Optical fiber landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment

Abstract

The invention discloses an optical fiber landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback regulation. The method comprises the steps of S1, system initialization, S2, signal acquisition, S3, signal data processing, S4, real-time signal-to-noise ratio SNR calculation, S5, system adoption frequency regulation and S6, sampling and feedback closed loop forming. The method solves the technical problem that an existing optical fiber landslide monitoring system lacks real-time identification and dynamic sampling control on the quality of collected data, can overcome the defects that a fixed sampling mode is low in efficiency, high in data redundancy and poor in environmental adaptability, and provides a self-adaptive sampling frequency adjusting method based on signal-to-noise ratio feedback. And more efficient data acquisition and landslide precursor capture are realized.

Description

Technical Field

[0001] This invention relates to the field of landslide monitoring and fiber optic sensing technology, and in particular to a fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment. Background Technology

[0002] Landslides are one of the most dangerous geological hazards worldwide, causing numerous casualties and property losses every year. Timely and accurate landslide monitoring and early warning are crucial for disaster reduction and prevention. In recent years, fiber optic distributed sensing technology has been widely used in the field of landslide monitoring due to its advantages such as resistance to electromagnetic interference, long-distance monitoring, and high sensitivity.

[0003] Currently, commonly used fiber optic sensing technologies include distributed acoustic sensing (DAS), Brillouin optical time-domain analysis (BOTDA), and Brillouin optical time-domain reflectometry (BOTDR).

[0004] These technologies enable continuous acquisition of strain, displacement, vibration, or temperature data in landslide areas, providing crucial data support for landslide early warning.

[0005] However, existing fiber optic landslide monitoring systems still have the following shortcomings in practical applications:

[0006] 1. Severe environmental noise interference

[0007] Fiber optic sensing data is susceptible to interference from various environmental factors, including:

[0008] 1.1 Meteorological factors: thermal expansion and contraction of optical fibers caused by wind, rain, lightning, and sudden temperature changes;

[0009] 1.2 Bio-disturbance: The mechanical effects of animal trampling and plant root growth on optical cables;

[0010] 1.3 Human activities: construction vibration, vehicle traffic, irrigation operations, etc.;

[0011] 1.4 System noise: light source jitter, detector dark current, and thermal noise from electronic equipment;

[0012] These interference factors cause a large amount of noise to be mixed in the collected signals, and the signal-to-noise ratio fluctuates significantly, affecting the identification of the true displacement signal of the landslide.

[0013] 2. Fixed sampling frequency mode is inefficient.

[0014] Most current monitoring systems use a fixed sampling frequency for data acquisition, such as continuous sampling at 1Hz, 10Hz, or 100Hz. This strategy has the following problems:

[0015] 2.1 When the signal-to-noise ratio is low (such as in strong winds or heavy rain), the system still collects data at a high frequency, resulting in a large amount of low-quality, low-value data. During the landslide stabilization period, the excessively high sampling frequency causes data redundancy, increasing transmission bandwidth usage and storage costs.

[0016] 2.2 During the active or precursory stages of a landslide, a fixed frequency may not be able to capture rapidly changing displacement signals in a timely manner;

[0017] 2.3 All-weather high-frequency sampling increases system energy consumption and affects the endurance of field equipment.

[0018] 3. Lack of data quality awareness mechanism

[0019] Existing systems generally lack real-time assessment and feedback on the quality of sensor data: they cannot identify the validity and reliability of the currently collected data; they cannot adaptively adjust monitoring strategies according to environmental changes; they are difficult to find a balance between data quality and sampling efficiency; and they affect the system's response sensitivity in identifying landslide precursors.

[0020] 4. Heavy data processing burden

[0021] The massive amounts of data generated by fixed high-frequency sampling put enormous pressure on the backend processing system: high-performance computing resources are needed for real-time data processing; long-term storage costs are high and data retrieval efficiency is low; communication bandwidth requirements are large, which is not conducive to remote wireless monitoring scenarios; a large amount of invalid data dilutes key information and reduces the effectiveness of early warning algorithms.

[0022] Therefore, there is an urgent need for a landslide monitoring method that can sense signal quality and adaptively adjust sampling strategies to improve the system's ability to acquire high-value data, reduce data redundancy, and enhance the timeliness and accuracy of early warning responses. Summary of the Invention

[0023] The purpose of this invention is to solve the above-mentioned problems and provide a fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment.

[0024] The technical solution of this invention is: a fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment, comprising the following steps.

[0025] S1. System initialization,

[0026] After the distributed fiber optic sensing system for landslide monitoring is installed, initial system parameters matching this method are set. These system parameters should include at least the basic sampling frequency f. base First, set the basic sampling frequency f base The current sampling rate f current ;

[0027] S2. Signal Acquisition

[0028] The sensing unit of the fiber optic sensing system acquires the time series signal x(t) of micro-deformation of the ground surface at the basic sampling frequency.

