CN110906859A - Tailing pond deformation monitoring system and data fusion method thereof - Google Patents
Tailing pond deformation monitoring system and data fusion method thereof Download PDFInfo
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Abstract
The invention discloses a tailing pond deformation monitoring system and a data fusion method thereof, and the invention adopts an advanced technical method, adopts a single-point monitoring module, a multipoint relative monitoring module and an integral displacement module for monitoring a tailing pond dam body, and measures the displacement change data of the dam body: inside displacement, outside displacement, inside displacement change rate, outside displacement change rate simultaneously carry out analysis and processing to the monitoring data is concentrated, come to carry out early warning in advance to tailing storehouse deformation strength and security through the result to data processing, definitely satisfy the enterprise's safety in production demand, effectively take precautions against and restrain the very big occurence of failure.
Description
Technical Field
The invention relates to the technical field of safety monitoring of a tailing pond, in particular to a tailing pond deformation monitoring system and a data fusion method thereof.
Background
China has abundant mineral resource types, and partial mineral reserves are in the top of the world. However, the mineral resources in China are generally characterized by more lean ores, less rich ores, more polymetallic ores, less single ore types, generally low metal grade, more solid wastes formed in the process of mining and selection and generation of a large number of mining storehouses. The development of mining industry makes great contribution to the economic development of China, but also brings serious environmental pollution, so that mining enterprises and society face severe environmental comprehensive treatment problems.
The development and utilization of mines often generate a large amount of tailings, and at present, no economic and effective treatment method for the tailings of the dressing plant exists. However, the tailings often contain various toxic and harmful substances, and the direct discharge causes serious pollution and ecological damage to the air, underground water and soil around the mine. Therefore, the tailing pond formed by damming and intercepting the valley opening or surrounding land is used as a place for discharging tailings or other industrial waste residues after ore sorting of the metal or nonmetal mines in stockpiling is very important.
At present, most tailings are stored in a tailing pond near a mining area, the tailing pond becomes an artificial debris flow dangerous source with high potential energy along with the change of conditions such as weathering and the like around the tailing pond, once a dam break accident occurs, debris flow with huge destructive power flows to a downstream area, and great harm is caused to living environments of human beings, such as surrounding air, underground water, soil, vegetation and ecological environments. Therefore, the safety monitoring of the tailing pond has important significance for monitoring the safety of the tailing pond, grasping the current safety situation of the tailing pond, reducing the occurrence rate of accidents of the tailing pond and the like.
Currently, main technical parameters of safe operation of the tailings reservoir in China, such as dam body deformation displacement, reservoir water level, infiltration line burial depth and the like, are measured on site by using a traditional instrument manually at regular intervals, the safety monitoring workload is large, and the system error and the manual error are large under the influence of many factors such as weather, manual work, site conditions and the like. Meanwhile, the manual monitoring has the characteristics that various technical parameters of the tailing pond cannot be monitored in time, various safety technical indexes of the tailing pond are difficult to master in time, and the like, and the safety production and management level of the tailing pond are affected. Therefore, the mine safety production in China urgently needs a real-time and automatic monitoring technology and system for a tailing pond.
The on-line monitoring system of the tailing pond utilizes a sensor technology, a signal transmission technology, a computer graphic image processing technology, a network technology and a software engineering technology to monitor various key technical indexes influencing the safety of the tailing pond and a dam body from the comprehensive angle of combining macroscopicity, microcosmicity, theory and practice, and analyzes the future trend according to the recorded historical data and the existing real-time data so as to assist enterprises and governments in making decisions, improve the safety guarantee level of the tailing pond and effectively prevent and restrain serious accidents.
