A deformation monitoring system for a tailings pond and its data fusion method

Abstract

The present invention discloses a deformation monitoring system for a tailings pond and its data fusion method. The present invention adopts advanced technical methods, including a single-point monitoring module for monitoring the dam body of the tailings pond, a multi-point relative monitoring module, and an overall displacement module, to measure the displacement change data of the dam body: internal displacement, external displacement, internal displacement change rate, and external displacement change rate. At the same time, the monitoring data is analyzed and processed centrally, and the deformation intensity and safety of the tailings pond are pre-warned based on the results of data processing, which exactly meets the enterprise's production safety requirements and effectively prevents and curbs the occurrence of major accidents.

Description

Technical Field

[0001] The present invention relates to the technical field of tailings pond safety monitoring, and particularly relates to a deformation monitoring system for a tailings pond and a data fusion method thereof. Background Art

[0002] China is rich in mineral resources, and the reserves of some mineral species rank among the top in the world. However, the overall characteristics of China's mineral resources are that there are many lean ores and few rich ores, many polymetallic ores and few single - mineral ores, the metal grade is generally low, and a large amount of solid waste is formed during the mining and beneficiation processes, resulting in a large number of mining reservoirs. The development of the mining industry has made great contributions to China's economic development, but at the same time, it has also brought serious environmental pollution, making mining enterprises and society face severe environmental comprehensive treatment problems.

[0003] The development and utilization of mines often produce a large amount of tailings. At present, there is no economically effective treatment method for the tailings of concentrators. However, the tailings often contain various toxic and harmful substances, and direct discharge will cause serious pollution and ecological damage to the air, groundwater, and soil around the mine. Therefore, it is crucial to use a tailings pond formed by building a dam to intercept the valley mouth or enclosing land as a place to store the tailings or other industrial waste residues discharged after ore separation in metal or non - metal mines.

[0004] At present, most tailings are stored in tailings ponds near the mining areas. With the changes in weathering and other conditions around the tailings ponds, the tailings pond has become an artificial debris - flow hazard source with high potential energy. Once a dam - break accident occurs, a debris - flow with great destructive power will rush downstream, causing great harm to the human living environment, such as the surrounding air, groundwater, soil, vegetation, and ecological environment. Therefore, the safety monitoring of tailings ponds is of great significance for strengthening the safety supervision of tailings ponds, grasping the safety status of tailings ponds, and reducing the occurrence of tailings pond accidents, etc.

[0005] Currently, the main technical parameters for the safe operation of tailings ponds in China, such as the deformation displacement of the dam body, the water level in the pond, and the buried depth of the phreatic line, are all measured manually on - site at regular intervals using traditional instruments. The workload of safety monitoring is large, and it is affected by many factors such as weather, manpower, and on - site conditions, resulting in large systematic errors and manual errors. At the same time, manual monitoring also has the characteristics of being unable to monitor the technical parameters of tailings ponds in a timely manner and being difficult to grasp the safety technical indicators of tailings ponds in a timely manner. These will all affect the safety production and management level of tailings ponds. Therefore, China's mine safety production urgently needs technologies and systems for real - time and automated monitoring of tailings ponds.

[0006] The on-line monitoring system for tailings ponds utilizes sensor technology, signal transmission technology, computer graphics and image processing technology, network technology, and software engineering technology to monitor various key technical indicators affecting the safety of tailings ponds and dams from an all-round perspective that combines macro and micro aspects, theory and practice. Based on the recorded historical data and existing real-time data, it analyzes future trends to assist enterprises and the government in making decisions, improving the safety guarantee level of tailings ponds, and effectively preventing and curbing the occurrence of major accidents.

