Method and device for automatic monitoring of vertical displacement of earth-rock dam

Abstract

The application provides a kind of earth-rock dam vertical displacement automated monitoring method and device, it is related to water conservancy and hydropower engineering safety technical field, method includes: for each intelligent measuring station in n intelligent measuring stations for monitoring earth-rock dam vertical displacement, according to the first elevation of the steel pipe target buried in bedrock, the elevation of intelligent measuring station is corrected, and the second elevation of intelligent measuring station is obtained;p observation values obtained by n intelligent measuring stations on m prism monitoring points on earth-rock dam body are acquired, and each observation value represents the observation height difference between a single intelligent measuring station and a corresponding single prism monitoring point;observation value vector is constructed according to p observation values, and the elevation of m prism monitoring points is taken as unknown parameter vector;unknown parameter vector is solved according to observation value vector, and vector solution is obtained, vector solution includes m target elevations corresponding to m prism monitoring points one by one;the vertical displacement change of earth-rock dam is analyzed according to m target elevations.

Description

Technical Field

[0001] This invention relates to the field of safety technology in water conservancy and hydropower engineering, and in particular to an automated monitoring method and device for the vertical displacement of earth-rock dams. Background Technology

[0002] Vertical displacement (settlement) of earth-rock dams is a core indicator reflecting their structural safety and stability, requiring precise monitoring to understand the dam's safety status. Currently, vertical displacement monitoring of earth-rock dams mainly employs methods such as leveling, trigonometric leveling, GPS measurement, and various automated monitoring instruments. Among these, leveling offers high accuracy but is labor-intensive and inefficient, making real-time automated monitoring difficult; GPS measurement is significantly affected by satellite signals and weather, resulting in relatively low elevation accuracy; traditional trigonometric leveling, while efficient, suffers from poor benchmark stability, significant temperature influence, and complex data processing. With the development of automation technology, surveying robots (automatic total stations) are increasingly being applied to vertical displacement monitoring, but current technologies still require manual intervention for benchmark verification, data acquisition, and processing, failing to achieve full automation of the vertical displacement monitoring process. Furthermore, the accuracy is low and real-time performance is poor, making it difficult to meet the needs of intelligent safety management of earth-rock dams. Summary of the Invention

[0003] This invention provides an automated monitoring method and device for the vertical displacement of earth-rock dams, which solves the problems of low accuracy and poor real-time performance caused by manual intervention in the monitoring of vertical displacement of dams in related technologies.

[0004] In a first aspect, embodiments of the present invention provide an automated monitoring method for the vertical displacement of an earth-rock dam, the method comprising: For each of the n intelligent monitoring stations monitoring the vertical displacement of the earth-rock dam, the elevation of the intelligent monitoring station is corrected based on the first elevation of the steel pipe marker buried in the bedrock to obtain the second elevation of the intelligent monitoring station; Obtain p observation values ​​obtained by the n intelligent monitoring stations observing m prism monitoring points on the earth-rock dam body, where each observation value represents the observation height difference between a single intelligent monitoring station and the corresponding single prism monitoring point; An observation value vector is constructed based on the p observation values, and the elevations of the m prism monitoring points are used as an unknown parameter vector. The unknown parameter vector is solved by the observed value vector to obtain a vector solution, which includes m target elevations corresponding one-to-one with the m prism monitoring points. The vertical displacement of the earth-rock dam is analyzed based on the m target elevations.

[0005] Optionally, before correcting the elevation of the intelligent station based on the first elevation of the steel pipe marker buried in the bedrock, the method further includes: obtaining a first elevation difference between the top of the steel pipe marker buried in the bedrock at the current moment and the top of the aluminum pipe marker adjacent to the steel pipe marker; determining the first elevation of the steel pipe marker based on the principle of thermal expansion, combined with the thermal expansion coefficient of the steel pipe marker, the thermal expansion coefficient of the aluminum pipe marker, and the first elevation difference, wherein the first elevation of the steel pipe marker is the elevation of the steel pipe marker at the current moment.

[0006] Optionally, the step of correcting the elevation of the intelligent measuring station based on the first elevation of the steel pipe marker buried in the bedrock to obtain the second elevation of the intelligent measuring station includes: obtaining the original elevation difference between the top of the steel pipe marker and the intelligent measuring station at an initial moment, and the real-time elevation difference between the top of the steel pipe marker and the intelligent measuring station at the current moment; determining the station settlement between the initial moment and the current moment based on the original elevation difference and the real-time elevation difference; and determining the second elevation of the intelligent measuring station based on the first elevation of the steel pipe marker and the station settlement, wherein the second elevation of the intelligent measuring station is the elevation of the intelligent measuring station at the current moment.

[0007] Optionally, obtaining p observation values ​​from the n intelligent monitoring stations observing m prism monitoring points on the earth-rock dam body includes: each of the p observation values ​​is obtained by the following steps: obtaining meteorological data of the area where the dam body is located, and the hypotenuse length between the intelligent monitoring station and the corresponding prism monitoring point; correcting the hypotenuse length using the meteorological data to obtain the corrected hypotenuse length; determining the one-way observation height difference between the intelligent monitoring station and the prism monitoring point based on the corrected hypotenuse length and the observation angle of the intelligent monitoring station relative to the prism monitoring point, and using the one-way observation height difference as the observation value.

[0008] Optionally, the step of solving for the unknown parameter vector based on the observed value vector to obtain a vector solution includes: constructing a design matrix based on the correspondence between the intelligent station and the prism monitoring point corresponding to each observed value, wherein the design matrix has a dimension of p×m, and if the i-th observed value corresponds to the j-th prism monitoring point, then the value at position (i,j) in the design matrix is ​​1; constructing an objective function based on the observed value vector, the design matrix, and the unknown parameter vector; and solving the objective function using the least squares method to obtain a vector solution for the unknown parameter vector.

[0009] Optionally, the step of analyzing the vertical displacement change of the earth-rock dam based on the m target elevations includes: determining the third elevation of the earth-rock dam body at the current moment based on the m target elevations; comparing the initial elevation of the earth-rock dam body at the initial moment with the third elevation of the earth-rock dam body at the current moment to determine the vertical displacement change of the earth-rock dam body; and generating an alarm signal when the vertical displacement change exceeds a preset safety threshold.

[0010] Secondly, embodiments of the present invention provide an automated monitoring device for the vertical displacement of an earth-rock dam, the device comprising: The correction module is used to correct the elevation of each of the n intelligent monitoring stations monitoring the vertical displacement of the earth-rock dam based on the first elevation of the steel pipe marker buried in the bedrock, so as to obtain the second elevation of the intelligent monitoring station. The acquisition module is used to acquire p observation values ​​obtained by the n intelligent monitoring stations observing m prism monitoring points on the earth-rock dam body, where each observation value represents the observation height difference between a single intelligent monitoring station and the corresponding single prism monitoring point; The construction module is used to construct an observation value vector based on the p observation values, and to use the elevations of the m prism monitoring points as an unknown parameter vector; The solution module is used to solve the unknown parameter vector based on the observed value vector to obtain a vector solution. The vector solution includes m target elevations that correspond one-to-one with the m prism monitoring points. The m target elevations are used to analyze the vertical displacement changes of the earth-rock dam. The analysis module is used to analyze the vertical displacement changes of the earth-rock dam based on the m target elevations.

