A method and device for early warning of rock slope protection net pre-landslide based on OTDR

Abstract

The present application relates to the field of rock slope disaster monitoring and early warning, in particular to a rock slope protection net pre-collapse and slide early warning method and device based on OTDR. The present application arranges optical fibers on the protection net. When rockfall impact occurs, the transmission of optical fiber signals will produce energy attenuation, phase disturbance or scattering mode change phenomenon. Real-time collection of reflection curve data along the optical fiber distribution, interpretation of the spatial distribution characteristics of the signal through algorithm, and realization of rockfall location positioning. At the same time, the present application can automatically adjust the determination threshold according to the baseline slope of different slope sections, the curve attenuation characteristics and the environmental noise, thereby effectively avoiding the false alarm and missed alarm problems caused by the traditional fixed threshold method in the presence of large noise or multi-stage attenuation. Further reduce the frequency of artificial inspection and emergency maintenance cost, reduce the risk of secondary disasters and traffic accidents, ensure the safety of road traffic, improve the efficiency of traffic operation, and have good popularization and application value and engineering feasibility.

Description

Technical Field

[0001] This invention relates to the field of rock slope disaster monitoring and early warning, and in particular to a method and equipment for early warning of impending collapse and landslide of rock slope protection nets based on OTDR. Background Technology

[0002] Due to their complex geological structure, weathering, and rainfall erosion, rock slopes along highways are prone to geological disasters such as rockfalls and landslides, which seriously threaten road safety. These disasters are characterized by their suddenness and high impact energy, especially on steep slopes or areas with well-developed joints.

[0003] Flexible protective netting is commonly used as a core protective measure in engineering projects, mitigating disaster impacts through interception and energy dissipation. However, existing protective systems lack real-time monitoring and awareness of the netting's operational status, resulting in significant blind spots in safety management. On one hand, the lack of real-time monitoring methods prevents the timely perception and feedback of dynamic information regarding slope rockfalls, including their frequency, scale, and specific location, making it difficult for maintenance units to assess the actual risk level and disaster evolution trend. On the other hand, the lack of continuous monitoring data on the netting's stress state, deformation, and interception effectiveness prevents engineers from accurately determining the netting's current safety margin and predicting its remaining service life. This monitoring deficiency not only exposes the potential failure risks of the protective system but also hinders data-driven, precise maintenance decisions, potentially triggering secondary disasters after protective failure and ultimately threatening highway traffic safety and operational efficiency.

[0004] Therefore, there is an urgent need for a method and equipment for early warning of landslides and collapses in rock slope protection nets that can take into account both adaptability to complex scenarios and intelligent anomaly recognition. Summary of the Invention

[0005] The purpose of this invention is to overcome the problem in the existing technology that it is not possible to monitor and provide feedback on rockfall conditions on slopes in real time, and to provide a method and equipment for early warning of landslides and collapses of rock slope protection nets based on OTDR.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0007] An OTDR-based method for early warning of landslides and collapses on rock slopes includes the following steps:

[0008] S1: Several sensing optical fibers parallel to the ground are continuously laid on the main beam of the protective net to be warned, and the light reflection curve sequence of the protective net to be warned is acquired in real time.

[0009] S2: Perform smoothing and noise reduction processing on the light reflection curve sequence to output a smooth sequence;

[0010] S3: Calculate the multi-scale slope features of the smoothed sequence, and generate an adaptive dynamic threshold based on the multi-scale slope features; wherein, the multi-scale slope features include the global average slope, global variance, and global standard deviation;

[0011] S4: Obtain the abnormal candidate points of the smoothed sequence based on the adaptively adjusted dynamic threshold;

[0012] S5: Cluster the abnormal candidate points into several abnormal segments according to the continuity, perform false positive detection, and output several valid abnormal segments;

[0013] S6: Calculate the confidence score of the effective abnormal segment, and output the current collapse warning result of the protective net to be warned based on the confidence score.

[0014] As a preferred embodiment of the present invention, step S2 includes the following steps:

[0015] S21: Calculate the local signal-to-noise ratio of the light reflection curve sequence;

[0016] S22: Determine the Gaussian filter radius based on the local signal-to-noise ratio;

[0017] S23: Perform smoothing and noise reduction processing on the light reflection curve sequence according to the Gaussian filter radius, and output a smooth sequence.