[0007] Currently, the more commonly used monitoring methods for tailings ponds mainly analyze the monitoring data of some single points. Firstly, the monitoring data of some single points is not comprehensive enough. Secondly, there are different errors in the monitoring results according to different monitoring means. In addition, such monitoring results cannot accurately grasp the deformation and its strength of the tailings pond as a whole. Finally, it is difficult to obtain information about the danger brought by the deformation of the tailings pond, or whether the tailings pond is safe, which is what we want to know more. Summary of the Invention

[0008] The technical problem to be solved by the present invention is: The present invention provides a deformation monitoring system for tailings ponds and its data fusion method, which mainly measures the displacement change data of the dam body, including internal displacement, external displacement, internal displacement change rate, and external displacement change rate. At the same time, it analyzes and processes the monitoring data set, and through the results of data processing, it gives early warnings about the deformation strength and safety of the tailings pond, precisely meets the needs of enterprise safe production, and effectively prevents and curbs the occurrence of major accidents.

[0009] To solve the above problems, the technical solution adopted by the present invention is as follows: A deformation monitoring system for tailings ponds includes a single-point monitoring module, a multi-point relative monitoring module, and an overall displacement module for monitoring the tailings pond dam body;

[0010] Among them, the multi-point relative monitoring module is used to receive the data of the single-point monitoring module, preprocess the data, and then transfer the preprocessed data to the overall displacement module;

[0011] The overall displacement module is used to perform fusion processing on the preprocessed data and transfer the processing results to the inclination module and the bending module to comprehensively characterize the deformation strength of the tailings pond dam;

[0012] The single-point monitoring module includes:

[0013] The external displacement module is used for real-time monitoring of the displacement parameters of the surface soil layer of the tailings pond;

[0014] The internal displacement module is used for real-time monitoring of the displacement parameters of the internal structure of the tailings pond.

[0015] Further, the external displacement module includes a horizontal displacement module and a vertical displacement module, and the combination of the horizontal displacement module and the vertical displacement module is used to monitor the displacement parameters of the surface soil layer of the tailings pond in real time;

[0016] The external displacement module includes a GNSS displacement monitoring device and a laser communication module. It monitors the displacement of each distribution point in real time through satellites, calculates the displacement in the three directions of the rectangular coordinate system X, Y, and H, then converts it into the horizontal displacement and vertical displacement in the spatial coordinate system, and calculates the displacement rate per unit time according to the displacement.

[0017] Further, the internal displacement module includes an inclination displacement module. The inclination displacement module includes an inclinometer and an MCU data processing module. Each monitoring point is a vertical deep well structure, and 3 to 15 inclinometer sensors are installed in each deep well. By monitoring the inclination angles of the sensors in real time, the internal inclination displacement is calculated according to the angles of the inclinometers, and the actual inclination displacement and the inclination displacement rate per unit time are calculated from top to bottom in combination with the GNSS sensors installed at the wellheads of the monitoring points.

[0018] A data fusion method for a deformation monitoring system of a tailings pond includes the following steps:

[0019] S1: Obtain the original data of the external displacement and the internal displacement from the external displacement module and the internal displacement module;

[0020] S2: Process the original data obtained from the GNSS external displacement monitoring device, store the valid data in the database, and perform judgment and early warning;

[0021] S3: Process the original data obtained from the internal displacement monitoring module, store the valid data in the database, and perform judgment and early warning;

[0022] S4: Combine the external displacement rate and the internal displacement rate of the same section for early warning;

[0023] S5: Delay and wait for the next calculation and early warning;

[0024] S6: Go back to S1 and continue.

[0025] Further, S2 includes the following steps:

[0026] S2.1: Subtract the latest coordinate data from the coordinate data obtained at the previous monitoring moment to obtain the displacement changes in the three directions 、 、 ;

[0027] S2.2: According to 、 Calculate the horizontal displacement ;

[0028] S2.3: Take directly as the vertical displacement;

[0029] S2.4: Take the horizontal displacement and the vertical displacement and store them in the database;

[0030] S2.5: Calculate the external displacement rates in different time periods, including the horizontal displacement rate and the vertical displacement rate, and store them in the database;

[0031] S2.6: Compare and judge the external displacement rates in each time period calculated in S2.5 with the warning value of each level of the external displacement rate designed for this tailings pond, and give a warning for a single monitoring point according to the situation;

[0032] S2.7: Calculate the distance between this monitoring point and the adjacent monitoring points of the same-level dam, subtract the data obtained at the previous monitoring moment, and calculate the relative displacement rate of the same-level dam in each time period according to different time periods, and store it in the database;

[0033] S2.8: Calculate the distance between this monitoring point and the adjacent monitoring points of the same cross-section, subtract the data obtained at the previous monitoring moment, and calculate the relative displacement rate of the same cross-section in each time period according to different time periods, and store it in the database;

[0034] S2.9: Compare and judge the relative external displacement rates in each time period calculated in S2.7 and 2.8 with the warning value of each level of the relative displacement rate of the same-level dam and the same cross-section designed for this tailings pond, and give a relative displacement warning according to the situation.