At present, the relatively common tailing pond monitoring method mainly analyzes monitoring data of a plurality of single points. First, the monitoring data of some single points is not comprehensive enough. Secondly, the monitoring result has different errors according to different monitoring means. In addition, the deformation and the strength of the tailings pond cannot be accurately grasped on the whole by the monitoring result. Finally, the danger caused by the deformation of the tailings pond which is more desirable to know is difficult to obtain, or whether the tailings pond is safe or not.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the invention provides a tailing pond deformation monitoring system and a data fusion method thereof, which mainly measure the displacement change data of a dam body, analyze and process the internal displacement, the external displacement, the internal displacement change rate and the external displacement change rate in a centralized manner, and early warn the deformation strength and the safety of a tailing pond in advance through the data processing result, thereby exactly meeting the safety production requirements of enterprises and effectively preventing and restraining the occurrence of serious accidents.
In order to solve the problems, the technical scheme adopted by the invention is as follows: a deformation monitoring system for a tailing pond comprises a single-point monitoring module, a multi-point relative monitoring module and an overall displacement module, wherein the single-point monitoring module, the multi-point relative monitoring module and the overall displacement module are used for monitoring a dam body of the tailing pond;
the multipoint relative monitoring module is used for receiving the data of the single-point monitoring module, preprocessing the data and transmitting the preprocessed data to the integral displacement module;
the integral displacement module is used for carrying out fusion processing on the preprocessed data and transmitting the processing result to the inclination module and the bending module to combine and represent the deformation strength of the tailing dam;
the single point monitoring module comprises:
the external displacement module is used for monitoring displacement parameters of the soil layer on the surface of the tailing pond in real time;
and the internal displacement module is used for monitoring the internal structure displacement parameters of the tailing pond in real time.
Furthermore, the external displacement module comprises a horizontal displacement module and a vertical displacement module, and the horizontal displacement module and the vertical displacement module are combined to be used for monitoring the displacement parameters of the soil layer on the surface of the tailing pond in real time;
the external displacement module comprises a GNSS displacement monitoring device and a laser communication module, displacement of each distribution point is monitored in real time through a satellite, displacement in three directions of the rectangular coordinate system X, Y, H is calculated, then horizontal displacement and vertical displacement in a space coordinate system are converted, and displacement rate in each unit time is calculated according to the displacement.
Further, the inside displacement module includes the displacement of tilt module, and the displacement of tilt module includes inclinometer and MCU data processing module, and each monitoring point is vertical deep well structure, installs 3~15 inclinometer sensors in every deep well, through the inclination of each sensor of real-time supervision, calculates inside displacement of tilt according to the inclinometer contained angle to combine the GNSS sensor of monitoring point well head installation to calculate actual displacement of tilt and the displacement of tilt rate of each unit interval from top to bottom.
A data fusion method of a tailing pond deformation monitoring system comprises the following steps:
s1: acquiring original data of the external displacement and the internal displacement from the external displacement module and the internal displacement module;
s2: processing original data obtained from a GNSS external displacement monitoring device, obtaining effective data, storing the effective data in a database, and performing judgment and early warning;
s3: processing the original data obtained from the internal displacement monitoring module, obtaining effective data, storing the effective data into a database, and performing judgment and early warning;
s4: early warning is combined on the external displacement rate and the internal displacement rate of the same section;
s5: delaying, and waiting for the next calculation early warning;
s6: transition to S1 continues.
Further, S2 includes the steps of:
s2.1: subtracting the latest coordinate data from the coordinate data obtained at the last monitoring moment to obtain displacement variation in three directions
、
、
；
S2.2: according to
、
Calculating horizontal displacement
；
S2.3: will be provided with
Directly as a vertical displacement;
s2.4: will be horizontally displaced
And vertical displacement
Storing the data into a database;
s2.5: calculating external displacement rates including a horizontal displacement rate and a vertical displacement rate according to different time periods, and storing the external displacement rates into a database;
s2.6: comparing and judging the external displacement rate calculated in the S2.5 in each time period with early warning values of each grade of the external displacement rate designed by the tailing pond, and early warning a single monitoring point according to the condition;
s2.7: calculating the distance between the monitoring point and the adjacent monitoring point of the same dam, subtracting the data obtained at the previous monitoring moment, calculating the relative displacement rate of the same dam in each time period according to different time periods, and storing the relative displacement rate into a database;
s2.8: calculating the distance between the monitoring point and the adjacent monitoring point on the same section, subtracting the data obtained at the last monitoring moment, calculating the relative displacement rate of the same section in each time period according to different time periods, and storing the relative displacement rate in a database;
s2.9: and comparing and judging the relative external displacement rate of each time period calculated in the S2.7 and the S2.8 with early warning values of each grade of relative displacement rate of the same grade dam and the same section designed in the tailing pond, and giving out relative displacement early warning according to conditions.