[0018] As a preferred embodiment of the present invention, the expression for the Gaussian filter radius is:

[0019] ,

[0020] Where r is the Gaussian filter radius, and f() is the radius calculation function. The maximum and minimum Gaussian filter radii are preset, and SNR is the local signal-to-noise ratio. The set signal-to-noise ratio threshold.

[0021] As a preferred embodiment of the present invention, the calculation formula for the multi-scale slope feature is as follows:

[0022] Global average slope (avgSlope):

[0023] ,

[0024] Global variance globalVar:

[0025] ,

[0026] Global standard deviation (globalStd):

[0027] ,

[0028] Where slope[i] is the slope at position i. x[i] is the distance at position i. The relative intensity of optical power at position i in the smoothed sequence is given by N, where N is the number of windows. The average slope of the smoothed sequence.

[0029] As a preferred embodiment of the present invention, the generation of the adaptively adjusted dynamic threshold includes the following steps:

[0030] S31: Calculate the local slope of each window;

[0031] ,

[0032] in, Let the starting position be i and the window size be... The local slope of the window, Let y[i] be the window scale, and y[i] be the relative intensity of light power at position i in the light reflection curve sequence.

[0033] S32: Calculate the local multi-scale slope characteristics of each window;

[0034] Where the starting position is i, and the window size is... The local multi-scale slope characteristics of the window are:

[0035] Local continuous slope average :

[0036] ,

[0037] Local variance :

[0038] ,

[0039] Local standard deviation :

[0040] ,

[0041] Where ω is the window size, Given a window size of ω, the slope value corresponding to the k-th position;

[0042] S33: Normalize the local variance; its expression is:

[0043] ,

[0044] Where localVarNorm is the normalized local variance, and ɛ is the set local variance correction factor;

[0045] S34: Generate an adaptively adjusted dynamic threshold, the expression of which is:

[0046] ,

[0047] in, To adaptively adjust the dynamic threshold, factor is the set detection sensitivity. This is the local variance suppression coefficient.

[0048] As a preferred embodiment of the present invention, the judgment of abnormal candidate points in S4 includes adaptive adjustment of dynamic threshold judgment and / or standard score statistic judgment;

[0049] The expression for the adaptive adjustment of the dynamic threshold is:

[0050] ,

[0051] The expression for judging the standard score statistic is:

[0052] ,

[0053] ,

[0054] Where z[i] is the standard score statistic at position i. This is the preset standard score threshold.

[0055] As a preferred embodiment of the present invention, step S6 includes the following steps:

[0056] S61: Calculate the abnormal segment parameters for each valid abnormal segment; the abnormal segment parameters include start and end positions, maximum loss amplitude, average slope within the segment, and local variance consistency.

[0057] S62: Calculate the confidence score of the valid outlier segment based on the outlier segment parameters;

[0058] S63: Output the current landslide warning result of the protective net to be warned based on the abnormal segment parameters and the confidence score; the landslide warning result includes the following steps:

[0059] Level 1 warning judgment: If the overall loss amplitude is greater than or equal to the set first loss amplitude threshold, the continuity of the effective abnormal segment is greater than or equal to medium, and the confidence score is greater than or equal to the set first confidence threshold, it is judged as a Level 1 alarm; otherwise, it proceeds to the Level 2 warning judgment.

[0060] Level 2 warning judgment: If the overall loss amplitude is greater than or equal to the set second loss amplitude threshold, the continuity of the effective abnormal segment is greater than or equal to medium, and the confidence score is greater than or equal to the set first confidence threshold, it is judged as a Level 2 alarm; otherwise, it proceeds to Level 3 warning judgment.

[0061] Level 3 warning judgment: If the set first loss amplitude threshold is greater than the comprehensive loss amplitude, the continuity of the effective abnormal segment is greater than or equal to low, and the confidence score is greater than or equal to the set second confidence threshold, it is judged as a level 3 alarm; otherwise, it is judged as an invalid abnormality.

[0062] The continuity of the effective abnormal segment is scored based on the continuous length of the abnormal segment and the proportion of abnormal gaps. The continuity is divided into high, medium and low according to the preset continuity score threshold.

[0063] As a preferred embodiment of the present invention, the expression for the confidence score is:

[0064]

[0065] Wherein, Score represents the confidence score. , , These are the weighting coefficients. For the maximum loss range, For local variance consistency, , The average slope within the segment. The average length of the valid outlier segments. Maximum length of valid outlier segments.