[0035] Furthermore, S3 includes the following steps:

[0036] S3.1: Obtain the basic displacement of this monitoring well from the GNSS sensor at the pipe orifice of the monitoring well;

[0037] S3.2: Process the data of all the internal displacement sensors in the monitoring well from top to bottom;

[0038] S3.3: Calculate the displacement of the current internal displacement sensor, superimpose it with the displacements of the internal displacement sensors above this sensor in this monitoring well, and then superimpose it with the basic displacement of the monitoring well obtained from the GNSS sensor at the pipe orifice of the monitoring well, and take it as the final internal displacement data at the position of the internal displacement sensor at this position, and store it in the database;

[0039] S3.4: Compare and judge the final internal displacement data at this position with the warning value of each level of the single-point internal displacement designed for this tailings pond, and give a warning for a single internal displacement monitoring point according to the situation;

[0040] S3.5: Calculate the internal displacement rate of each monitoring point according to different time periods, store it in the database, and compare it with the warning values of each level of the single-point internal displacement rate designed for this tailings pond to make a judgment, and issue a warning for the single internal displacement rate according to the situation.

[0041] S3.6: Calculate the weighted average of the internal displacement rates of all monitoring points of this monitoring well to obtain the overall internal displacement rate of this monitoring well, and store it in the database.

[0042] S3.7: Subtract the overall internal displacement rate of this monitoring well from the overall internal displacement rate at the previous monitoring moment to obtain the acceleration value of the internal displacement rate of this monitoring well, and store it in the database.

[0043] S3.8: Compare and judge the internal displacement acceleration value calculated in S3.7 with the warning values of each level of the internal displacement acceleration of the monitoring well designed for this tailings pond, and issue a warning for the internal displacement acceleration according to the situation.

[0044] Further, S4 includes the following steps:

[0045] S4.1: Calculate the weighted average of the external displacement rate data obtained from all external displacement monitoring points of the same section according to the importance of each monitoring point to obtain the external displacement rate of the section.

[0046] S4.2: Calculate the weighted average of the internal displacement rate data obtained from all internal displacement monitoring points of the same section according to the importance of each monitoring point to obtain the internal displacement rate of the section.

[0047] S4.3: Combine the external displacement rate and the internal displacement rate of the section according to a certain weight value to calculate the dam body displacement rate data, and issue a warning according to the situation.

[0048] The present invention adopts advanced technical methods to monitor the key technical indicators of the safety of the tailings pond from the combination of macro and micro aspects, conducts data analysis based on all historical records, assists enterprises and the government in decision-making, improves the safety guarantee level of the tailings pond, and effectively prevents and curbs the occurrence of major accidents. Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 It is a schematic structural diagram of a deformation monitoring system for a tailings pond of the present invention;

[0051] Figure 2 is a schematic structural diagram of the external displacement profile of the present invention;

[0052] Figure 3 is a schematic structural diagram of the internal displacement profile of the present invention;

[0053] Markings in the figure: 1 - tailings pond deformation monitoring system, 2 - single - point monitoring module, 3 - multi - point relative monitoring module, 4 - external displacement module, 5 - internal displacement module, 6 - overall displacement module, 7 - horizontal displacement module, 8 - vertical displacement module, 9 - tilt displacement module, 10 - tilt module, 11 - bending module. Specific embodiments

[0054] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0055] As Figure 1 shown, a tailings pond deformation monitoring 1 system includes a single - point monitoring module 2, a multi - point relative monitoring module 3, and an overall displacement module 6 for monitoring the tailings pond dam body;