[0029] S3. Signal Data Processing

[0030] Transform the signal from the time domain to the frequency domain and calculate the signal power P. s and noise power P n ;

[0031] S4. Calculate the real-time signal-to-noise ratio (SNR).

[0032] The real-time signal-to-noise ratio (SNR) is calculated based on the signal power and noise power.

[0033] S5. The system employs frequency regulation.

[0034] Based on the real-time signal-to-noise ratio (SNR), the update sampling frequency f for the next cycle is adjusted through the established mapping relationship. new ,

[0035] S6. Form a closed loop of sampling and feedback.

[0036] Based on the update sampling frequency f obtained in step S5 new Replace the currently executing sampling rate f in S2 current The sampling of the time series signal of surface micro-deformation is performed in the next cycle, and the cycle runs from S2 to S6, forming a dynamic loop of feedback and sampling.

[0037] Preferably, the system parameters mentioned in step S1 also include an upper limit for the sampling frequency f. max Sampling frequency lower limit f min Signal-to-noise ratio reference threshold θ, and sensitivity coefficient α.

[0038] Preferably, the installation requirements for the distributed optical fiber sensing system in step S1 are that the optical fiber sensing units are laid out along the landslide body, covering the key monitoring profile, and the spatial resolution of the sampling points is 0.5m to 5m.

[0039] Preferably, step S3 includes sub-steps.

[0040] S31. Window Splitting

[0041] The continuous time series signal x(t) is divided into a fixed time window T. w The sampled segments are segmented to obtain the sequence {x1, x2, ..., x}. N The length of the time window is determined based on the landslide deformation characteristics. The window can be divided into non-overlapping or 50% overlapping segments. Overlapping can improve signal continuity but increase computational load.

[0042] Rapid landslides or collapses: T w = 10s ~ 30s

[0043] Slow creep landslide: T w = 30s ~ 120s

[0044] Extremely slow landslide: T w = 120s ~ 300s

[0045] The number of data points N contained in each window points = f current × T w , where f current This is the current sampling rate.

[0046] S32. Signal and noise separation

[0047] For each time window, the signal segment x i Frequency domain analysis is performed by using Fast Fourier Transform (FFT) to transform the signal from the time domain to the frequency domain:

[0048] S321. FFT Transform

[0049] (1)

[0050] The spectrum X(f) is obtained, where f is the frequency component, N is the number of sampling points, and x is the frequency component. i (t) Time-domain signal, n sampling point number, N total number of sampling points, j imaginary unit, e base of natural logarithm;

[0051] S322. Frequency Threshold Setting

[0052] Based on the frequency characteristics of different types of landslide displacement signals, a signal frequency threshold f is set. c :

[0053] Sudden landslide: f c = 5Hz ~ 20Hz, capturing rapid displacement and vibration;

[0054] Creep-type landslides: f c = 0.1Hz ~ 5Hz, pay attention to slow deformation at low frequencies;

[0055] Complex landslides: set as multi-band analysis, f c1 = 0.1Hz ~ 1Hz, f c2 = 1Hz ~ 10Hz;

[0056] S323. Signal separation and power calculation

[0057] The spectrum is set according to the threshold f. c The signal is divided into a low-frequency signal band and a high-frequency noise band, and the signal power P is calculated separately. s and noise power P n :

[0058] (2)

[0059] (3)

[0060] Where f max The Nyquist frequency corresponding to the system sampling frequency, i.e., f max = f current / 2, signal frequency threshold f c upper limit of sampling frequency f max , spectrum X(f).

[0061] Preferably, in step S4, the signal power P is used as the basis for the calculation. s and noise power P n Calculate the signal-to-noise ratio (SNR) for the current time window:

[0062] (4)

[0063] Where ε is a small positive constant to prevent division by zero, typically taking the value 10. -10 ;P s For signal power, P n This represents noise power.

[0064] Physical meaning of signal-to-noise ratio:

[0065] SNR > 20dB: Excellent signal quality, obvious landslide characteristics, and low environmental interference;

[0066] 10dB < SNR ≤ 20dB: Signal quality is good and can be monitored normally;

[0067] 0dB < SNR ≤ 10dB: The noise is relatively strong, the signal quality is average, and the sampling frequency needs to be reduced.

[0068] SNR ≤ 0dB: Noise dominates, signal is overwhelmed, sampling frequency should be significantly reduced.

[0069] Preferably, step S4 further includes an SNR calculation and optimization step.

[0070] Exponential moving average is used to smooth the SNR sequence to eliminate short-term fluctuations.

[0071] (5)

[0072] Where β is the smoothing coefficient, typically ranging from 0.2 to 0.5, and SNR... smooth (k) represents the smoothed signal-to-noise ratio, k is the time window number, and SNR(k) represents the original signal-to-noise ratio. smooth (k-1) is the signal-to-noise ratio after the previous smoothing, and (1-β) is the historical weight;

[0073] Set the valid range for SNR (-10dB~50dB), and truncate outliers that exceed the range.

