Early warning method for riverway bank collapse based on soil surface displacement acceleration trend analysis

Abstract

The invention discloses a riverway bank collapse early warning method based on soil body surface displacement acceleration trend analysis, and relates to the technical field of riverway bank collapse early warning. The method comprises the following steps: laying GNSS measurement site positions according to a potential risk area of river beach collapse and a transverse distance and a longitudinal distance of a collapse pit in a region where the GNSS measurement site positions are located; carrying out real-time traceability filtering and denoising calculation on the continuously online monitored soil body surface displacement data based on a Butterworth low-pass filter; carrying out real-time acceleration trend discrimination calculation on the filtered soil body surface displacement data, and carrying out risk grading early warning discrimination on bank collapse risk threshold values of different bank collapse risk grades by utilizing calculated linear acceleration trend intensity; and carrying out riverway bank collapse risk site survey and result verification according to a risk grading early warning judgment result. The method has important engineering application value in the aspects of river channel safety monitoring, disaster early warning, emergency management and the like, and scientific support can be provided for river treatment, dike safety maintenance, intelligent water conservancy construction and the like.

Description

Technical Field

[0001] The invention relates to the technical field of river bank collapse warning, and in particular to a river bank collapse warning method based on soil surface displacement acceleration trend analysis. Background Art

[0002] River bank collapse is a common river disaster phenomenon, which seriously affects river flood control safety, embankment stability, and coastal ecological environment. Especially in large river basins, river bank collapse not only threatens flood control embankments, ports and docks, and coastal infrastructure, but may also cause hidden dangers to ship navigation safety. Therefore, timely and accurate monitoring of river bank collapse and early warning of bank collapse are of great significance for disaster prevention and mitigation.

[0003] Traditional methods of early warning of bank collapse mainly include nearshore underwater topography measurement, manual bank inspection, drone inspection, etc. Although these methods can provide certain information about bank deformation, they are limited in the real-time and continuity of data acquisition, and it is difficult to meet the early warning needs of sudden bank collapse disasters. In recent years, the rapid development of GNSS (Global Navigation Satellite System) high-frequency online monitoring technology has made it possible to dynamically monitor the surface displacement of river bank soil with millimeter-level accuracy.

[0004] However, due to the complexity of river bank collapse and the large amount of environmental noise and system errors in high-frequency GNSS measurement data, direct use for bank collapse warning is likely to lead to misjudgment or missed judgment. Therefore, how to reasonably deploy GNSS in potential areas of bank collapse, how to effectively denoise GNSS high-frequency monitoring data and extract real trend displacement signals, and how to identify different acceleration trends under different risk signs before collapse, so as to establish a real-time classification model for accelerating trend to identify bank collapse risk based on online monitoring data of soil surface displacement, are technical problems that need to be solved urgently. Summary of the invention

[0005] In view of the above problems, the present invention proposes a river bank collapse early warning method based on soil surface displacement acceleration trend analysis, aiming to achieve real-time, accurate and intelligent bank collapse risk assessment and early warning.

[0006] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0007] A river bank collapse early warning method based on soil surface displacement acceleration trend analysis, the method comprising:

[0008] According to the potential risk areas of river bank collapse and the lateral and longitudinal distances of collapse pits in the area, GNSS measurement stations are deployed to collect real-time soil surface displacement data at the points;

[0009] A low-pass filter algorithm is designed based on the Butterworth low-pass filter to perform real-time source tracing filtering and denoising calculation on the soil surface displacement data monitored online;

[0010] The filtered soil surface displacement data is calculated and judged in real time based on the linear fitting method. The calculated linear acceleration trend intensity is used to judge the bank collapse risk thresholds of different bank collapse risk levels.

[0011] According to the results of the risk classification and early warning, on-site investigation of river bank collapse risk and verification of the results are carried out, the accuracy of the risk classification and early warning is verified by the results of the on-site investigation, and the surface displacement threshold of the soil surface displacement is further optimized.

[0012] As a preferred solution of the present invention, the method for arranging GNSS measurement sites includes:

[0013] According to the slope ratio of the bank and the height difference of the beach channel, the general monitoring area and the key monitoring area are divided. The key monitoring area refers to the area with a slope ratio of S≤1:3 or a beach channel height difference H≥20 meters. All areas outside the key monitoring area are general monitoring areas.

