A high-precision monitoring method for earth-rock dam surface deformation based on GNSS

Abstract

The present invention discloses a high-precision monitoring method for the surface deformation of an earth-rock dam based on GNSS. The method comprises the following steps: conducting an on-site investigation, laying out a wireless communication network and a power system according to on-site conditions, formulating GNSS monitoring points according to the size and structural design of the dam, setting up GNSS deformation monitoring equipment, constructing a closed triangle control network according to the locations of the monitoring points, selecting an initial time point, remotely and in real time acquiring monitoring data of each monitoring point in different time periods at a service end through a wireless communication system, performing denoising and adjustment processing on the monitoring data of each monitoring point, solving the closed loop error of the control network according to the closed triangle control network, intercepting monitoring data of different time periods, and averaging and storing the denoised results.

Description

Technical Field

[0001] The present invention relates to a high-precision monitoring method for the surface deformation of an earth-rock dam based on GNSS, belonging to the technical field of deformation monitoring of earth-rock dams in water conservancy and hydropower projects. Background Art

[0002] External deformation monitoring of earth-rockfill dams can intuitively and accurately reflect the dam's operating status and is a key component of reservoir dam deformation monitoring. Traditional methods for monitoring the external deformation of rockfill dams often require regular on-site data collection, resulting in poor timeliness and impracticality in harsh climatic conditions. Furthermore, the sensor elements used, such as resistance strain gauges, inductive sensors, and steel chord sensors, generally suffer from poor anti-interference and stability, and are prone to zero-point drift. Rockfill dams are non-rigid structures that deform significantly when subjected to external forces. Furthermore, uneven force distribution across different parts leads to irregular deformation.

[0003] Currently, the bottleneck restricting improvements in observation accuracy remains the stability and precision of the observation instruments themselves. Conventional monitoring methods have long contributed to deformation monitoring of dams, large buildings, and structures. However, these methods suffer from low timeliness and inconsistent measurement results, which reduces their scientific validity and practical value. Furthermore, conventional methods require long observation cycles, making it impossible to understand building deformation in real time. Summary of the Invention

[0004] In order to solve the shortcomings of the existing technology, the purpose of the present invention is to provide a high-precision monitoring method for the surface deformation of earth-rock dams based on GNSS, which solves the problems of the traditional monitoring method in the existing technology that the data cannot be statistically analyzed in real time, the monitoring points cannot be monitored synchronously, and the monitoring data is discontinuous.

[0005] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0006] A high-precision monitoring method for surface deformation of earth-rock dams based on GNSS includes the following steps:

[0007] Acquiring monitoring data of each GNSS monitoring point in different time periods, wherein there are multiple GNSS monitoring points forming at least one closed triangular control network;

[0008] De-noising and adjustment processing are performed on the acquired monitoring data;

[0009] Based on the monitoring data after denoising and adjustment, the closed loop error is calculated for each closed triangle control network;

[0010] Perform weighted adjustment on the closed loop difference results to correct the coordinates of the monitoring points in each closed triangle control network;

[0011] The corrected coordinates are averaged and stored.

[0012] Furthermore, the monitoring data of the aforementioned GNSS monitoring points at different time periods are obtained by the following method:

[0013] Through wireless communication systems and power systems, real-time, remote access is provided to monitoring data collected by GNSS deformation monitoring equipment at each monitoring point over different time periods. The GNSS deformation monitoring equipment is installed at each monitoring point, which is designed based on the dam's size and structural design. Furthermore, the wireless communication network utilizes any of 4G, 5G, Wi-Fi, or Zigbee.

[0014] Furthermore, the aforementioned power system adopts a solar photovoltaic power generation system.

[0015] Furthermore, the aforementioned single closed triangle control network is constructed in such a way that every three adjacent monitoring points form a closed triangle control network.

[0016] Furthermore, the aforementioned step of performing denoising and adjustment processing on the acquired monitoring data of the monitoring points includes:

[0017] Take one day as a cycle and extract monitoring data every hour within the cycle;

[0018] The number of smoothing cycles S is set according to the accuracy requirement. The five-point smoothing processing method is called according to the set number of smoothing cycles to filter the monitoring data of different monitoring points and different time periods to obtain the noise-filtered monitoring data.