[0074] Preferably, in step S5, based on the real-time signal-to-noise ratio (SNR) obtained in S4, a linear feedback control model is used to adjust the sampling frequency f of the next cycle. new :

[0075] (6)

[0076] Where f base : Base sampling frequency, the standard operating frequency of the system design; α: Sampling adjustment sensitivity coefficient, controlling the response speed and amplitude of frequency adjustment; θ: Signal-to-noise ratio reference threshold, the target signal-to-noise ratio level desired by the system; Update sampling frequency f new Real-time signal-to-noise ratio (SNR);

[0077] Frequency constraints:

[0078]

[0079] Prevent frequency adjustment from exceeding system physical limits or excessively reducing monitoring capabilities:

[0080] When SNR > θ (signal quality better than expected): numerator (SNR - θ) > 0

[0081] f new > f base The system increases the sampling frequency and data acquisition density to capture more detailed displacement changes;

[0082] When SNR = θ (signal quality meets expectations): numerator (SNR - θ) = 0

[0083] f new = f base Maintain the current sampling frequency;

[0084] When SNR < θ (signal quality is lower than expected): numerator (SNR - θ) < 0

[0085] f new < f base The system reduces the sampling frequency, decreases the collection of low-quality data, and saves resources.

[0086] Preferably, in step S5, to avoid drastic frequency fluctuations, a piecewise linear adjustment function is used:

[0087] (7)

[0088] Where Δ1 and Δ2 are the signal-to-noise ratio (SNR) tolerance bands, typically 5dB~10dB; α: sampling adjustment sensitivity coefficient; θ: SNR reference threshold; update sampling frequency f. new， Real-time signal-to-noise ratio (SNR), upper limit of sampling frequency f max Sampling frequency lower limit f min ;

[0089] Frequency change rate limit:

[0090] To prevent sudden frequency changes from affecting system stability, the amplitude of frequency variation between adjacent periods is limited:

[0091] (8)

[0092] Where Δf max The maximum allowable frequency variation, typically f base 20%~50%; f new It updates the sampling frequency, f current This is the currently executing sampling rate.

[0093] Preferably, step S6 is specifically...

[0094] The updated sampling frequency f obtained in step S5 new The control interface of the fiber optic sensing system sends the data to its fiber optic sensing sampling controller.

[0095] After receiving the frequency command, the sampling controller reconfigures the sampling timer parameters and adjusts the data acquisition clock period: T new = 1 / f new、 Update the cache size and data packaging strategy, and synchronize the timestamp recording system.

[0096] Then the fiber optic sensing system enters the next sampling cycle, running in a loop from S2 to S6 to form closed-loop feedback control.

[0097] Preferably, the fiber optic sensing system records the following information in real time for analysis and debugging: SNR value for each time window, sampling frequency adjustment history, signal power and noise power change trends, system alarm events such as abnormally low SNR and frequency adjustment saturation.

[0098] The beneficial effects of this invention are:

[0099] This invention presents a sampling frequency control method for fiber optic landslide monitoring based on signal-to-noise ratio feedback adjustment. This method solves the technical problem of lacking real-time identification and dynamic sampling control of the acquired data quality in existing fiber optic landslide monitoring systems. At the same time, it overcomes the shortcomings of low efficiency, high data redundancy, and poor environmental adaptability of fixed sampling modes. It proposes an adaptive sampling frequency adjustment method based on signal-to-noise ratio feedback to achieve more efficient data acquisition and landslide precursor detection.

[0100] 1. Real-time signal quality sensing capability

[0101] This method accurately quantifies and assesses the intensity of landslide-related signals and the level of environmental noise interference through comparative analysis of frequency domain energy and power. It can distinguish between real displacement signals and various environmental disturbances (wind, rain, temperature, vibration, etc.), providing a reliable basis for sampling decisions. Compared to traditional methods that rely solely on displacement thresholds, this method analyzes and evaluates from the perspective of real-time signal quality, making it more scientific and reasonable.

[0102] 2. Adaptive Intelligent Control

[0103] This method enables dynamic intelligent adjustment of the sampling frequency, increasing the sampling density when signal quality is good and decreasing the sampling frequency when noise is high. This adaptive mechanism allows the system to: automatically increase monitoring intensity during active landslide periods, automatically enter energy-saving mode during stable periods, and reduce invalid data collection under severe weather conditions; thus improving the ability to acquire key data and enhancing system flexibility.

[0104] 3. Significantly improved data efficiency

[0105] By employing an intelligent frequency reduction strategy, the sampling frequency can be reduced to 30%–50% of the reference frequency in interference-prone environments, significantly reducing the amount of invalid data collected. Real-world testing of this system shows that it saves 40%–60% of data storage space, reduces communication bandwidth requirements by 35%–55%, reduces backend processing computation by 30%–50%, and lowers overall system energy consumption by 20%–40%.

