optionally, the elevation weight, the storage capacity weight, and the dam height weight are all one third.

[0190] In several embodiments provided by the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the division of the units is only a logical function division, and there can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0191] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to the actual needs to achieve the purposes of the embodiments of the present application.

[0192] In addition, each functional unit in the various embodiments of the present application can be integrated in a processing unit, or each unit physically exists alone, or two or more units are integrated in a unit. The integrated unit can be in the form of hardware, or in the form of a software functional unit.

[0193] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A comprehensive monitoring and measurement method for tailings dams, characterized in that, include: S1, The monitoring parameters of the tailings dam are obtained through a sensor-based monitoring system using a fixed acquisition cycle; S2, calculate the drone inspection probability value based on monitoring parameters; S3. If the inspection probability value is greater than the set probability value threshold, proceed to S4; otherwise, proceed to S1. S4 uses drones to monitor tailings ponds using various methods, obtaining multiple types of monitoring data; S5, monitor the tailings dam based on the monitoring data and obtain the monitoring results; By using drones to monitor tailings ponds through various means, multiple types of monitoring data were obtained, including: The monitoring data includes visible light images, point cloud data, and infrared images; By using drones to acquire visible light images, point cloud data, and infrared images of tailings ponds, multiple methods of monitoring of tailings ponds can be achieved. The tailings dam is monitored based on monitoring data to obtain monitoring results, including: The DSM of the tailings dam is obtained based on visible light images, and the first elevation, first reservoir capacity and first dam height of the tailings dam are obtained based on the DSM. Based on the point cloud data of the tailings dam, the second elevation, second capacity and second dam height of the tailings dam are obtained. Identifying potential landslide hazard zones in tailings ponds using infrared images; Calculate the third elevation, third reservoir capacity, and third dam height based on the first elevation, first reservoir capacity, and first dam height; The risk coefficient of the tailings dam is calculated by comparing the third elevation, third reservoir capacity, and third dam height obtained from two adjacent monitoring sessions. The monitoring results include potential landslide hazard zones and risk coefficients in tailings ponds; The third elevation, third reservoir capacity, and third dam height are calculated based on the first elevation, first reservoir capacity, and first dam height, as well as the second elevation, second reservoir capacity, and second dam height, including: The function for calculating the third elevation is: elev1, elev2, and elev3 represent the first elevation, the second elevation, and the third elevation, respectively. The function for calculating the third storage capacity is: stocap1, stocap, and stocap3 represent the first, second, and third storage capacities, respectively. The function for calculating the height of the third dam is: damhei1, damhei2, and damhei3 represent the first, second, and third dam heights, respectively. By comparing the third elevation, third reservoir capacity, and third dam height obtained from two adjacent monitoring sessions, the risk coefficient of the tailings dam is calculated, including: The risk coefficient is calculated using the following function: risk represents the risk coefficient, elev 3,i and elev 3,i-1 Let elevate represent the third elevation obtained from the i-th and (i-1)-th monitoring, respectively. std stocap represents the maximum elevation of the tailings dam. 3,i and stocap 3,i-1 Stocap represents the third reservoir capacity obtained from the i-th and (i-1)-th monitoring, respectively. std damhei is the maximum capacity of the tailings dam. 3,i and damhei 3,i-1 damhei represents the third dam height obtained from the i-th and (i-1)-th monitoring, respectively. std λ represents the maximum value of the tailings dam height, and λ1, λ2 and λ3 represent the elevation weight, dam capacity weight and dam height weight, respectively.

2. The comprehensive monitoring and measurement method for tailings dams according to claim 1, characterized in that, The sensor-based monitoring system includes a seepage line monitoring module, a deep horizontal displacement monitoring module, a reservoir water level monitoring module, a rainfall monitoring module, a dry beach length monitoring module, and a data center. The data center is used to receive and store monitoring parameters from the infiltration line monitoring module, the cross-sectional deep horizontal displacement monitoring module, the reservoir water level monitoring module, the rainfall monitoring module, and the dry beach length monitoring module, which send monitoring parameters at a fixed acquisition cycle.

3. The comprehensive monitoring and measurement method for tailings dams according to claim 2, characterized in that, The monitoring parameters sent by the phreatic line monitoring module include the phreatic line height of the tailings dam; the monitoring parameters sent by the cross-sectional deep horizontal displacement monitoring module include the deep horizontal displacement of the tailings dam; the monitoring parameters sent by the reservoir water level monitoring module include the water level of the tailings dam; and the monitoring parameters sent by the rainfall monitoring module include the rainfall in the tailings dam. The monitoring parameters sent by the dry beach length monitoring module include the dry beach length of the tailings dam.

4. The comprehensive monitoring and measurement method for tailings dams according to claim 3, characterized in that, The probability value of drone inspection is calculated based on monitoring parameters, including: The following function is used to calculate the probability value of drone inspection: profins k =w1×infheivar k +w2×depdisvar k +w3×watlevvar k +w4×raifalvar k +w5×bealenvar k profins k Infheivar represents the drone inspection probability value calculated based on the monitoring parameters obtained in the kth acquisition period. k ,depdisvar k ,watlevvar k raifalvar k and bealenvar k , respectively, represent the infiltration line height parameter, deep horizontal displacement parameter, water level parameter, rainfall parameter, and dry beach length parameter calculated based on the monitoring parameters obtained in the kth acquisition period; w1, w2, w3, w4, and w5 represent the weights of the infiltration line height parameter, deep horizontal displacement parameter, water level parameter, rainfall parameter, and dry beach length parameter, respectively.

5. The comprehensive monitoring and measurement method for tailings dams according to claim 4, characterized in that, The function for calculating the wetting line height parameter is: nor indicates that the variables within the parentheses are normalized during the calculation; infhei k and infhei k-1 represents the phreatic line height of the tailings pond collected in the k-th and (k-1)-th acquisition cycles, respectively; bsinfhei represents the preset phreatic line height. The function for calculating deep horizontal displacement parameters is: depdis k and depdis k-1 represents the deep horizontal displacement of the tailings pond collected in the k-th acquisition cycle and the (k-1)-th acquisition cycle, respectively, and bsdepdis represents the preset deep horizontal displacement. The function for calculating water level parameters is: watlev k and watlev k-1 represents the water level of the tailings pond collected in the k-th acquisition cycle and the (k-1)-th acquisition cycle, respectively, and bswatlev represents the preset water level; The function for calculating rainfall parameters is: raifal k and raifal k-1 represents the rainfall in the tailings pond collected in the k-th and (k-1)-th acquisition cycles, respectively, and bsraifal represents the preset rainfall. The function for calculating the dry beach length parameter is: bealen k and bealen k-1 represents the dry beach length of the tailings pond collected in the k-th and (k-1)-th acquisition cycles, respectively, and bsbealen represents the preset dry beach length.

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