Further, S3 includes the steps of:
s3.1: acquiring basic displacement of the monitoring well from a GNSS sensor at the pipe orifice of the monitoring well;
s3.2: processing data of all internal displacement sensors in the monitoring well from top to bottom;
s3.3: calculating the displacement of the current internal displacement sensor, superposing the displacement with the displacement of each internal displacement sensor above the sensor in the monitoring well, superposing the basic displacement of the monitoring well obtained by the GNSS sensor at the pipe orifice of the monitoring well, and storing the final internal displacement data on the internal displacement sensor at the position in a database;
s3.4: comparing and judging the final internal displacement data on the position with early warning values of all levels of single-point internal displacement designed by the tailing pond, and performing early warning on a single internal displacement monitoring point according to conditions;
s3.5: calculating the internal displacement rate of each monitoring point according to different time periods, storing the internal displacement rate into a database, comparing and judging the internal displacement rate with early warning values of each grade of the internal displacement rate of a single point designed by the tailing pond, and early warning the internal displacement rate of each monitoring point according to conditions;
s3.6: carrying out weighted average on the internal displacement rates of all monitoring points of the monitoring well to obtain the overall internal displacement rate of the monitoring well, and storing the overall internal displacement rate into a database;
s3.7: subtracting the integral internal displacement rate of the monitoring well from the integral internal displacement rate at the last monitoring moment to obtain an acceleration value of the integral internal displacement rate of the monitoring well, and storing the acceleration value into a database;
s3.8: and (4) comparing and judging the internal displacement acceleration value calculated in the S3.7 with each grade of early warning value of the internal displacement acceleration of the monitoring well designed in the tailing pond, and making internal displacement acceleration early warning according to the condition.
Further, S4 includes the steps of:
s4.1: weighting and averaging the external displacement rate data obtained by all external displacement monitoring points of the same section according to the importance degree of each monitoring point to obtain the external displacement rate of the section;
s4.2: weighting and averaging the internal displacement rate data obtained by all internal displacement monitoring points of the same section according to the importance degree of each monitoring point to obtain the internal displacement rate of the section;
s4.3: and combining the external displacement rate and the internal displacement rate of the section according to a certain weight, calculating dam body displacement rate data, and performing early warning according to the condition.
The invention adopts an advanced technical method, combines macroscopical and microscopic aspects to monitor key technical indexes of the safety of the tailing pond, performs data analysis according to all historical records, assists enterprise and government decisions, improves the safety guarantee level of the tailing pond, and effectively prevents and restrains major accidents.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a deformation monitoring system for a tailings pond of the present invention;
FIG. 2 is a schematic structural view of the external displacement section of the present invention;
FIG. 3 is a schematic structural view of the internal displacement section of the present invention;
the labels in the figure are: the method comprises the following steps of 1-a tailing pond deformation monitoring system, 2-a single-point monitoring module, 3-a multipoint relative monitoring module, 4-an external displacement module, 5-an internal displacement module, 6-an overall displacement module, 7-a horizontal displacement module, 8-a vertical displacement module, 9-an inclined displacement module, 10-an inclined module and 11-a bending module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a system for monitoring deformation of a tailing pond 1 comprises a single-point monitoring module 2, a multi-point relative monitoring module 3 and an overall displacement module 6, wherein the single-point monitoring module 2, the multi-point relative monitoring module 3 and the overall displacement module are used for monitoring a dam body of the tailing pond;
the multipoint relative monitoring module 3 is used for receiving the data of the single-point monitoring module 2, preprocessing the data and then transmitting the preprocessed data to the integral displacement module 6;
the integral displacement module 6 is used for carrying out fusion processing on the preprocessed data and transmitting the processing result to the inclination module 10 and the bending module 11 to combine and represent the deformation strength of the tailing dam;
the single-point monitoring module 2 includes:
the external displacement module 4 is used for monitoring displacement parameters of the soil layer on the surface of the tailing pond in real time;
and the internal displacement module 5 is used for monitoring the internal structure displacement parameters of the tailing pond in real time.