[0106] 4. Enhanced early warning and response capabilities

[0107] In the early stages of landslides, weak displacement signals are often accompanied by an increase in SNR (Spot Reduction Rate). This method can capture this feature in a timely manner, automatically increasing the sampling frequency and data acquisition density, thereby improving the probability of detecting sudden deformation events. Compared with fixed-frequency sampling, the early warning response time can be shortened by 20% to 40%, reducing the false negative rate.

[0108] 5. Highly adaptable to engineering projects

[0109] This method features a simple and efficient algorithm with clearly defined physical meanings for its parameters, making it easy to implement in engineering and conduct on-site debugging. It is applicable to various fiber optic sensing technologies (DAS, BOTDA, BOTDR) and different types of landslides (collapses, landslides, debris flows). It can be flexibly deployed for: monitoring of mountain highway slopes, railway tunnel deformation, reservoir bank landslides, mine slope stability, and urban underground space settlement.

[0110] 6. Improved system reliability

[0111] By reducing the collection of low-quality data, the risk of misjudgment in the data processing system is reduced. Meanwhile, adaptive frequency adjustment prevents the system from operating under overload conditions, extending equipment lifespan and improving long-term system stability. Attached Figure Description

[0112] Figure 1 The flowchart shows the overall process of the fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment.

[0113] Figure 2 Here is the signal processing flowchart for step S3;

[0114] Figure 3 The flowcharts for S4 to S5 in Example 2 include real-time signal-to-noise ratio (SNR) calculation optimization and piecewise linear frequency adjustment. Detailed Implementation

[0115] Example 1: See Figure 1-2 The figure shows a method for controlling the sampling frequency of fiber optic landslide monitoring based on signal-to-noise ratio feedback adjustment, which includes the following steps.

[0116] S1. System initialization,

[0117] After the distributed fiber optic sensing system for landslide monitoring is installed, initial system parameters matching this method are set. These system parameters should include at least the basic sampling frequency f. base First, set the basic sampling frequency f base The current sampling rate f current The system parameters mentioned in step S1 also include the upper limit of the sampling frequency f. max Sampling frequency lower limit f min Signal-to-noise ratio reference threshold θ, and sensitivity coefficient α.

[0118] The installation requirements for the distributed optical fiber sensing system described in step S1 are that the optical fiber sensing units are laid out along the landslide body, covering the key monitoring profiles, and the spatial resolution of the sampling points is 0.5m to 5m.

[0119] S2. Signal Acquisition

[0120] The sensing unit of the fiber optic sensing system acquires the time series signal x(t) of micro-deformation of the ground surface at the basic sampling frequency.

[0121] S3. Signal Data Processing

[0122] Transform the signal from the time domain to the frequency domain and calculate the signal power P. s and noise power P n Step S3 specifically includes sub-steps.

[0123] S31. Window Splitting

[0124] The continuous time series signal x(t) is divided into a fixed time window T. w The sampled segments are segmented to obtain the sequence {x1, x2, ..., x}. N The length of the time window is determined based on the landslide deformation characteristics.

[0125] Rapid landslides or collapses: T w = 10s ~ 30s

[0126] Slow creep landslide: T w = 30s ~ 120s

[0127] Extremely slow landslide: T w = 120s ~ 300s

[0128] The number of data points N contained in each window points = f current × T w , where f current This is the current sampling rate.

[0129] The window can be segmented with no overlap or with 50% overlap. Overlapping can improve signal continuity, but it increases the computational load.

[0130] S32. Signal and noise separation

[0131] For each time window, the signal segment x i Frequency domain analysis is performed by using Fast Fourier Transform (FFT) to transform the signal from the time domain to the frequency domain:

[0132] S321 FFT Transform

[0133] (1)

[0134] The spectrum X(f) is obtained, where f is the frequency component, N is the number of sampling points, and x is the frequency component. i (t) Time-domain signal, n sampling point number, N total number of sampling points, j imaginary unit, e base of natural logarithm;

[0135] S322 Frequency Threshold Setting

[0136] Based on the frequency characteristics of different types of landslide displacement signals, a signal frequency threshold f is set. c :

[0137] Sudden landslide: f c = 5Hz ~ 20Hz, which can capture rapid displacement and vibration.

[0138] Creep-type landslides: f c = 0.1Hz ~ 5Hz, pay attention to slow deformation at low frequencies.

[0139] Complex landslides: set as multi-band analysis, f c1 = 0.1Hz ~ 1Hz, f c2 = 1Hz ~ 10Hz;

[0140] Separation and power calculation of S323 signal

[0141] The spectrum is set according to the threshold f. c The signal is divided into a low-frequency signal band and a high-frequency noise band, and the signal power P is calculated separately. s and noise power P n :

[0142] (2)

[0143] (3)

[0144] Where f max The Nyquist frequency corresponding to the system sampling frequency, i.e., f max = f current / 2, signal power P s and noise power P n， Spectrum X(f).