[0014] In the direction of the coastline, the density of GNSS measurement stations is determined according to the lateral scale characteristics of the collapse at the site, and the spacing L log ≤2B, where B represents the lateral distance of continuous collapsed banks or the lateral width of collapsed pits;

[0015] In the direction perpendicular to the shoreline, the density of GNSS measurement stations is determined according to the longitudinal distance characteristics of the on-site collapse. The longitudinal spacing L lat ≤2D, where D represents the average retreat distance of the collapsed bank or the longitudinal scale of the collapsed pit;

[0016] The GNSS measurement station is deployed in the beach area between the river embankment and the water edge. The deployment elevation is lower than the embankment elevation and is located between the embankment elevation and the average high water level of the river.

[0017] As a preferred solution of the present invention, the expression of the soil surface displacement data is:

[0018] ;

[0019] In the formula, Time series data of high-frequency soil surface displacement monitored by GNSS measurement stations; Represents the low-frequency trend soil displacement part; represents the random noise part;

[0020] Using Butterworth low-pass filter to design low-pass filtering algorithm for random noise part Perform filtering and denoising calculations for forward tracing.

[0021] As a preferred solution of the present invention, the low-pass filtering algorithm requires two parameters to characterize, namely, the order of the filter N and the cutoff frequency at -3dB ;

[0022] For the soil surface displacement data monitored by the GNSS measurement station, the normalized amplitude-frequency response function of the Butterworth filter is It is expressed as:

[0023] ;

[0024] In the formula, is the signal frequency, is the cut-off frequency, N is the order of the filter.

[0025] As a preferred solution of the present invention, the order of the filter is N Determined by the following formula:

[0026] ;

[0027] In the formula, is the passband attenuation; is the stop-band attenuation; is the sampling frequency;

[0028] The cut-off frequency Determine as follows:

[0029] Time series data of soil surface displacement Use fast Fourier transform to perform spectrum analysis and calculate the corresponding spectrum :

[0030] ;

[0031] In the formula, M is the number of fast Fourier transform points, is an imaginary unit; The frequency index represents the frequency component in the spectrum, The time index represents the sampling point in the time series;

[0032] Calculate the spectrum amplitude :

[0033] ;

[0034] In the formula, and are the real and imaginary parts after fast Fourier transform respectively;

[0035] By analyzing the spectrum amplitude, the cumulative contribution rate of frequency energy is statistically calculated , the formula is:

[0036] ;

[0037] In the formula, Represents the index corresponding to the candidate cutoff frequency; M / 2 represents the maximum index of the positive frequency range;

[0038] Choose to meet the cumulative contribution rate The minimum frequency is used as the cut-off frequency .

[0039] As a preferred solution of the present invention, the real-time acceleration trend discrimination calculation is performed on the filtered soil surface displacement, and the calculated linear acceleration trend intensity is used to perform risk classification warning discrimination on the surface displacement thresholds of different bank collapse risk levels, specifically including:

[0040] The soil surface displacement after denoising and filtering is calculated in real time according to the least square method to determine the trend, and the linear increase rate of the trend displacement is calculated, which represents the movement speed of the soil surface monitoring displacement during collapse. ;

[0041] When calculating the linear increase rate of the trend displacement, the data of 5 consecutive hours before the displacement judgment time point is selected as the calculation period, which is recorded as , the sampling frequency of the GNSS measurement station is ;

[0042] for At this moment, in order to calculate the moving speed of the soil surface displacement And determine the risk of bank collapse, use it in calculation The number of previous data points within 5 consecutive hours moving forward for:

[0043] ;

[0044] Perform the least squares calculation on the previous sampling data:

[0045] ;

[0046] In the formula, The current calculation time point Calculate the corresponding time of the sampling points in the domain forward; The current calculation time point The soil surface displacement corresponding to the sampling points in the forward calculation domain.

