Ground surface deformation height difference evolution measurement system based on multi-source data

Abstract

The present application relates to the technical field of ground deformation monitoring, and particularly relates to a ground deformation height difference evolution measurement system based on multi-source data, comprising: a signal acquisition module, used for acquiring radio signals of a target area, including direct path signals and reflected signals; a reflected signal identification module, used for establishing a signal reflection model based on gain coefficients and time delay parameters of the reflected signals; and a signal interference calculation module, used for calculating a signal interference index for evaluating the interference degree of the reflected signals on the received signals based on the signal reflection model.The present application establishes a signal propagation model containing direct signals and reflected paths by laying out multi-channel radio receivers and combining a three-dimensional terrain model.The technology can accurately identify the geometric characteristics and signal attenuation law of different reflected paths, providing a theoretical basis for subsequent interference elimination, and significantly improves the explainability of signal propagation paths under complex terrain, creating conditions for accurately quantifying multipath interference.

Description

Technical Field

[0001] This invention relates to the field of land surface deformation monitoring technology, and more specifically to a land surface deformation elevation difference evolution measurement system based on multi-source data. Background Technology

[0002] Surface deformation, including subsidence, uplift, and horizontal displacement, is a dynamic response of the Earth's surface to the combined effects of natural factors (such as tectonic movements, earthquakes, volcanic activity, groundwater extraction, and glacial melting) and human activities (such as urban expansion, large-scale engineering construction, and mineral resource extraction). Accurate and continuous monitoring of the spatiotemporal evolution of surface deformation is of crucial scientific value and practical significance for ensuring urban safety, assessing geological hazard risks, monitoring the stability of major engineering projects, and studying global climate change.

[0003] In the field of surface deformation monitoring, especially in landslide monitoring in mountainous areas, landslides are often caused by small-scale, uneven deformation. This type of deformation may be overlooked in most monitoring systems because they typically focus on the overall deformation trend. Therefore, current technologies for monitoring small-scale uneven deformation in landslide monitoring mainly rely on high-density sensor networks and high-resolution remote sensing technologies (such as small drones or lidar). These technologies enable detailed monitoring of local areas, capturing minute deformation changes, and identifying potential landslide risks through data fusion and analysis algorithms.

[0004] When landslide monitoring stations are located in high mountain valleys, these valleys are typically composed of steep cliffs that reflect radio signals at various angles. During propagation, the signals are reflected off the cliffs, resulting in multiple signal paths overlapping and creating a multipath effect. This phenomenon means that the receiver receives not only signals from the direct path but also signals reflected back from the cliffs. This multipath propagation effect causes phase differences in the received signals, affecting signal clarity and stability. This interference can lead to variations in signal strength, making it difficult for equipment to accurately receive and decode data. Furthermore, because the received signals include reflected signals from different paths, it can increase errors in the positioning system, affecting the accuracy and real-time performance of surface deformation data, and consequently impacting landslide risk assessment and early warning.

[0005] Therefore, a land surface deformation and elevation difference evolution measurement system based on multi-source data is proposed to solve the problems mentioned above. Summary of the Invention

[0006] Technical problems to be solved

[0007] To address the aforementioned shortcomings of existing technologies, this invention provides a surface deformation elevation difference evolution measurement system based on multi-source data. This system effectively solves the signal interference problem caused by multipath effects in surface deformation monitoring in high mountain and canyon areas. It achieves high-precision surface deformation monitoring in complex terrain environments, significantly improving the reliability of early warning systems for geological disasters such as landslides.

[0008] Technical solution

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] This invention provides a system for measuring the evolution of surface deformation and elevation difference based on multi-source data, comprising:

[0011] The signal acquisition module is used to acquire radio signals in the target area, including direct path signals and reflected signals;

[0012] The reflected signal identification module establishes a signal reflection model based on the gain coefficient and time delay parameters of the reflected signal;

[0013] The signal interference calculation module calculates signal interference indices based on the signal reflection model to assess the degree of interference of reflected signals to received signals.

[0014] The signal optimization module dynamically adjusts the received signal based on signal interference indicators to eliminate the influence of signals from indirect paths.

[0015] Furthermore, establishing the signal reflection model includes:

[0016] S1: Construct a 3D terrain model using elevation data of the target area, and determine the transmitter location within the 3D terrain model. and receiver location ;

[0017] S2: Calculate the direct path from the transmitter to the receiver, i.e.:

[0018]

[0019] In the formula, d is the signal propagation distance;

[0020] S3: Define a reflection point located on the signal propagation path that satisfies the condition that the angle of incidence equals the angle of reflection, i.e.:

[0021]

[0022] In the formula, This indicates the geometric relationship between the reflection point and the transmitter and receiver; This represents the three-dimensional coordinates of the i-th reflection point; i is the index number of the reflection point. Let be the angle of incidence at the i-th reflection point;

[0023] S4: Measure the signal strength along the direct path from the base station to the target point without reflection, i.e.:

[0024]

[0025] In the formula, For the direct path signal; A is the initial signal strength. It is the attenuation factor;

[0026] S5: Under the same conditions, measure the reflected signals received from reflection points along different reflection paths. For each reflected signal, calculate its corresponding gain coefficient. ,Right now:

[0027]

[0028] In the formula, Let be the gain coefficient of the i-th reflected signal relative to the direct path signal;

[0029] S6: The expression for the signal reflection model, which calculates the composite signal received in a complex environment based on the direct path signal, reflected signal, and gain coefficient, is as follows:

[0030]

[0031] In the formula, This represents the composite signal received at time t; n is the number of reflected signals. Indicates time The i-th reflected signal received; Let be the time delay of the i-th reflected signal.