[0145] S4. Calculate the real-time signal-to-noise ratio (SNR).

[0146] In step S4, based on the signal power P s and noise power P n Calculate the signal-to-noise ratio (SNR) for the current time window:

[0147] (4)

[0148] Where ε is a small positive constant to prevent division by zero, typically taking the value 10. -10 P s For signal power, P n Noise power;

[0149] Physical meaning of signal-to-noise ratio (SNR):

[0150] SNR > 20dB: Excellent signal quality, obvious landslide characteristics, and low environmental interference;

[0151] 10dB < SNR ≤ 20dB: Signal quality is good and can be monitored normally;

[0152] 0dB < SNR ≤ 10dB: The noise is relatively strong, the signal quality is average, and the sampling frequency needs to be reduced.

[0153] SNR ≤ 0dB: Noise dominates, signal is overwhelmed, sampling frequency should be significantly reduced;

[0154] S5. The system employs frequency regulation.

[0155] In step S5, based on the real-time signal-to-noise ratio (SNR) obtained in S4, a linear feedback control model is used to adjust the sampling frequency f for the next cycle. new :

[0156] (6)

[0157] Where f base : Base sampling frequency, the standard operating frequency of the system design; α: Sampling adjustment sensitivity coefficient, controlling the response speed and amplitude of frequency adjustment; θ: Signal-to-noise ratio reference threshold, the target signal-to-noise ratio level desired by the system; Update sampling frequency f new Real-time signal-to-noise ratio (SNR);

[0158] Frequency constraints:

[0159]

[0160] To prevent frequency adjustment from exceeding system physical limitations or excessively reducing monitoring capabilities, the adjustment logic is analyzed as follows:

[0161] When SNR > θ (signal quality better than expected): numerator (SNR - θ) > 0

[0162] f new > f base The system increases the sampling frequency and data acquisition density to capture more detailed displacement changes;

[0163] When SNR = θ (signal quality meets expectations): numerator (SNR - θ) = 0

[0164] f new = f base Maintain the current sampling frequency;

[0165] When SNR < θ (signal quality is lower than expected): numerator (SNR - θ) < 0

[0166] f new < f base The system reduces the sampling frequency, decreases the collection of low-quality data, and saves resources.

[0167] S6. Form a closed loop of sampling and feedback.

[0168] Based on the update sampling frequency f obtained in step S5 new Replace the currently executing sampling rate f in S2 current The sampling of the time series signal of surface micro-deformation is performed in the next cycle, and the cycle runs from S2 to S6, forming a dynamic loop of feedback and sampling.

[0169] Step S6 is specifically...

[0170] The updated sampling frequency f obtained in step S5 new The control interface of the fiber optic sensing system sends the data to its fiber optic sensing sampling controller.

[0171] After receiving the frequency command, the sampling controller reconfigures the sampling timer parameters and adjusts the data acquisition clock period: T new = 1 / f new、 Update the cache size and data packaging strategy, and synchronize the timestamp recording system.

[0172] Then the fiber optic sensing system enters the next sampling cycle, running in a loop from S2 to S6 to form closed-loop feedback control.

[0173] The fiber optic sensing system records the following information in real time for analysis and debugging:

[0174] The system alarm events include the SNR value for each time window, the history of sampling frequency adjustment, and the trend of signal power and noise power changes, such as abnormally low SNR and frequency adjustment saturation.

[0175] Example 2: Example 2 is a further optimized scheme based on Example 1. See [link to example]. Figure 1-3 The figure shows a method for controlling the sampling frequency of fiber optic landslide monitoring based on signal-to-noise ratio feedback adjustment, which includes the following steps.

[0176] S1. System initialization,

[0177] After the distributed fiber optic sensing system for landslide monitoring is installed, initial system parameters matching this method are set. These system parameters should include at least the basic sampling frequency f. base First, set the basic sampling frequency f base The current sampling rate f currentThe system parameters mentioned in step S1 also include the upper limit of the sampling frequency f. max Sampling frequency lower limit f min Signal-to-noise ratio reference threshold θ, and sensitivity coefficient α.

[0178] The installation requirements for the distributed optical fiber sensing system described in step S1 are that the optical fiber sensing units are laid out along the landslide body, covering the key monitoring profiles, and the spatial resolution of the sampling points is 0.5m to 5m.

[0179] S2. Signal Acquisition

[0180] The sensing unit of the fiber optic sensing system acquires the time series signal x(t) of micro-deformation of the ground surface at the basic sampling frequency.

[0181] S3. Signal Data Processing

[0182] Transform the signal from the time domain to the frequency domain and calculate the signal power P. s and noise power P n Step S3 specifically includes sub-steps.