[0047] As a preferred solution of the present invention, the bank collapse risk thresholds of different bank collapse risk levels are set according to different risk situations of river bank collapse, specifically divided into:

[0048] Low risk state, i.e. safe zone: The trend deformation is within the set acceptable range and close to the static stable state. At this time, the bank collapse risk threshold is recorded as , the corresponding trend deformation rate range is ;

[0049] Medium risk status, i.e. warning zone: Trend deformation has a cumulative trend, cracks occur inside the soil, and the cracks continue to extend. At this time, the bank collapse risk threshold is recorded as , the corresponding trend deformation rate range is , corresponding to the issuance of a medium risk warning for river bank collapse;

[0050] High-risk state, i.e. critical zone: when the soil surface displacement continues in the warning zone for a preset period of time, The trend deformation has an accelerating development trend. At this time, through cracks appear inside the soil, and the local soil reaches a critical sliding state, facing an unstable collapse situation. After the collapse, the entire collapse body will quickly lose its bearing capacity. The corresponding trend deformation rate range is , and a corresponding high-risk warning for river bank collapse was issued.

[0051] As a preferred solution of the present invention, the on-site investigation of river bank collapse risk and result verification are carried out according to the results of risk classification warning and discrimination, the accuracy of risk classification warning and discrimination is verified by using the results of the on-site investigation, and the bank collapse risk threshold of soil surface displacement is further optimized, specifically including:

[0052] Early warning response survey: When the soil surface displacement is in a medium-risk state or a high-risk state and an early warning is issued, an on-site survey is organized to observe the cracks on the slope, including the width, depth and direction of the cracks, check for signs of local collapse, and assess the threat of bank collapse;

[0053] On-site data comparison: compare the on-site measured data, including the landslide location and crack expansion, with the warning status predicted in the real-time classification and judgment results of the acceleration trend of soil surface displacement;

[0054] If the on-site bank collapse situation is stronger than the warning state, the critical bank collapse risk threshold needs to be lowered;

[0055] If the on-site bank collapse situation is lower than the warning level, the critical bank collapse risk threshold needs to be increased.

[0056] By deploying GNSS measurement stations in areas with potential risks of river collapse, soil surface displacement data can be collected in real time, providing real-time and accurate data support for early identification and early warning of bank collapse risks; the Butterworth low-pass filter is used to denoise the soil surface displacement data, significantly reducing environmental interference and measurement errors, improving data accuracy, and ensuring the reliability of subsequent trend analysis; the filtered displacement data is analyzed by a linear fitting method, the acceleration trend intensity is calculated in real time, and a graded early warning of bank collapse risks is carried out based on this trend, effectively improving the accuracy of the warning and the response speed; on-site surveys are carried out based on the risk classification early warning results, and the accuracy of the warning is verified in combination with measured data, and the risk threshold is further optimized, making the early warning system more accurate and operational; the on-site survey and verification results provide a basis for optimizing the warning threshold, ensuring that the early warning system can be dynamically adjusted according to actual conditions, thereby improving the adaptability and long-term effectiveness of the system. The present invention combines GNSS real-time monitoring, low-pass filtering denoising, accelerated trend analysis, risk classification warning and field verification to form an efficient, intelligent and accurate river bank collapse warning method, which can capture soil displacement changes in real time and conduct scientific risk assessment, providing strong support for early warning and prevention of bank collapse disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them: Figure 1 is a flow chart of the method of the present invention; Figure 2 This is a schematic diagram of the on-site deployment of GNSS measurement stations in an embodiment of the present invention; Figure 3 A comparison diagram of the original soil surface displacement data sequence and the displacement data sequence after Butterworth low-pass filtering in an embodiment of the present invention; Figure 4 Schematic diagram of risk classification warning and discrimination of accelerated change trend of soil surface displacement in an embodiment of the present invention; Figure 5 This is a schematic diagram of a site photo of a low-risk state of bank collapse in an embodiment of the present invention; Figure 6 This is a schematic diagram of an on-site photo of a high-risk state of bank collapse in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0059] like Figure 1 FIG. 1 is an embodiment of the present invention, which provides a river bank collapse early warning method based on soil surface displacement acceleration trend analysis, comprising the following steps:

[0060] S1: GNSS (Global Navigation Satellite System) measurement sites are arranged horizontally and vertically

[0061] According to the potential risk areas of river bank collapse in the area and the lateral and longitudinal distances of collapse pits, GNSS measurement stations are reasonably arranged to collect soil surface displacement data at the points in real time.