[0066] As a preferred embodiment of the present invention, the false positive detection in S5 includes:

[0067] Before the starting point of each abnormal segment, backtrack to set a sampling point, calculate the preceding slope of the sampling point. If the preceding slope is less than or equal to 0, output the abnormal segment as a valid abnormal segment; otherwise, discard it.

[0068] An OTDR-based rock slope protection netting early warning device for impending landslides includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform any of the aforementioned OTDR-based rock slope protection netting early warning methods for impending landslides.

[0069] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0070] This invention deploys optical fibers in a specific manner on a protective net. When a rockfall occurs, the signal transmitted through the fiber optic cable exhibits energy attenuation, phase disturbance, or changes in scattering modes. Real-time data acquisition of the reflection curve along the fiber optic cable is used, and algorithms interpret the spatial distribution characteristics of the signal to pinpoint the location of the rockfall. Furthermore, this invention automatically adjusts the judgment threshold based on the baseline slope, curve attenuation characteristics, and environmental noise of different slope sections, effectively avoiding false alarms and missed alarms caused by traditional fixed threshold methods in situations with high noise levels or multi-level attenuation. This reduces the frequency of manual inspections and emergency maintenance costs, minimizes the risk of secondary disasters and traffic accidents, ensures road safety, and improves traffic efficiency, demonstrating significant potential for widespread application and engineering feasibility. Attached Figure Description

[0071] Figure 1 This is a flowchart illustrating an OTDR-based method for early warning of landslides and collapses in rock slopes, as described in Embodiment 1 of the present invention.

[0072] Figure 2 This is a schematic diagram of the optical time-domain reflectometry system characteristic curves under different states in the OTDR-based rock slope protection net early warning method for landslides and collapses described in Embodiment 2 of the present invention.

[0073] Figure 3 This is a schematic diagram of the fiber optic cable layout on the slope protection net in the OTDR-based early warning method for rock slope protection nets described in Embodiment 2 of the present invention.

[0074] Figure 4 This is a schematic diagram of the visualization detection results of a practical application example of the OTDR-based rock slope protection net early warning method for landslides and collapses described in Embodiment 2 of the present invention.

[0075] Figure 5 This is a schematic diagram of the structure of an OTDR-based rock slope protection net for early warning of landslides and collapses, as described in Embodiment 3 of the present invention.

[0076] In the diagram, the markings are: 11 - anchor bolt, 22 - optical fiber encased in a flexible tube, and 33 - weather-resistant strapping. Detailed Implementation

[0077] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0078] Example 1

[0079] like Figure 1 As shown, an early warning method for landslides and collapses in rock slope protection netting based on OTDR includes the following steps:

[0080] S1: Several sensing optical fibers parallel to the ground are continuously laid on the main beam of the protective net to be warned, and the light reflection curve sequence of the protective net to be warned is acquired in real time.

[0081] S2: Perform smoothing and noise reduction processing on the light reflection curve sequence to output a smooth sequence.

[0082] S3: Calculate the multi-scale slope features of the smoothed sequence, and generate an adaptive dynamic threshold based on the multi-scale slope features; wherein, the multi-scale slope features include the global average slope, global variance, and global standard deviation.

[0083] S4: Obtain the abnormal candidate points of the smooth sequence based on the adaptively adjusted dynamic threshold.

[0084] S5: Cluster the abnormal candidate points into several abnormal segments according to continuity, perform false positive detection, and output several valid abnormal segments.

[0085] S6: Calculate the confidence score of the effective abnormal segment, and output the current collapse warning result of the protective net to be warned based on the confidence score.

[0086] Example 2

[0087] This embodiment is a specific implementation of the OTDR-based rock slope protection net pre-collapse and landslide early warning method described in Embodiment 1, including the following steps:

[0088] S1: Several sensing optical fibers parallel to the ground are continuously laid on the main beam of the protective net to be warned, and the light reflection curve sequence of the protective net to be warned is acquired in real time.

[0089] The light reflection curves are obtained by sampling at a distance using an OTDR device, resulting in a light reflection curve sequence (x[i], y[i]), where x[i] is the distance (m) and y[i] is the optical power / loss (dB).

[0090] S2: Perform smoothing and noise reduction processing on the light reflection curve sequence to output a smooth sequence.

[0091] S21: Calculate the local signal-to-noise ratio of the light reflection curve sequence; its expression is:

[0092] ,

[0093] Where mean(|y|) is the mean of the absolute value sequence of relative light power intensity, and std(|y|) is the standard deviation of the absolute value sequence of relative light power intensity.

