A land-air dual-modal slope monitoring method, device, electronic equipment, and medium based on fiber optic data.

Abstract

This invention discloses a land-air dual-modal slope monitoring method, device, electronic equipment, and medium based on fiber optic data. It belongs to the field of fiber optic sensing and monitoring technology. The slope monitoring method includes acquiring fiber optic sensor data and coordinates within the slope area; calculating deformation anomaly segments using a sliding window variance analysis method based on the fiber optic sensor data and coordinates; acquiring UAV data and establishing a digital altitude difference model based on the UAV data; generating a three-dimensional displacement distribution map of the slope surface based on the UAV data and the digital altitude difference model; performing spatiotemporal matching between the three-dimensional displacement distribution map of the slope surface and the deformation anomaly segments to obtain an anomaly correlation map; and generating slope monitoring results based on the anomaly correlation map. This invention improves monitoring effectiveness by employing a land-air collaborative approach, combining fiber optic sensor data with UAV data, and comprehensively monitoring slopes using the three-dimensional displacement distribution map of the slope surface and deformation anomaly segments.

Description

Technical Field

[0001] This invention belongs to the field of fiber optic sensing and monitoring technology, specifically relating to a land-air dual-mode slope monitoring method, device, electronic equipment, and medium based on fiber optic data. Background Technology

[0002] Open-pit mine slope slippage is a core hazard threatening mine safety. It requires a simultaneous understanding of both "internal rock mass deformation" and "surface displacement" characteristics. However, existing technologies have significant limitations, as detailed below:

[0003] 1. Deficiencies in open-pit mine slope internal displacement monitoring technology:

[0004] Traditional open-pit mine slope internal monitoring relies on contact equipment such as borehole inclinometers and multi-point displacement meters, which require manual drilling and installation. The monitoring range of a single point is only 1-2m. In loose rock slopes of open-pit mines, the boreholes are prone to collapse and fail, and continuous monitoring cannot be achieved.

[0005] Existing distributed optical fiber sensing (DAS / DTS) technology can achieve continuous monitoring at the kilometer level, but it can only output strain and temperature data along the optical fiber. It cannot directly correlate with the three-dimensional slip morphology of the slope, and it lacks coupling analysis with surface deformation, making it difficult to determine whether "internal deformation has been transmitted to the surface".

[0006] 2. Bottlenecks in surface deformation monitoring technology:

[0007] While surface monitoring equipment for open-pit mine slopes, such as Global Navigation Satellite System (GNSS) and total station, can achieve millimeter-level accuracy, the large area and steep terrain of open-pit mine slopes (>45°) result in low deployment density of surface monitoring equipment, typically one monitoring point per 1000m². This leads to blind spots in monitoring and poor data continuity due to vibrations from mining vehicles and dust obstruction.

[0008] Using a single unmanned aerial vehicle (UAV) for remote sensing monitoring, such as photogrammetry or LiDAR, can achieve large-scale surface modeling. However, the "single-flight" mode has the following problems: ① Lack of ground base station coordination, in the strong electromagnetic interference environment of open-pit mines, the positioning accuracy of UAVs can easily drop to the decimeter level; ② It can only capture surface texture or elevation changes, and cannot distinguish between "slope slippage" and "surface changes caused by mine accumulation / excavation", resulting in a high misjudgment rate (measured misjudgment rate > 15%); ③ Data processing and surface deformation calculation rely on manual labor, with a long lag time (usually > 24 hours), making it difficult to meet the needs of real-time early warning.

[0009] 3. Existing monitoring technologies lack data fusion technology:

[0010] Existing monitoring schemes mostly operate independently of "internal" and "surface" technologies, lacking a standardized fusion method. This results in the following problems: ① Strain anomaly areas detected by internal fiber optic monitoring cannot be verified for corresponding displacement using surface data; ② Slippage areas detected by surface monitoring are difficult to trace the deformation initiation depth and transmission path using internal data; ③ Open-pit mines experience large diurnal temperature variations (up to 20℃) and frequent blasting vibrations, which easily lead to fiber optic strain data drift and increased UAV image matching errors. Existing technologies lack targeted error correction mechanisms, resulting in insufficient overall monitoring accuracy after fusion (vertical error > 10cm).

