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

Abstract

The invention relates to the technical field of earth surface deformation monitoring, in particular to an earth surface deformation altitude difference evolution measurement system based on multi-source data, which comprises a signal acquisition module used for acquiring radio signals of a target area, including direct path signals and reflection signals; the reflected signal identification module is used for establishing a signal reflection model based on the gain coefficient and the time delay parameter of the reflected signal; and the signal interference calculation module is used for calculating a signal interference index for evaluating the interference degree of the reflected signal on the received signal based on the signal reflection model. According to the method, a multi-channel radio receiver is arranged, a three-dimensional terrain model is combined, and a signal propagation model containing a direct signal and a reflection path is established. According to the technology, the geometrical characteristics and signal attenuation laws of different reflection paths can be accurately identified, and a theoretical basis is provided for subsequent interference elimination. The interpretability of a signal propagation path in a complex terrain is remarkably improved, and conditions are created for accurately quantifying multipath interference.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ground surface deformation monitoring, and particularly relates to a ground surface deformation height difference evolution measurement system based on multi-source data. BACKGROUND

[0002] Ground surface deformation, including subsidence, uplift, horizontal displacement, etc., is the dynamic response of the earth's surface under the joint action of natural factors (such as tectonic movement, earthquake, volcanic activity, groundwater exploitation, glacier melting) and human activities (such as urban expansion, large-scale engineering construction, mineral resources exploitation). Accurately and continuously monitoring the spatio-temporal evolution of ground surface deformation has important scientific value and practical significance for ensuring urban safety, assessing geological disaster risk, monitoring major engineering stability, and studying global climate change.

[0003] In the field of ground surface deformation monitoring, especially in the monitoring of landslides in mountainous areas, the occurrence of landslides is often caused by small and uneven deformation in a small area. This deformation may be ignored in most monitoring systems, as the systems usually focus on the overall deformation trend. Therefore, for small and uneven deformation in landslide monitoring, the existing technology mainly relies on high-density sensor networks and high-resolution remote sensing technologies (such as small unmanned aerial vehicles or laser radars), which can achieve detailed monitoring of local areas, capture small deformation changes, and identify potential landslide risks through data fusion and analysis algorithms.

[0004] When the landslide monitoring station in the mountainous area is located in a high mountain valley, the valley is usually composed of steep cliffs, which reflect radio signals at different angles. During signal propagation, multiple signal paths are superimposed due to reflection from the cliffs, forming a multipath effect. This phenomenon causes the receiver to receive not only the direct path signal but also the reflected signal from the cliffs. The multipath propagation effect causes a phase difference in the received signal, affecting the clarity and stability of the signal. This interference can cause changes in signal strength, making it difficult for devices to accurately receive and decode data. In addition, since the received signal contains reflected signals from different paths, it can increase the error of the positioning system, affecting the accuracy and real-time performance of the ground surface deformation data, and further affecting the risk assessment and early warning of landslides.

[0005] Therefore, a ground surface deformation height difference evolution measurement system based on multi-source data is proposed to solve the above problems. SUMMARY

[0006] Technical problems solved

[0007] In view of the above-mentioned defects of the prior art, the present application provides a ground surface deformation height difference evolution measurement system based on multi-source data, which can effectively solve the problem of signal interference caused by multipath effect in ground surface deformation monitoring in high mountain and canyon areas in the prior art. High-precision ground surface deformation monitoring is realized in a complex topographic environment, and the reliability of geological disaster warning such as landslides is significantly improved.

[0008] Technical scheme

[0009] In order to achieve the above-mentioned purpose, the present application is realized by the following technical scheme:

[0010] The present application provides a ground surface deformation height difference evolution measurement system based on multi-source data, comprising:

[0011] A signal acquisition module for acquiring radio signals of a target area, including direct path signals and reflected signals;

[0012] A reflected signal identification module for establishing a signal reflection model based on the gain coefficient and time delay parameter of the reflected signal;

[0013] A signal interference calculation module for calculating a signal interference index for evaluating the interference degree of the reflected signal on the received signal based on the signal reflection model;

[0014] A signal optimization module for dynamically adjusting the received signal based on the signal interference index to eliminate the influence of non-direct path signals.