[0094] S22: Determine the Gaussian filter radius based on the local signal-to-noise ratio;

[0095] Furthermore, the expression for the Gaussian filter radius is:

[0096] ,

[0097] Where r is the Gaussian filter radius, and f() is the radius calculation function. The maximum and minimum Gaussian filter radii are preset, and SNR is the local signal-to-noise ratio. The set signal-to-noise ratio threshold.

[0098] This step uses a larger radius to enhance smoothness when the SNR is low, and a smaller radius to preserve details when the SNR is high, based on the calculation of the dynamic radius.

[0099] S23: Perform smoothing and noise reduction processing on the light reflection curve sequence according to the Gaussian filter radius, and output a smooth sequence.

[0100] This step uses Gaussian smoothing to suppress random noise and ensures the stability of subsequent detection.

[0101] S3: Calculate the multi-scale slope features of the smoothed sequence, and generate an adaptive dynamic threshold based on the multi-scale slope features; wherein, the multi-scale slope features include the global average slope, global variance, and global standard deviation.

[0102] Furthermore, the calculation formula for the multi-scale slope feature is as follows:

[0103] Global average slope (avgSlope):

[0104] ,

[0105] Global variance globalVar:

[0106] ,

[0107] Global standard deviation (globalStd):

[0108] ,

[0109] Where slope[i] is the slope at position i. x[i] is the distance at position i. The relative intensity of optical power at position i in the smoothed sequence is given by N, where N is the number of windows. The average slope of the smoothed sequence.

[0110] Furthermore, the generation of the adaptively adjusted dynamic threshold includes the following steps:

[0111] S31: Calculate the local slope of each window (e.g., calculate the local slope of each window). (in the case of =2,5,10);

[0112] ,

[0113] in, Let the starting position be i and the window size be... The local slope of the window, Let y[i] be the window scale, and y[i] be the relative intensity of light power at position i in the light reflection curve sequence.

[0114] S32: Calculate the local multi-scale slope characteristics of each window;

[0115] Where the starting position is i, and the window size is... The local multi-scale slope characteristics of the window are:

[0116] Local continuous slope average :

[0117] ,

[0118] Local variance :

[0119] ,

[0120] Local standard deviation :

[0121] ,

[0122] Where ω is the window size, Given a window size of ω, the slope value corresponding to the k-th position;

[0123] S33: To counteract the overall curve noise level, the local variance is normalized; its expression is:

[0124] ,

[0125] Where localVarNorm is the normalized local variance, and ɛ is the set local variance correction factor;

[0126] S34: Using the global average slope as a baseline, generate an adaptively adjusted dynamic threshold, the expression of which is:

[0127] ,

[0128] in, To adaptively adjust the dynamic threshold, factor is the set detection sensitivity (in this embodiment, the overall sensitivity is manually adjusted within the range of 0.6–1.0). .

[0129] When local fluctuations increase (i.e., when localVarNorm is high), the threshold adaptively decreases; when local noise decreases, the threshold adaptively increases, thereby improving the robustness of detection. If a certain segment has a continuous slope less than the threshold for at least minLength sampling points, it is considered that an abnormal descent segment may exist.

[0130] S4: Obtain the abnormal candidate points of the smooth sequence based on the adaptively adjusted dynamic threshold.

[0131] Furthermore, the identification of abnormal candidate points includes adaptive adjustment of dynamic thresholds and / or standard score statistics.

[0132] The expression for the adaptive adjustment of the dynamic threshold is:

[0133] ,

[0134] The expression for judging the standard score statistic is:

[0135] ,

[0136] ,

[0137] Where z[i] is the standard score statistic at position i. This is the preset standard score threshold.

[0138] This embodiment uses two discrimination conditions and two discrimination strategies for mixed judgment.

[0139] When a lenient detection strategy is required, an anomaly can be identified as long as any one of the judgment conditions is met, which can improve the recall rate. When a strict detection strategy is required, both judgment conditions must be met simultaneously to identify an anomaly, which can reduce false positives.

[0140] This embodiment fuses candidate segments obtained from different window scales and uses a logical OR method to obtain the final macro-bend candidate segments and micro-bend candidate segments: that is, if a point is detected as an anomaly at any scale, it is retained; this ensures that anomaly features at different scales can be detected, improving overall detection sensitivity and adaptability; and it takes into account both macro-bend (large-scale changes) and micro-bend (small-scale perturbations) detection. Figure 2 As shown, the characteristic curve of the optical time-domain reflectometry system under normal conditions is as follows: Figure 2 The left-middle figure shows the characteristic curves of the time-domain reflectometry system during macro-bending deformation. Figure 2 As shown in the middle right figure.