[0011] Thirdly, embodiments of the present invention provide an electronic device, including: processor; A memory for storing instructions to be executed by the processor, wherein the processor is configured to execute the instructions to implement the method as described in the first aspect.

[0012] Fourthly, embodiments of the present invention provide a storage medium that, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the method described in the first aspect.

[0013] Fifthly, embodiments of the present invention provide a computer program product, the program product comprising a computer program, the computer program being executed by a processor as described in the first aspect.

[0014] According to an embodiment of the present invention, an automated monitoring method for the vertical displacement of an earth-rock dam is provided. For each of n intelligent monitoring stations monitoring the vertical displacement of the earth-rock dam, the elevation of the intelligent monitoring station is corrected based on the first elevation of a steel pipe marker buried in the bedrock, resulting in a second elevation of the intelligent monitoring station. P observation values ​​are obtained from the observations of m prism monitoring points on the dam body by the n intelligent monitoring stations, each observation value representing the observation elevation difference between a single intelligent monitoring station and its corresponding single prism monitoring point. An observation value vector is constructed based on the p observation values, and the elevations of the m prism monitoring points are used as an unknown parameter vector. The unknown parameter vector is solved based on the observation value vector to obtain a vector solution, which includes m target elevations corresponding one-to-one with the m prism monitoring points. The vertical displacement change of the earth-rock dam is analyzed based on the m target elevations. The elevation of each intelligent monitoring station is corrected, which makes the second elevation of the intelligent monitoring station more accurate. Then, combined with the observation values ​​obtained from the intelligent monitoring station, the vertical displacement change of the dam body obtained from the observation values ​​has higher accuracy. Moreover, the whole process is fully automated and does not require human intervention. To a certain extent, it solves the problem of low accuracy and poor real-time performance caused by human intervention in the monitoring of vertical displacement of the dam body in related technologies. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 The execution architecture of an automated monitoring method for vertical displacement of earth-rock dams provided in some embodiments of the present invention is illustrated. Figure 2 The flowchart of an automated monitoring method for vertical displacement of earth-rock dams provided in some embodiments of the present invention is shown. Figure 3 The architecture of an automated monitoring system for vertical displacement of earth-rock dams provided in some embodiments of the present invention is shown; Figure 4 This invention illustrates the concept of an automated monitoring method for the vertical displacement of earth-rock dams provided by some embodiments of the present invention; Figure 5 The structure of an automated monitoring device for vertical displacement of earth-rock dams provided in some embodiments of the present invention is shown; Figure 6 The diagram shows a structural block diagram of an electronic device provided in some embodiments of this application. Detailed Implementation

[0017] As described in the background section, the vertical displacement (settlement) of earth-rock dams is a core indicator reflecting their structural safety and stability, requiring precise monitoring to understand the dam's safety status. Currently, vertical displacement monitoring of earth-rock dams mainly employs methods such as leveling, trigonometric leveling, GPS measurement, and various automated monitoring instruments. Among these, leveling offers high accuracy but is labor-intensive and inefficient, making real-time automated monitoring difficult; GPS measurement is significantly affected by satellite signals and weather, resulting in relatively low elevation accuracy; while traditional trigonometric leveling is more efficient, it suffers from poor benchmark stability, significant temperature influence, and complex data processing. With the development of automation technology, surveying robots (automatic total stations) are gradually being applied to vertical displacement monitoring. However, existing technologies still require manual intervention for benchmark verification, data acquisition, and processing, failing to achieve full automation of the vertical displacement monitoring process. Furthermore, the accuracy is low and real-time performance is poor, making it difficult to meet the needs of intelligent safety management of earth-rock dams.

[0018] According to an embodiment of the present invention, an automated monitoring method for the vertical displacement of an earth-rock dam is provided. For each of n intelligent monitoring stations monitoring the vertical displacement of the earth-rock dam, the elevation of the intelligent monitoring station is corrected based on the first elevation of a steel pipe marker buried in the bedrock, resulting in a second elevation of the intelligent monitoring station. P observation values ​​are obtained from the observations of m prism monitoring points on the dam body by the n intelligent monitoring stations, each observation value representing the observation elevation difference between a single intelligent monitoring station and its corresponding single prism monitoring point. An observation value vector is constructed based on the p observation values, and the elevations of the m prism monitoring points are used as an unknown parameter vector. The unknown parameter vector is solved based on the observation value vector to obtain a vector solution, which includes m target elevations corresponding one-to-one with the m prism monitoring points. The vertical displacement change of the earth-rock dam is analyzed based on the m target elevations. The elevation of each intelligent monitoring station is corrected, which makes the second elevation of the intelligent monitoring station more accurate. Then, combined with the observation values ​​obtained from the intelligent monitoring station, the vertical displacement change of the dam body obtained from the observation values ​​has higher accuracy. Moreover, the whole process is fully automated and does not require human intervention. To a certain extent, it solves the problem of low accuracy and poor real-time performance caused by human intervention in the monitoring of vertical displacement of the dam body in related technologies.

[0019] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0020] It should be understood that the automated monitoring method for vertical displacement of earth-rock dams provided in this embodiment of the invention can be executed by a target device. The target device can be a single electronic device or multiple electronic devices working together. The electronic device can be a server, such as a standalone physical server, a server cluster consisting of multiple servers, or a cloud server capable of cloud computing.

[0021] Before introducing the automated monitoring method for vertical displacement of earth-rock dams provided in this embodiment of the invention, it is necessary to understand that elevation refers to the distance from a ground point along the vertical direction to a certain reference surface. In the method for monitoring vertical displacement of earth-rock dams provided in this embodiment of the invention, the elevation of the steel pipe marker refers to the vertical height of the top of the steel pipe marker relative to the stable bedrock, and the elevation of the intelligent monitoring station (measuring robot) refers to the vertical height of the center point of the top surface of the intelligent monitoring station's observation pier relative to the bedrock. Correspondingly, the elevation of each observation point (the center of the prism monitoring point) on the dam body is the vertical height of the prism monitoring point relative to the bedrock, which can reflect the settlement or uplift of the dam body. Figure 1 The implementation architecture of an automated monitoring method for vertical displacement of earth-rock dams provided in some embodiments of the present invention is shown, such as... Figure 1 As shown, the bedrock is located underground and serves as a stable reference standard. The earth-rock dam is situated on the riverbed at ground level, and multiple prism monitoring points are set on the dam to monitor its vertical displacement. The implementation architecture of this invention also includes a bimetallic beacon device deeply buried within the stable bedrock to construct the core benchmark system. The beacon core tubes (steel pipe beacon and aluminum pipe beacon) of this device are made from materials from the same furnace and their thermal expansion coefficients have been pre-determined with precision. Furthermore, this embodiment of the invention provides a bimetallic beacon instrument I. The two dashed lines emitted by the bimetallic beacon instrument I point to the aluminum pipe beacon and the steel pipe beacon, respectively, indicating that the bimetallic beacon instrument I is used to monitor the height difference between the tops of the aluminum pipe beacon and the steel pipe beacon. In this embodiment of the invention, the intelligent station, observation pier, and bimetallic beacon can be deployed as an integrated device. Corresponding observation piers are deployed adjacent to the steel pipe beacon wall. It should be noted that this embodiment of the invention includes multiple observation piers; only one is shown as an example in the figure. This invention also provides a bimetallic beacon II. The dashed lines emitted by the bimetallic beacon II point to the steel pipe beacon and the observation pier, respectively, indicating that the bimetallic beacon II is used to monitor the height difference between the top of the steel pipe beacon and the top of the observation pier. Since the intelligent measuring robot station is located on the top of the observation pier, the height of the measuring robot can be ignored. Therefore, the height difference between the top of the steel pipe beacon and the top of the observation pier can be understood as the height difference between the top of the steel pipe beacon and the measuring robot. The multiple dashed lines emitted by the measuring robot are examples of prism monitoring points on the earth-rock dam body observed by the measuring robot.