[0019] Furthermore, the calculation formula of the above five-point smoothing method is:

[0020] b(1)=[3×a(1)+2×a(2)+a(3)-a(4)] / 5

[0021] b(2)=p4×a(1)+3×a(2)+2×a(3)+a(4)] / 10

[0022] b(i)=[a(i-2)+a(i-1)+a(i)+a(i+1)+a(i+2)] / 5 i=3,...,n-2

[0023] b(n-1)=(a(n-3)+2×a(n-2)+3×a(n-3)+4×a(n)) / 10

[0024] b(n)=[-a(n-3)+a(n-2)+2×a(n-1)+3×a(n)] / 5

[0025] Where n is the number of monitoring data within a specified time period at the monitoring point, a(i) is the i-th monitoring data before smoothing, and b(i) is the i-th monitoring data after smoothing;

[0026] The monitoring data after smoothing in the previous cycle is brought back into the calculation formula of the five-point smoothing method as input data, and the final monitoring data is obtained by performing S cycles of calculation.

[0027] Furthermore, the aforementioned step of calculating the closed loop difference includes:

[0028] Number each closed triangle control network according to the monitoring point number;

[0029] Construct a space vector for the coordinates of each monitoring point in the closed triangle control network;

[0030] According to the numbering sequence of the closed triangle control network, solve the closed loop difference of each closed triangle control network

[0031]

[0032] Where B m1 , L m1 、H m1 are the coordinates of the first monitoring point in the mth closed triangle control network, B m2 , L m2 、H m2 are the coordinates of the second monitoring point in the mth closed triangle control network, B m3 , L m3 、H m3 are the coordinates of the third monitoring point in the mth closed triangle control network, ΔB m , ΔL m , ΔH m are the closed loop differences of the three coordinate components in the mth closed triangle control network.

[0033] Furthermore, the aforementioned step of performing weighted adjustment processing on the closed loop difference results to correct the coordinates of the monitoring points in each closed triangle control network includes:

[0034] Compare the closed loop difference result with the preset closed loop difference threshold. If the closed loop difference is less than or equal to the closed loop difference threshold, no coordinate adjustment is performed. If it is greater than the closed loop difference threshold, a weighted adjustment is performed according to the baseline distance. The formula for weighted adjustment is:

[0035]

[0036] Where B′ mj , L′ mj , H′ mjis the coordinate of the jth monitoring point in the mth closed triangle control network after adjustment, B mj , L mj 、H mj is the coordinate of the jth monitoring point in the mth closed triangle control network; They respectively represent the space vector constructed between the first monitoring point and the second monitoring point in the mth closed triangle control network, the space vector constructed between the second monitoring point and the third monitoring point in the mth closed triangle control network, and the space vector constructed between the third monitoring point and the first monitoring point in the mth closed triangle control network.

[0037] The beneficial effects achieved by the present invention are:

[0038] 1. With high-precision positioning technology as the core, this paper proposes an automatic and real-time monitoring method for the deformation and displacement of earth-rock dams. By establishing a triangular control network and using a closed loop difference method to correct the monitoring points within each triangular control network, the observation accuracy is improved, providing an effective support basis for the deformation and displacement of earth-rock dams and enabling management personnel to make scientific decisions.

[0039] 2. By adopting this method, data processing can be directly performed using the program, reducing personnel cost investment and data errors caused by manual monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of the GNSS earth-rock dam apparent deformation monitoring process of the present invention;

[0041] Figure 2 This is a schematic diagram of the triangulated control network of the present invention;

[0042] Figure 3 It is the original observation record diagram of the present invention;

[0043] Figure 4 This is the result diagram after noise filtering and adjustment of the monitoring results of the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0045] This embodiment discloses a high-precision monitoring method for surface deformation of earth-rock dams based on GNSS, the flow chart of which is as follows: Figure 1 As shown in FIG, the monitoring model for collecting, denoising and adjusting the surface deformation data of earth-rock dams based on GNSS monitoring equipment specifically includes the following steps:

[0046] Step 1: Conduct on-site surveys and deploy wireless communication networks and power systems based on on-site conditions.

[0047] Among them, the wireless communication network adopts 4G, 5G, WiFi or Zigbee and other communication methods, and the power system adopts a solar photovoltaic power generation system to avoid the large-scale deployment of network cables and cables.

[0048] Step 2: According to the dam's shape and structural design, GNSS monitoring points are planned, GNSS deformation monitoring equipment is set up, and the monitoring points are arranged as follows: Figure 2 The triangular GNSS closed loop control network shown,

[0049] The specific method is to form a closed triangle control network for each three adjacent monitoring points based on the layout of the GNSS monitoring points.