Further, the external displacement module 4 comprises a horizontal displacement module 7 and a vertical displacement module 8, and the horizontal displacement module 7 and the vertical displacement module 8 are combined to monitor displacement parameters of the soil layer on the surface of the tailing pond in real time;
the external displacement module 4 comprises a GNSS displacement monitoring device and a laser communication module, and is configured to monitor displacement of each distribution point in real time through a satellite, calculate displacements in three directions of the rectangular coordinate system X, Y, H, convert the displacements into horizontal displacement and vertical displacement in a spatial coordinate system, and calculate displacement rate of each unit time according to the displacements, where an external displacement cross-sectional view is shown in fig. 2;
furthermore, the internal displacement module 5 comprises an inclined displacement module 9, the inclined displacement module 9 comprises an inclinometer and an MCU data processing module, each monitoring point is of a vertical deep well structure, 3-15 inclinometer sensors are installed in each deep well, the inclination angle of each sensor is monitored in real time, the internal inclined displacement is calculated according to the included angle of the inclinometer, the actual inclined displacement and the inclined displacement rate of each unit time are calculated from top to bottom by combining GNSS sensors installed at the well mouths of the monitoring points, and the internal displacement profile is shown in FIG. 3;
a data fusion method of a tailing pond deformation monitoring system comprises the following steps:
s1: acquiring original data of the external displacement and the internal displacement from the external displacement module 4 and the internal displacement module 5;
s2: processing original data obtained from a GNSS external displacement monitoring device, obtaining effective data, storing the effective data in a database, and performing judgment and early warning;
s3: processing the original data obtained from the internal displacement monitoring module, obtaining effective data, storing the effective data into a database, and performing judgment and early warning;
s4: early warning is combined on the external displacement rate and the internal displacement rate of the same section;
s5: delaying, and waiting for the next calculation early warning;
s6: transition to S1 continues.
Further, S2 includes the steps of:
s2.1: subtracting the latest coordinate data from the coordinate data obtained at the last monitoring moment to obtain displacement variation in three directions
、
、
；
S2.2: according to
、
Calculating horizontal displacement
；
S2.3: will be provided with
Directly as a vertical displacement;
s2.4: will be horizontally displaced
And vertical displacement
Storing the data into a database;
s2.5: calculating external displacement rates including a horizontal displacement rate and a vertical displacement rate according to different time periods, and storing the external displacement rates into a database;
s2.6: comparing and judging the external displacement rate calculated in the S2.5 in each time period with early warning values of each grade of the external displacement rate designed by the tailing pond, and early warning a single monitoring point according to the condition;
s2.7: calculating the distance between the monitoring point and the adjacent monitoring point of the same dam, subtracting the data obtained at the previous monitoring moment, calculating the relative displacement rate of the same dam in each time period according to different time periods, and storing the relative displacement rate into a database;
s2.8: calculating the distance between the monitoring point and the adjacent monitoring point on the same section, subtracting the data obtained at the last monitoring moment, calculating the relative displacement rate of the same section in each time period according to different time periods, and storing the relative displacement rate in a database;
s2.9: and comparing and judging the relative external displacement rate of each time period calculated in the S2.7 and the S2.8 with early warning values of each grade of relative displacement rate of the same grade dam and the same section designed in the tailing pond, and giving out relative displacement early warning according to conditions.