[0015] Further, the signal reflection model is established, comprising:

[0016] S1: constructing a three-dimensional topographic model using the elevation data of the target area, and determining the transmitter position and receiver position in the three-dimensional topographic model; ;

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

[0018]

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

[0020] S3: defining a reflection point located on the signal propagation path that satisfies the condition that the incident angle is equal to the reflection angle, i.e.:

[0021]

[0022] In the formula, represents the geometric relationship between the reflection point and the transmitter and receiver; represents the three-dimensional coordinates of the i-th reflection point; i is the index number of the reflection point; ​is the incident angle at the i-th reflection point;

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

[0024]

[0025] wherein, is the direct path signal; A is the initial strength of the signal; is the attenuation factor;

[0026] S5: Measure the reflected signals received from the reflection points in different reflection paths under the same condition For each reflected signal, calculate its corresponding gain coefficient i.e.,

[0027]

[0028] wherein, is the gain coefficient of the i-th reflected signal relative to the direct path signal;

[0029] S6: Calculate the composite signal received in the complex environment based on the direct path signal, the reflected signal and the gain coefficient, i.e., the expression of the signal reflection model is:

[0030]

[0031] wherein, represents the composite signal received at time t; n is the number of reflected signals; represents the i-th reflected signal received at time . is the time delay of the i-th reflected signal.

[0032] Further, the time delay 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.,

[0033]

[0034] wherein, is the distance from the transmitter position to the reflection point position; is the distance from the reflection point position to the receiver position; c is the signal propagation speed.

[0035] Further, the signal interference index for evaluating the interference degree of the reflected signal on the received signal based on the signal reflection model includes:

[0036]

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

[0038] Further, the dynamic adjustment of the received signal based on the signal interference index comprises:

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

[0040]

[0041] In the formula, is the average strength of the composite signal; m is the number of composite signal collections in the continuous time period;

[0042] S8: Calculate the signal strength variation rate for quantifying the signal fluctuation based on the average strength of the composite signal, i.e.

[0043]

[0044] In the formula, is the signal variation rate; is the composite signal strength received at a specific time point

[0045] S9: Define a preliminary smoothing factor based on the signal variation rate and a preset fluctuation range, i.e.

[0046]

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

[0048] S10: Calculate the composite signal variation amount of adjacent time points collected in the continuous time period, i.e.

[0049]

[0050] In the formula, is the composite signal variation amount at a specific time point

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

[0052]

[0053] In the formula, is the signal fluctuation threshold; is a proportional constant;

[0054] ​​S12: comparing the signal change rate and the signal fluctuation threshold, dynamically adjusting the smoothing factor based on the initial smoothing factor and the comparison result to adapt to the real-time changing signal environment, i.e.

[0055]

[0056] In the formula, is a dynamic balance factor;

[0057] S13: real-time adjustment of the received signal based on the signal interference index and the dynamic balance factor, i.e.

[0058]

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

[0060] Further, the dynamic adjustment of the received signal based on the signal interference index further comprises:

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

[0062]

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

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

[0065]

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

[0067] S16: based on the instantaneous phase difference and the gain coefficient, a compensation weight for offsetting signal interference is generated, i.e.

[0068]

[0069] In the formula, is the phase compensation weight of the i-th reflected signal, which is used to accurately subtract the phase distortion component caused by multipath effect in subsequent signal synthesis; is the sine function of the phase difference; is the exponential decay term; wherein is the decay coefficient;

[0070] S17: superimpose the phase compensation weight into the filtered signal to generate the final optimized signal, i.e.

[0071]

[0072] wherein, is the final optimized signal.

[0073] Further, the generating the compensation weight for offsetting the signal interference based on the instantaneous phase difference and the gain coefficient comprises:

[0074] S161: collecting the time delay of the reflection signal in a continuous time period in the past, and calculating the time delay standard deviation, i.e.

[0075]

[0076] wherein, is the time delay fluctuation rate; M is the number of reflection paths; is the time delay value of the i-th reflection path at time t; is the average value of the time delay values of the M reflection paths in the continuous time period in the past;

[0077] S162: calculating the severity of the comprehensive quantified environmental interference based on the time delay fluctuation rate and the interference index, i.e.

[0078]

[0079] wherein, is the coupling factor of the interference and the time delay; is the preset maximum allowed value of the interference index; is the preset maximum allowed value of the time delay fluctuation rate;

[0080] S163: dynamically adjusting the attenuation coefficient based on the value of the coupling factor to balance the phase compensation strength, i.e.

[0081]

[0082] wherein, is the dynamic attenuation factor.

[0083] Further, the generating the compensation weight for offsetting the signal interference based on the instantaneous phase difference and the gain coefficient further comprises:

[0084] S164: calculating the dynamic offset of the reflection path caused by the micro-deformation of the mountain wall or the swing of the vegetation based on the coordinates of the reflection points in the continuous time period in the past, i.e.