[0062] Specifically, the method for deploying GNSS measurement sites includes:

[0063] According to the slope ratio of the bank and the height difference of the beach channel, the general monitoring area and the key monitoring area are divided. The spacing of GNSS measurement stations in the key monitoring area is more dense. The key monitoring area refers to the area with a slope ratio of S≤1:3 or a beach channel height difference H≥20 meters. All areas outside the key monitoring area are general monitoring areas;

[0064] In the direction of the coastline, the density of GNSS measurement stations is determined according to the lateral scale characteristics of the collapse at the site, and the spacing L log ≤2B, where B represents the lateral distance of continuous collapsed banks or the lateral width of collapsed pits; for example, the lateral width of a large pit collapse in a certain section can reach about 500m, so the layout spacing can be 1km;

[0065] In the direction perpendicular to the shoreline, the density of GNSS measurement stations is determined according to the longitudinal distance characteristics of the on-site collapse. The longitudinal spacing L lat ≤2D, where D represents the average retreat distance of the collapsed bank or the longitudinal scale of the collapsed pit; for example, the average retreat depth of a large collapsed pit in a certain section can reach 60m, so the layout spacing can be 120m;

[0066] GNSS measurement stations are deployed in the beach area between the river embankment and the waterside, which has been eroded and collapsed under the conditions of water flow, and can take into account the requirements of construction convenience and long-term maintenance and use. Generally speaking, the equipment is deployed at an elevation lower than the embankment elevation and between the embankment elevation and the average high water level of the river (the average high water level is taken for tidal river sections).

[0067] like Figure 2 As shown in the figure, this is an online measurement station for GNSS soil surface displacement measurement deployed on-site at a certain section of the river where the average high tide level is about +2m and the embankment elevation is about +5m, so the elevation of the measurement station deployment area is +3m.

[0068] S2: Spectral analysis and denoising calculation of high-frequency online displacement data

[0069] A low-pass filtering algorithm is designed based on the Butterworth low-pass filter, and real-time source tracing filtering and denoising calculation is performed on the soil surface displacement data monitored continuously online.

[0070] In one embodiment, the soil surface displacement data is expressed as:

[0071] ;

[0072] In the formula, The time series data of high-frequency soil surface displacement monitored by the GNSS measurement station is used to obtain the synthetic displacement in the three-dimensional direction. The sampling frequency is , generally 1 data per 1 to 3 minutes; Represents the low-frequency trend soil displacement part; It represents the random noise part, which is mainly caused by measurement errors, environmental interference, instrument stability and other factors, including receiver white noise, tropospheric and ionospheric delay noise, vehicle passing, wind effect, temperature change and other noise in the observation environment;

[0073] Using Butterworth low-pass filter to design low-pass filtering algorithm for random noise part Perform forward tracing filtering and denoising calculations, where the low-pass filtering algorithm requires two parameters to characterize, namely the order of the filter N and the cutoff frequency at -3dB ;

[0074] For the soil surface displacement data monitored by the GNSS measurement station, the normalized amplitude-frequency response function of the Butterworth filter is It is expressed as:

[0075] ;

[0076] In the formula, is the signal frequency, is the cut-off frequency, N is the order of the filter;

[0077] when hour, There is almost no signal attenuation;

[0078] when hour, The signal is effectively attenuated;

[0079] The order of the filter N The larger it is, the steeper the filter's transition band (the area from 1 to 0).

[0080] According to the general characteristics of soil surface displacement changes during bank collapse, the order of the Butterworth low-pass filter is N Choose a more suitable medium level, balancing the smoothing effect and signal fidelity at the same time;

[0081] Furthermore, the filter order N Determined by the following formula:

[0082] ;

[0083] In the formula, is the passband attenuation, in dB, and the default value is ; is the stopband attenuation in dB. For soil surface displacement data, the default value is ; is the sampling frequency; the sampling frequency of this data sequence is 1 data per minute, which is converted into a frequency of 1 / 60Hz.