Further, S3 includes the steps of:
s3.1: acquiring basic displacement of the monitoring well from a GNSS sensor at the pipe orifice of the monitoring well;
s3.2: processing data of all internal displacement sensors in the monitoring well from top to bottom;
s3.3: calculating the displacement of the current internal displacement sensor, superposing the displacement with the displacement of each internal displacement sensor above the sensor in the monitoring well, superposing the basic displacement of the monitoring well obtained by the GNSS sensor at the pipe orifice of the monitoring well, and storing the final internal displacement data on the internal displacement sensor at the position in a database;
s3.4: comparing and judging the final internal displacement data on the position with early warning values of all levels of single-point internal displacement designed by the tailing pond, and performing early warning on a single internal displacement monitoring point according to conditions;
s3.5: calculating the internal displacement rate of each monitoring point according to different time periods, storing the internal displacement rate into a database, comparing and judging the internal displacement rate with early warning values of each grade of the internal displacement rate of a single point designed by the tailing pond, and early warning the internal displacement rate of each monitoring point according to conditions;
s3.6: carrying out weighted average on the internal displacement rates of all monitoring points of the monitoring well to obtain the overall internal displacement rate of the monitoring well, and storing the overall internal displacement rate into a database;
s3.7: subtracting the integral internal displacement rate of the monitoring well from the integral internal displacement rate at the last monitoring moment to obtain an acceleration value of the integral internal displacement rate of the monitoring well, and storing the acceleration value into a database;
s3.8: and (4) comparing and judging the internal displacement acceleration value calculated in the S3.7 with each grade of early warning value of the internal displacement acceleration of the monitoring well designed in the tailing pond, and making internal displacement acceleration early warning according to the condition.
Further, S4 includes the steps of:
s4.1: weighting and averaging the external displacement rate data obtained by all external displacement monitoring points of the same section according to the importance degree of each monitoring point to obtain the external displacement rate of the section;
s4.2: weighting and averaging the internal displacement rate data obtained by all internal displacement monitoring points of the same section according to the importance degree of each monitoring point to obtain the internal displacement rate of the section;
s4.3: and combining the external displacement rate and the internal displacement rate of the section according to a certain weight, calculating dam body displacement rate data, and performing early warning according to the condition.
The deformation monitoring system further comprises a real-time deformation monitoring data module and a historical monitoring data module, the real-time deformation monitoring data module transmits data to the data preprocessing module, and the preprocessing process comprises data cleaning, data integration and data transformation. Data cleaning refers to cleaning unreasonable parts of monitoring data, such as monitored outliers; data integration means that data of different sensor sources and different formats are logically or physically organically integrated; data transformation refers to the spatial-temporal correspondence of data with different characteristics and different spaces.
The result of the data preprocessing module is combined with the historical monitoring data module to analyze and process the data in the statistical characteristic analysis module, and the statistical characteristic analysis comprises data association, error elimination and the like. And then fusing a plurality of layers of data in a data multilayer fusion module, transmitting the fusion result to a data storage management module for management, and transmitting the fusion result to a historical monitoring data module for reserved display. The invention considers the engineering practicability and real-time property, firstly, the fusion is directly carried out on the collected original data layer, and the data is integrated and analyzed before various sensors are preprocessed. Then, data from each sensor is subjected to statistical feature extraction and fusion processing in a feature layer, and finally, decision layer fusion is carried out on the data in a safety assessment early warning module, and then display and early warning are carried out.
The method comprises the steps of combining engineering feasibility and real-time performance, setting a weight of monitoring data of each sensor by taking different importance and stability of the monitoring data of each sensor into consideration, carrying out weighted averaging on the data, calculating an average value of each displacement and average displacement rate of each displacement in different unit time, and representing the displacement intensity of a dam body of a tailing pond, so that deformation, safety and early warning conditions of the dam body of the tailing pond are represented better.