[0085]

[0086] wherein, is the displacement vector of the i-th reflection point in the time interval . is the three-dimensional coordinate of the i-th reflection point at time t; is the three-dimensional coordinate of the i-th reflection point at the previous moment; The time interval between calculations of the reflection point positions;

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

[0088]

[0089] Where, is the average offset direction of the reflection points; Q is the number of reflection points; is the unit offset vector, indicating the direction;

[0090] S166: Obtain the main direction of the surface deformation from the 3D terrain model and calculate the angle between the average offset direction and the main direction of the surface deformation, that is:

[0091]

[0092] Where, It is the angle between the average offset direction and the main direction of surface deformation, which is used to determine the cause of the reflection path offset; is the main direction of surface deformation;

[0093] S167: Based on the angle between the average offset direction and the main direction of the surface deformation, the degree of consistency between the offset direction of the reflection path and the true surface deformation direction is defined, namely:

[0094]

[0095] Where, is the directional correlation factor;

[0096] S168: Couple the directional correlation factor to the attenuation coefficient and modify the dynamic attenuation factor, that is:

[0097]

[0098] Where, is the optimal attenuation coefficient.

[0099] Beneficial effects

[0100] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0101] The present application establishes a signal propagation model containing direct signals and reflected paths by laying out a multi-channel radio receiver combined with a three-dimensional terrain model. This technology can accurately identify the geometric characteristics and signal attenuation law of different reflection paths, providing a theoretical basis for subsequent interference cancellation. It significantly improves the explainability of signal propagation paths in complex terrain, creating conditions for accurate quantification of multipath interference.

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

[0103] In addition, the present application also innovatively introduces a dynamic balance factor, which intelligently adjusts the filtering strength by comparing the signal fluctuation with the preset threshold in real time. This method can effectively suppress reflected interference while preserving effective deformation signals, significantly improving the signal-to-noise ratio and making millimeter-level deformation monitoring possible.

[0104] And by extracting the signal instantaneous phase through Hilbert transform and combining with the design of directional compensation weight, the system realizes accurate cancellation of multipath phase interference. This technology especially uses an exponential decay term to suppress invalid reflection paths, effectively solving the phase aliasing problem and significantly improving the time resolution of deformation monitoring.

[0105] Especially through the coupling analysis of time delay fluctuation rate and interference index, a dynamic adjustment strategy of attenuation coefficient is established. This innovation enables the system to automatically adjust the compensation strength according to environmental changes, balancing the needs of noise suppression and signal preservation, and enhancing the robustness of the system in dynamic environments.

[0106] And by analyzing the spatial relationship between the reflection point offset direction and the surface deformation direction, the system realizes the directional optimization of the compensation strategy. This technology can distinguish between real deformation signals and environmental interference, avoiding the mis-suppression of effective signals, and significantly improving the reliability of landslide warning. BRIEF DESCRIPTION OF DRAWINGS

[0107] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0108] Figure 1 The working flowchart of the ground surface deformation height difference evolution measurement system in the embodiment of the present application. DETAILED DESCRIPTION

[0109] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

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

[0111] Embodiment one:

[0112] Referring to the accompanying Figure 1 The present application proposes a surface deformation height difference evolution measurement system based on multi-source data, aiming at the multipath effect interference problem existing in surface deformation monitoring in high mountain and canyon areas. Firstly, a signal propagation basic model is established through three-dimensional terrain modeling and multi-channel signal acquisition; secondly, a composite signal model containing direct signal and reflected path is constructed, and the gain coefficient and time delay of each reflected path are quantified; then a dynamic interference evaluation mechanism is innovatively proposed, which distinguishes environmental interference from real deformation through time delay fluctuation analysis and direction correlation detection; finally, adaptive filtering and phase compensation technology are used, combined with dynamic attenuation factor optimization algorithm, to realize accurate 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 is used to provide basic signal data, providing necessary input for subsequent processing. By arranging multiple radio receivers in the canyon, real-time signal data under different paths, including direct signal and reflected signal, are collected.

[0115] The reflected signal identification module is used to determine the gain coefficient of each reflected 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 influence of each reflected signal on the received signal.

[0116] The signal interference calculation module calculates the signal interference index based on the signal reflection model, evaluates the influence degree of the reflected signal on the received signal, and provides quantitative interference degree evaluation.

[0117] The signal optimization module adjusts the received signal in real time according to the interference index, reduces the influence of non-direct path signals, ensures more accurate monitoring data, and reduces the influence of false signals.

[0118] More specifically, the working process steps of the system are as follows:

[0119] S1: Use known elevation data of the canyon area to build a three-dimensional terrain model, representing the complex structure of the terrain, such as cliffs, canyons, vegetation, etc. Use a multi-channel radio receiver to set up multiple signal collection points in the canyon to obtain radio signals under different paths. Preprocess the received radio signals, including noise removal and signal enhancement, to improve signal quality. And the location of the transmitter and receiver in the three-dimensional terrain model, that is, the transmitter position and receiver position .