[0056] Among them, the multi - point relative monitoring module 3 is used to receive the data of the single - point monitoring module 2, pre - process the data, and then transfer the pre - processed data to the overall displacement module 6;

[0057] The overall displacement module 6 is used to fuse - process the pre - processed data and transfer the processing result to the tilt module 10 and the bending module 11 to comprehensively characterize the deformation strength of the tailings pond dam;

[0058] The single - point monitoring module 2 includes:

[0059] An external displacement module 4 for real - time monitoring of the displacement parameters of the surface soil layer of the tailings pond;

[0060] An internal displacement module 5 for real - time monitoring of the displacement parameters of the internal structure of the tailings pond.

[0061] Furthermore, the external displacement module 4 includes a horizontal displacement module 7 and a vertical displacement module 8. The horizontal displacement module 7 and the vertical displacement module 8 are combined to perform real - time monitoring of the displacement parameters of the surface soil layer of the tailings pond;

[0062] The external displacement module 4 includes a GNSS displacement monitoring device and a laser communication module, which monitors the displacement of each distribution point in real time through satellites, calculates the displacement in the three directions of the rectangular coordinate system X, Y, and H, then converts it into the horizontal displacement and vertical displacement in the space coordinate system, and calculates the displacement rate per unit time according to the displacement. The external displacement profile is as shown in Figure 2 shown;

[0063] Furthermore, the internal displacement module 5 includes an inclination displacement module 9. The inclination displacement module 9 includes an inclinometer and an MCU data processing module. Each monitoring point is a vertical deep well structure, and 3 to 15 inclinometer sensors are installed in each deep well. By monitoring the inclination angle of each sensor in real time, the internal inclination displacement is calculated according to the angle of the inclinometer, and the actual inclination displacement and the inclination displacement rate per unit time are calculated from top to bottom in combination with the GNSS sensor installed at the wellhead of the monitoring point. The internal displacement profile is shown in Figure 3 ;

[0064] A data fusion method for a tailings pond deformation monitoring system includes the following steps:

[0065] S1: Obtain the original data of the external displacement and the internal displacement from the external displacement module 4 and the internal displacement module 5;

[0066] S2: Process the original data obtained from the GNSS external displacement monitoring device, obtain the valid data and store it in the database, and perform judgment and early warning;

[0067] S3: Process the original data obtained from the internal displacement monitoring module, obtain the valid data and store it in the database, and perform judgment and early warning;

[0068] S4: Combine the external displacement rate and the internal displacement rate of the same section for early warning;

[0069] S5: Delay and wait for the next calculation and early warning;

[0070] S6: Go back to S1 and continue.

[0071] Furthermore, S2 includes the following steps:

[0072] S2.1: Subtract the latest coordinate data from the coordinate data obtained at the previous monitoring moment to obtain the displacement change amounts in the three directions , , ;

[0073] S2.2: Calculate the horizontal displacement , ; ;

[0074] S2.3: Substitute Directly as the vertical displacement;

[0075] S2.4: Store the horizontal displacement and the vertical displacement in the database;

[0076] S2.5: Calculate the external displacement rate according to different time periods, including the horizontal displacement rate and the vertical displacement rate, and store them in the database;

[0077] S2.6: Compare and judge the external displacement rates of each time period calculated in S2.5 with the warning value of each level of the external displacement rate designed for this tailings pond, and make a warning for a single monitoring point according to the situation;

[0078] S2.7: Calculate the distance between this monitoring point and the adjacent monitoring points of the same-level dam, subtract the data obtained at the previous monitoring moment, and calculate the relative displacement rate of the same-level dam in each time period according to different time periods, and store it in the database;

[0079] S2.8: Calculate the distance between this monitoring point and the adjacent monitoring points of the same cross-section, subtract the data obtained at the previous monitoring moment, and calculate the relative displacement rate of the same cross-section in each time period according to different time periods, and store it in the database;

[0080] S2.9: Compare and judge the relative external displacement rates of each time period calculated in S2.7 and 2.8 with the warning value of each level of the relative displacement rate of the same-level dam and the same cross-section designed for this tailings pond, and make a relative displacement warning according to the situation.