[0183] S31. Window Splitting

[0184] The continuous time series signal x(t) is divided into a fixed time window T. w The sampled segments are segmented to obtain the sequence {x1, x2, ..., x}. N The length of the time window is determined based on the landslide deformation characteristics.

[0185] Rapid landslides or collapses: T w = 10s ~ 30s

[0186] Slow creep landslide: T w = 30s ~ 120s

[0187] Extremely slow landslide: T w = 120s ~ 300s

[0188] The number of data points N contained in each window points = f current × T w , where f current This is the current sampling rate.

[0189] The window can be segmented with no overlap or with 50% overlap. Overlapping can improve signal continuity, but it increases the computational load.

[0190] S32. Signal and noise separation

[0191] For each time window, the signal segment xi Frequency domain analysis is performed by using Fast Fourier Transform (FFT) to transform the signal from the time domain to the frequency domain:

[0192] S321 FFT Transform

[0193] (1)

[0194] The spectrum X(f) is obtained. In formula (1), f is the frequency component, and N is the number of sampling points. i (t) Time-domain signal, n sampling point number, N total number of sampling points, j imaginary unit, e base of natural logarithm;

[0195] S322 Frequency Threshold Setting

[0196] Based on the frequency characteristics of different types of landslide displacement signals, a signal frequency threshold f is set. c :

[0197] Sudden landslide: f c = 5Hz ~ 20Hz, which can capture rapid displacement and vibration.

[0198] Creep-type landslides: f c = 0.1Hz ~ 5Hz, pay attention to slow deformation at low frequencies.

[0199] Complex landslides: set as multi-band analysis, f c1 = 0.1Hz ~ 1Hz, f c2 = 1Hz ~ 10Hz;

[0200] Separation and power calculation of S323 signal

[0201] The spectrum is set according to the threshold f. c The signal is divided into a low-frequency signal band and a high-frequency noise band, and the signal power P is calculated separately. s and noise power P n :

[0202] (2)

[0203] (3)

[0204] Where f max The Nyquist frequency corresponding to the system sampling frequency, i.e., f max = f current / 2, signal frequency threshold f c upper limit of sampling frequency f max , spectrum X(f).

[0205] S4. Calculate the real-time signal-to-noise ratio (SNR).

[0206] In step S4, based on the signal power P s and noise power P n Calculate the signal-to-noise ratio (SNR) for the current time window:

[0207] (4)

[0208] Where ε is a small positive constant to prevent division by zero, typically taking the value 10. -10 ;P s For signal power, P n This represents noise power.

[0209] Physical meaning of signal-to-noise ratio:

[0210] SNR > 20dB: Excellent signal quality, obvious landslide characteristics, and low environmental interference;

[0211] 10dB < SNR ≤ 20dB: Signal quality is good and can be monitored normally;

[0212] 0dB < SNR ≤ 10dB: The noise is relatively strong, the signal quality is average, and the sampling frequency needs to be reduced.

[0213] SNR ≤ 0dB: Noise dominates, signal is overwhelmed, sampling frequency should be significantly reduced;

[0214] Step S4 also includes an SNR calculation and optimization step.

[0215] Exponential moving average is used to smooth the SNR sequence to eliminate short-term fluctuations.

[0216] (5)

[0217] Where β is the smoothing coefficient, SNR smooth (k) represents the smoothed signal-to-noise ratio, k is the time window number, and SNR(k) represents the original signal-to-noise ratio. smooth (k-1) is the signal-to-noise ratio after the previous smoothing, and (1-β) is the historical weight;

[0218] Set the valid range for SNR (-10dB~50dB), and truncate outliers that exceed the range.

[0219] S5. The system employs frequency regulation.

[0220] In step S5, based on the real-time signal-to-noise ratio (SNR) obtained in S4, a linear feedback control model is used to adjust the sampling frequency f for the next cycle. new :

[0221] (6)

[0222] Where f base : Base sampling frequency, the standard operating frequency of the system design; α: Sampling adjustment sensitivity coefficient, controlling the response speed and amplitude of frequency adjustment; θ: Signal-to-noise ratio reference threshold, the target signal-to-noise ratio level desired by the system; Update sampling frequency f new Real-time signal-to-noise ratio (SNR);

[0223] Frequency constraints:

[0224]

[0225] To prevent frequency adjustment from exceeding system physical limitations or excessively reducing monitoring capabilities, the adjustment logic is analyzed as follows:

[0226] When SNR > θ (signal quality better than expected): numerator (SNR - θ) > 0

[0227] f new > f base The system increases the sampling frequency and data acquisition density to capture more detailed displacement changes;

[0228] When SNR = θ (signal quality meets expectations): numerator (SNR - θ) = 0

[0229] f new = f base Maintain the current sampling frequency;

[0230] When SNR < θ (signal quality is lower than expected): numerator (SNR - θ) < 0

[0231] f new < f base The system reduces the sampling frequency, decreases the collection of low-quality data, and saves resources.