[0120] Among them, the radio signal includes the direct path signal from the transmitter directly transmitted to the receiver without any reflection, which has strong signal strength and small delay, and is the benchmark for signal analysis; the reflected signal is reflected by the canyon wall or other objects during propagation and indirectly reaches the receiver.

[0121] S2: Collect signals from multiple directions respectively, record the strength and delay of the signals through time delay measurement and signal strength analysis, and establish a signal reflection model to quantify the influence of reflected signals on received signals, to ensure the accuracy of high mountain landslide monitoring. Specifically:

[0122] S201: Calculate the direct path from the transmitter to the receiver, that is:

[0123]

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

[0125] S202: Define the reflection point located on the signal propagation path that satisfies the condition that the incident angle is equal to the reflection angle, that is:

[0126]

[0127] In the formula, represents the geometric relationship between the reflection point and the transmitter and receiver, that is, the incident angle is equal to the reflection angle in geometric optics; represents the three-dimensional coordinates of the i-th reflection point, which is the place where the signal meets and reflects during propagation; i is the index number of the reflection point; is the incident angle (or reflection angle) at the i-th reflection point, the tangent value of which is combined with the height of the reflection point position and the horizontal distance between it and 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 position, thereby helping to obtain more accurate signal data.

[0128] S203: In the absence of reflection, measure the direct path signal strength from the base station to the target point to ensure the most accurate benchmark signal, that is:

[0129]

[0130] wherein, is the direct path signal; A is the initial strength of the signal; is the attenuation factor.

[0131] S204: Measure the reflected signals received from the reflection points in different reflection paths under the same condition For each reflected signal, calculate its corresponding gain coefficient i.e.:

[0132]

[0133] wherein, is the gain coefficient of the ith reflected signal relative to the direct path signal, reflecting the strength of the reflected signal compared to the direct path signal, usually a value less than 1, indicating the degree of attenuation of the reflected signal, affecting the overall received composite signal quality.

[0134] S205: Calculate the composite signal received in a complex environment based on the direct path signal, the reflected signal and the gain coefficient, i.e. the expression of the signal reflection model is:

[0135]

[0136] wherein, represents the composite signal received at time t, containing the information of the direct path signal and all reflected signals, which can help analyze and process the received signal; n is the number of reflected signals; represents the ith reflected signal received at time reflects the time variation of the arrival of the reflected signal, representing the signal that has experienced a time delay when received by the receiver; is the time delay of the ith 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 to the actual received time, so that the contribution of the reflected signal can be correctly identified in the received signal; its acquisition method is to combine the time from the transmitter to the reflection point and the time from the reflection point to the receiver, i.e.

[0137]

[0138] wherein, is the distance from the transmitter position to the reflection point position; is the distance from the reflection point position to the receiver position; c is the signal propagation speed.

[0139] S3: Calculate the degree of interference caused by multipath propagation of radio signals in high mountain valley environment, that is:

[0140]

[0141] In the formula, I is the signal interference index, which is the result value for quantifying the degree of interference, and the higher the value, the more serious the interference; T is the measurement window, that is, the length of time for analyzing the signal.

[0142] S4: Based on the signal interference index, improve the quality of the radio signal, eliminate the influence of reflected signals caused by multipath propagation and environmental interference, in order to improve the accuracy and reliability of the received signal, specifically:

[0143] S401: Collect the composite signal in the continuous time period, and calculate the average signal strength in the continuous time period, that is:

[0144]

[0145] In the formula, is the average strength of the composite signal; m is the number of composite signal collections in the continuous time period.

[0146] S402: Calculate the signal strength variation rate for quantifying the signal volatility based on the average strength of the composite signal, that is:

[0147]

[0148] In the formula, is the signal variation rate, which represents the difference between the received composite signal strength at a specific time point and the average strength of the composite signal, reflecting whether the signal is normal at that time point, whether there is interference or reflection, etc. is the received composite signal strength at a specific time point , which contains direct path signals and noise and interference due to reflection or other factors.

[0149] S403: Define the preliminary smoothing factor based on the signal variation rate and the preset fluctuation range, that is:

[0150]

[0151] In the formula, is the initial smoothing factor, which represents the parameter for dynamically adjusting signal filtering in the signal processing process; R is the preset fluctuation range, which represents the acceptable variation range of signal strength under normal operating conditions, obtained based on historical data.