[0084] Cutoff frequency The selection needs to be analyzed based on the spectrum characteristics of the actual signal of the GNSS soil surface displacement. It can be determined by using the FFT (Fast Fourier Transform) spectrum analysis method combined with the energy contribution method;

[0085] Specifically, the cutoff frequency Determine as follows:

[0086] Time series data of soil surface displacement Use fast Fourier transform to perform spectrum analysis and calculate the corresponding spectrum :

[0087] ;

[0088] In the formula, M is the number of fast Fourier transform points, is an imaginary unit; The frequency index represents the frequency component in the spectrum, The time index represents the sampling point in the time series;

[0089] Calculate the spectrum amplitude :

[0090] ;

[0091] In the formula, and are the real and imaginary parts after fast Fourier transform respectively;

[0092] By analyzing the spectrum amplitude, the time series data of soil surface displacement is determined In which frequency ranges is the main energy of the signal concentrated? Specifically, the cumulative contribution rate of the statistical frequency energy , the formula is:

[0093] ;

[0094] In the formula, Indicates the index corresponding to the candidate cutoff frequency; M / 2 indicates the maximum index of the positive frequency range;

[0095] Choose to meet the cumulative contribution rate The minimum frequency is used as the cut-off frequency In this embodiment, the cut-off frequency can be taken as 0.03 Hz to 0.05 Hz according to calculation.

[0096] like Figure 3 The figure shows the comparison between the original high-frequency data sequence of the GNSS measurement station during operation and the displacement data sequence after Butterworth low-pass filtering. The original high-frequency data in the upper part of the figure fluctuates frequently and with large amplitudes, indicating that the data is affected by more high-frequency noise, which may be caused by the external environment (such as wind, temperature changes, etc.) or measurement errors. The fluctuation pattern of the original data is relatively complex, and it is difficult to extract a clear trend displacement from it.

[0097] The data below, after Butterworth filtering, has significantly reduced volatility, showing a smooth and gradually changing curve. After filtering, most of the high-frequency noise is removed, making the data closer to the actual trend displacement. The filtered data shows a more obvious and continuous change trend (such as a gentle curve of rising or falling), which shows that the long-term trend displacement is effectively extracted through the low-pass filter, and irrelevant high-frequency fluctuations are removed, which can help better analyze the long-term changes in soil displacement.

[0098] S3: Risk classification and early warning of soil surface displacement acceleration trend

[0099] The filtered soil surface displacement data is subjected to real-time acceleration trend discrimination calculation according to the linear fitting method. The calculated linear acceleration trend intensity is used to perform risk classification and early warning discrimination on the bank collapse risk thresholds of different bank collapse risk levels.

[0100] In one embodiment, the implementation method of step S3 includes:

[0101] The soil surface displacement after denoising and filtering is calculated in real time according to the least square method to determine the trend, and the linear increase rate of the trend displacement is calculated, which represents the movement speed of the soil surface monitoring displacement during collapse. ;

[0102] According to the general characteristics of river bank collapse, the soil displacement shows a significant trend of cumulative increase within 3 to 5 hours before the bank collapse occurs. Therefore, when calculating the linear increase rate of the trend displacement, the data of 5 consecutive hours before the displacement judgment time point is selected as the calculation period, which is recorded as , the sampling frequency of the GNSS measurement station is (1 sampling data per minute);

[0103] for At this moment, in order to calculate the moving speed of the soil surface displacement And determine the risk of bank collapse, use it in calculation The number of previous data points within 5 consecutive hours moving forward for:

[0104] ;

[0105] Perform the least squares calculation on the previous data:

[0106] ;

[0107] In the formula, The current calculation time point Calculate the corresponding time of the sampling points in the domain forward; The current calculation time point The soil surface displacement corresponding to the sampling points in the forward calculation domain.

[0108] like Figure 4 As shown, there are 40,800 data points in total during the example calculation process, and the data duration is 20 days.