[0032] Furthermore, the delay is obtained by combining the time from the transmitter to the reflection point and the time from the reflection point to the receiver, that is:

[0033]

[0034] In the formula, This is the distance from the transmitter location to the reflection point location; denoted as ρ, where ρ is the distance from the reflection point to the receiver; c is the signal propagation speed.

[0035] Furthermore, the signal interference index calculated based on the signal reflection model to assess the degree of interference of the reflected signal to the received signal includes:

[0036]

[0037] In the formula, I represents the signal interference index; T represents the measurement window.

[0038] Furthermore, the dynamic adjustment of the received signal based on the signal interference index includes:

[0039] S7: Collect the composite signal over a continuous time period and calculate the average signal strength over that continuous time period, i.e.:

[0040]

[0041] In the formula, The average intensity of the composite signal is m; m is the number of composite signal samples collected within a continuous time period.

[0042] S8: Calculate the rate of change of signal strength based on the average strength of the composite signal to quantify signal volatility, i.e.:

[0043]

[0044] In the formula, The rate of change of the signal; For a specific point in time Received composite signal strength;

[0045] S9: Define a preliminary smoothing factor based on the signal rate of change and a preset fluctuation range, namely:

[0046]

[0047] In the formula, R is the initial smoothing factor; R is the preset fluctuation range.

[0048] S10: Calculate the change in the composite signal collected at adjacent time points within a continuous time period, i.e.:

[0049]

[0050] In the formula, For a specific time point The change in the composite signal at that time;

[0051] S11: Select the maximum value of the composite signal change and calculate it with a preset empirical coefficient to measure the maximum acceptable signal change range of the system under normal operating conditions, i.e.:

[0052]

[0053] In the formula, The threshold for signal fluctuation; It is a proportionality constant;

[0054] S12: Compare the signal rate of change with the signal fluctuation threshold, and dynamically adjust the smoothing factor based on the initial smoothing factor and the comparison results to adapt to the real-time changing signal environment, i.e.:

[0055]

[0056] In the formula, As a dynamic equilibrium factor;

[0057] S13: Adjust the received signal in real time based on the signal interference index and dynamic balance factor, that is:

[0058]

[0059] In the formula, This is the filtered signal after adaptive filtering.

[0060] Furthermore, the method of dynamically adjusting the received signal based on the signal interference index also includes:

[0061] S14: Perform a Hilbert transform on the direct path signal to obtain its instantaneous phase, i.e.:

[0062]

[0063] In the formula, The instantaneous phase of the direct path signal;

[0064] S15: Calculate the phase difference between the reflected signal and the direct path signal, i.e.:

[0065]

[0066] In the formula, The instantaneous phase difference between the i-th reflected signal and the direct path signal; Let be the instantaneous phase of the i-th reflected signal;

[0067] S16: Generate compensation weights for canceling signal interference based on instantaneous phase difference and gain coefficient, i.e.:

[0068]

[0069] In the formula, This is the phase compensation weight for the i-th reflected signal, used to accurately subtract the phase distortion component caused by multipath effects in subsequent signal synthesis; It is a sine function of the phase difference; For exponential decay term; where The attenuation coefficient;

[0070] S17: The phase compensation weights are superimposed on the filtered signal to generate the final optimized signal, i.e.:

[0071]

[0072] In the formula, This is the final optimized signal.

[0073] Furthermore, the generation of compensation weights for canceling signal interference based on instantaneous phase difference and gain coefficient includes:

[0074] S161: Collect the time delay of reflected signals over consecutive past time periods and calculate the standard deviation of the time delay, i.e.:

[0075]

[0076] In the formula, Where M is the time delay volatility; and M is the number of reflection paths. Let be the time delay value of the i-th reflection path at time t; This represents the average delay values ​​of M reflection paths over a consecutive time period in the past.

[0077] S162: Calculate the severity of comprehensive quantitative environmental interference based on latency volatility and interference indicators, i.e.:

[0078]

[0079] In the formula, This is the coupling factor between interference and time delay; The maximum allowable value preset for interference indicators; The preset maximum allowable value for latency volatility;

[0080] S163: Based on the value of the coupling factor, the attenuation coefficient is dynamically adjusted to balance the phase compensation intensity, i.e.:

[0081]

[0082] In the formula, This is the dynamic decay factor.