[0152] S404: Calculate the composite signal variation of adjacent time points collected in a continuous time period, that is,

[0153]

[0154] In the formula, is the composite signal variation at a specific time point ;

[0155] S405: Select the maximum value of the composite signal variation and calculate the maximum signal variation range acceptable by the system under normal operating conditions with a preset empirical coefficient, that is,

[0156]

[0157] In the formula, is the signal fluctuation threshold, which is a standard for monitoring the system to determine whether the signal is within the normal fluctuation range; is a proportional constant less than 1, used to generate a reasonable fluctuation threshold.

[0158] S406: Compare the signal change rate with the signal fluctuation threshold, and dynamically adjust the smoothing factor based on the initial smoothing factor and the comparison result to adapt to the real-time changing signal environment, that is,

[0159]

[0160] In the formula, is the dynamic balance factor, which represents the value of the smoothing factor used at the current time t and will affect the sensitivity of signal processing; when the signal change rate is greater than the signal fluctuation threshold, it means 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, which means that in the case of large signal fluctuations, the system will adapt to these changes more quickly; on the contrary, when the signal change rate is less than the signal fluctuation threshold, it means that the signal change is normal and small, and such fluctuations do not require overly 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: Real-time adjust the received signal based on the signal interference index and the dynamic balance factor to eliminate the impact of non-direct path signals as much as possible, that is,

[0162]

[0163] In the formula, For the filtered signal after adaptive filtering processing, the accuracy of the positioning system can be improved by accurately processing and removing the interference of the reflected signal. In high mountains and valleys, clean signals help to more accurately calculate the position of the sensor and environmental parameters, which will more accurately reflect the true deformation of the ground. Accurate signals can capture small changes in ground deformation, which is crucial for studying the details and changes of ground deformation.

[0164] It should be noted that in the present scheme, the multipath effect not only causes signal strength fluctuations, but also causes signal phase difference fluctuations that affect the accuracy of ground deformation monitoring. The phase offset of the reflected signal will superimpose on the direct signal, causing the phase of the received signal to be distorted, which in turn affects the time resolution of deformation measurement. Therefore, in S407, the phase interference cannot be completely eliminated by intensity filtering, and the multipath phase difference needs to be quantized to eliminate the time domain aliasing of high frequency deformation signals; and the noise of invalid reflection paths is suppressed by an exponential decay term. Specifically:

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

[0166]

[0167] In the formula, is the instantaneous phase of the direct path signal, which is used as a phase reference, and the phases of all subsequent reflected signals will be compared with it to determine how much deviation is caused; is the result of the Hilbert transform of the direct path signal; is the arctangent function, which 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, is the instantaneous phase difference between the i-th reflected signal and the direct path signal, which quantifies how much phase distortion is caused by the multipath effect; is the instantaneous phase of the i-th reflected signal, which has a time delay relative to the direct signal due to the longer reflection path.

[0171] S410: Based on the instantaneous phase difference and the gain coefficient, generate a compensation weight for offsetting signal interference, i.e.

[0172]

[0173] In the formula, is the phase compensation weight for the i-th reflected signal, which is used to accurately subtract the phase distortion component caused by multipath effect in the subsequent signal synthesis; is a sinusoidal function of the phase difference, which is used to provide the compensated directivity; that is, when the phase difference is 90° or 270°, the sinusoidal function takes the maximum value ±1, at which time the reflected signal is orthogonal to the direct signal, the interference is the largest, and the maximum compensation strength is needed; when the phase difference is 0° or 180°, the sinusoidal function is 0, at which time the reflected signal is in phase or anti-phase with the direct signal, which is manifested as pure intensity superposition, and the phase interference is the smallest, so the phase compensation amount is also zero; is an exponential decay term, which will become very small when the phase difference is very large, for example, due to random noise or invalid reflection path, thereby suppressing the entire phase compensation weight to near zero, avoiding excessive compensation for these abnormal values, and maintaining the stability of the signal; wherein is the attenuation coefficient, which is used to control the degree of attenuation.

[0174] S411: superimpose the phase compensation weight into the filtered signal to generate the final optimized signal, that is:

[0175]

[0176] wherein, is the final optimized signal, from which the phase interference component caused by all reflection paths is subtracted from the intensity filtered signal, thereby obtaining an output result that is purified in intensity and phase and closest to the ideal direct signal.