[0081] Furthermore, S3 includes the following steps:

[0082] S3.1: Obtain the basic displacement of this monitoring well from the GNSS sensor at the pipe orifice of the monitoring well;

[0083] S3.2: Process the data of all the internal displacement sensors in the monitoring well from top to bottom;

[0084] S3.3: Calculate the displacement of the current internal displacement sensor, superimpose it with the displacements of each internal displacement sensor above this sensor in this monitoring well, and then superimpose it with the basic displacement of the monitoring well obtained from the GNSS sensor at the pipe orifice of the monitoring well, and use it as the final internal displacement data at the position of this internal displacement sensor, and store it in the database;

[0085] S3.4: Compare and judge the final internal displacement data at this position with the warning value of each level of the single-point internal displacement designed for this tailings pond, and make a warning for a single internal displacement monitoring point according to the situation;

[0086] S3.5: Calculate the internal displacement rate of each monitoring point according to different time periods, store it in the database, and compare it with the warning values of each level of the single-point internal displacement rate designed for this tailings pond to make a judgment, and issue a warning for the single internal displacement rate according to the situation.

[0087] S3.6: Calculate the weighted average of the internal displacement rates of all monitoring points of this monitoring well to obtain the overall internal displacement rate of this monitoring well, and store it in the database.

[0088] S3.7: Subtract the overall internal displacement rate of this monitoring well from the overall internal displacement rate at the previous monitoring moment to obtain the acceleration value of the internal displacement rate of this monitoring well, and store it in the database.

[0089] S3.8: Compare and judge the internal displacement acceleration value calculated in S3.7 with the warning values of each level of the internal displacement acceleration of the monitoring well designed for this tailings pond, and issue a warning for the internal displacement acceleration according to the situation.

[0090] Furthermore, S4 includes the following steps:

[0091] S4.1: Calculate the weighted average of the external displacement rate data obtained from all external displacement monitoring points of the same cross-section according to the importance of each monitoring point to obtain the external displacement rate of the cross-section.

[0092] S4.2: Calculate the weighted average of the internal displacement rate data obtained from all internal displacement monitoring points of the same cross-section according to the importance of each monitoring point to obtain the internal displacement rate of the cross-section.

[0093] S4.3: Combine the external displacement rate and the internal displacement rate of the cross-section according to a certain weight value, calculate the dam body displacement rate data, and issue a warning according to the situation.

[0094] The deformation monitoring system further includes a real-time deformation monitoring data module and a historical monitoring data module. The real-time deformation monitoring data module transmits the data to the data preprocessing module, and the preprocessing process includes data cleaning, data integration, and data transformation. Data cleaning refers to cleaning the unreasonable parts of the monitoring data, such as the outliers in the monitoring; data integration refers to organically concentrating the data from different sensor sources and different formats logically or physically; data transformation refers to corresponding the data with different characteristics and different spaces in space and time.

[0095] The results of the data preprocessing module are combined with the historical monitoring data module to analyze and process the data in the statistical feature analysis module. Statistical feature analysis includes data association, error elimination, etc. Then, after fusing the data at multiple levels in the data multi-level fusion module, the fusion results are transmitted to the data storage and management module for management and then transmitted to the historical monitoring data module for retention and display. Considering engineering practicability and real-time performance, the present invention first performs fusion directly on the acquired raw data layer, and conducts data synthesis and analysis before the preprocessing of various sensors. Then, statistical features are extracted from the data of each sensor and fused at the feature layer. Finally, decision-level fusion of the data is performed in the safety assessment and early warning module for display and early warning.

[0096] Among them, combining engineering feasibility and real-time performance, the algorithm executed in the data multi-level fusion module is a weighted fusion scheme. Weights of the monitoring data of each sensor are determined considering the different importance and stability of the monitoring data of each sensor, and then the data are weighted and averaged to obtain the average value of each displacement amount and the average displacement rate at different unit times, which represents the displacement intensity of the tailings dam, thus better characterizing the deformation, safety, and early warning conditions of the tailings dam.