[0083] Furthermore, the step of generating compensation weights for canceling signal interference based on instantaneous phase difference and gain coefficient further includes:

[0084] S164: Based on the coordinates of reflection points over consecutive past time periods, calculate the change in the position of the reflection points over time and the dynamic shift of the reflection path caused by slight deformation of the mountain wall or vegetation movement, i.e.:

[0085]

[0086] In the formula, For the i-th reflection point in the time interval Displacement vector within; Let be the three-dimensional coordinates of the i-th reflection point at time t; The three-dimensional coordinates of the i-th reflection point at the previous moment; The time interval between the positions of the reflection points is calculated.

[0087] S165: Calculate the average offset direction of all reflection points based on the displacement vector, used to determine the main direction of environmental interference, i.e.:

[0088]

[0089] In the formula, The average offset direction of the reflection points; Q is the number of reflection points; The unit offset vector indicates the direction;

[0090] S166: Obtain the principal direction of surface deformation from the 3D terrain model, and calculate the angle between the average offset direction and the principal direction of surface deformation, i.e.:

[0091]

[0092] In the formula, The angle between the average offset direction and the main direction of surface deformation is used to determine the cause of the reflection path offset. This indicates the main direction of surface deformation;

[0093] S167: Based on the angle between the average offset direction and the main direction of surface deformation, a definition is used to quantify the consistency between the offset direction of the reflection path and the actual surface deformation direction, i.e.:

[0094]

[0095] In the formula, Directional correlation factor;

[0096] S168: Couple the directional correlation factor to the attenuation coefficient to correct the dynamic attenuation factor, i.e.:

[0097]

[0098] In the formula, This is the optimal attenuation coefficient.

[0099] Beneficial effects

[0100] The technical solution provided by this invention has the following advantages compared with the prior art:

[0101] This invention establishes a signal propagation model that includes both direct and reflected signals by deploying multi-channel radio receivers and combining them with a three-dimensional terrain model. This technology can accurately identify the geometric features and signal attenuation patterns of different reflected paths, providing a theoretical basis for subsequent interference cancellation. It significantly improves the interpretability of signal propagation paths in complex terrain, creating conditions for the accurate quantification of multipath interference.

[0102] This invention also achieves a quantitative assessment of the signal distortion caused by multipath effects by designing an interference index in the form of time-domain integral. This mechanism can dynamically reflect real-time changes in environmental interference, solving the problem of difficulty in quantifying the degree of interference in traditional monitoring, and providing a key decision-making basis for adaptive signal optimization.

[0103] Furthermore, this invention innovatively introduces a dynamic balance factor, which intelligently adjusts the filtering intensity by comparing signal fluctuations with a preset threshold in real time. This method can effectively suppress reflection interference while preserving the valid deformation signal, significantly improving the signal-to-noise ratio and making millimeter-level deformation monitoring possible.

[0104] By extracting the instantaneous phase of the signal through Hilbert transform and combining it with directional compensation weight design, the system achieves precise elimination of multipath phase interference. This technique specifically employs an exponential decay term to suppress invalid reflection paths, effectively solving the phase aliasing problem and significantly improving the temporal resolution of deformation monitoring.

[0105] In particular, by coupling analysis of time delay volatility and interference indicators, a dynamic adjustment strategy for the attenuation coefficient was established. This innovation enables the system to automatically adjust the compensation intensity according to environmental changes, balancing the needs of noise suppression and signal preservation, and enhancing the system's robustness in dynamic environments.

[0106] By analyzing the spatial relationship between the direction of reflection point offset and the direction of surface deformation, the system achieves directional optimization of the compensation strategy. This technology can distinguish between real deformation signals and environmental interference, avoids false suppression of effective signals, and significantly improves the reliability of landslide early warning. Attached Figure Description

[0107] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0108] Figure 1 This is a schematic diagram of the working process of the surface deformation and elevation difference evolution measurement system in an embodiment of the present invention. Detailed Implementation

[0109] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0110] The present invention will be further described below with reference to embodiments.

[0111] Example 1:

[0112] See appendix Figure 1 This paper proposes a surface deformation elevation difference evolution measurement system based on multi-source data to address the multipath interference problem in surface deformation monitoring in high mountain and canyon areas. First, a basic signal propagation model is established through 3D terrain modeling and multi-channel signal acquisition. Second, a composite signal model including direct signals and reflection paths is constructed, quantifying the gain coefficient and time delay of each reflection path. Then, an innovative dynamic interference assessment mechanism is proposed, distinguishing between environmental interference and actual deformation through time delay fluctuation analysis and directional correlation detection. Finally, adaptive filtering and phase compensation techniques, combined with a dynamic attenuation factor optimization algorithm, are employed to achieve precise suppression of signal interference.

[0113] The system includes a signal acquisition module, a reflected signal identification module, a signal interference calculation module, and a signal optimization module.

[0114] Specifically, the signal acquisition module provides basic signal data, offering the necessary input for subsequent processing. Multiple radio receivers are deployed throughout the canyon to collect signal data in real time along different paths, including direct and reflected signals.

[0115] The reflected signal identification module is used to determine the gain coefficient of each reflection path, providing a mathematical basis for quantifying signal interference. Through time delay measurement and signal strength analysis, a signal reflection model is established to quantify the impact of each reflected signal on the received signal.