[0050] Step 3: Select the initial time point and use it as a benchmark to calculate the deformation of each monitoring point.

[0051] Step 4: Through the wireless communication system, the server remotely obtains the monitoring data of each monitoring point in real time, performs denoising and adjustment on the monitoring data of each monitoring point to eliminate signal interference and error effects.

[0052] 4.1 For the monitoring data at different times of each monitoring point, a five-point smoothing program is compiled using Matlab, Fortran, etc., and smoothing is performed using the five-point smoothing method. The calculation formula of the five-point smoothing method is as follows:

[0053] b(i)=[a(i-2)+a(i-1)+a(i)+a(i+1)+a(i+2)] / 5 i=3,...,n-2 (1)

[0054]

[0055] Where n is the number of monitoring data in the specified time period of each monitoring point, a(i) is the i-th monitoring data before smoothing, and b(i) is the i-th monitoring data after smoothing;

[0056] 4.2 Take one day as the cycle and extract monitoring data every 1 hour within the cycle. For example, after the end of Monday, start noise filtering and adjustment on Tuesday with zero data as the starting point;

[0057] 4.3 Assign the data in the selected time period to a, call the five-point smoothing process to filter out the noise, and obtain b;

[0058] 4.4 Set the number of smoothing cycles S according to the accuracy requirements, assign b to a, and repeat steps 4.1 to 4.3 according to the set number of smoothing cycles to obtain the noise-filtered monitoring data for the selected time period of the monitoring point;

[0059] 4.5 Perform steps 4.2 to 4.4 for all monitoring points to obtain processed data from all monitoring points.

[0060] Step 5: Based on the closed triangle control network established previously, solve the closed loop error of the control network and improve the monitoring accuracy through adjustment.

[0061] 5.1 Number each closed triangle control network according to the monitoring point number;

[0062] 5.2 From step 4, read the noise-filtered monitoring data of each measuring point at the calculation time;

[0063] 5.3 Construct a space vector for the coordinates of each monitoring point in the closed triangle control network;

[0064] 5.4 Set the closed loop difference threshold;

[0065] 5.5 Solving the closed loop error of a single closed triangle control network

[0066]

[0067] Where B m1 , L m1 、H m1 are the coordinates of the first monitoring point in the mth closed triangle control network, B m2 , L m2 、H m2 are the coordinates of the second monitoring point in the mth closed triangle control network, B m3 , L m3 、H m3 are the coordinates of the third monitoring point in the mth closed triangle control network, ΔB m , ΔL m , ΔH m are the closed loop differences of the three coordinate components in the mth closed triangle control network.

[0068] 5.6 If the closed loop error is less than or equal to the pre-set threshold, no coordinate adjustment is performed. If it is greater, a weighted adjustment is performed according to the baseline distance to correct the coordinates of all monitoring points in the closed triangle control network:

[0069]

[0070] Where B′ mj , L′ mj , H′ mjis the coordinate of the jth monitoring point in the mth closed triangle control network after adjustment, B mj , L mj 、H mj is the coordinate of the jth monitoring point in the mth closed triangle control network; They respectively represent the space vector constructed between the first monitoring point and the second monitoring point in the mth closed triangle control network, the space vector constructed between the second monitoring point and the third monitoring point in the mth closed triangle control network, and the space vector constructed between the third monitoring point and the first monitoring point in the mth closed triangle control network.

[0071] 5.6 Repeat steps 5.4 to 5.5 until the closed loop errors of all closed triangle control networks are less than the threshold.

[0072] Step six: intercept the monitoring data of different time periods, average the results after noise removal and adjustment, and store the data.

[0073] Because the monitoring cycle of earth-rock dams is very long, general engineering reports and records require average data of 1 day, 3 days, 7 days or 1 month. Therefore, the data of the monitoring points need to be averaged at the end.

[0074] The high-precision monitoring method for earth-rock dam surface deformation based on GNSS in this embodiment is used to monitor the original monitoring data of the earth-rock dam surface ( Figure 3 ) is processed to obtain Figure 4 From the results, it can be seen that the original monitoring method contains noise interference such as random signals, which greatly affects the accuracy of monitoring. The monitoring value cannot be used to evaluate the deformation of the measuring point. Through the noise filtering, closed-loop adjustment and other processing of the present invention, these noisy observation signals are processed, and the excessive singular values ​​are filtered out by the five-point smoothing method, making the deformation data more accurate, and being able to obtain an estimate of the real signal with the minimum error in the average sense. The embodiment also uses leveling measurement to demonstrate the accuracy of GNSS monitoring data. The comparison results are shown in Table 1, indicating that the data processing method proposed by the present invention has good accuracy.