[0022] In an embodiment of the present invention, Figure 2 The flowchart illustrates an automated monitoring method for the vertical displacement of an earth-rock dam provided in some embodiments of the present invention. For example... Figure 2As shown, the automated monitoring method for vertical displacement of earth-rock dams provided in this embodiment of the invention includes steps 210 to 250.

[0023] Step 210: For each of the n intelligent monitoring stations monitoring the vertical displacement of the earth-rock dam, the elevation of the intelligent monitoring station is corrected according to the first elevation of the steel pipe marker buried in the bedrock, so as to obtain the second elevation of the intelligent monitoring station.

[0024] In embodiments of the present invention, intelligent monitoring stations can be as follows: Figure 1 The measurement robot shown can have n intelligent stations monitoring the dam's elevation. To ensure greater accuracy in dam elevation measurements, n is typically a positive integer greater than 2. Since the earth-rock dam is constructed outdoors, the bimetallic marker's measurement results are prone to errors due to environmental influences. Therefore, the first elevation of the steel pipe marker can be the corrected real-time elevation of the steel pipe marker at the current moment. Accordingly, it can be determined based on... Figure 1 The first elevation of the steel pipe marker, which is integrated with the intelligent measuring station, is shown. The elevation of the intelligent measuring station on the measuring pier is corrected to obtain the real-time elevation of the intelligent measuring station, which is also the second elevation of the intelligent measuring station.

[0025] Step 220: Obtain p observation values ​​obtained by the n intelligent monitoring stations observing m prism monitoring points on the earth-rock dam body. Each observation value represents the observation height difference between a single intelligent monitoring station and the corresponding single prism monitoring point.

[0026] In this embodiment of the invention, n intelligent monitoring stations are set outside the earth-rock dam, and m prism monitoring points can be set on the earth-rock dam, where m is usually a positive integer not less than 1, such as... Figure 1 In the scenario shown, m is 4. The observation relationship between the n intelligent stations and the m prism monitoring points can be any preset relationship, and this embodiment of the invention does not impose any restrictions on this. For example, when n is 3, there are three intelligent stations: n1, n2, and n3; and when m is 4, there are four prism monitoring points: m1, m2, m3, and m4. Intelligent station n1 can observe all four prism monitoring points; intelligent station n2 can observe both m1 and m3; and intelligent station n3 can observe both m2 and m4. After the n intelligent stations have observed the m prism monitoring points, p observation values ​​can be obtained, where p is a positive integer. For example, in the distance example above, p is 8 (obtained from 4 + 2 + 2). Each of the observations represents the observational height difference between a single smart station and its corresponding single prism monitoring point in the vertical direction.

[0027] Step 230: Construct an observation value vector based on the p observation values, and use the elevations of the m prism monitoring points as an unknown parameter vector.

[0028] In this embodiment of the invention, the p observations can be arranged into an observation vector in a preset order, and the dimension of the observation vector is p×1. An unknown parameter vector is constructed using the elevations of the m prism monitoring points as unknown parameters, and the dimension of the unknown parameter vector is m×1. The preset order can be determined from the preset correspondence in step 220.

[0029] Step 240: Solve for the unknown parameter vector based on the observed value vector to obtain a vector solution. The vector solution includes m target elevations that correspond one-to-one with the m prism monitoring points.

[0030] In this embodiment of the invention, the position parameter vector can be solved using the least squares method based on the observation value vector, thereby obtaining the vector solution corresponding to the unknown parameter vector. The dimension of the vector solution is m×1, including m target elevations corresponding one-to-one with m prism monitoring points. The target elevations are the real-time elevations of the prism monitoring points at the current moment.

[0031] Step 250: Analyze the vertical displacement change of the earth-rock dam based on the m target elevations.

[0032] In this embodiment of the invention, the presence or absence of abnormal phenomena such as subsidence or rise in the dam body can be determined based on m elevations obtained from two consecutive measurements. The vertical displacement change of the earth-rock dam is obtained by comparing the target elevation determined at the current moment with the target elevation determined at the initial moment. The initial moment can be a preset fixed moment, or, in the case of periodically executing the automated monitoring method for the vertical displacement of the earth-rock dam provided in this embodiment of the invention, the moment of the last execution can be used as the initial moment. Furthermore, the m target elevations can be compared with a preset static threshold. When any target elevation is detected to exceed the threshold, it is considered that an abnormal settlement phenomenon has occurred in the dam body, thus enabling timely detection of local settlement in the earth-rock dam.

[0033] In this embodiment of the invention, the elevation of each intelligent monitoring station is corrected, thereby making the second elevation of the intelligent monitoring station more accurate. In addition, combined with the observation values ​​obtained from the intelligent monitoring station, the vertical displacement change of the dam body obtained from the observation values ​​has higher accuracy. Moreover, the whole process is fully automated and does not require human intervention, which to some extent solves the problem of low accuracy and poor real-time performance caused by human intervention in the monitoring of vertical displacement of the dam body in related technologies.

[0034] In this embodiment of the invention, before correcting the elevation of the intelligent station based on the first elevation of the steel pipe marker buried in the bedrock in step 210, the method further includes obtaining the first elevation of the steel pipe marker. Specifically, the method obtains the first elevation difference between the top of the steel pipe marker buried in the bedrock at the current moment and the top of the aluminum pipe marker adjacent to the steel pipe marker; based on the principle of thermal expansion, and combining the thermal expansion coefficients of the steel pipe marker, the aluminum pipe marker, and the first elevation difference, the method determines the first elevation of the steel pipe marker, which is the elevation of the steel pipe marker at the current moment.

[0035] In this embodiment of the invention, the core tubes (steel tube markers and aluminum tube markers) of the bimetallic marker device, deeply buried in stable bedrock, are made of materials from the same furnace and their thermal expansion coefficients have been precisely measured in advance. Through, as... Figure 1 The bimetallic beacon I shown continuously and in real-time collects data on the height difference between the steel and aluminum bimetallic beacons. Combined with pre-determined differences in the linear expansion coefficients of steel and aluminum, a temperature-deformation correlation model can be established to accurately calculate the deformation of the beacon core length caused by temperature changes. Furthermore, this deformation can be used as a temperature correction parameter to adjust all subsequent data, eliminating the interference of ambient temperature fluctuations on the stability of the benchmark height and ensuring the temperature adaptability and data reliability of the measurement benchmark.