[0177] It is worth noting that in S410, in the actual mountain landslide monitoring scene, the reflection point position changes dynamically due to the micro-deformation or vegetation swing of the canyon wall, which will cause the time delay to be time-varying, and thus the fixed attenuation coefficient value cannot be applicable. When the attenuation coefficient value is too large, it will cause over-compensation to suppress the effective high-frequency deformation signal (such as the instantaneous jitter of the landslide precursor); when the attenuation coefficient value is too small, it will cause under-compensation to cause residual phase noise, which will mask the millimeter / hour-level deformation. Therefore, the fluctuation degree of the reflection signal time delay needs to be calculated, and then combined with the signal interference intensity to generate a coupling factor, and finally the attenuation coefficient is dynamically adjusted according to the value of the coupling factor. Specifically, S410 includes:

[0178] S4101: collect the time delay of the reflection signal in the past continuous time period, and calculate the time delay standard deviation, that is:

[0179]

[0180] wherein, is the time delay fluctuation rate, which quantifies the dispersion degree of all the reflection path time delay values. The greater the value, the more intense the environment (such as the surface of a mountain, vegetation) changes, resulting in very unstable reflection path length; M is the number of reflection paths; is the time delay value of the ith reflection path at time t; is the average value of all M reflection path time delay values in the past continuous time period, representing the central tendency of the time delay.

[0181] S4102: Based on the time delay fluctuation rate and the interference index, the severity of the environment interference is calculated, that is:

[0182]

[0183] In the formula, is the coupling factor of interference and time delay, which combines the information of two dimensions of interference intensity and time delay stability to form a comprehensive environment dynamic index; when the interference intensity is large and the time delay fluctuation is also large, the coupling factor value will be very large, indicating that the environment is very complex and unstable, and stronger phase compensation suppression is needed; on the contrary, when both are small, the coupling factor value is small, indicating that the environment is stable, and the compensation should be weakened to avoid damaging the effective signal; is the maximum allowed value of the interference index preset; is the maximum allowed value of the time delay fluctuation rate preset.

[0184] S4103: Based on the value of the coupling factor, the attenuation coefficient is dynamically adjusted to balance the phase compensation strength, that is:

[0185]

[0186] In the formula, is the dynamic attenuation factor, which is the result of using different strategies according to the value range of the coupling factor, and is used to ensure smooth and reasonable control; when the value of the coupling factor is less than or equal to 1, it indicates that the environment is normal to moderately dynamic, so the logarithmic function is used to grow; it grows faster at the beginning and then slows down, which ensures that when the environment starts to fluctuate, the compensation strength can respond quickly; but when the fluctuation continues to increase, it will not be overreacted, preventing overcorrection and protecting the effective signal; when the value of the coupling factor is greater than 1, it indicates that the environment is highly dynamic, and the function value starts from 1 and tends to 2 as the coupling factor increases, which is a saturated growth strategy; to avoid the dynamic attenuation factor increasing indefinitely when the environment is extremely unstable, and to avoid the system gain being too high to cause instability or noise amplification, ensuring the robustness of the system.

[0187] By solving the problem of time delay fluctuation caused by environmental changes, the dynamic attenuation factor is adaptively changed, and the strength of phase compensation is intelligently balanced, so as to achieve the optimal effect between suppressing noise interference and preserving effective signals, and to improve the stability and monitoring accuracy of the system in a dynamic environment.

[0188] It is particularly emphasized that in S410, although the compensation intensity is adjusted by the dynamic attenuation factor, the coupling relationship between the reflection path stability and the ground deformation monitoring accuracy is not fully quantified, especially in the canyon wall, the reflection point coordinates are dynamically offset due to micro-deformation or vegetation swing, causing high-frequency jitter of the time delay and gain coefficient. The current dynamic attenuation factor only depends on the time delay fluctuation rate and the interference index, and does not associate the spatial correlation between the reflection path offset direction and the ground deformation direction. For example, if the reflection point is offset to the southeast due to landslides, and the monitoring system defaults to isotropic compensation, it will cause the phase difference compensation direction to be wrong; or the vertical high-frequency jitter caused by vegetation swing may overlap with the real deformation signal frequency band, and the existing filtering may mis-suppress the effective signal. Therefore, it is necessary to dynamically analyze the spatial correlation between the reflection path offset direction and the ground deformation direction, optimize the phase compensation weight, adaptively suppress multipath interference, and at the same time preserve the effective deformation signal. To solve the problem of dynamic interference of reflection signals caused by micro-deformation of mountain or swing of vegetation in high mountains and canyons, and improve the millimeter-level accuracy and reliability of ground deformation monitoring. Specifically, S410 further comprises:

[0189] S4104: Based on the reflection point coordinates in the past continuous time period, the change of the reflection point position with time and the dynamic offset of the reflection path caused by the micro-deformation of the mountain wall or the swing of the vegetation is calculated, that is:

[0190]

[0191] In the formula, is the displacement vector of the i-th reflection point in the time interval , reflecting the path change; is the three-dimensional coordinates of the i-th reflection point at time t; is the three-dimensional coordinates of the i-th reflection point at the previous moment; is the time interval between the reflection point position calculations.