[0109] The bank collapse risk thresholds of different bank collapse risk levels are set according to different risk scenarios of river bank collapse, specifically divided into:

[0110] Low risk status (safe zone): The trend deformation is within the set acceptable range and close to the static stable state. At this time, the bank collapse risk threshold is recorded as , the corresponding trend deformation rate range is In this calculation The value is 0;

[0111] Medium risk status (warning zone): Trend deformation has a cumulative trend. Generally, cracks occur inside the soil and the cracks continue to extend. At this time, the bank collapse risk threshold is recorded as , the corresponding trend deformation rate range is , corresponding to the issuance of a medium risk warning for river bank collapse; in this calculation The value is 0.0005;

[0112] High risk state (critical zone): When the soil surface displacement continues in the warning zone for a preset period of time, The trend deformation has an obvious accelerating development trend. At this time, through cracks appear inside the soil, and the local soil reaches a critical sliding state, facing an unstable collapse situation. After the collapse, the entire collapse body will quickly lose its bearing capacity. The corresponding trend deformation rate range is , and a corresponding high-risk warning for river bank collapse was issued.

[0113] Among them, the bank collapse risk thresholds of different bank collapse risk levels are , The value-taking process is determined comprehensively with reference to bank collapse characteristics, geological conditions, protection conditions, historical bank collapse characteristics and other conditions in different regions.

[0114] like Figure 4 The figure shows the risk level warning judgment result based on the accelerated change trend of the soil surface displacement of the sample measurement data during operation. It can be seen from the figure that from April 24 to May 5, the fluctuation of the soil surface displacement data was generally stable, and the judgment result was that it was in a low risk state of bank collapse; from May 5 to May 12, the soil surface displacement data increased, and the judgment result was that it was in a medium risk state of bank collapse; from May 12 to May 15, the soil surface displacement data increased rapidly, and the judgment result was that it was in a high risk state of bank collapse.

[0115] S4: On-site survey, verification and optimization of river bank collapse risk classification thresholds after early warning

[0116] Based on the results of risk classification and early warning, on-site investigation and verification of river bank collapse risk are carried out. The accuracy of risk classification and early warning is verified by using the results of on-site investigation, and the bank collapse risk threshold of soil surface displacement is further optimized.

[0117] Specifically, step S4 includes:

[0118] Early warning response survey: When medium-risk and high-risk deformation of the soil surface occurs and an early warning is issued, an on-site survey is organized to observe the cracks on the bank slope (width, depth, direction), check for signs of local collapse (collapse, sliding soil blocks), and assess the threat of bank collapse;

[0119] Field data comparison: compare the predicted warning status in the real-time classification and judgment results of the measured field data, including the landslide location, crack expansion and acceleration trend of soil surface displacement. If the on-site bank collapse situation is stronger than the warning status, it means that the warning model is too conservative and the critical bank collapse risk threshold needs to be lowered; if the on-site bank collapse threat is lower than the warning status, the critical bank collapse risk threshold needs to be further increased.

[0120] The accuracy of the judgment results is verified through multiple on-site surveys, and the bank collapse risk threshold of soil surface displacement is further optimized to improve the scientific nature of the use of early warning methods and reduce false alarms and missed alarms.

[0121] On April 30 and May 15, we visited the site to check the collapse of the river channel where the GNSS measurement station is located. See the site photos for details. Figure 5 and Figure 6 On April 30, there was no obvious scouring at the front of the river channel, the beach was relatively stable, and there were no cracks in the soil at the site, which was in a low risk state of bank collapse; on May 15, large through cracks appeared at the front of the beach, and the front was scoured violently, which was indeed facing a high risk state of imminent bank collapse. This early warning method was well verified in the field environment.

[0122] In summary, the present invention can accurately capture the displacement changes of river bank collapse in real time by deploying GNSS measurement stations and combining real-time online monitoring data of soil surface displacement, ensuring timely warning before bank collapse occurs, and using Butterworth low-pass filter to effectively remove high-frequency noise, significantly improving the accuracy of displacement data, thereby improving the accuracy of warning;

[0123] By denoising the displacement data on the soil surface and identifying the acceleration trend based on the linear fitting method, the acceleration trend of the soil surface can be analyzed and the potential risk of bank collapse can be accurately identified. The linear growth rate of soil displacement can be revealed through acceleration trend analysis, and risk classification can be performed according to different speed ranges, effectively improving the response speed and accuracy of bank collapse risk warning.