[0036] In this embodiment of the invention, the coefficient of thermal expansion of the steel pipe is assumed to be... The coefficient of thermal expansion of aluminum tubes is 1. ,and The bimetallic marker I measured the change in height difference between the steel pipe marker and the aluminum pipe marker as follows: Because the bimetallic marker's bottom end is fixed to the bedrock, temperature changes cause variations in the length of the steel pipe marker. And the change in the length of the aluminum tube label The first difference in elevation between them has the following relationship:

[0037] in, The first elevation difference between the steel pipe marker and the aluminum pipe marker, measured by bimetallic marker I. For the change in the length of the steel pipe, This refers to the variation in the length of the aluminum tube.

[0038] According to the principle of thermal expansion:

[0039] in, This represents the original length of the steel pipe marker and aluminum pipe marker from the fixed end in the bedrock to the measuring point at the top. This represents the temperature change between the two measurements. The coefficient of thermal expansion of the steel pipe is given. The coefficient of thermal expansion of the aluminum tube is known. Solving the above equations simultaneously, we can calculate the change in the reference (standard) length of the steel pipe caused by temperature changes. (i.e., temperature correction):

[0040] Based on the temperature correction, the elevation of the steel pipe marker is adjusted, and the real-time first elevation of the steel pipe marker is as follows:

[0041] in, This is the first elevation of the steel pipe marker.

[0042] In this embodiment of the invention, after determining the first elevation of the steel pipe marker, step 210, which involves correcting the elevation of the intelligent measuring station based on the first elevation of the steel pipe marker buried in the bedrock to obtain the second elevation of the intelligent measuring station, includes: obtaining the original elevation difference between the top of the steel pipe marker and the intelligent measuring station at an initial moment, and the real-time elevation difference between the top of the steel pipe marker and the intelligent measuring station at the current moment; determining the station settlement between the initial moment and the current moment based on the original elevation difference and the real-time elevation difference; and determining the second elevation of the intelligent measuring station based on the first elevation of the steel pipe marker and the station settlement, wherein the second elevation of the intelligent measuring station is the elevation of the intelligent measuring station at the current moment.

[0043] In this embodiment of the invention, the vertical displacement of the intelligent measuring station can be corrected based on the first elevation of the steel pipe marker to obtain the second elevation of the intelligent measuring station. Utilizing, for example... Figure 1 The bimetallic beacon II, as shown, performs synchronous monitoring, measuring the vertical displacement difference between the top of the deeply buried bimetallic steel beacon and the main body of the intelligent measuring robot station. Since the bimetallic steel beacon is deeply buried in stable bedrock, its position can be considered an absolutely stable benchmark. This difference can be used to calculate the vertical settlement of the intelligent measuring robot station in real time. When subsequently calculating the elevation of the monitoring points on the dam body, this settlement is included as a real-time correction term to offset measurement deviations caused by the settlement of the intelligent measuring station itself, thus ensuring the dynamic stability of the monitoring benchmark from the source. Specifically, it can be calculated using the following formula:

[0044] Among them, the height difference between the top of the bedrock steel pipe marker and the observation pier of the intelligent measuring station, measured by the bimetallic marker II, was: , For the initial time ( The elevation difference between the top of the steel pipe marker and the original elevation of the intelligent measuring station. For the current moment ( The real-time elevation difference between the top of the steel pipe marker and the intelligent measuring station. This refers to the vertical displacement (station settlement) between the initial time and the current time. When the intelligent station sinks... Decrease A positive value indicates sinking.

[0045] The real-time second elevation of the intelligent surveying station of the measurement robot, after correction for the first elevation, can be determined by the following formula:

[0046] in, This is the second elevation of the intelligent monitoring station. This is the first elevation of the revised steel pipe marker. This represents the settlement of the monitoring station.

[0047] In this embodiment of the invention, each of the p observations in step 220 can be obtained by the following steps: obtaining meteorological data of the area where the dam is located, and the length of the hypotenuse between the intelligent station and the corresponding prism monitoring point; correcting the hypotenuse length using the meteorological data to obtain the corrected hypotenuse length; determining the one-way observation height difference between the intelligent station and the prism monitoring point based on the corrected hypotenuse length and the observation vertical angle of the intelligent station relative to the prism monitoring point, and using the one-way observation height difference as the observation value.

[0048] In this embodiment of the invention, the measuring robot performs trigonometric leveling measurements on the corresponding prism monitoring points according to a preset correspondence and preset working parameters. The preset working parameters may include parameters such as the observation sequence, observation frequency, and observation accuracy threshold of the dam monitoring points (prism monitoring points). The intelligent measuring station automatically initiates trigonometric leveling measurements on the dedicated prism monitoring points deployed at key locations on the dam body based on the preset working parameters, simultaneously recording core observation index values ​​such as the observed side length and the observed vertical angle. The observed side length is also the hypotenuse length between the intelligent measuring station and the corresponding prism monitoring point (e.g.,...). Figure 1 The dashed line shown indicates the distance between the intelligent station and the prism monitoring point. The vertical angle is the angle between the hypotenuse length and the horizontal plane.

[0049] In this embodiment of the invention, before processing the observation index values ​​of the intelligent monitoring station, meteorological data of the area where the dam is located can be obtained. A meteorological field model is then established based on the meteorological data, and meteorological corrections are applied to the hypotenuse length. The meteorological data includes multi-dimensional meteorological data such as temperature, humidity, and air pressure within the monitoring area. A dynamic meteorological field model covering the entire region is constructed based on the collected meteorological data. This model is used to perform targeted real-time meteorological corrections on the observation side lengths and angles collected by the measurement robot, focusing on reducing the interference of meteorological factors such as atmospheric refraction and air pressure changes on the accuracy of trigonometric leveling measurements, thereby improving the accuracy of the original observation data. The meteorological correction and trigonometric leveling calculation formulas are as follows:

[0050] Where D is the corrected hypotenuse length. Here, K represents the length of the hypotenuse collected by the intelligent weather station, and K is the meteorological correction coefficient, which can be obtained using the following formula:

[0051] in, To obtain the refractive index of the instrument under reference meteorological conditions, the atmospheric refractive index can be calculated using the Kohlrousch formula or a corresponding simplified formula. This represents the actual atmospheric refractive index in meteorological data.

[0052] In this embodiment of the invention, the unidirectional observation height difference of the intelligent station relative to the prism monitoring point can be determined by the following formula when determining the unidirectional observation height difference of the intelligent station relative to the prism monitoring point based on the corrected hypotenuse length and the observation vertical angle of the intelligent station relative to the prism monitoring point:

[0053] in, The elevation difference between the intelligent station and the prism monitoring point is denoted by D, where D is the corrected hypotenuse length. To observe the vertical angle, i is the instrument height of the measuring robot, and v is the target height of the prism monitoring point (the vertical height from the center of the prism monitoring point to the monitoring point on the dam). This is the correction factor for the spherical-atmospheric difference in meteorological data (including differences in Earth's curvature and atmospheric refraction), where the spherical-atmospheric difference factor is calculated using the following formula:

[0054] in, The radius of curvature of the Earth, This is the meteorological correction coefficient.