[0192] S4105: Calculate the average offset direction of all reflection points based on the displacement vector, which is used to judge the main direction of environmental interference, that is:

[0193]

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

[0195] S4106: Obtain the main direction of ground deformation from the three-dimensional terrain model, and calculate the included angle between the average offset direction and the main direction of ground deformation, that is:

[0196]

[0197] in the formula, is the included angle between the average offset direction and the main direction of ground deformation, used to determine the cause of the offset of the reflection path; when , the offset direction of the reflection path is highly consistent with the deformation direction, so it is likely to be the same displacement caused by a landslide; when , the offset direction is orthogonal to the deformation direction, which is likely to be the swing of vegetation caused by lateral wind; when , the offset direction is completely opposite to the deformation direction, which is likely to be caused by local geological rebound; is the main direction of ground deformation.

[0198] S4107: Based on the included angle between the average offset direction and the main direction of ground deformation, the consistency between the offset direction of the reflection path and the real ground deformation direction is defined to avoid misjudging the real deformation signal as noise, that is:

[0199]

[0200] in the formula, is a direction correlation factor, used to optimize phase compensation and finally improve deformation monitoring accuracy.

[0201] S4108: The direction correlation factor is coupled into the attenuation coefficient to correct the dynamic attenuation factor, that is:

[0202]

[0203] in the formula, is an optimal attenuation coefficient optimized by both direction correlation and time delay stability; when the offset direction of the reflection path is highly correlated with the deformation direction and the time delay fluctuation rate is low, the optimal attenuation coefficient will be increased to strengthen the compensation of the path and avoid misjudging the real deformation signal as noise; otherwise, the optimal attenuation coefficient will be reduced to prevent excessive suppression of effective signals.

[0204] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the protection scope of the technical solutions of the embodiments of the present application.

Claims

1. A surface deformation height difference evolution measurement system based on multi-source data, characterized in that, The method comprises the following steps: The signal acquisition module is used to collect the radio signal of the target area, including the direct path signal and the reflected signal; The reflected signal identification module establishes a signal reflection model based on the gain coefficient and the time delay parameter of the reflected signal; The signal interference calculation module calculates a signal interference index for evaluating the interference degree of the reflected signal to the received signal based on the signal reflection model; The signal optimization module dynamically adjusts the received signal based on the signal interference index to eliminate the influence of the non-direct path signal.

2. The multi-source data based earth surface deformation elevation evolution measurement system according to claim 1, wherein, The signal reflection model is established by the following steps: S1: Construct a three-dimensional terrain model using the elevation data of the target area, and determine the transmitter position in the three-dimensional terrain model and the receiver position ; S2: calculating the direct path from the transmitter to the receiver, i.e. In the formula, d is the propagation distance of the signal; S3: defining the reflection point located on the signal propagation path that satisfies the condition that the incident angle is equal to the reflection angle, i.e. wherein represents the geometric relationship between the reflection point and the transmitter and receiver; represents the three-dimensional coordinates of the ith reflection point; i is the index number of the reflection point; is the angle of incidence at the ith reflection point; S4: measuring the direct path signal strength from the base station to the target point without reflection, i.e. wherein is the direct path signal; A is the initial strength of the signal; is the attenuation factor; S5: Measure the reflected signal received by the reflection point in different reflection paths under the same condition For each reflected signal, calculate its corresponding gain coefficient That is: wherein is the gain factor of the ith reflected signal with respect to the direct path signal; S6: calculating the composite signal received in the complex environment based on the direct path signal, the reflected signal and the gain coefficient, i.e. the expression of the signal reflection model is: wherein represents the composite signal received at time t; n is the number of reflected signals; represents the composite signal received at time the ith reflected signal received at time t; is the time delay of the ith reflected signal.

3. The multi-source data based earth surface deformation elevation evolution measurement system of claim 2, wherein, The time delay 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. wherein is the distance from the transmitter location to the reflection point location; is the distance from the reflection point location to the receiver location; and c is the signal propagation speed.

4. The multi-source data based earth surface deformation elevation evolution measurement system of claim 2, wherein, The signal interference calculation module calculates a signal interference index for evaluating the interference degree of the reflected signal to the received signal based on the signal reflection model, which comprises: In the formula, I is the signal interference index; T is the measurement window.