[0116] The signal interference calculation module calculates signal interference indices based on a signal reflection model, assesses the impact of reflected signals on received signals, and provides a quantitative assessment of interference levels.

[0117] The signal optimization module adjusts the received signal in real time according to the interference index, reduces the impact of indirect path signals, ensures more accurate monitoring data, and reduces the impact of erroneous signals.

[0118] More specifically, the workflow steps of this system are as follows:

[0119] S1: Using known elevation data of the canyon area, construct a 3D terrain model to represent the complex structure of the terrain, such as mountain walls, canyons, and vegetation. Establish multiple signal acquisition points within the canyon using a multi-channel radio receiver to acquire radio signals along different paths. Preprocess the received radio signals, including noise reduction and signal enhancement, to improve signal quality. Determine the positions of the transmitter and receiver within the 3D terrain model, i.e., the transmitter location. and receiver location .

[0120] Among them, radio signals include direct path signals that are transmitted directly from the transmitter to the receiver without any reflection. These signals are strong and have little delay, and serve as the benchmark for signal analysis. They also include reflected signals that are reflected by canyon walls or other objects during propagation and indirectly reach the receiver.

[0121] S2: Signals from multiple directions are collected, and signal strength and delay are recorded through time delay measurement and signal strength analysis. A signal reflection model is established to quantify the impact of reflected signals on received signals, ensuring the accuracy of high-altitude landslide monitoring. Specifically:

[0122] S201: Calculate the direct path from the transmitter to the receiver, i.e.:

[0123]

[0124] In the formula, d is the propagation distance of the signal.

[0125] S202: Define a reflection point located on the signal propagation path that satisfies the condition that the angle of incidence equals the angle of reflection, i.e.:

[0126]

[0127] In the formula, This indicates the geometric relationship between the reflection point and the transmitter and receiver, that is, in geometric optics, the angle of incidence equals the angle of reflection; Represents the three-dimensional coordinates of the i-th reflection point, which is the location where the signal encounters and is reflected by a surface during propagation; i is the index number of the reflection point; Let be the incident angle (or reflection angle) at the i-th reflection point. The tangent of this value is combined with the relationship between the height of the reflection point and its horizontal distance from the transmitter or receiver to establish the geometric relationship with the signal propagation path. By ensuring that the reflection angle and the incident angle are equal, we can find the correct reflection point location, thereby helping to obtain more accurate signal data.

[0128] S203: Measure the signal strength along the direct path from the base station to the target point in the absence of reflection to ensure the most accurate reference signal is obtained, i.e.:

[0129]

[0130] In the formula, For the direct path signal; A is the initial signal strength. This is the attenuation factor.

[0131] S204: Under the same conditions, measure the reflected signals received from reflection points in different reflection paths. For each reflected signal, calculate its corresponding gain coefficient. ,Right now:

[0132]

[0133] In the formula, The gain coefficient of the i-th reflected signal relative to the direct path signal reflects the strength of the reflected signal compared to the direct path signal. It is usually a value less than 1, indicating the degree of attenuation of the reflected signal and affecting the overall quality of the received composite signal.

[0134] S205: The expression for the signal reflection model, calculated based on the direct path signal, reflected signal, and gain coefficient, in a complex environment, is as follows:

[0135]

[0136] In the formula, This represents the composite signal received at time t, which includes information about the direct path signal and all reflected signals, and can help analyze and process the received signal; n is the number of reflected signals; Indicates time The received i-th reflected signal reflects the time variation of the arrival of the reflected signal, representing the time delay experienced by the receiver. The signal; Let be the time delay of the i-th reflected signal, reflecting the total time delay experienced by the signal from the transmitter to the reflection point and then to the receiver. By adding the time delay, the reflected signal can be made to correspond with the actual received time, thus correctly identifying the contribution of the reflected signal in the received signal. It is obtained by combining the time from the transmitter to the reflection point and the time from the reflection point to the receiver, i.e.:

[0137]

[0138] In the formula, This is the distance from the transmitter location to the reflection point location; denoted as ρ, where ρ is the distance from the reflection point to the receiver; c is the signal propagation speed.

[0139] S3: Calculate the degree of interference caused by multipath propagation of radio signals in high mountain and canyon environments, i.e.:

[0140]

[0141] In the formula, I is the signal interference index, which is used to quantify the degree of interference. The higher the value, the more severe the interference. T is the measurement window, which is the length of time for analyzing the signal.

[0142] S4: Improve radio signal quality based on signal interference indicators, eliminate the effects of reflected signals caused by multipath propagation and environmental interference, thereby improving the accuracy and reliability of received signals. Specifically:

[0143] S401: Collect the composite signal over a continuous time period and calculate the average signal strength over that continuous time period, i.e.:

[0144]

[0145] In the formula, denoted as the average intensity of the composite signal; m represents the number of composite signal samples collected within a continuous time period.