[0232] In step S5, to avoid drastic frequency fluctuations, a piecewise linear adjustment function is used:

[0233] (7)

[0234] Where Δ1 and Δ2 are the signal-to-noise ratio (SNR) tolerance bands, typically 5dB~10dB; α: sampling adjustment sensitivity coefficient; θ: SNR reference threshold; update sampling frequency f. new， Real-time signal-to-noise ratio (SNR), upper limit of sampling frequency f max Sampling frequency lower limit f min ;

[0235] Frequency change rate limit:

[0236] To prevent sudden frequency changes from affecting system stability, the amplitude of frequency variation between adjacent periods is limited:

[0237] (8)

[0238] Where Δf max The maximum allowable frequency variation, typically f base 20%~50%; f new It updates the sampling frequency, f current This is the current sampling rate.

[0239] S6. Form a closed loop of sampling and feedback.

[0240] Based on the update sampling frequency f obtained in step S5 new Replace the currently executing sampling rate f in S2 current The sampling of the time series signal of surface micro-deformation is performed in the next cycle, and the cycle runs from S2 to S6, forming a dynamic loop of feedback and sampling.

[0241] Step S6 is specifically...

[0242] The updated sampling frequency f obtained in step S5 new The control interface of the fiber optic sensing system sends the data to its fiber optic sensing sampling controller.

[0243] After receiving the frequency command, the sampling controller reconfigures the sampling timer parameters and adjusts the data acquisition clock period: T new = 1 / f new、 Update the cache size and data packaging strategy, and synchronize the timestamp recording system.

[0244] Then the fiber optic sensing system enters the next sampling cycle, running in a loop from S2 to S6 to form closed-loop feedback control.

[0245] The fiber optic sensing system records the following information in real time for analysis and debugging:

[0246] The system alarm events include the SNR value for each time window, the history of sampling frequency adjustment, and the trend of signal power and noise power changes, such as abnormally low SNR and frequency adjustment saturation.

[0247] In steps S321 of embodiments one and two above, the Fast Fourier Transform (FFT) can be replaced with the Discrete Wavelet Transform (DWT), and the piecewise function adjustment in step S5 can be replaced with a nonlinear adjustment function. More embodiments can be formed.

[0248] The methods described in Examples 1 and 2 above are applicable to various fiber optic sensing technologies (DAS, BOTDA, BOTDR) and different types of landslides (collapse, landslide, debris flow). They can be flexibly deployed for: mountain highway slope monitoring, railway tunnel deformation monitoring, reservoir bank landslide monitoring, mine slope stability monitoring, and urban underground space settlement monitoring.

Claims

1. A method for controlling the sampling frequency of fiber optic landslide monitoring based on signal-to-noise ratio feedback adjustment, comprising the following steps: S1. System initialization, After the distributed fiber optic sensing system for landslide monitoring is installed, initial system parameters matching this method are set. These system parameters should include at least the basic sampling frequency f. base First, set the basic sampling frequency f base The current sampling rate f current ; S2. Signal Acquisition The sensing unit of the fiber optic sensing system acquires the time series signal x(t) of micro-deformation of the ground surface at the basic sampling frequency. S3. Signal Data Processing Transform the signal from the time domain to the frequency domain and calculate the signal power P. s and noise power P n ; S4. Calculate the real-time signal-to-noise ratio (SNR). The real-time signal-to-noise ratio (SNR) is calculated based on the signal power and noise power. S5. The system employs frequency regulation. Based on the real-time signal-to-noise ratio (SNR), the update sampling frequency f for the next cycle is adjusted through the established mapping relationship. new , S6. Form a closed loop of sampling and feedback. Based on the update sampling frequency f obtained in step S5 new Replace the currently executing sampling rate f in S2 current The sampling of the time series signal of surface micro-deformation is performed in the next cycle, and the cycle runs from S2 to S6, forming a dynamic loop of feedback and sampling.

2. The fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment according to claim 1, characterized in that: The system parameters mentioned in step S1 also include the upper limit of the sampling frequency f. max Sampling frequency lower limit f min Signal-to-noise ratio reference threshold θ, and sensitivity coefficient α.

3. The fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment according to claim 1, characterized in that: The installation requirements for the distributed optical fiber sensing system described in step S1 are that the optical fiber sensing units are laid out along the landslide body, covering the key monitoring profiles, and the spatial resolution of the sampling points is 0.5m to 5m.