[0055] In this embodiment of the invention, step 230, which constructs an observation vector based on p observations, can be achieved using the following formula:

[0056] in, The elevation difference observed by each measuring robot after various corrections is the unidirectional elevation difference observed in step 220, where p is the total number of observations.

[0057] In this embodiment of the invention, step 230, which uses the elevations of the m prism monitoring points as an unknown parameter vector, can be achieved using the following formula:

[0058] in, for The unknown target elevation of the prism monitoring point.

[0059] In this embodiment of the invention, when the number of measurement robots deployed in the monitoring system exceeds two (i.e., n is greater than 2), the unidirectional observation elevation differences of each intelligent monitoring station after temperature correction, intelligent station vertical displacement correction, and meteorological correction can be summarized and integrated to construct a triangular elevation monitoring network covering the entire dam area in real time. The least squares method is then used to perform unified adjustment calculations on the entire network's observation data, fully utilizing the redundancy and complementarity of data from multiple intelligent monitoring stations, eliminating random and systematic errors in the observation data, and obtaining the most probable value of the elevation of each monitoring point on the dam body. Finally, the data processing center further analyzes the adjusted elevation data, generating vertical displacement monitoring reports, displacement change curves, and other results, simultaneously completing data storage, backup, and visualization, providing data support for dam safety assessment. Specifically, step 240, which involves solving for the unknown parameter vector based on the observed value vector to obtain a vector solution, includes: constructing a design matrix based on the correspondence between the intelligent station and the prism monitoring point corresponding to each observed value. The design matrix has a dimension of p×m, where if the i-th observed value corresponds to the j-th prism monitoring point, then the value at position (i,j) in the design matrix is ​​1; constructing an objective function based on the observed value vector, the design matrix, and the unknown parameter vector; and solving the objective function using the least squares method to obtain a vector solution for the unknown parameter vector.

[0060] In this embodiment of the invention, in order to obtain the vector solution using the least squares method, the observation error vector (random error) should also be constructed according to the following formula:

[0061] in, For the first The error (residual) of each observation.

[0062] Therefore, the error variance column matrix (covariance matrix) can be expressed as follows:

[0063] in, For the first The variance of each observation is determined by factors such as the accuracy of the observation instrument and the observation environment, and can be obtained through calibration of previous observation data or instrument parameters. Assuming that the observations are independent, the covariance matrix is ​​a diagonal matrix (off-diagonal elements are 0). If there are correlated observations, the off-diagonal elements are the covariances of the corresponding observations.

[0064] In this embodiment of the invention, the weight matrix is: The weight matrix is ​​the inverse of the variance matrix, used to represent the precision weights of different observations (higher precision results in larger weights). The adjustment can be adjusted according to the accuracy of the observation instrument and the number of observations. For observations with higher accuracy (such as data from bimetallic spectrometers), the weight can be appropriately increased to improve the reliability of the adjustment results.

[0065] In this embodiment of the invention, based on the correspondence between the intelligent station and the prism monitoring point corresponding to each observation value, a design matrix is ​​constructed according to the following formula, wherein the dimension of the design matrix is ​​p×m: Design matrix (coefficient matrix):

[0066] Wherein, if the i-th observation corresponds to the j-th prism monitoring point, then the value at position (i,j) in the design matrix is ​​1; if they do not correspond, then... (Determined based on the correspondence of points in the network observation).

[0067] Vector of constant terms:

[0068] in That is, the first Initial elevation of the intelligent station corresponding to each observation Settlement correction amount of intelligent monitoring station Reference temperature correction sum.

[0069] In this embodiment of the invention, the error equation matrix form constructed based on the above parameters is as follows: Summarized as follows:

[0070] The left side of the equation is the sum of the linear combination of the unknown parameter vectors and the observation error, while the right side is the difference between the observed value after previous correction and the constant term.

[0071] In this embodiment of the invention, the core criterion for least squares adjustment is to minimize the weighted sum of squares of the observation error vector, i.e. Based on this criterion, by differentiating the error equation and setting the derivative to zero, the least squares solution for the unknown parameter (the most likely value of the monitoring point elevation) is derived:

[0072] , For unknown parameter vectors The least squares estimate (i.e., the final elevation of the monitoring point or the least squares value vector). For designing a matrix The transpose of the matrix; Let be the inverse matrix of the coefficient matrix of the normal equation, and let the normal equation be . ; Let be the vector of constant terms in the normal equations; In this embodiment of the invention, after using least squares adjustment, the accuracy of the results can be evaluated, and the reliability of the data can be verified. Key indicators include the unit weighted mean square error and the covariance matrix of the unknown parameters. The unit weighted mean square error (root mean square error) reflects the average accuracy of the observations, and its formula can be expressed as follows:

[0073] in, Redundant observations (total number of observations) Subtract the number of unknown parameters The more redundant observations, the higher the adjustment accuracy.

[0074] The covariance matrix of the unknown parameters (accuracy matrix key) reflects the accuracy of the most probable elevation values ​​of each monitoring point, and the formula can be expressed as follows:

[0075] Among them, the diagonal elements of the matrix For the first The variance of the elevation of each monitoring point, and its square root, is the standard error of that monitoring point, used to measure the accuracy of the elevation observation.

[0076] In this embodiment of the invention, combining the least squares solution results, the comprehensive model (matrix form) of the final elevation of the monitoring point can be expressed as the following formula:

[0077] in, The final elevation or maximum value vector matrix of the monitoring points, with dimension . ; The initial elevation vector matrix of the intelligent measuring station has dimensions of . ; This is the settlement correction vector matrix of the intelligent station retrieved by the Bimetallic Gauge II, with dimensions of... ; This is the unidirectional observational elevation difference vector matrix after meteorological and spherical atmospheric difference corrections, with dimensions of [missing information]. ; The temperature correction vector matrix for the reference steel pipe standard calculated by the bimetallic standard instrument I has dimensions of When the reference transfer involves temperature deformation; The residual correction term vector matrix of the network adjustment has dimensions of . ,Right now (Observational residuals) have the smallest weighted sum of squares.

[0078] In this embodiment of the invention, the vector solution can be obtained through the above formula, thereby obtaining the target elevations corresponding to the m prism monitoring points. After solving the unknown parameter vector based on the observed value vector in step 240 to obtain the vector solution, step 250 includes: determining the third elevation of the earth-rock dam body at the current moment based on the m target elevations; comparing the initial elevation of the earth-rock dam body at the initial moment with the third elevation of the earth-rock dam body at the current moment to determine the vertical displacement change of the earth-rock dam body; and generating an alarm signal when the vertical displacement change exceeds a preset safety threshold.