[0097] The multi-sensor based tailings dam deformation monitoring system and its data fusion method proposed in this patent can better realize the automatic monitoring of the tailings dam online, and accurately achieve the real-time performance, authenticity, stability, and accuracy of the monitoring data, so as to effectively help mining enterprises master the deformation conditions of the tailings dam and perform risk control for safe production. The multi-sensor based tailings dam deformation monitoring system and its data fusion method mainly consist of two parts: a deformation monitoring plan and a data processing algorithm. By using a variety of sensor monitoring and data processing technologies, combining macro and micro aspects, the key technical indicators for monitoring the safety of the tailings dam are monitored. Data analysis is carried out based on all historical records to assist enterprises and the government in making decisions, improving the safety guarantee level of the tailings dam, and effectively preventing and curbing the occurrence of major accidents.

[0098] The present invention mainly measures the displacement change data of the dam body, including internal displacement (internal displacement monitoring mainly focuses on the stability monitoring of the tailings inside the dam), external displacement (external displacement monitoring mainly focuses on the stability monitoring of the soil layer outside the dam body), internal displacement change rate, and external displacement change rate. At the same time, the monitoring data set is analyzed and processed, and early warnings are given for the deformation intensity and safety of the tailings dam based on the results of data processing, accurately meeting the safety production requirements of enterprises and effectively preventing and curbing the occurrence of serious and particularly serious accidents.

[0099] The present invention realizes functions such as the acquisition, processing, and integration of tailings dam deformation monitoring data. The main data includes external displacement data, internal displacement data, etc. During the software design process, improvements have been made to the acquisition and calculation methods of multiple data;

[0100] I. Application of multi-sensor multi-layer information fusion in tailings pond monitoring: The present invention preprocesses various monitoring results such as deformation data of each point in the tailings pond at different times, stores them in a database in a certain format, and conducts trend analysis on historical data at the same time, realizes multi-layer information fusion of the multi-sensor acquisition layer, data processing layer and decision-making layer, and makes early warning judgments according to the alarm thresholds of various parameters, and then judges and makes decisions on the overall deformation situation of the tailings pond, obtains a monitoring safety assessment and decision-making with better performance than a single sensor, and further stores and manages the fused data.

[0101] II. Analysis of multi-point relative displacement in deformation monitoring: Since the deformation information of a single monitoring point in deformation monitoring cannot reflect the actual shape of the overall deformation, the present invention uses the absolute numerical data of external displacement and internal displacement, analyzes the average displacement of multiple monitoring points according to the multi-point relative displacement, and calculates the average displacement rate of the monitoring points and the intensity of the displacement in time units such as hours, days, and months, so as to reflect the spatial state and time characteristics of the shape, size and position change of the deformed body from the entire monitoring period and as a whole, which is very helpful for the early warning of the monitoring system.

[0102] III. Internal displacement calculation scheme: The conventional method for internal displacement is to install multiple (3 - 15) inclinometer sensors in the monitoring well and calculate the displacement of each internal point and the comprehensive displacement from bottom to top. In this invention, the method of accumulating and calculating the change of displacement of each internal detection point from top to bottom is adopted. This can effectively reduce the installation cost, engineering difficulty, maintenance, etc. of multi-sensors, and improve the feasibility and stability of the monitoring system.

[0103] IV. Proposing a monitoring index for internal displacement acceleration: Internal displacement is an important index for monitoring the deformation of the tailings pond dam. When the internal displacement rate suddenly increases, it often indicates the risk of possible collapse. Therefore, calculating the acceleration of the internal displacement rate and making safety predictions according to the value of this index is of great significance.

[0104] V. Proposing a monitoring index for dam body displacement rate: The dam body displacement mainly includes internal displacement and external displacement, and the monitoring indexes include the absolute value of displacement, the displacement rate value, and the displacement acceleration value. On this basis, the present invention patent proposes the concept of the dam body displacement rate based on the combination of internal displacement and external displacement. The external displacement rate data obtained from all external displacement monitoring points of a certain section are weighted and averaged according to the importance of each monitoring point to obtain the external displacement rate of the section; the internal displacement rate data obtained from all internal displacement monitoring points of a certain section are weighted and averaged according to the importance of each monitoring point to obtain the internal displacement rate of the section. Then, the two are calculated according to a certain weight value to obtain the dam body section displacement rate, and then compared with the early warning values of each level for early warning.