[0141] S5: Cluster the abnormal candidate points into several abnormal segments according to continuity, perform false positive detection, and output several valid abnormal segments.

[0142] Furthermore, to avoid misjudgments caused by isolated noise points, this embodiment also includes false positive detection, which includes:

[0143] Before the starting point of each abnormal segment, a sampling point is set back, and the preceding slope of that sampling point is calculated. If the preceding slope is less than or equal to 0, it indicates that the preceding curve is in a downward trend, and the abnormal segment is output as a valid abnormal segment; otherwise, the abnormality is considered not to belong to the abnormal bending event and is discarded. This step can effectively filter out false anomalies caused by isolated spikes or electronic noise.

[0144] Furthermore, the preceding slope The expression is:

[0145] ,

[0146] in, This indicates the starting position of the current abnormal segment. The relative intensity of optical power at the starting position of the smoothed anomalous segment. To trace the location of the sampling point, The relative intensity of optical power at the backsampling points after smoothing.

[0147] S6: Calculate the confidence score and continuity score of the effective abnormal segment, and output the current collapse warning result of the protective net to be warned based on the comprehensive loss magnitude, confidence score and continuity score.

[0148] Furthermore, this includes the following steps:

[0149] S61: Calculate the outlier parameters for each valid outlier segment; the outlier parameters include:

[0150] Start and end positions ;

[0151] Maximum loss range ;

[0152] Average slope within the segment ;

[0153] Local variance consistency .

[0154] S62: Calculate the confidence score of the valid outlier segment based on the outlier segment parameters;

[0155] Furthermore, the expression for the confidence score is:

[0156]

[0157] Wherein, Score represents the confidence score. , , These are weighting coefficients used to balance the importance of intensity features, fluctuation features, and spatial features. The average length of the valid outlier segments. The maximum length of a valid anomaly segment. The confidence score ranges from 0 to 1; when the score is close to 1, it indicates that the intensity shift, fluctuation characteristics, and spatial continuity characteristics of the anomaly are all significant, and the anomaly is highly reliable; when the score is close to 0, it indicates that no valid anomaly has been formed.

[0158] This confidence score is used to screen high-confidence anomaly segments from multiple detection results and provides a weighted basis for subsequent graded early warning. The higher the score, the more likely the anomaly segment corresponds to a real fiber macrobend or breakage event.

[0159] S63: Calculate the continuity score of the valid abnormal segment based on the abnormal segment parameters;

[0160] Furthermore, the expression for the continuity score is:

[0161]

[0162] Where C represents continuous rating. , , These are preset weighting coefficients; This represents the actual length of the continuous abnormal segment. This represents the total span of the anomalous segment; the larger the continuous coverage ratio, the more complete the anomalous structure. This represents the number of breakpoints in the abnormal segment. This represents the total number of outliers. The lower the percentage of gaps, the better the continuity of the outlier segments. As a trend consistency index, it counts the slope direction within the abnormal segment. If the direction changes are consistent, it is considered to be trend consistent and is assigned a value of 1; otherwise, it is assigned a value of 0.

[0163] S64: Output the current landslide warning result of the protective net to be warned based on the abnormal segment parameters and the confidence score; the landslide warning result includes the following steps:

[0164] (1) Level 1 warning judgment: If the overall loss amplitude is greater than or equal to the set first loss amplitude threshold, the continuity of the effective abnormal segment is greater than or equal to medium, and the confidence score is greater than or equal to the set first confidence threshold, it is judged as a Level 1 alarm; otherwise, it enters the Level 2 warning judgment.

[0165] For example: when the comprehensive loss amplitude ≥ the set first loss amplitude threshold, the continuity of the effective abnormal segment is high, and the confidence score ≥ the set first confidence threshold, it may be a fiber break or an overall damage of the protective net, and it is determined as a first-level alarm.

[0166] For example: when the comprehensive loss amplitude significantly exceeds the set first loss amplitude threshold, the continuity of the effective abnormal segment is medium, and the confidence score is within the allowable credible range higher than the set first confidence threshold, it can also be determined as a first-level alarm to avoid missing serious structural damage situations.

[0167] (2) Second-level early warning judgment: If the comprehensive loss amplitude ≥ the set second loss amplitude threshold, the continuity of the effective abnormal segment ≥ medium, and the confidence score ≥ the set first confidence threshold, it is determined as a second-level alarm; otherwise, it enters the third-level early warning judgment.