[0124] By setting different risk level thresholds (low risk, medium risk, high risk), different soil displacement trends are carefully graded, so that the early warning system can issue corresponding early warning information according to different risk states; combined with on-site surveys and result verification, the bank collapse risk threshold is further optimized. This verification process can adjust and improve the reliability of the early warning model in real time, and improve the effectiveness and pertinence of the early warning;

[0125] The graded warning scheme and on-site verification mechanism proposed in the present invention can not only realize intelligent decision-making, but also make dynamic adjustments according to the on-site environment and actual conditions, thereby enhancing the scientificity and practicality of bank collapse prevention and control; through continuous optimization and real-time data feedback, the bank collapse risk threshold can be automatically adjusted, further improving the accuracy and timeliness of bank collapse warning.

[0126] The present invention provides an efficient, intelligent and accurate river bank collapse warning method through precise displacement monitoring, effective noise filtering, accelerated trend identification and multi-level risk classification warning, which greatly improves the warning capability and prevention effect of river bank collapse disasters.

[0127] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or other arbitrary combinations. When implemented using software, it can be implemented in whole or in part in the form of a computer program product, which includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0128] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0129] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A river bank collapse early warning method based on soil surface displacement acceleration trend analysis, characterized in that: The method comprises: According to the potential risk areas of river bank collapse and the lateral and longitudinal distances of collapse pits in the area, GNSS measurement stations are deployed to collect real-time soil surface displacement data at the points; A low-pass filter algorithm is designed based on the Butterworth low-pass filter to perform real-time source tracing filtering and denoising calculation on the soil surface displacement data monitored online; The filtered soil surface displacement data is subjected to real-time acceleration trend discrimination calculation based on the linear fitting method, and the calculated linear acceleration trend intensity is used to perform risk classification and early warning discrimination on the bank collapse risk thresholds of different bank collapse risk levels; According to the results of the risk classification and early warning, on-site investigation of river bank collapse risk and verification of the results are carried out, the accuracy of the risk classification and early warning is verified by the results of the on-site investigation, and the surface displacement threshold of the soil surface displacement is further optimized.

2. A river bank collapse early warning method based on soil surface displacement acceleration trend analysis as claimed in claim 1, characterized in that: The method for arranging GNSS measurement sites includes: According to the slope ratio of the bank and the height difference of the beach channel, the general monitoring area and the key monitoring area are divided. The key monitoring area refers to the area with a slope ratio of S≤1:3 or a beach channel height difference H≥20 meters. All areas outside the key monitoring area are general monitoring areas. In the direction of the coastline, the density of GNSS measurement stations is determined according to the lateral scale characteristics of the collapse at the site, and the spacing L log ≤2B, where B represents the lateral distance of continuous collapsed banks or the lateral width of collapsed pits; In the direction perpendicular to the shoreline, the density of GNSS measurement stations is determined according to the longitudinal distance characteristics of the on-site collapse. The longitudinal spacing L lat ≤2D, where D represents the average retreat distance of the collapsed bank or the longitudinal scale of the collapsed pit; The GNSS measurement station is deployed in the beach area between the river embankment and the water edge. The deployment elevation is lower than the embankment elevation and is located between the embankment elevation and the average high water level of the river.

3. The river bank collapse early warning method based on soil surface displacement acceleration trend analysis as claimed in claim 1, characterized in that: The expression of the soil surface displacement data is: ； In the formula, Time series data of high-frequency soil surface displacement monitored by GNSS measurement stations; Represents the low-frequency trend soil displacement part; represents the random noise part; Using Butterworth low-pass filter to design low-pass filtering algorithm for random noise part Perform filtering and denoising calculations for forward tracing.

4. A river bank collapse early warning method based on soil surface displacement acceleration trend analysis as claimed in claim 3, characterized in that: The low-pass filtering algorithm requires two parameters to characterize, namely the order of the filter N and the cutoff frequency at -3dB ; For the soil surface displacement data monitored by the GNSS measurement station, the normalized amplitude-frequency response function of the Butterworth filter is It is expressed as: ； In the formula, is the signal frequency, is the cut-off frequency, N is the order of the filter.