The multi-sensor-based tailing pond deformation monitoring system and the data fusion method thereof can better realize online monitoring automation monitoring of the tailing pond, exactly achieve real-time performance, authenticity, stability and accuracy of monitoring data, effectively master deformation conditions of the tailing pond for mine enterprises, and conduct risk management and control on safety production. The deformation monitoring system based on the multi-sensor tailing pond and the data fusion method thereof mainly comprise two parts, namely a deformation monitoring scheme and a data processing algorithm, a plurality of sensor monitoring and data processing technologies are utilized, the macroscopical and microscopic combination is adopted to monitor key technical indexes of tailing pond safety, data analysis is carried out according to all historical records, enterprise and government decision making is assisted, the safety guarantee level of the tailing pond is improved, and major accidents are effectively prevented and restrained.
The method mainly measures the displacement change data of the dam body, analyzes and processes the monitored data in a centralized manner, and performs early warning on the deformation strength and the safety of the tailing pond according to the data processing result, thereby accurately meeting the safety production requirements of enterprises and effectively preventing and restraining the occurrence of serious accidents.
The invention realizes the functions of acquiring, processing, integrating and the like of the deformation monitoring data of the tailing pond, the main data comprises external displacement data, internal displacement data and the like, and the acquisition and calculation methods of a plurality of data are improved in the software design process;
firstly, the application of multi-sensor multi-layer information fusion in tailing pond monitoring; the invention preprocesses various monitoring results such as measured deformation data of various points of the tailing pond at different times, stores the monitoring results in the database according to a certain format, analyzes the trend of historical data, realizes the multi-layer information fusion of a multi-sensor acquisition layer, a data processing layer and a decision layer, carries out early warning judgment according to the alarm threshold value of each parameter, further judges and decides the whole deformation condition of the tailing pond, obtains better monitoring safety evaluation and decision compared with the performance of a single sensor, and further stores and manages the fused data.
Secondly, analyzing the relative displacement of multiple points in deformation monitoring; because the deformation information of a single monitoring point in the deformation monitoring can not reflect the actual shape of the whole deformation, the invention adopts absolute numerical data of external displacement and internal displacement, analyzes the average displacement of a plurality of monitoring points according to the relative displacement of a plurality of points, calculates the average displacement rate of the monitoring points and the displacement intensity in each time unit according to the time units of hours, days, months and the like, reflects the space state and the time characteristics of the change of the shape, the size and the position of the deformation body in the whole monitoring period and the whole and is greatly helpful for the early warning of a monitoring system.
Thirdly, calculating the internal displacement; the conventional method for internal displacement is to install a plurality of (3-15) inclinometer sensors in a monitoring well and calculate each internal point and the comprehensive displacement from bottom to top. Therefore, the installation cost, the engineering difficulty, the maintenance and the like of the multi-sensor can be effectively reduced, and the feasibility and the stability of the monitoring system are improved.
Fourthly, providing monitoring indexes of internal displacement acceleration; the internal displacement is an important index for monitoring deformation of a tailing reservoir dam body, and when the internal displacement rate is suddenly increased, the risk of collapse is often predicted, so that the acceleration of the internal displacement rate is calculated, and the safety prediction is performed according to the value, so that the important significance is achieved.
Fifthly, providing a monitoring index of the dam displacement rate; the dam displacement mainly comprises internal displacement and external displacement, and the monitoring indexes comprise a displacement absolute value, a displacement speed value and a displacement acceleration value. On the basis, the invention provides a dam body displacement rate concept based on the combination of internal displacement and external displacement, external displacement rate data obtained by all external displacement monitoring points of a certain section are weighted and averaged according to the importance degree of each monitoring point, and the external displacement rate of the section is obtained; and weighting and averaging the internal displacement rate data obtained by all internal displacement monitoring points of a certain section according to the importance degree of each monitoring point to obtain the internal displacement rate of the section. And calculating the displacement rate of the dam body section according to a certain weight value and comparing the displacement rate with the early warning values of all levels to perform early warning.