[0146] S402: Calculates the rate of change of signal strength used to quantify signal volatility based on the average strength of the composite signal, i.e.:

[0147]

[0148] In the formula, The rate of change of the signal represents the change at a specific point in time. The difference between the received composite signal strength and the average composite signal strength reflects whether the signal is normal at that point in time, and whether there are problems such as interference or reflection. For a specific point in time The received composite signal strength includes the direct path signal as well as noise and interference caused by reflections or other factors.

[0149] S403: Define a preliminary smoothing factor based on the signal rate of change and a preset fluctuation range, namely:

[0150]

[0151] In the formula, R is the initial smoothing factor, representing the parameter used to dynamically adjust signal filtering during signal processing; R is the preset fluctuation range, representing the acceptable range of signal strength variation under normal operating conditions, obtained based on historical data.

[0152] S404: Calculate the change in the composite signal collected at adjacent time points within a continuous time period, i.e.:

[0153]

[0154] In the formula, For a specific time point The change in the composite signal at that time;

[0155] S405: Select the maximum value of the composite signal variation and calculate it with a preset empirical coefficient to measure the maximum acceptable signal variation range of the system under normal operating conditions, i.e.:

[0156]

[0157] In the formula, The signal fluctuation threshold serves as a standard for the monitoring system to determine whether a signal is within the normal fluctuation range. It is a proportionality constant less than 1, used to generate a reasonable fluctuation threshold.

[0158] S406: Compare the signal rate of change with the signal fluctuation threshold, and dynamically adjust the smoothing factor based on the initial smoothing factor and the comparison results to adapt to the real-time changing signal environment, i.e.:

[0159]

[0160] In the formula, The dynamic balance factor represents the value of the smoothing factor used at the current time t, which will affect the sensitivity of signal processing. When the rate of change of the signal is greater than the signal fluctuation threshold, it indicates that the signal fluctuation is abnormally severe, and the value of the dynamic balance factor needs to be increased to make the system more sensitive to signal changes. This means that the system will adapt to these changes more quickly when the signal fluctuation is large. Conversely, when the rate of change of the signal is less than the signal fluctuation threshold, it indicates that the signal change is normal and small. Such fluctuations do not require a sensitive response, so the value of the dynamic balance factor is reduced to slow down the system's response to small changes and reduce the lag during filtering.

[0161] S407: Based on signal interference indicators and dynamic balance factors, the received signal is adjusted in real time to minimize the impact of signals from indirect paths, i.e.:

[0162]

[0163] In the formula, The filtered signal after adaptive filtering can improve the accuracy of the positioning system by accurately processing and removing interference from reflected signals. In high mountains and canyons, clean signals help to calculate the position of sensors and environmental parameters more accurately, and will more accurately reflect the true deformation of the earth's surface. Accurate signals can capture minute changes in earth's surface deformation, which is crucial for studying the details and patterns of earth's surface deformation.

[0164] It should be noted that in this scheme, multipath effects not only cause signal strength fluctuations but also signal phase difference fluctuations, affecting the accuracy of surface deformation monitoring. The phase shift of the reflected signal superimposed on the direct signal leads to phase distortion of the received signal, thus affecting the temporal resolution of deformation measurement. Therefore, intensity filtering in S407 cannot completely eliminate phase interference; quantization of the multipath phase difference is needed to eliminate temporal aliasing of high-frequency deformation signals; and exponential attenuation terms are used to suppress noise from invalid reflection paths. Specifically:

[0165] S408: Perform a Hilbert transform on the direct path signal to obtain its instantaneous phase, i.e.:

[0166]

[0167] In the formula, The instantaneous phase of the direct path signal serves as a phase reference, and the phase of all subsequent reflected signals will be compared with it to determine the extent of the deviation. The result of the direct path signal after undergoing the Hilbert transform; The arctangent function is used to calculate the instantaneous phase value from the above ratio.

[0168] S409: Calculate the phase difference between the reflected signal and the direct path signal, i.e.:

[0169]

[0170] In the formula, The instantaneous phase difference between the i-th reflected signal and the direct path signal quantifies the magnitude of phase distortion caused by multipath effects. Let be the instantaneous phase of the i-th reflected signal. Due to the longer reflection path, this signal has a time delay relative to the direct signal.

[0171] S410: Generates compensation weights for canceling signal interference based on instantaneous phase difference and gain coefficient, i.e.:

[0172]

[0173] In the formula, This is the phase compensation weight for the i-th reflected signal, used to accurately subtract the phase distortion component caused by multipath effects in subsequent signal synthesis; The sine function of the phase difference is used to provide the directionality of the compensation. When the phase difference is 90° or 270°, the sine function reaches its maximum value of ±1. At this time, the reflected signal and the direct signal are orthogonal, the interference is the greatest, and the maximum compensation is required. When the phase difference is 0° or 180°, the sine function is 0. At this time, the reflected signal and the direct signal are in phase or out of phase, which is a pure intensity superposition. The phase interference is the least, so the phase compensation amount is also zero. This is an exponential decay term. When the phase difference is very large, such as due to random noise or invalid reflection paths, the value of this term becomes very small, thus suppressing the entire phase compensation weight to near zero, avoiding overcompensation for these outliers, and maintaining signal stability; where... This is the attenuation coefficient, used to control the degree of attenuation.