5. A river bank collapse early warning method based on soil surface displacement acceleration trend analysis as claimed in claim 4, characterized in that: The order of the filter N Determined by the following formula: ； In the formula, is the passband attenuation; is the stop-band attenuation; is the sampling frequency; The cut-off frequency Determine as follows: Time series data of soil surface displacement Use fast Fourier transform to perform spectrum analysis and calculate the corresponding spectrum : ； In the formula, M is the number of fast Fourier transform points, is an imaginary unit; The frequency index represents the frequency component in the spectrum, The time index represents the sampling point in the time series; Calculate the spectrum amplitude : ； In the formula, and are the real and imaginary parts after fast Fourier transform respectively; By analyzing the spectrum amplitude, the cumulative contribution rate of frequency energy is statistically calculated , the formula is: ； In the formula, Represents the index corresponding to the candidate cutoff frequency; M / 2 represents the maximum index of the positive frequency range; Choose to meet the cumulative contribution rate The minimum frequency is used as the cut-off frequency .

6. A river bank collapse early warning method based on soil surface displacement acceleration trend analysis as claimed in claim 1, characterized in that: The real-time acceleration trend discrimination calculation is performed on the filtered soil surface displacement, and the calculated linear acceleration trend intensity is used to perform risk classification warning discrimination on the surface displacement thresholds of different bank collapse risk levels, specifically including: The soil surface displacement after denoising and filtering is calculated in real time according to the least square method to determine the trend, and the linear increase rate of the trend displacement is calculated, which represents the movement speed of the soil surface monitoring displacement during collapse. ; When calculating the linear increase rate of the trend displacement, the data of 5 consecutive hours before the displacement judgment time point is selected as the calculation period, which is recorded as , the sampling frequency of the GNSS measurement station is ; for At this moment, in order to calculate the moving speed of the soil surface displacement And determine the risk of bank collapse, use it in calculation The number of previous data points within 5 consecutive hours moving forward for: ； Perform the least squares calculation on the previous sampling data: ； In the formula, The current calculation time point Calculate the corresponding time of the sampling points in the domain forward; The current calculation time point The soil surface displacement corresponding to the sampling points in the forward calculation domain.

7. A river bank collapse early warning method based on soil surface displacement acceleration trend analysis as claimed in claim 6, characterized in that: The bank collapse risk thresholds of different bank collapse risk levels are set according to different risk situations of river bank collapse, and are specifically divided into: Low risk state, i.e. safe zone: The trend deformation is within the set acceptable range and close to the static stable state. At this time, the bank collapse risk threshold is recorded as , the corresponding trend deformation rate range is ; Medium risk status, i.e. warning zone: Trend deformation has a cumulative trend, cracks occur inside the soil, and the cracks continue to extend. At this time, the bank collapse risk threshold is recorded as , the corresponding trend deformation rate range is , corresponding to the issuance of a medium risk warning for river bank collapse; High-risk state, i.e. critical zone: when the soil surface displacement continues in the warning zone for a preset period of time, The trend deformation has an accelerating development trend. At this time, through cracks appear inside the soil, and the local soil reaches a critical sliding state, facing an unstable collapse situation. After the collapse, the entire collapse body will quickly lose its bearing capacity. The corresponding trend deformation rate range is , and a corresponding high-risk warning for river bank collapse was issued.

8. The river bank collapse early warning method based on soil surface displacement acceleration trend analysis as claimed in claim 1, characterized in that: The on-site investigation and verification of river bank collapse risk are carried out according to the results of risk classification and early warning, and the accuracy of risk classification and early warning is verified by the results of the on-site investigation, and the bank collapse risk threshold of soil surface displacement is further optimized, which specifically includes: Early warning response survey: When the soil surface displacement is in a medium-risk state or a high-risk state and an early warning is issued, an on-site survey is organized to observe the cracks on the slope, including the width, depth and direction of the cracks, check for signs of local collapse, and assess the threat of bank collapse; On-site data comparison: compare the on-site measured data, including the landslide location and crack expansion, with the warning status predicted in the real-time classification and judgment results of the acceleration trend of soil surface displacement; If the on-site bank collapse situation is stronger than the warning state, the critical bank collapse risk threshold needs to be lowered; If the on-site bank collapse situation is lower than the warning level, the critical bank collapse risk threshold needs to be increased.

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