[0079] In this embodiment of the invention, the third elevation of the earth-rock dam body at the current moment can be determined based on the m target elevations. Since the intelligent monitoring station observes the prism monitoring points periodically, the automated monitoring method for the vertical displacement of the earth-rock dam provided in this embodiment can be executed periodically. The time of the last execution of the method is taken as the initial time, and the dam body elevation determined by the last execution is taken as the initial elevation. By comparing the initial elevation and the third elevation, the change in vertical displacement of the earth-rock dam body between the initial time and the current time is determined. Furthermore, it is determined whether the change in vertical displacement exceeds a preset safety threshold. If the change in vertical displacement exceeds the safety threshold, an alarm signal is generated.

[0080] In this embodiment of the invention, the safety threshold may include multiple thresholds. Based on the comparison of the vertical displacement change with multiple safety thresholds, a graded alarm is performed so that staff can be notified in a timely manner when a serious settlement accident occurs.

[0081] To better understand the automated monitoring method for vertical displacement of earth-rock dams provided in the embodiments of the present invention, Figure 3 The architecture of an automated monitoring system for vertical displacement of earth-rock dams provided in some embodiments of the present invention is shown. Figure 3 The monitoring system shown can perform the automated monitoring method for the vertical displacement of earth-rock dams described above. For example... Figure 3As shown, the automated monitoring system for the vertical displacement of the earth-rock dam is divided into a “field monitoring layer” and a “data processing layer”. Among them, (1) the field monitoring layer is responsible for data acquisition and preliminary calibration, and includes components: a deep-buried bimetallic marker device, a bimetallic marker instrument (I / II), a measurement robot intelligent station (including a measurement robot and a data processing center), a dam body monitoring point prism monitoring point group, and a meteorological sensor. Among them, the measurement robot intelligent station is installed on the observation pier in the dam site area and is firmly fixed. The deep-buried bimetallic marker device has been drilled and buried according to the specifications, and the marker core tube is deeply buried in the stable bedrock. The bimetallic marker instruments I and II are precisely connected with the bimetallic steel pipe marker, aluminum pipe marker, and intelligent station body, respectively. The meteorological sensor is deployed in key locations throughout the dam area (including the area around the intelligent station and different elevation areas of the dam body). The measurement robot special prism monitoring points are deployed in key monitoring sections such as the dam top, dam slope, and dam foundation according to the monitoring plan. The data processing center completed wireless / wired communication link tests with the measurement robot, bimetallic beacon, and meteorological sensors to ensure stable data transmission without packet loss. Simultaneously, it synchronized the clocks of all devices to ensure consistent timestamps for all monitoring data. The data processing center pre-sets observation plans, including the observation sequence for each monitoring point on the dam body, the frequency of single observations (e.g., once every 30 minutes), the number of repeated observations, and accuracy thresholds (e.g., angle measurement accuracy, side measurement accuracy requirements). It also inputs pre-determined thermal expansion coefficients of the deeply buried bimetallic beacon steel / aluminum pipe, meteorological correction parameters, etc., and sets data storage paths, backup rules, and early warning thresholds.

[0082] In this embodiment of the invention, a deeply buried bimetallic marker device serves as an elevation benchmark. It is transmitted to bimetallic marker I via a "benchmark transfer," where bimetallic marker I calculates a "temperature correction," and bimetallic marker II collects "vertical displacement" observation data. Both results are transmitted to the data processing center. A surveying robot performs trigonometric leveling observations on the "dam monitoring point prism monitoring point group," and the observation data is transmitted to the data processing center. Simultaneously, a "real-time benchmark correction" is synchronized to the data processing center for data calibration. Meteorological sensors collect "meteorological elements" and transmit them to the data processing center. Combined with "meteorological field model correction," the influence of environmental factors on the observation data is eliminated. Subsequently, the data processing layer is responsible for data optimization and result output. Specifically, the data preprocessing module receives data from the field monitoring layer, performs preliminary cleaning and organization, and then, through the multi-intelligent station trigonometric leveling network construction module, builds an elevation observation network based on the preprocessed data. Data accuracy is optimized through "least square adjustment," and finally, through the elevation or maximum / minimum value generation module, the elevation data of the dam monitoring points is output. The vertical displacement of the earth-rock dam is obtained through re-measurement.

[0083] In an embodiment of the present invention, Figure 4 This invention illustrates the concept of an automated monitoring method for the vertical displacement of earth-rock dams, provided by some embodiments of the present invention. For example... Figure 4As shown, the automated monitoring method for vertical displacement of earth-rock dams provided in this embodiment of the invention includes: Data Acquisition. ① Dam Body Observation Data Acquisition: The intelligent station of the measurement robot, based on a preset observation plan, automatically searches, coarsely aims, and finely aims to lock onto dedicated prism monitoring points at each monitoring point on the dam body. It simultaneously collects angle (horizontal angle, vertical angle) and side (slope distance) data. After completing the observation of each monitoring point, it automatically jumps to the next monitoring point until a round of observation is completed. If aiming fails during this period, it automatically retryes 2-3 times. If it still fails, it records the anomaly and continues subsequent observations. ② Meteorological Data Acquisition: Each meteorological sensor simultaneously collects real-time temperature, air pressure, and humidity data of the monitoring area. The collection frequency matches the dam body observation frequency to ensure that each round of dam body observation is supported by corresponding meteorological data. ③ Benchmark and Intelligent Station Monitoring Data Acquisition: Bimetallic Beacon I collects real-time data on the elevation difference between the bimetallic steel pipe beacon and the aluminum pipe beacon. Bimetallic Beacon II simultaneously collects data on the elevation difference between the bimetallic steel pipe beacon and the intelligent station of the measurement robot. The two types of data form a dataset with timestamps that correspond one-to-one with the dam body observation data and meteorological data.

[0084] Calculation of Automatic Temperature Correction and Real-Time Vertical Displacement of the Intelligent Station: Two core correction parameters are calculated based on the elevation difference data collected by the bimetallic beacon. ① Automatic Temperature Correction Calculation: The data processing center uses the pre-entered thermal expansion coefficient of the steel / aluminum pipe beacon, combined with the elevation difference change data of the steel / aluminum pipe beacon collected by bimetallic beacon I, and substitutes it into the temperature-deformation correlation model to accurately calculate the deformation of the core tube length caused by temperature changes. This deformation is the automatic temperature correction, used to offset the influence of temperature fluctuations on the benchmark elevation. ② Vertical Displacement Calculation of the Intelligent Station: Since the bimetallic steel pipe beacon is deeply buried in stable bedrock, its position is considered an absolutely stable benchmark. The data processing center uses the elevation difference data between the steel pipe beacon and the intelligent station body collected by bimetallic beacon II to inversely derive the real-time vertical settlement of the intelligent station body. This settlement is used as the displacement correction value of the intelligent station to eliminate the interference of the intelligent station's own deformation on the observation results.

[0085] Data transmission to the processing center: Raw data and preliminary calculated correction parameters collected by the measuring robot, bimetallic marker, and meteorological sensors are transmitted in real time to the data processing center via a pre-set communication link (such as 4G / 5G, fiber optic). During transmission, data encryption and integrity verification mechanisms (such as CRC cyclic redundancy check) are employed to ensure that the data is not tampered with or lost during transmission. Upon receiving the data, the processing center automatically classifies and stores the data, and simultaneously generates a data reception log, recording information such as data source, transmission time, and data integrity, facilitating subsequent data traceability and troubleshooting.