[0174] S411: The phase compensation weights are superimposed on the filtered signal to generate the final optimized signal, i.e.:

[0175]

[0176] In the formula, To obtain the final optimized signal, all phase interference components caused by reflection paths are subtracted from the intensity-filtered signal, resulting in an output that is purified in both intensity and phase and is closest to the ideal direct signal.

[0177] It is worth noting that in the S410, under actual landslide monitoring scenarios, the dynamic changes in the position of the reflection point caused by slight deformation of the canyon walls or swaying vegetation can lead to time delays. The time-varying nature of the attenuation factor renders fixed attenuation coefficient values ​​inapplicable. For example, an excessively large attenuation coefficient can lead to overcompensation, effectively suppressing high-frequency deformation signals (such as the instantaneous jitter of landslide precursors); an excessively small attenuation coefficient can cause undercompensation, resulting in residual phase noise that masks millimeter-level / hourly deformation. Therefore, it is necessary to calculate the fluctuation degree of the reflected signal's time delay, combine it with the signal interference intensity to generate a coupling factor, and finally dynamically adjust the attenuation coefficient based on the value of the coupling factor. Specifically, S410 includes:

[0178] S4101: Collect the time delay of reflected signals over a continuous historical time period and calculate the standard deviation of the time delay, i.e.:

[0179]

[0180] In the formula, The time delay volatility quantifies the dispersion of time delay values ​​for all reflection paths. The larger the value, the more drastic the changes in the environment (such as the mountain surface and vegetation), resulting in highly unstable reflection path lengths; M represents the number of reflection paths. Let be the time delay value of the i-th reflection path at time t; The average delay values ​​of all M reflection paths over a continuous time period represent the central trend of the delay.

[0181] S4102: Calculate the severity of comprehensive quantitative environmental interference based on latency volatility and interference indicators, i.e.:

[0182]

[0183] In the formula, The coupling factor is a combination of interference and time delay, which combines information from two dimensions: interference intensity and time delay stability, forming a comprehensive environmental dynamic index. When the interference intensity is high and the time delay fluctuation is also large, the coupling factor value will be large, indicating that the environment is very complex and unstable, and stronger phase compensation suppression is required. Conversely, when both are small, the coupling factor value is small, indicating that the environment is stable, and compensation should be weakened to avoid damaging the effective signal. The maximum allowable value preset for interference indicators; The maximum allowable value for time delay volatility is preset.

[0184] S4103: Based on the value of the coupling factor, the attenuation coefficient is dynamically adjusted to balance the phase compensation intensity, i.e.:

[0185]

[0186] In the formula, The dynamic attenuation factor is the result of different strategies based on the value range of the coupling factor, used to ensure smooth and reasonable control. When the value of the coupling factor is less than or equal to 1, it indicates a normal to moderately dynamic environment, so a logarithmic function growth is adopted. The growth is relatively fast in the initial stage, and then slows down. This ensures that the compensation strength can respond quickly when the environment begins to fluctuate. However, when the fluctuation continues to increase, it will not overreact, preventing overcorrection and protecting the effective signal. When the value of the coupling factor is greater than 1, it indicates a highly dynamic environment. The function value starts from 1 and approaches 2 as the coupling factor increases, which is a saturation growth strategy. This avoids the dynamic attenuation factor from increasing infinitely when the environment is extremely unstable, thereby avoiding excessive system gain and instability or noise amplification, ensuring system robustness.

[0187] By addressing the time delay fluctuations caused by environmental changes, and by allowing the dynamic attenuation factor to adapt and intelligently balance the intensity of phase compensation, the system achieves optimal results in suppressing noise interference and preserving effective signals, thereby improving the system's stability and monitoring accuracy in dynamic environments.

[0188] It is particularly important to emphasize that while S410 adjusts the compensation intensity through a dynamic attenuation factor, it does not fully quantify the coupling relationship between reflection path stability and surface deformation monitoring accuracy. This is especially true in canyon walls, where micro-deformation or vegetation movement can cause dynamic shifts in the coordinates of reflection points, resulting in high-frequency jitter in time delay and gain coefficients. Currently, the dynamic attenuation factor only relies on time delay volatility and interference indicators, without considering the spatial correlation between the reflection path shift direction and the surface deformation direction. For example, if a reflection point shifts southeast due to a landslide, and the monitoring system defaults to isotropic compensation, it will lead to incorrect phase difference compensation direction; or high-frequency jitter in the vertical direction caused by vegetation movement may overlap with the frequency band of the actual deformation signal, potentially causing existing filtering to incorrectly suppress the effective signal. Therefore, it is necessary to dynamically analyze the spatial correlation between the reflection path shift direction and the surface deformation direction, optimize the phase compensation weight, adaptively suppress multipath interference, and retain the effective deformation signal. This will solve the problem of dynamic interference in reflection signals caused by micro-deformation of mountains or vegetation movement in high mountain canyons, improving the millimeter-level accuracy and reliability of surface deformation monitoring. Specifically, S410 also includes:

[0189] S4104: Based on the coordinates of reflection points over consecutive past time periods, calculate the change in the position of the reflection points over time and the dynamic shift of the reflection path caused by slight deformation of the mountain wall or vegetation movement, i.e.:

[0190]

[0191] In the formula, For the i-th reflection point in the time interval The displacement vector within reflects the path change; Let be the three-dimensional coordinates of the i-th reflection point at time t; The three-dimensional coordinates of the i-th reflection point at the previous moment; The time interval between the calculated reflection point locations.