[0105] It should also be noted that in this text, relational terms such as I, II, III, and IV are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article, or device comprising the said element.

[0106] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference may be made to each other.

Claims

1. A deformation monitoring system for a tailings pond, characterized in that: it includes a single-point monitoring module, a multi-point relative monitoring module, and an overall displacement module for monitoring the dam body of the tailings pond; Among them, the multi-point relative monitoring module is used to receive the data of the single-point monitoring module, preprocess the data, and then transfer the preprocessed data to the overall displacement module; The overall displacement module is used to perform fusion processing on the preprocessed data, and transfer the processing result to the inclination module and the bending module to comprehensively represent the deformation intensity of the tailings pond dam; The deformation monitoring system uses the absolute numerical data of external displacement and internal displacement, analyzes the average displacement of multiple monitoring points according to the multi-point relative displacement, and calculates the average displacement rate of the monitoring points and the displacement intensity under each time unit such as hour, day, month, etc., and reflects the spatial state and time characteristics of the shape, size and position change of the deformed body from the entire monitoring period and overall; Among them, the single-point monitoring module includes: An external displacement module for real-time monitoring of the displacement parameters of the surface soil layer of the tailings pond; An internal displacement module for real-time monitoring of the displacement parameters of the internal structure of the tailings pond; The internal displacement module includes an inclination displacement module. The inclination displacement module includes an inclinometer and an MCU data processing module. Each monitoring point is a vertical deep well structure. 3 to 15 inclinometer sensors are installed in each deep well. By real-time monitoring the inclination angles of each sensor, the internal inclination displacement is calculated according to the inclinometer angle, and the actual inclination displacement and the inclination displacement rate per unit time are calculated from top to bottom in combination with the GNSS sensor installed at the wellhead of the monitoring point. Then, the overall internal displacement rate of this monitoring well is subtracted from the overall internal displacement rate of the previous monitoring moment to obtain the acceleration value of the internal displacement rate of this monitoring well.

2. A deformation monitoring system for a tailings pond according to claim 1, characterized in that: The external displacement module includes a horizontal displacement module and a vertical displacement module, and the horizontal displacement module and the vertical displacement module are combined to perform real-time monitoring of the displacement parameters of the surface soil layer of the tailings pond; The external displacement module includes a GNSS displacement monitoring device and a laser communication module. The displacement of each distribution point is monitored in real time through satellites, the displacement in the three directions of the rectangular coordinate system X, Y, and H is calculated, and then it is converted into the horizontal displacement and vertical displacement in the spatial coordinate system, and the displacement rate per unit time is calculated according to the displacement.

3. A deformation monitoring system for a tailings pond according to claim 1, characterized in that: The internal displacement module processes the data of all internal displacement sensors in the monitoring well from top to bottom, calculates the displacement of the current internal displacement sensor, superimposes it with the displacements of each internal displacement sensor above this sensor in this monitoring well, and then superimposes it with the basic displacement of the monitoring well obtained by the GNSS sensor at the wellhead of the monitoring well as the final internal displacement data at the position of this internal displacement sensor.

4. A data fusion method for a deformation monitoring system for a tailings pond according to any one of claims 1 to 3, characterized in that, it includes the following steps: S1: Obtain the original data of external displacement and internal displacement from the external displacement module and the internal displacement module; S2: Process the original data obtained from the GNSS displacement monitoring device, obtain the valid data and store it in the database, and make judgment and early warning; the valid data includes horizontal displacement, vertical displacement, horizontal displacement rate, vertical displacement rate, relative displacement rate of the same-level dam, and relative displacement rate of the same cross-section; S3: Process the original data obtained from the internal displacement monitoring module, obtain the valid data and store it in the database, and make judgment and early warning. The valid data includes internal displacement data, internal displacement rate, and acceleration value of the internal displacement rate; among them, for all the internal displacement sensors in the monitoring well, the change amount of the displacement of each internal monitoring point is cumulatively calculated from top to bottom; S4: Combine the external displacement rate and the internal displacement rate of the same cross-section for early warning; S5: Delay and wait for the next calculation and early warning; S6: Go back to S1 and continue.