[0075] Table 1 Comparison of level monitoring data and GNSS equipment monitoring data

[0076]

[0077] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A high-precision monitoring method for surface deformation of earth-rock dams based on GNSS, characterized in that: The steps include: Acquiring monitoring data of each GNSS monitoring point in different time periods, wherein there are multiple GNSS monitoring points forming at least one closed triangular control network; De-noising and adjustment processing are performed on the acquired monitoring data; Based on the monitoring data after denoising and adjustment, the closed loop error is calculated for each closed triangle control network; Perform weighted adjustment on the closed loop difference results to correct the coordinates of the monitoring points in each closed triangle control network; Perform average processing and data storage on the corrected coordinates; The step of calculating the closed loop difference comprises: Number each closed triangle control network according to the monitoring point number; Construct a space vector for the coordinates of each monitoring point in the closed triangle control network; According to the numbering sequence of the closed triangle control network, solve the closed loop difference of each closed triangle control network: ， Where, 、 、 are the coordinates of the first monitoring point in the mth closed triangle control network, 、 、 are the coordinates of the second monitoring point in the mth closed triangle control network, 、 、 are the coordinates of the third monitoring point in the mth closed triangle control network, 、 、 are the closed loop differences of the three coordinate components in the mth closed triangle control network; The step of performing weighted adjustment processing on the closed loop difference results to correct the coordinates of the monitoring points in each closed triangle control network includes: Compare the closed loop difference result with a preset closed loop difference threshold; if the closed loop difference is less than or equal to the closed loop difference threshold, no coordinate adjustment is performed; if it is greater than the closed loop difference threshold, a weighted adjustment is performed according to the baseline distance; The formula for the weighted adjustment is: ， Where, 、 、 is the coordinate of the jth monitoring point in the mth closed triangle control network after adjustment, 、 、 is the coordinate of the jth monitoring point in the mth closed triangle control network; , 、 、 They respectively represent the space vector constructed between the first monitoring point and the second monitoring point in the mth closed triangle control network, the space vector constructed between the second monitoring point and the third monitoring point in the mth closed triangle control network, and the space vector constructed between the third monitoring point and the first monitoring point in the mth closed triangle control network.

2. The high-precision monitoring method for surface deformation of earth-rock dams based on GNSS according to claim 1, characterized in that: The monitoring data of each GNSS monitoring point in different time periods are obtained by the following method: Through wireless communication systems and power systems, monitoring data collected by GNSS deformation monitoring equipment at each monitoring point in different time periods is obtained in real time and remotely. The GNSS deformation monitoring equipment is set up at each monitoring point, and the monitoring points are designed based on the size and structural design of the dam.

3. The high-precision monitoring method for surface deformation of earth-rock dams based on GNSS according to claim 2, characterized in that: The communication mode of the wireless communication network is any one of 4G, 5G, WiFi or Zigbee.

4. The high-precision monitoring method for surface deformation of earth-rock dams based on GNSS according to claim 2, characterized in that: The power system adopts a solar photovoltaic power generation system.

5. The high-precision monitoring method for surface deformation of earth-rock dams based on GNSS according to claim 1, characterized in that: The closed triangle control network is constructed in such a way that every three adjacent monitoring points form a closed triangle control network.

6. The high-precision monitoring method for surface deformation of earth-rock dams based on GNSS according to claim 1, characterized in that: The step of performing denoising and adjustment processing on the monitoring data of each monitoring point comprises: Take one day as a cycle and extract monitoring data every hour within the cycle; The number of smoothing cycles S is set according to the accuracy requirement, and the pre-programmed five-point smoothing processing program is called according to the set number of smoothing cycles to filter the monitoring data of different monitoring points and different time periods to obtain the noise-filtered monitoring data.

7. The high-precision monitoring method for surface deformation of earth-rock dams based on GNSS according to claim 6, characterized in that: The calculation formula of the five-point smoothing method is: ， ， Where n is the number of monitoring data in the specified time period at each monitoring point, a(i) is the i-th monitoring data before smoothing, and b(i) is the i-th monitoring data after smoothing; The monitoring data after smoothing in the previous cycle is brought back into the calculation formula of the five-point smoothing method as input data, and S cycles of calculation are performed to obtain the final monitoring data.

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