[0192] S4105: Calculates the average offset direction of all reflection points based on the displacement vector, used to determine the main direction of environmental interference, i.e.:

[0193]

[0194] In the formula, The average offset direction of the reflection points reflects the overall trend of environmental disturbance; Q is the number of reflection points. It is a unit offset vector, indicating the direction.

[0195] S4106: Obtain the principal direction of surface deformation from the 3D terrain model, and calculate the angle between the average offset direction and the principal direction of surface deformation, i.e.:

[0196]

[0197] In the formula, The angle between the average offset direction and the main direction of surface deformation is used to determine the cause of the reflection path offset; when At that time, the direction of the reflection path offset was highly consistent with the direction of deformation, so it was likely a displacement in the same direction caused by a landslide; when When the offset direction is orthogonal to the deformation direction, it may be due to vegetation swaying caused by lateral wind force; when At this time, the direction of offset is completely opposite to the direction of deformation, which may be caused by local geological rebound; This indicates the main direction of surface deformation.

[0198] S4107: Based on the angle between the average offset direction and the main direction of surface deformation, a method is defined to quantify the consistency between the offset direction of the reflection path and the actual surface deformation direction, avoiding misjudging the actual deformation signal as noise.

[0199]

[0200] In the formula, This is a directional correlation factor used to optimize phase compensation and ultimately improve deformation monitoring accuracy.

[0201] S4108: Couples the directional correlation factor to the attenuation coefficient to correct the dynamic attenuation factor, i.e.:

[0202]

[0203] In the formula, It is an optimal attenuation coefficient after dual optimization of directional correlation and time delay stability. When the reflection path offset direction is highly correlated with the deformation direction and the time delay fluctuation rate is low, the optimal attenuation coefficient will be increased to enhance the compensation of the path and avoid misjudging the real deformation signal as noise. Conversely, the optimal attenuation coefficient will be reduced to prevent excessive suppression of the effective signal.

[0204] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A surface deformation and elevation difference evolution measurement system based on multi-source data, characterized in that, include: The signal acquisition module is used to acquire radio signals in the target area, including direct path signals and reflected signals; A reflected signal identification module establishes a signal reflection model based on the gain coefficient and time delay parameters of the reflected signal; the establishment of the signal reflection model includes: S1: Construct a 3D terrain model using elevation data of the target area, and determine the transmitter location within the 3D terrain model. and receiver location ; S2: Calculate the direct path from the transmitter to the receiver, i.e.: In the formula, d is the signal propagation distance; S3: Define a reflection point located on the signal propagation path that satisfies the condition that the angle of incidence equals the angle of reflection, i.e.: In the formula, This indicates the geometric relationship between the reflection point and the transmitter and receiver; This represents the three-dimensional coordinates of the i-th reflection point; i is the index number of the reflection point. Let be the angle of incidence at the i-th reflection point; S4: Measure the signal strength along the direct path from the base station to the target point without reflection, i.e.: In the formula, For the direct path signal; A is the initial signal strength. It is the attenuation factor; S5: Under the same conditions, measure the reflected signals received from reflection points along different reflection paths. For each reflected signal, calculate its corresponding gain coefficient. ,Right now: In the formula, Let be the gain coefficient of the i-th reflected signal relative to the direct path signal; S6: The expression for the signal reflection model, which calculates the composite signal received in a complex environment based on the direct path signal, reflected signal, and gain coefficient, is as follows: In the formula, This represents the composite signal received at time t; n is the number of reflected signals. Indicates time The i-th reflected signal received; Let be the time delay of the i-th reflected signal; The signal interference calculation module calculates signal interference indices based on the signal reflection model to assess the degree of interference of reflected signals to received signals. The signal optimization module dynamically adjusts the received signal based on signal interference indicators to eliminate the influence of signals from indirect paths.

2. The surface deformation and elevation difference evolution measurement system based on multi-source data according to claim 1, characterized in that, The delay is obtained by combining the time from the transmitter to the reflection point and the time from the reflection point to the receiver, that is: In the formula, This is the distance from the transmitter location to the reflection point location; denoted as ρ, where ρ is the distance from the reflection point to the receiver; c is the signal propagation speed.

3. The surface deformation and elevation difference evolution measurement system based on multi-source data according to claim 2, characterized in that, The signal interference index, calculated based on the signal reflection model to assess the degree of interference of the reflected signal to the received signal, includes: In the formula, I represents the signal interference index; T represents the measurement window.