It is further understood that the use of relational terms such as i, ii, iii, iv, and the like may be used solely to distinguish one from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
Claims (7)
1. A tailing storehouse deformation monitoring system which characterized in that: the system comprises a single-point monitoring module, a multi-point relative monitoring module and an integral displacement module, wherein the single-point monitoring module, the multi-point relative monitoring module and the integral displacement module are used for monitoring a dam body of a tailing pond;
the multipoint relative monitoring module is used for receiving the data of the single-point monitoring module, preprocessing the data and transmitting the preprocessed data to the integral displacement module;
the integral displacement module is used for carrying out fusion processing on the preprocessed data and transmitting the processing result to the inclination module and the bending module to combine and represent the deformation strength of the tailing dam;
the single point monitoring module comprises:
the external displacement module is used for monitoring displacement parameters of the soil layer on the surface of the tailing pond in real time;
and the internal displacement module is used for monitoring the internal structure displacement parameters of the tailing pond in real time.
2. The tailings pond deformation monitoring system of claim 1, wherein: the external displacement module comprises a horizontal displacement module and a vertical displacement module, and the horizontal displacement module and the vertical displacement module are combined to monitor the displacement parameters of the soil layer on the surface of the tailing pond in real time;
the external displacement module comprises a GNSS displacement monitoring device and a laser communication module, displacement of each distribution point is monitored in real time through a satellite, displacement in three directions of the rectangular coordinate system X, Y, H is calculated, then horizontal displacement and vertical displacement in a space coordinate system are converted, and displacement rate in each unit time is calculated according to the displacement.
3. The tailings pond deformation monitoring system of claim 1, wherein: the inside displacement module includes the displacement of tilt module, and the displacement of tilt module includes inclinometer and MCU data processing module, and each monitoring point is vertical deep well structure, installs 3~15 inclinometer sensors in every deep well, through the inclination of each sensor of real-time supervision, calculates inside displacement of tilt according to the inclinometer contained angle to combine the GNSS sensor of monitoring point well head installation to calculate actual displacement of tilt and the displacement of tilt speed of each unit interval from top to bottom.
4. The data fusion method for the tailings pond deformation monitoring system according to any one of claims 1 to 3, comprising the following steps:
s1: acquiring original data of the external displacement and the internal displacement from the external displacement module and the internal displacement module;
s2: processing original data obtained from a GNSS displacement monitoring device, obtaining effective data, storing the effective data into a database, and performing judgment and early warning;
s3: processing the original data obtained from the internal displacement monitoring module, obtaining effective data, storing the effective data into a database, and performing judgment and early warning;
s4: early warning is combined on the external displacement rate and the internal displacement rate of the same section;
s5: delaying, and waiting for the next calculation early warning;
s6: transition to S1 continues.
5. The data fusion method for the tailings pond deformation monitoring system according to claim 4, wherein the S2 comprises the following steps:
s2.1: subtracting the latest coordinate data from the coordinate data obtained at the last monitoring moment to obtain displacement variation in three directions
、
、
；
S2.2: according to
、
Calculating horizontal displacement
；
S2.3: will be provided with
Directly as a vertical displacement;
s2.4: will be horizontally displaced
And vertical displacement
Storing the data into a database;
s2.5: calculating external displacement rates including a horizontal displacement rate and a vertical displacement rate according to different time periods, and storing the external displacement rates into a database;
s2.6: comparing and judging the external displacement rate calculated in the S2.5 in each time period with early warning values of each grade of the external displacement rate designed by the tailing pond, and early warning a single monitoring point according to the condition;
s2.7: calculating the distance between the monitoring point and the adjacent monitoring point of the same dam, subtracting the data obtained at the previous monitoring moment, calculating the relative displacement rate of the same dam in each time period according to different time periods, and storing the relative displacement rate into a database;
s2.8: calculating the distance between the monitoring point and the adjacent monitoring point on the same section, subtracting the data obtained at the last monitoring moment, calculating the relative displacement rate of the same section in each time period according to different time periods, and storing the relative displacement rate in a database;
s2.9: and comparing and judging the relative external displacement rate of each time period calculated in the S2.7 and the S2.8 with early warning values of each grade of relative displacement rate of the same grade dam and the same section designed in the tailing pond, and giving out relative displacement early warning according to conditions.