[0168] For example: when the set first loss amplitude threshold > the comprehensive loss amplitude ≥ the set second loss amplitude threshold, and the set first confidence threshold > the confidence score ≥ the set second confidence threshold, it may be macro bending or local deformation, and it is determined as a second-level alarm.

[0169] (3) Third-level early warning judgment: If the set first loss amplitude threshold > the comprehensive loss amplitude, the continuity of the effective abnormal segment ≥ low level, and the confidence score ≥ the set second confidence threshold, it is determined as a third-level alarm; otherwise, it is determined as an invalid abnormality.

[0170] For example: when the set second loss amplitude threshold > the comprehensive loss amplitude, and the set second confidence threshold > the confidence score, it may be a slight perturbation or impact, and it is determined as a third-level alarm.

[0171] For example: when the comprehensive loss amplitude ≥ the set second loss amplitude threshold, but the confidence score is significantly lower than the set first confidence threshold and higher than the set second confidence threshold; or when the confidence score is greater than the set first confidence threshold but the comprehensive loss amplitude is less than the set second loss amplitude threshold, it is also determined as a third-level alarm.

[0172] For example: when the continuity of the effective abnormal segment is lower than the low-level range, or when the confidence score is lower than the lowest credibility threshold (i.e., the set second confidence threshold), it is determined as an invalid abnormality.

[0173] Among them, the continuity of the effective abnormal segment is continuously scored according to the continuous length of the abnormal segment and the abnormal gap ratio. According to the preset continuous scoring threshold, the continuity is divided into high level (C>0.6), medium level (0.4<C<0.6), and low level (C<0.4). When the continuous scoring is higher than the preset continuous scoring threshold interval, it is determined as high continuity; when it is within the preset continuous scoring threshold interval, it is determined as medium continuity; and when it is lower than the preset continuous scoring threshold interval, it is determined as low continuity.

[0174] The following is an example of actual operation based on the scheme described in this embodiment, and the specific results are as follows:

[0175] In the protective netting, flexible tubes containing optical fibers are fixed to the wire mesh at certain vertical intervals and in a continuous horizontal manner using weather-resistant straps. For example... Figure 3 As shown, 11 is the anchor bolt, 22 is the optical fiber encased in the flexible hose, and 33 is the weather-resistant strap. The fiber optic hose simultaneously serves the functions of optical signal transmission and structural stress feedback. When the network is subjected to impact or deformation, the optical fiber undergoes a slight bend or breakage, and the corresponding reflected signal changes accordingly.

[0176] An optical time-domain reflectometer (OTDR) was used to transmit test pulses to the fiber optic link at a fixed sampling period, simultaneously acquiring Rayleigh scattering and Fresnel reflection signals to form an OTDR curve data sequence showing the variation of reflection power with distance. Specific test results include:

[0177] The coordinates of the abnormal segment (X) are: 235.620 236.630 237.650 238.670 239.680 240.700 241.710 242.730 243.740 244.760 245.780 246.790 247.810 248.820 249.840 250.850 251.870;

[0178] Corresponding loss values ​​(dB): 24.870 24.860 24.860 24.850 24.840 24.830 24.820 24.810 24.800 24.800 24.790 24.780 24.770 24.770 24.770 24.760 24.750;

[0179] Confidence score: 0.94;

[0180] Continuity score: 1.0;

[0181] Runtime: 0.015 milliseconds.

[0182] Secondly, the detection results can be visualized: the blue curve is the original curve acquired by the OTDR, and the green segment is the abnormal curvature region determined by the algorithm (e.g., Figure 4(As shown). Based on experimental data, the first loss amplitude threshold is 1.5dB, the second loss amplitude threshold is 0.5dB, the first confidence threshold is 0.6, and the second confidence threshold is 0.4. Combining the abnormal segment parameters (amplitude change of 0.12dB), confidence score, and continuity score, the overall loss amplitude is less than the second loss amplitude threshold, and the confidence threshold is high, thus it can be determined as a level three alarm. After field verification, the accuracy of this warning result was confirmed.