[0086] Meteorological field model construction and meteorological correction: The data processing center aggregates multi-dimensional meteorological data collected by various meteorological sensors and uses spatial interpolation algorithms (such as Kriging interpolation) to construct a dynamic meteorological field model covering the entire dam area, accurately reflecting the differences in meteorological parameter distribution in different areas of the dam area; based on the constructed meteorological field model, targeted meteorological corrections are made to the observation side lengths and angles collected by the measurement robot, focusing on correcting the influence of meteorological factors such as atmospheric refraction, air pressure changes, and temperature gradients on trigonometric leveling, replacing the traditional fixed parameter correction method and improving the accuracy of observation data.

[0087] Application of intelligent station displacement correction and temperature correction: The data processing center integrates the previously calculated intelligent station displacement correction value and automatic temperature correction value with the meteorological correction observation data. Through the preset calculation model, the original observation data is corrected a second time, completely eliminating the influence of the three core factors of intelligent station settlement, temperature fluctuation and meteorological interference on the observation results, and obtaining accurate basic observation data of dam monitoring points.

[0088] Determining the Number of Intelligent Monitoring Stations: The data processing center automatically counts the number of currently operating intelligent monitoring stations and selects the corresponding elevation calculation method based on the number of stations. The core purpose of this logic is to adapt to the monitoring needs of earth-rock dams of different sizes. The single-station / dual-station mode is suitable for small and medium-sized earth-rock dams (small monitoring range, few monitoring points), while the multi-station mode is suitable for large earth-rock dams (wide monitoring range, many monitoring points), ensuring a balance between monitoring efficiency and data accuracy.

[0089] Direct calculation of measuring point elevations (single-station / dual-station mode): When there are one or two intelligent measuring stations, there is no need to construct a complex monitoring network. Based on the corrected observation data, the data processing center directly calculates the elevation values ​​of each monitoring point on the dam body using the basic formula of trigonometric leveling. After the calculation is completed, the elevation data is initially verified. If the data exceeds the preset reasonable range (such as excessive deviation from the historical initial elevation), it is marked as abnormal data and the original data of the corresponding observation period is retrieved for recalculation.

[0090] Constructing a trigonometric elevation network and network adjustment (multi-station mode): When the number of intelligent monitoring stations exceeds two, a multi-intelligent-station collaborative processing flow is initiated. ① Constructing a trigonometric elevation network: The data processing center aggregates the corrected observation data from each intelligent monitoring station, constructing a trigonometric elevation monitoring network covering the entire dam area, using dam monitoring points, each intelligent monitoring station, and deeply buried bimetallic benchmark points as nodes. This ensures that each monitoring point is covered by at least two intelligent monitoring stations, creating data redundancy. ② Least squares network adjustment: Based on the constructed trigonometric elevation network, error equations are established, and the least squares method is used to perform unified adjustment calculations on the entire network's observation data. This fully utilizes the complementarity of data from multiple intelligent monitoring stations to eliminate random and systematic errors, obtaining the most probable value of the elevation of each dam monitoring point, thus improving the overall accuracy and reliability of large-scale monitoring.

[0091] Output the most probable elevation values ​​of monitoring points: After single-station calculation or multi-station adjustment, the data processing center outputs the final most probable elevation values ​​of each dam monitoring point, and generates standardized monitoring reports. The report content includes monitoring point number, observation time, elevation value, correction parameters, accuracy assessment results, etc. Simultaneously, vertical displacement change curves are generated to intuitively display the elevation change trend of each monitoring point in different observation periods. The data results support local storage, cloud backup, and integration with third-party systems (such as dam safety management platforms).

[0092] Data Analysis and Early Warning: The data processing center performs in-depth analysis of the output elevation data, calculating core indicators such as the cumulative vertical displacement and displacement change rate of each monitoring point. The analysis results are compared with preset early warning thresholds (such as the maximum allowable settlement and the maximum allowable settlement rate). If an indicator at a monitoring point exceeds the early warning threshold, the system immediately triggers a multi-level early warning mechanism, including local audible and visual alarms, sending SMS / APP push notifications to maintenance personnel, and automatically retrieving historical observation data, surrounding meteorological data, and intelligent station status data for the monitoring point, providing comprehensive data support for maintenance personnel to investigate potential safety hazards. If the data does not exceed the threshold, the system enters the next observation cycle, achieving continuous closed-loop monitoring operation.

[0093] The automated monitoring method for vertical displacement of earth-rock dams provided in this invention uses a bimetallic beacon to monitor the vertical displacement between the intelligent station and the stable bedrock benchmark in real time, enabling dynamic correction of station settlement. This fundamentally solves the benchmark drift problem caused by the instability of intelligent stations in traditional monitoring. Combined with the stabilizing anchoring effect of the deeply buried bimetallic beacon, the long-term reliability of the monitoring benchmark is ensured. Simultaneously, the bimetallic beacon principle is used to automatically compensate for temperature effects, accurately offsetting the interference of temperature changes on the benchmark elevation. Combined with precise meteorological correction based on a meteorological field model, this effectively weakens the interference of multiple environmental factors, achieving a monitoring accuracy better than ±3mm, meeting the actual needs of high-precision monitoring of earth-rock dams. Furthermore, it supports the network operation of multiple intelligent measurement robot stations. Through the construction of a global trigonometric elevation network and least squares adjustment, efficient fusion of data from multiple intelligent stations is achieved, fully leveraging the complementary advantages of multi-source data. This not only improves the coverage of large earth-rock dams and long-distance monitoring areas but also reduces the impact of a single intelligent station failure on the overall monitoring effect through data redundancy verification, further improving the reliability and comprehensiveness of the monitoring data. Simultaneously, the entire process is automated, achieving breakthroughs in monitoring efficiency and real-time performance. From intelligent station baseline monitoring and dam body point observation to data correction, adjustment processing, and results output, the entire monitoring process is automated, requiring no manual intervention. This significantly reduces the labor intensity and human error associated with traditional manual monitoring, while also significantly increasing monitoring frequency and data update speed. It enables real-time monitoring of the vertical displacement of earth-rock dams, timely capturing even minute changes in dam vertical displacement, providing crucial support for early warning of dam safety hazards. This method is adaptable to the monitoring needs of earth-rock dams of different sizes and geological conditions. The deployment of each component of the monitoring system is flexible; the core deeply buried bimetallic marker device can adapt to complex bedrock environments, and the meteorological field model can be dynamically adjusted according to the meteorological characteristics of the dam area, ensuring stable operation under various environmental conditions. Through high-precision, real-time monitoring data output, it can provide scientific and accurate decision-making basis for dam operation and maintenance management, effectively improving the dam's safe operation guarantee capability and reducing safety risks such as dam failure.