6. The data fusion method for the tailings pond deformation monitoring system according to claim 4, wherein the S3 comprises the following steps:
s3.1: acquiring basic displacement of the monitoring well from a GNSS sensor at the pipe orifice of the monitoring well;
s3.2: processing data of all internal displacement sensors in the monitoring well from top to bottom;
s3.3: calculating the displacement of the current internal displacement sensor, superposing the displacement with the displacement of each internal displacement sensor above the sensor in the monitoring well, superposing the basic displacement of the monitoring well obtained by the GNSS sensor at the pipe orifice of the monitoring well, and storing the final internal displacement data on the internal displacement sensor at the position in a database;
s3.4: comparing and judging the final internal displacement data on the position with early warning values of all levels of single-point internal displacement designed by the tailing pond, and performing early warning on a single internal displacement monitoring point according to conditions;
s3.5: calculating the internal displacement rate of each monitoring point according to different time periods, storing the internal displacement rate into a database, comparing and judging the internal displacement rate with early warning values of each grade of the internal displacement rate of a single point designed by the tailing pond, and early warning the internal displacement rate of each monitoring point according to conditions;
s3.6: carrying out weighted average on the internal displacement rates of all monitoring points of the monitoring well to obtain the overall internal displacement rate of the monitoring well, and storing the overall internal displacement rate into a database;
s3.7: subtracting the integral internal displacement rate of the monitoring well from the integral internal displacement rate at the last monitoring moment to obtain an acceleration value of the integral internal displacement rate of the monitoring well, and storing the acceleration value into a database;
s3.8: and (4) comparing and judging the internal displacement acceleration value calculated in the S3.7 with each grade of early warning value of the internal displacement acceleration of the monitoring well designed in the tailing pond, and making internal displacement acceleration early warning according to the condition.
7. The data fusion method for the tailings pond deformation monitoring system according to claim 4, wherein the S4 comprises the following steps:
s4.1: weighting and averaging the external displacement rate data obtained by all external displacement monitoring points of the same section according to the importance degree of each monitoring point to obtain the external displacement rate of the section;
s4.2: weighting and averaging the internal displacement rate data obtained by all internal displacement monitoring points of the same section according to the importance degree of each monitoring point to obtain the internal displacement rate of the section;
s4.3: and combining the external displacement rate and the internal displacement rate of the section according to a certain weight, calculating dam body displacement rate data, and performing early warning according to the condition.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111504238A (en) * | 2020-04-29 | 2020-08-07 | 河南柴油机重工有限责任公司 | Micro-amplitude displacement testing method and device for vibration isolation device in diesel engine running state |
| CN111964621A (en) * | 2020-08-14 | 2020-11-20 | 洛阳理工学院 | Layout method of displacement monitoring points in tailing pond based on dangerous sliding arc |
| CN113309990A (en) * | 2021-05-28 | 2021-08-27 | 深圳四维集思技术服务有限公司 | Pipeline detection early warning method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111504238A (en) * | 2020-04-29 | 2020-08-07 | 河南柴油机重工有限责任公司 | Micro-amplitude displacement testing method and device for vibration isolation device in diesel engine running state |
| CN111504238B (en) * | 2020-04-29 | 2021-12-03 | 河南柴油机重工有限责任公司 | Micro-amplitude displacement testing method and device for vibration isolation device in diesel engine running state |
| CN111964621A (en) * | 2020-08-14 | 2020-11-20 | 洛阳理工学院 | Layout method of displacement monitoring points in tailing pond based on dangerous sliding arc |
| CN113309990A (en) * | 2021-05-28 | 2021-08-27 | 深圳四维集思技术服务有限公司 | Pipeline detection early warning method and system |
| CN113309990B (en) * | 2021-05-28 | 2023-01-03 | 深圳四维集思技术服务有限公司 | Pipeline detection early warning method and system |
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