4. The surface deformation and elevation difference evolution measurement system based on multi-source data according to claim 3, characterized in that, The dynamic adjustment of the received signal based on the signal interference index includes: S7: Collect the composite signal over a continuous time period and calculate the average signal strength over that continuous time period, i.e.: In the formula, The average intensity of the composite signal is m; m is the number of composite signal samples collected within a continuous time period. S8: Calculate the rate of change of signal strength based on the average strength of the composite signal to quantify signal volatility, i.e.: In the formula, The rate of change of the signal; For a specific point in time Received composite signal strength; S9: Define a preliminary smoothing factor based on the signal rate of change and a preset fluctuation range, namely: In the formula, R is the initial smoothing factor; R is the preset fluctuation range. S10: Calculate the change in the composite signal collected at adjacent time points within a continuous time period, i.e.: In the formula, For a specific time point The change in the composite signal at that time; S11: Select the maximum value of the composite signal change and calculate it with a preset empirical coefficient to measure the maximum acceptable signal change range of the system under normal operating conditions, i.e.: In the formula, The threshold for signal fluctuation; It is a proportionality constant; S12: Compare the signal rate of change with the signal fluctuation threshold, and dynamically adjust the smoothing factor based on the initial smoothing factor and the comparison results to adapt to the real-time changing signal environment, i.e.: In the formula, It is a dynamic equilibrium factor; S13: Adjust the received signal in real time based on the signal interference index and dynamic balance factor, that is: In the formula, This is the filtered signal after adaptive filtering.

5. The surface deformation and elevation difference evolution measurement system based on multi-source data according to claim 4, characterized in that, The method of dynamically adjusting the received signal based on the signal interference index also includes: S14: Perform a Hilbert transform on the direct path signal to obtain its instantaneous phase, i.e.: In the formula, The instantaneous phase of the direct path signal; S15: Calculate the phase difference between the reflected signal and the direct path signal, i.e.: In the formula, The instantaneous phase difference between the i-th reflected signal and the direct path signal; Let be the instantaneous phase of the i-th reflected signal; S16: Generate compensation weights for canceling signal interference based on instantaneous phase difference and gain coefficient, i.e.: In the formula, This is the phase compensation weight for the i-th reflected signal, used to accurately subtract the phase distortion component caused by multipath effects in subsequent signal synthesis; It is a sine function of the phase difference; For exponential decay term; where The attenuation coefficient; S17: The phase compensation weights are superimposed on the filtered signal to generate the final optimized signal, i.e.: In the formula, This is the final optimized signal.

6. The surface deformation and elevation difference evolution measurement system based on multi-source data according to claim 5, characterized in that, The method of generating compensation weights for canceling signal interference based on instantaneous phase difference and gain coefficient includes: S161: Collect the time delay of reflected signals over consecutive past time periods and calculate the standard deviation of the time delay, i.e.: In the formula, Where M is the time delay volatility; and M is the number of reflection paths. Let be the time delay value of the i-th reflection path at time t; This represents the average delay values ​​of M reflection paths over a consecutive time period in the past. S162: Calculate the severity of comprehensive quantitative environmental interference based on latency volatility and interference indicators, i.e.: In the formula, This is the coupling factor between interference and time delay; The maximum allowable value preset for interference indicators; The preset maximum allowable value for latency volatility; S163: Based on the value of the coupling factor, the attenuation coefficient is dynamically adjusted to balance the phase compensation intensity, i.e.: In the formula, This is the dynamic decay factor.

7. The surface deformation and elevation difference evolution measurement system based on multi-source data according to claim 6, characterized in that, The method of generating compensation weights for canceling signal interference based on instantaneous phase difference and gain coefficient further includes: S164: Based on the coordinates of reflection points over consecutive past time periods, calculate the change in the position of the reflection points over time and the dynamic shift of the reflection path caused by slight deformation of the mountain wall or vegetation movement, i.e.: In the formula, For the i-th reflection point in the time interval Displacement vector within; Let be the three-dimensional coordinates of the i-th reflection point at time t; The three-dimensional coordinates of the i-th reflection point at the previous moment; The time interval between the positions of the reflection points is calculated. S165: Calculates the average offset direction of all reflection points based on the displacement vector, used to determine the main direction of environmental interference, i.e.: In the formula, The average offset direction of the reflection points; Q is the number of reflection points; The unit offset vector indicates the direction; S166: Obtain the principal direction of surface deformation from the 3D terrain model, and calculate the angle between the average offset direction and the principal direction of surface deformation, i.e.: In the formula, The angle between the average offset direction and the main direction of surface deformation is used to determine the cause of the reflection path offset. This indicates the main direction of surface deformation; S167: Based on the angle between the average offset direction and the principal direction of surface deformation, a definition is used to quantify the consistency between the offset direction of the reflection path and the actual surface deformation direction, i.e.: In the formula, Directional correlation factor; S168: Couple the directional correlation factor to the attenuation coefficient to correct the dynamic attenuation factor, i.e.: In the formula, This is the optimal attenuation coefficient.

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