[0183] Based on the above results, this invention addresses the monitoring of rockfall and protective netting on highway rock slopes by introducing a dynamic discrimination method based on adaptive slope analysis. This method adaptively extracts features and identifies anomalies in OTDR fiber optic curves, automatically adjusting the judgment threshold according to the baseline slope, curve attenuation characteristics, and environmental noise of different slope sections. This effectively avoids the false alarms and missed alarms caused by traditional fixed threshold methods in situations with high noise or multi-level attenuation. This method significantly improves the accuracy and robustness of detecting rockfall events and abnormal stress on protective netting. Furthermore, the algorithm has low computational complexity and can be directly integrated into existing OTDR equipment, significantly improving fault location efficiency and maintenance response speed. In engineering applications, it can reduce the frequency of manual inspections and emergency maintenance costs by approximately 30% to 50%, reducing the risk of secondary disasters and traffic accidents. In terms of social benefits, this invention provides continuous and accurate safety monitoring and early warning for highway rock slopes, ensuring road safety, improving traffic efficiency, and demonstrating significant application value and engineering feasibility.

[0184] Example 3

[0185] like Figure 5 As shown, an OTDR-based early warning device for rock slope protection netting against imminent landslides includes at least one processor, a memory communicatively connected to the at least one processor, and at least one input / output interface communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which, when executed, enables the at least one processor to perform the OTDR-based early warning method for rock slope protection netting against imminent landslides described in the foregoing embodiments. The input / output interface may include a display, keyboard, mouse, and USB interface for inputting and outputting data.

[0186] Furthermore, the OTDR-based rock slope protection netting early warning device for impending collapse and landslide can be a desktop computer, mobile phone, tablet computer, wearable OTDR-based rock slope protection netting early warning device for impending collapse and landslide, etc., capable of deep information recognition.

[0187] Furthermore, the processor may include one or more processing cores. The processor connects various parts of the OTDR-based rock slope protection netting early warning system using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory, and by calling data stored in memory. Optionally, the processor may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also be implemented separately as a communication chip, without being integrated into the processor.

[0188] The memory may include random access memory (RAM) or read-only memory (ROM). The memory can be used to store instructions, programs, code, code sets, or instruction sets, such as instructions or code sets used to implement the OTDR-based rock slope protection net pre-collapse early warning method provided in this application embodiment. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described above, etc. The data storage area may also store data created during the use of the OTDR-based rock slope protection net pre-collapse early warning device (such as a modulation sequence-depth mapping table, image data, spectrogram data, etc.).

[0189] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory, magnetic disks, or optical disks.

[0190] When the integrated units of the present invention are implemented as software functional units and sold or used as independent products, they can also be stored in a computer-readable storage medium. The computer-readable storage medium stores program code, which can be called by a processor to execute the methods described in the above method embodiments. Based on this understanding, the technical solution of the embodiments of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes electronic memories such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium has storage space for program code that executes any of the method steps described above. This program code can be read from or written to one or more computer program products. The program code can be compressed, for example, in an appropriate form.

[0191] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for early warning of landslides and collapses in rock slope protection nets based on OTDR, characterized in that, Includes the following steps: S1: Several sensing optical fibers parallel to the ground are continuously laid on the main beam of the protective net to be warned, and the light reflection curve sequence of the protective net to be warned is acquired in real time. S2: Perform smoothing and noise reduction processing on the light reflection curve sequence to output a smooth sequence; S3: Calculate the multi-scale slope features of the smoothed sequence, and generate an adaptive dynamic threshold based on the multi-scale slope features; wherein, the multi-scale slope features include the global average slope, global variance, and global standard deviation; S4: Obtain the abnormal candidate points of the smoothed sequence based on the adaptively adjusted dynamic threshold; S5: Cluster the abnormal candidate points into several abnormal segments according to the continuity, perform false positive detection, and output several valid abnormal segments; S6: Calculate the confidence score of the effective abnormal segment, and output the current collapse and landslide warning result of the protective net to be warned based on the confidence score; The generation of the adaptive dynamic threshold includes the following steps: S31: Calculate the local slope of each window; ， in, Let the starting position be i and the window size be... The local slope of the window, Let y[i] be the window scale, and y[i] be the relative intensity of light power at position i in the light reflection curve sequence; S32: Calculate the local multi-scale slope characteristics of each window; Where the starting position is i, and the window size is... The local multi-scale slope characteristics of the window are: Local continuous slope average : ， Local variance : ， Local standard deviation : ， Where ω is the window size, Given a window size of ω, the slope value corresponding to the k-th position; S33: Normalize the local variance; its expression is: ， Where localVarNorm is the normalized local variance, ɛ is the set local variance correction factor, and globalVar is the global variance; S34: Generate an adaptively adjusted dynamic threshold, the expression of which is: ， in, To adaptively adjust the dynamic threshold, factor is the set detection sensitivity. is the local variance suppression coefficient, and avgSlope is the global average slope.