4. The fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment according to claim 1, characterized in that: Step S3 includes sub-steps S31. Window Splitting The continuous time series signal x(t) is divided into a fixed time window T. w The sampled segments are segmented to obtain the sequence {x1, x2, ..., x}. N The length of the time window is determined based on the landslide deformation characteristics. Rapid landslides or collapses: T w = 10s ~ 30s Slow creep landslide: T w = 30s ~ 120s Extremely slow landslide: T w = 120s ~ 300s The number of data points N contained in each window points = f current × T w , where f current This is the current sampling rate. S32. Signal and noise separation For each time window, the signal segment x i Frequency domain analysis is performed by using Fast Fourier Transform (FFT) to transform the signal from the time domain to the frequency domain: S321. FFT Transform （1） The spectrum X(f) is obtained, where f is the frequency component, N is the number of sampling points, and x is the frequency component. i (t) Time-domain signal, n sampling point number, N total number of sampling points, j imaginary unit, e base of natural logarithm; S322. Frequency Threshold Setting Based on the frequency characteristics of different types of landslide displacement signals, a signal frequency threshold f is set. c : Sudden landslide: f c = 5Hz ~ 20Hz, Creep-type landslides: f c = 0.1Hz ~ 5Hz, Complex landslides: set as multi-band analysis, f c1 = 0.1Hz ~ 1Hz, f c2 = 1Hz ~ 10Hz; S323. Signal separation and power calculation The spectrum is set according to the threshold f. c The signal is divided into a low-frequency signal band and a high-frequency noise band, and the signal power P is calculated separately. s and noise power P n : （2） （3） Where f max The Nyquist frequency corresponding to the system sampling frequency, i.e., f max = f current / 2, signal frequency threshold f c upper limit of sampling frequency f max , spectrum X(f).

5. The fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment according to claim 1, characterized in that: In step S4, based on the signal power P s and noise power P n Calculate the signal-to-noise ratio (SNR) for the current time window: （4） Where ε is a small positive constant to prevent division by zero; P s For signal power, P n This represents noise power.

6. The fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment according to claim 5, characterized in that: Step S4 also includes an SNR calculation and optimization step. Exponential moving average is used to smooth the SNR sequence to eliminate short-term fluctuations. （5） Where β is the smoothing coefficient, SNR smooth (k) represents the smoothed signal-to-noise ratio, k is the time window number, and SNR(k) represents the original signal-to-noise ratio. smooth (k-1) is the signal-to-noise ratio after the previous smoothing, and (1-β) is the historical weight; Set the valid range for SNR (-10dB~50dB), and truncate outliers that exceed the range.

7. The fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment according to claim 1, characterized in that: In step S5, based on the real-time signal-to-noise ratio (SNR) obtained in S4, a linear feedback control model is used to adjust the sampling frequency f for the next cycle. new : （6） Where f base : Base sampling frequency, the standard operating frequency of the system design; α: Sampling adjustment sensitivity coefficient, controlling the response speed and amplitude of frequency adjustment; θ: Signal-to-noise ratio reference threshold, the target signal-to-noise ratio level desired by the system; update sampling frequency f new Real-time signal-to-noise ratio (SNR); Frequency constraints: ； Prevent frequency adjustment from exceeding system physical limits or excessively reducing monitoring capabilities: When SNR > θ (signal quality better than expected): numerator (SNR - θ) > 0 f new > f base The system increases the sampling frequency and data acquisition density; When SNR = θ (signal quality meets expectations): numerator (SNR - θ) = 0 f new = f base Maintain the current sampling frequency; When SNR < θ (signal quality is lower than expected): numerator (SNR - θ) < 0 f new < f base The system reduces the sampling frequency.

8. The fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment according to claim 7, characterized in that: In step S5, to avoid drastic frequency fluctuations, a piecewise linear adjustment function is used: （7） Δ1 and Δ2 are the signal-to-noise ratio tolerance bands, with typical values ​​of 5dB~10dB; α: Sampling adjustment sensitivity coefficient, θ: Signal-to-noise ratio reference threshold, update sampling frequency f new， Real-time signal-to-noise ratio (SNR), upper limit of sampling frequency f max Sampling frequency lower limit f min ; Frequency change rate limit: To prevent sudden frequency changes from affecting system stability, the amplitude of frequency variation between adjacent periods is limited: （8） Where Δf max The maximum allowable frequency variation, typically f base 20%~50%; f new It updates the sampling frequency, f current This is the current sampling rate.

9. The fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment according to claim 1, characterized in that: Step S6 is specifically... The updated sampling frequency f obtained in step S5 new The control interface of the fiber optic sensing system sends the data to its fiber optic sensing sampling controller. After receiving the frequency command, the sampling controller reconfigures the sampling timer parameters and adjusts the data acquisition clock period: T new = 1 / f new、 Update the cache size and data packaging strategy, and synchronize the timestamp recording system. Then the fiber optic sensing system enters the next sampling cycle, running in a loop from S2 to S6 to form closed-loop feedback control.

10. The fiber optic landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback adjustment according to claim 1, characterized in that: The fiber optic sensing system records the following information in real time for analysis and debugging: The system alarm events include the SNR value for each time window, the history of sampling frequency adjustment, and the trends in signal power and noise power changes.

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