[0011] Furthermore, the unique operating environment of open-pit mines (high dust concentration, numerous large pieces of equipment, and strong electromagnetic interference) further exacerbates the difficulty of monitoring. Traditional fiber optic cables are easily damaged by being run over by mining trucks, and drones are prone to visual obstacle avoidance failure due to dust obstruction, both of which limit the engineering application of existing technologies. Summary of the Invention

[0012] In view of the technical problems existing in the prior art, the present invention provides a land-air dual-mode slope monitoring method, device, electronic equipment and medium based on optical fiber data.

[0013] According to a first aspect of the technical solution of the present invention, a land-air dual-mode slope monitoring method based on optical fiber data is provided, which includes the following steps:

[0014] Step S1: Obtain fiber optic sensor data and fiber optic sensor coordinates within the slope area;

[0015] Step S2: The deformation anomaly segment is calculated using the sliding window variance analysis method based on the fiber optic sensor data and the fiber optic sensor coordinates.

[0016] Step S3: Acquire drone data;

[0017] Step S4: Establish a digital altitude difference model based on the UAV data obtained in step S3;

[0018] Step S5: Generate a three-dimensional displacement distribution map of the slope surface based on the UAV data obtained in step S3 and the digital height difference model obtained in step S4.

[0019] Step S6: Perform spatiotemporal matching on the three-dimensional displacement distribution map and deformation anomaly segment of the slope surface obtained in step S5 to obtain an anomaly correlation map;

[0020] Step S7: Generate slope monitoring results based on the anomaly correlation diagram obtained in step S6.

[0021] A further improvement of the present invention is that: acquiring fiber optic sensor data and fiber optic sensor coordinates within the slope area includes the following steps:

[0022] Step S11: Obtain the raw data of the fiber optic sensing and the coordinates of the fiber optic sensor;

[0023] The raw fiber optic sensing data obtained in step S11 includes temperature data and strain data;

[0024] Step S12: The strain data obtained in step S11 is subjected to wavelet threshold denoising and high-frequency interference removal to obtain the first data;

[0025] Step S13: Correct the first data obtained in step S12 based on the temperature data obtained in step S11 to obtain fiber optic sensor data.

[0026] A further improvement of the present invention is that the step of calculating the deformation anomaly segment using the sliding window variance analysis method based on the fiber optic sensor data and the fiber optic sensor coordinates includes the following steps:

[0027] Step S21: Based on the coordinates of the fiber optic sensor obtained in step S1, divide the fiber optic sensor data into several windows.

[0028] Step S22: Calculate the variance of the fiber optic sensor data within each window;

[0029] Step S23: Select windows that are greater than or equal to the first threshold from the variance of fiber optic sensor data within several windows obtained in step S22 as abnormal windows.

[0030] Step S24: Output the fiber optic sensor coordinates and fiber optic sensor data obtained in step S1 corresponding to the abnormal window obtained in step S23 as the deformation abnormal segment.

[0031] A further improvement of the present invention is that: the step of establishing a digital altitude difference model based on the UAV data includes the following steps:

[0032] Step S41: Obtain UAV data from two adjacent sampling periods;

[0033] Step S42: Establish two digital surface models or two digital terrain models based on the UAV data from two adjacent sampling periods obtained in step S41.

[0034] Step S43: Preprocess the two digital surface models or two digital terrain models obtained in step S42;

[0035] Step S44: Based on the two preprocessed digital surface models or two digital terrain models obtained in step S43, a digital height difference model is obtained by differential calculation.

[0036] A further improvement of the present invention is that the step of generating a three-dimensional displacement distribution map of the slope surface based on the UAV data and the digital height difference model includes the following steps:

[0037] Step S51: Calculate the vertical displacement of each pixel based on the digital height difference model;

[0038] Step S52: Perform geometric correction and image enhancement on the UAV data to obtain enhanced data;

[0039] Step S53: The augmented data obtained in step S52 and the augmented data from the previous time step are processed using a feature algorithm to obtain several matching feature points.

[0040] Step S54: Calculate the x-axis pixel displacement and y-axis pixel displacement of each pair of matching feature points obtained in step S53.