2. The method for early warning of landslides and collapses in rock slope protection nets based on OTDR as described in claim 1, characterized in that, S2 includes the following steps: S21: Calculate the local signal-to-noise ratio of the light reflection curve sequence; S22: Determine the Gaussian filter radius based on the local signal-to-noise ratio; S23: Perform smoothing and noise reduction processing on the light reflection curve sequence according to the Gaussian filter radius, and output a smooth sequence.

3. The method for early warning of landslides and collapses in rock slope protection nets based on OTDR as described in claim 2, characterized in that, The expression for the Gaussian filter radius is: ， Where r is the Gaussian filter radius, and f() is the radius calculation function. The maximum and minimum Gaussian filter radii are preset, and SNR is the local signal-to-noise ratio. The set signal-to-noise ratio threshold.

4. The method for early warning of landslides and collapses in rock slope protection nets based on OTDR as described in claim 1, characterized in that, The formula for calculating the multi-scale slope feature is: Global average slope (avgSlope): ， Global variance globalVar: ， Global standard deviation (globalStd): ， Where slope[i] is the slope at position i. x[i] is the distance at position i. The relative intensity of optical power at position i in the smoothed sequence is given by N, where N is the number of windows. The average slope of the smoothed sequence.

5. The method for early warning of landslides and collapses in rock slope protection nets based on OTDR as described in claim 4, characterized in that, The judgment of abnormal candidate points in S4 includes adaptive adjustment of dynamic threshold judgment and / or standard score statistic judgment. The expression for the adaptive adjustment of the dynamic threshold is: ， The expression for judging the standard score statistic is: ， ， Where z[i] is the standard score statistic at position i. This is the preset standard score threshold.

6. The method for early warning of landslides and collapses in rock slope protection nets based on OTDR as described in claim 5, characterized in that, S6 includes the following steps: S61: Calculate the abnormal segment parameters for each valid abnormal segment; the abnormal segment parameters include start and end positions, maximum loss amplitude, average slope within the segment, and local variance consistency. S62: Calculate the confidence score of the valid outlier segment based on the outlier segment parameters; S63: Output the current landslide warning result of the protective net to be warned based on the abnormal segment parameters and the confidence score; the landslide warning result includes the following steps: Level 1 warning judgment: If the overall loss amplitude is greater than or equal to the set first loss amplitude threshold, the continuity of the effective abnormal segment is greater than or equal to medium, and the confidence score is greater than or equal to the set first confidence threshold, it is judged as a Level 1 alarm; otherwise, it proceeds to the Level 2 warning judgment. Level 2 warning judgment: If the overall loss amplitude is greater than or equal to the set second loss amplitude threshold, the continuity of the effective abnormal segment is greater than or equal to medium, and the confidence score is greater than or equal to the set first confidence threshold, it is judged as a Level 2 alarm; otherwise, it proceeds to Level 3 warning judgment. Level 3 warning judgment: If the set first loss amplitude threshold is greater than the comprehensive loss amplitude, the continuity of the effective abnormal segment is greater than or equal to low, and the confidence score is greater than or equal to the set second confidence threshold, it is judged as a level 3 alarm; otherwise, it is judged as an invalid abnormality. The continuity of the effective abnormal segment is scored based on the continuous length of the abnormal segment and the proportion of abnormal gaps. The continuity is divided into high, medium and low according to the preset continuity score threshold.

7. The method for early warning of landslides and collapses in rock slope protection nets based on OTDR as described in claim 6, characterized in that, The expression for the confidence score is: Wherein, Score represents the confidence score. , , These are the weighting coefficients. For the maximum loss range, For local variance consistency, , The average slope within the segment. The average length of the valid outlier segments. Maximum length of valid outlier segments.

8. The method for early warning of landslides and collapses in rock slope protection nets based on OTDR as described in claim 1, characterized in that, The false positive detection in S5 includes: Before the starting point of each abnormal segment, backtrack to set a sampling point, calculate the preceding slope of the sampling point. If the preceding slope is less than or equal to 0, output the abnormal segment as a valid abnormal segment; otherwise, discard it.

9. An OTDR-based early warning device for landslides and collapses on rock slope protection nets, characterized in that, It includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform an OTDR-based rock slope protection net pre-collapse and landslide early warning method according to any one of claims 1 to 8.

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