5. A data fusion method for a tailings pond deformation monitoring system according to claim 4, wherein, S2 includes the following steps: S2.1: Subtract the latest coordinate data from the coordinate data obtained at the previous monitoring moment to obtain the displacement changes in three directions , , ; S2.2: According to , calculate the horizontal displacement ; S2.3: Use directly as the vertical displacement; S2.4: Store the horizontal displacement and the vertical displacement into the database; S2.5: Calculate the external displacement rate according to different time periods, including horizontal displacement rate and vertical displacement rate, and store it in the database; S2.6: Compare and judge the external displacement rates of each time period calculated in S2.5 with the early warning values of each level of the external displacement rate designed for this tailings pond, and make an early warning for a single monitoring point according to the situation; S2.7: Calculate the distance between this monitoring point and the adjacent monitoring points of the same-level dam, subtract the data obtained at the previous monitoring moment, and calculate the relative displacement rate of the same-level dam in each time period according to different time periods, and store it in the database; S2.8: Calculate the distance between this monitoring point and the adjacent monitoring points of the same cross-section, subtract the data obtained at the previous monitoring moment, and calculate the relative displacement rate of the same cross-section in each time period according to different time periods, and store it in the database; S2.9: Compare and judge the relative external displacement rates of each time period calculated in S2.7 and 2.8 with the early warning values of each level of the relative displacement rates of the same-level dam and the same cross-section designed for this tailings pond, and make a relative displacement early warning according to the situation.

6. A data fusion method for a tailings pond deformation monitoring system according to claim 4, wherein, S3 includes the following steps: S3.1: Obtain the basic displacement amount of this monitoring well from the GNSS sensor at the pipe orifice of the monitoring well; S3.2: Process the data of all the internal displacement sensors in the monitoring well from top to bottom; S3.3: Calculate the displacement amount of the current internal displacement sensor, superimpose it with the displacement amounts of each internal displacement sensor above this sensor in this monitoring well, and then superimpose it with the basic displacement amount of the monitoring well obtained from the GNSS sensor at the pipe orifice of the monitoring well, and use it as the final internal displacement data at the position of the internal displacement sensor at this position and store it in the database; S3.4: Compare and judge the final internal displacement data at this position with the early warning values of each level of the single-point internal displacement designed for this tailings pond, and make an early warning for a single internal displacement monitoring point according to the situation; S3.5: Calculate the internal displacement rate of each monitoring point according to different time periods, store it in the database, and compare it with the warning values of each level of the single-point internal displacement rate designed for this tailings pond to make a judgment, and issue a single internal displacement rate warning according to the situation; S3.6: Weight-average the internal displacement rates of all monitoring points of this monitoring well to obtain the overall internal displacement rate of this monitoring well, and store it in the database; S3.7: Subtract the overall internal displacement rate of this monitoring well from the overall internal displacement rate at the previous monitoring moment to obtain the acceleration value of the internal displacement rate of this monitoring well, and store it in the database; S3.8: Compare and judge the internal displacement acceleration value calculated in S3.7 with the warning values of each level of the internal displacement acceleration of the monitoring well designed for this tailings pond, and issue an internal displacement acceleration warning according to the situation.

7. According to the data fusion method of a tailings pond deformation monitoring system described in claim 4, characterized in that, S4 includes the following steps: S4.1: Weight-average the external displacement rate data obtained from all external displacement monitoring points of the same cross-section according to the importance of each monitoring point to obtain the external displacement rate of the cross-section; S4.2: Weight-average the internal displacement rate data obtained from all internal displacement monitoring points of the same cross-section according to the importance of each monitoring point to obtain the internal displacement rate of the cross-section; S4.3: Combine the external displacement rate and the internal displacement rate of the cross-section according to certain weights, calculate the dam body displacement rate data, and issue a warning according to the situation.

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