[0094] In an embodiment of the present invention, Figure 5 The structure of an automated monitoring device for the vertical displacement of an earth-rock dam, according to an embodiment of the present invention, is shown. Figure 5 As shown, an automated monitoring device 500 for vertical displacement of an earth-rock dam provided in this embodiment of the invention includes: The correction module 510 is used to correct the elevation of each of the n intelligent monitoring stations monitoring the vertical displacement of the earth-rock dam based on the first elevation of the steel pipe marker buried in the bedrock, so as to obtain the second elevation of the intelligent monitoring station. The acquisition module 520 is used to acquire p observation values ​​obtained by the n intelligent monitoring stations observing m prism monitoring points on the earth-rock dam body, where each observation value represents the observation height difference between a single intelligent monitoring station and the corresponding single prism monitoring point. The construction module 530 is used to construct an observation value vector based on the p observation values ​​and to use the elevations of the m prism monitoring points as an unknown parameter vector; The solution module 540 is used to solve the unknown parameter vector based on the observed value vector to obtain a vector solution, wherein the vector solution includes m target elevations that correspond one-to-one with the m prism monitoring points; Analysis module 550 is used to analyze the vertical displacement change of the earth-rock dam based on the m target elevations.

[0095] It should be noted that the embodiments of the automated monitoring device for the vertical displacement of earth-rock dams in this specification and the embodiments of the automated monitoring method for the vertical displacement of earth-rock dams in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the corresponding embodiments of the automated monitoring method for the vertical displacement of earth-rock dams mentioned above, and the repeated parts will not be described again.

[0096] Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device provided in this application embodiment includes a processor 610 and a memory 620, wherein the memory is used to store instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the above method.

[0097] In an exemplary embodiment, the electronic device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0098] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory including instructions that can be executed by a processor of a device to perform the described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. When the instructions in this non-transitory computer-readable storage medium are executed by a processor of an electronic device, the electronic device is able to perform the described method.

[0099] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0100] The above description does not provide detailed technical specifications regarding the structure of each layer. However, those skilled in the art should understand that layers and regions of desired shapes can be formed using various technical means. Furthermore, to form the same structure, those skilled in the art can also design methods that are not entirely identical to those described above. Additionally, although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be advantageously combined.

[0101] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0102] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An automated monitoring method for the vertical displacement of an earth-rock dam, characterized in that, The method includes: For each of the n intelligent monitoring stations monitoring the vertical displacement of the earth-rock dam, the elevation of the intelligent monitoring station is corrected based on the first elevation of the steel pipe marker buried in the bedrock to obtain the second elevation of the intelligent monitoring station; Obtain p observation values ​​obtained by the n intelligent monitoring stations observing m prism monitoring points on the earth-rock dam body, where each observation value represents the observation height difference between a single intelligent monitoring station and the corresponding single prism monitoring point; An observation value vector is constructed based on the p observation values, and the elevations of the m prism monitoring points are used as an unknown parameter vector. The unknown parameter vector is solved by the observed value vector to obtain a vector solution, which includes m target elevations corresponding one-to-one with the m prism monitoring points. The vertical displacement of the earth-rock dam is analyzed based on the m target elevations.

2. The method according to claim 1, characterized in that, Before correcting the elevation of the intelligent station based on the first elevation of the steel pipe marker buried in the bedrock, the method further includes: Obtain the first height difference between the top of the steel pipe target buried in the bedrock at the current moment and the top of the aluminum pipe target adjacent to the steel pipe target; Based on the principle of thermal expansion, and combining the thermal expansion coefficients of the steel pipe marker, the aluminum pipe marker, and the first elevation difference, the first elevation of the steel pipe marker is determined, and the first elevation of the steel pipe marker is the elevation of the steel pipe marker at the current moment.

3. The method according to claim 2, characterized in that, The process of correcting the elevation of the intelligent monitoring station based on the first elevation of the steel pipe marker buried in the bedrock to obtain the second elevation of the intelligent monitoring station includes: Obtain the initial height difference between the top of the steel pipe target and the intelligent measuring station at the initial moment, and the real-time height difference between the top of the steel pipe target and the intelligent measuring station at the current moment; Based on the original elevation difference and the real-time elevation difference, determine the station settlement between the initial time and the current time; Based on the first elevation of the steel pipe marker and the settlement of the station, the second elevation of the intelligent station is determined, and the second elevation of the intelligent station is the elevation of the intelligent station at the current moment.

4. The method according to claim 3, characterized in that, The acquisition of p observation values ​​obtained by the n intelligent monitoring stations observing m prism monitoring points on the earth-rock dam body includes: Each of the p observations is obtained by the following steps: Acquire meteorological data of the area where the dam is located, as well as the hypotenuse length between the intelligent station and the corresponding prism monitoring point; The length of the hypotenuse is corrected using the meteorological data to obtain the corrected length of the hypotenuse; The one-way observation height difference of the intelligent station relative to the prism monitoring point is determined based on the corrected hypotenuse length and the observation angle of the intelligent station relative to the prism monitoring point, and the one-way observation height difference is used as the observation value.

5. The method according to claim 4, characterized in that, The step of solving for the unknown parameter vector based on the observed value vector to obtain a vector solution includes: Based on the correspondence between the intelligent station and the prism monitoring point corresponding to each observation value, a design matrix is ​​constructed. The dimension of the design matrix is ​​p×m. If the i-th observation value corresponds to the j-th prism monitoring point, then the value at position (i,j) in the design matrix is ​​1. Construct an objective function based on the observed value vector, the design matrix, and the unknown parameter vector; The objective function is solved by the least squares method to obtain the vector solution of the unknown parameter vector.

6. The method according to claim 1, characterized in that, The analysis of the vertical displacement change of the earth-rock dam based on the m target elevations includes: The third elevation of the earth-rock dam body at the current moment is determined based on the m target elevations; By comparing the initial elevation of the earth-rock dam body at the initial moment with the third elevation of the earth-rock dam body at the current moment, the vertical displacement change of the earth-rock dam body is determined. An alarm signal is generated when the change in vertical displacement exceeds a preset safety threshold.

7. An automated monitoring device for the vertical displacement of an earth-rock dam, characterized in that, The device includes: The correction module is used to correct the elevation of each of the n intelligent monitoring stations monitoring the vertical displacement of the earth-rock dam based on the first elevation of the steel pipe marker buried in the bedrock, so as to obtain the second elevation of the intelligent monitoring station. The acquisition module is used to acquire p observation values ​​obtained by the n intelligent monitoring stations observing m prism monitoring points on the earth-rock dam body, where each observation value represents the observation height difference between a single intelligent monitoring station and the corresponding single prism monitoring point; The construction module is used to construct an observation value vector based on the p observation values, and to use the elevations of the m prism monitoring points as an unknown parameter vector; The solution module is used to solve the unknown parameter vector based on the observed value vector to obtain a vector solution, wherein the vector solution includes m target elevations that correspond one-to-one with the m prism monitoring points; The analysis module is used to analyze the vertical displacement changes of the earth-rock dam based on the m target elevations.

8. An electronic device, characterized in that, The electronic device includes: processor; A memory for storing instructions to be executed by the processor, wherein the processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as claimed in any one of claims 1 to 6.

10. A computer program product, characterized in that... The program product includes a computer program that is executed by a processor using the method as described in any one of claims 1 to 6.

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