[0041] Step S55: Calculate the slope direction displacement and slope dip displacement based on the x-axis pixel displacement and y-axis pixel displacement obtained in step S54.

[0042] Step S56: Establish a three-dimensional displacement distribution map of the slope surface based on the vertical displacement obtained in step S51, the slope direction displacement obtained in step S55, and the slope dip displacement obtained in step S55.

[0043] A further improvement of the present invention is that: the process of obtaining an anomaly correlation map by spatiotemporal matching of the three-dimensional displacement distribution map and the deformation anomaly segment of the slope surface includes the following steps:

[0044] Step S61: Use GPS timestamps to unify the sampling time of the fiber optic sensor data obtained in step S1 and the UAV data obtained in step S3.

[0045] Step S62: Establish a three-dimensional coordinate system for the slope, with the ground base station as the origin, the slope direction as the X-axis, the slope dip as the Y-axis, and the vertical direction of the ground surface as the Z-axis.

[0046] Step S63: Spatially correlate the three-dimensional displacement distribution map of the slope surface obtained in step S5 and the abnormal deformation segment obtained in step S2 in the three-dimensional coordinate system of the slope to obtain an abnormal correlation map.

[0047] A further improvement of the present invention is that: the anomaly correlation diagram obtained in step S6 at the current moment is compared with the anomaly correlation diagram at the previous moment, and the strain amplitude is greater than or equal to the second threshold and the three-dimensional displacement is greater than or equal to the third threshold, which are marked as slip risk areas and an alarm is issued.

[0048] Potential slip zones are defined as those with strain amplitudes greater than or equal to the second threshold and three-dimensional displacements less than the third threshold.

[0049] The strain amplitude is less than the second threshold and the three-dimensional displacement is greater than or equal to the third threshold, which are marked as non-slip regions;

[0050] Areas with strain amplitudes less than the second threshold and three-dimensional displacements less than the third threshold are designated as safe zones.

[0051] According to a second aspect of the technical solution of the present invention, a land-air dual-mode slope monitoring device based on optical fiber data is provided, comprising:

[0052] The fiber optic data acquisition module is used to acquire fiber optic sensor data and fiber optic sensor coordinates within the slope area.

[0053] The strain anomaly calculation module calculates the deformation anomaly segment based on the fiber optic sensor data and the fiber optic sensor coordinates using the sliding window variance analysis method.

[0054] The drone module is used to acquire drone data;

[0055] The modeling module is used to build a digital altitude difference model based on the UAV data;

[0056] The displacement anomaly calculation module is used to generate a three-dimensional displacement distribution map of the slope surface based on the UAV data and the digital height difference model.

[0057] The correlation module is used to perform spatiotemporal matching of the three-dimensional displacement distribution map and deformation anomaly segment of the slope surface to obtain the anomaly correlation map;

[0058] The output module is used to generate monitoring results based on the anomaly correlation diagram.

[0059] According to a third aspect of the present invention, an electronic device is provided, comprising one or more processors, a storage device for storing one or more programs, and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the above-described land-air dual-modal slope monitoring method based on fiber optic data.

[0060] According to a fourth aspect of the technical solution of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the above-described method for land-air dual-mode slope monitoring based on optical fiber data.

[0061] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial technical effects:

[0062] 1. This invention adopts a land-air collaborative approach, combining fiber optic sensor data with UAV data, and uses a three-dimensional displacement distribution map of the slope surface and abnormal deformation sections to comprehensively monitor the slope, thereby improving the monitoring effect and avoiding misjudgment and delayed judgment.

[0063] 2. This invention optimizes the data processing flow by using wavelet threshold denoising, high-frequency interference removal, and temperature correction to effectively reduce the impact of environmental interference on fiber optic data and improve the accuracy of internal deformation monitoring.

[0064] 3. This invention establishes a standardized spatiotemporal matching mechanism, which unifies sampling time through GPS timestamps and constructs a three-dimensional coordinate system for slopes, thereby achieving a precise correlation between internal strain anomalies and surface displacement and clarifying the deformation transmission path.

[0065] 4. This invention refines the risk classification of monitoring areas, formulates multi-dimensional judgment criteria by combining strain amplitude and three-dimensional displacement, and provides targeted monitoring frequencies and engineering measures for different risk areas, thereby improving the scientific nature of early warning response.