5. The multi-source data based earth surface deformation elevation evolution measurement system according to claim 4, wherein, The signal optimization module dynamically adjusts the received signal based on the signal interference index, which comprises: S7: collecting the composite signal in the continuous time period and calculating the average signal strength in the continuous time period, i.e. wherein is the average intensity of the composite signal; m is the number of composite signal acquisitions over a continuous time period; S8: calculating the signal strength variation rate for quantifying the signal fluctuation based on the composite signal average strength, i.e. wherein is the signal rate of change; is the composite signal strength received at a particular point in time in time; S9: defining the preliminary smoothing factor based on the signal variation rate and the preset fluctuation range, i.e. In the formula, is an initial smoothing factor; R is a preset fluctuation range; S10: calculating the composite signal variation amount of the adjacent time points collected in the continuous time period, i.e. In the formula, is the composite signal change amount at a specific time point t. S11: selecting the maximum value of the composite signal variation amount and calculating the maximum signal variation range that the system can accept under normal operating conditions based on the preset empirical coefficient, i.e. In the formula, is a signal fluctuation threshold value; is a proportionality constant; S12: comparing the signal variation rate with the signal fluctuation threshold, and dynamically adjusting the smoothing factor based on the initial smoothing factor and the comparison result to adapt to the real-time changing signal environment, i.e. In the formula, is a dynamic balance factor; S13: adjusting the received signal in real time based on the signal interference index and the dynamic balance factor, i.e. In the formula, is a filtered signal after adaptive filtering processing.

6. The multi-source data based earth surface deformation elevation evolution measurement system of claim 5, wherein, The signal optimization module dynamically adjusts the received signal based on the signal interference index, which further comprises: S14: performing Hilbert transform on the direct path signal to obtain the instantaneous phase, i.e. In the formula, is the instantaneous phase of the direct path signal; S15: calculating the phase difference between the reflected signal and the direct path signal, i.e. wherein the instantaneous phase difference between the ith reflected signal and the direct path signal; the instantaneous phase of the ith reflected signal; S16: generating a compensation weight for offsetting the signal interference based on the instantaneous phase difference and the gain coefficient, i.e. wherein is the phase compensation weight for the i-th reflected signal, used to precisely subtract the phase distortion component caused by the multipath effect in the subsequent signal synthesis; is a sinusoidal function of the phase difference; is an exponential decay term; wherein is a decay coefficient; S17: superimposing the phase compensation weight to the filtered signal to generate the final optimized signal, i.e. In the formula, is the final optimized signal.

7. The multi-source data based earth surface deformation elevation evolution measurement system of claim 6, wherein, The signal optimization module dynamically adjusts the received signal based on the signal interference index, which further comprises: S161: collecting the time delay of the reflected signal in the past continuous time period and calculating the time delay standard deviation, i.e. wherein is the delay fluctuation rate; M is the number of reflection paths; is the delay value of the i-th reflection path at time t; is the average of the delay values of the M reflection paths over the past consecutive time period; S162: calculating the severity of the environmental interference based on the time delay fluctuation rate and the interference index, i.e. wherein is a coupling factor for interference and latency; is a maximum allowed value preset for the interference indicator; is a maximum allowed value preset for the latency fluctuation rate; S163: dynamically adjust the attenuation coefficient based on the value of the coupling factor to balance the strength of phase compensation, that is: In the formula, is a dynamic attenuation factor.

8. The multi-source data based earth surface deformation elevation evolution measurement system of claim 7, wherein, The generating compensation weight for offsetting signal interference based on the instantaneous phase difference and the gain coefficient further comprises: S164: Based on the coordinates of the reflection points in the past continuous time period, the change of the reflection point position with time and the dynamic offset of the reflection path caused by the micro-deformation of the mountain wall or the swing of the vegetation are calculated, that is: wherein is the displacement vector of the ith reflection point within the time interval ; and is the three-dimensional coordinate of the ith reflection point at time t; is the three-dimensional coordinate of the ith reflection point at the previous time instant; is the time interval between the reflection point position calculations; S165: Calculate the average offset direction of all reflection points based on the displacement vector, which is used to judge the main direction of environmental interference, that is: wherein is the average direction of the offsets of the reflection points; Q is the number of reflection points; is the unit offset vector, indicating the direction; S166: Obtain the main direction of ground deformation from the three-dimensional terrain model, and calculate the included angle between the average offset direction and the main direction of ground deformation, that is: In the formula, is the included angle between the average direction of the offset and the main direction of the ground deformation, used to determine the cause of the offset of the reflection path; is the main direction of the ground deformation; S167: Based on the included angle between the average offset direction and the main direction of ground deformation, define a direction correlation factor to quantify the consistency between the offset direction of the reflection path and the real ground deformation direction, that is: wherein is a directional dependence factor; S168: Coupling the direction correlation factor into the attenuation coefficient to correct the dynamic attenuation factor, that is: In the formula, is the optimum attenuation coefficient.

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