[0066] 5. This invention is applicable to the complex operating environment of open-pit mines. It adopts designs such as mine-grade armored optical cables and ground base station differential positioning to resist the effects of dust, electromagnetic interference and equipment crushing, thereby enhancing the engineering practicality of the technology.

[0067] 6. This invention enables real-time processing and rapid feedback of monitoring data, compressing the data processing cycle to within 30 minutes, solving the problem of traditional UAV monitoring lagging for more than 24 hours, and meeting the real-time early warning needs of slope slippage in open-pit coal mines. Attached Figure Description

[0068] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0069] Figure 1 This is a flowchart of a land-air dual-modal slope monitoring method based on optical fiber data according to the present invention;

[0070] Figure 2 This is a structural block diagram of a land-air dual-mode slope monitoring device based on fiber optic data according to the present invention;

[0071] Figure 3 This is a schematic diagram of the structure of the computer system in an embodiment of the present invention. Detailed Implementation

[0072] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0073] Example 1

[0074] like Figure 1 As shown, this invention provides a land-air dual-mode slope monitoring method based on fiber optic data, which includes the following steps:

[0075] S1: Acquire fiber optic sensor data and coordinates within the slope area;

[0076] Specifically, the fiber optic sensor data is acquired through a distributed optical fiber system deployed in the slope area. This distributed optical fiber system includes distributed acoustic sensors and distributed temperature sensors. The distributed acoustic sensors are used to monitor changes in rock strain within the slope and capture micro-vibration signals during slip surface initiation. The distributed temperature sensors are used to monitor the temperature along the optical cable route and correct for spurious strain caused by temperature, thereby mitigating the interference from day-night temperature differences in the open-pit mine.

[0077] Specifically, the distributed optical fibers are installed in the slope area to be monitored by drilling or ground deployment, thereby forming a monitoring network.

[0078] Specifically, step S1 includes the following steps:

[0079] S11. Obtain raw data and fiber optic sensor coordinates;

[0080] The raw data includes temperature data and strain data;

[0081] S12. Wavelet threshold denoising and high-frequency interference removal are applied to the strain data to obtain the first data.

[0082] S13. Correct the first data based on the temperature data to obtain fiber optic sensor data.

[0083] Specifically, the raw data consists of vibration signals (i.e., strain data) obtained from distributed acoustic wave sensing and temperature signals (i.e., temperature data) obtained from distributed temperature sensing. The coordinates of the fiber optic sensors are the coordinates of the corresponding distributed acoustic wave sensing and distributed temperature sensing. The fiber optic sensor coordinates are obtained based on the layout diagram of the previous construction. The types of raw data are not limited to the two mentioned above. Data types can be added according to the slope conditions. By using multiple types of raw data, the strain data can be corrected, thereby improving the accuracy of the data.

[0084] Specifically, in S12, the wavelet threshold denoising uses a wavelet basis of db4 and a threshold of 0.1 to remove wavelet signals. The high-frequency interference includes, but is not limited to, interference such as blasting (500Hz-200Hz) and truck vibration (10Hz-50Hz), while retaining low-frequency signals (frequency <1Hz) related to slip.

[0085] Specifically, in S13, according to the formula:

[0086]

[0087] In the formula, This is temperature-corrected strain data, i.e., fiber optic sensor data. The data is the uncorrected strain data, i.e., the first data, where α is the calculation coefficient and ΔT is the temperature data difference. Preferably, α = 12 με / ℃.

[0088] Specifically, the fiber optic sensor data after S12 and S13 is a pure strain sequence. =[ , , ..., (n is the total number of data points, and the sampling interval is usually 1 second).

[0089] S2: The deformation anomaly segment is calculated using the sliding window variance analysis method based on the fiber optic sensor data and the fiber optic sensor coordinates;

[0090] Specifically, S2 includes the following steps:

[0091] S21: Divide the fiber optic sensor data into several windows according to the coordinates of the fiber optic sensor;

[0092] Specifically, the window length is set to 10m (a window length of 10m corresponds to the spatial resolution of the optical fiber, thus ensuring coverage of deformation information), and the step size is 1m (a step size of 1m ensures that adjacent windows overlap, avoiding missing small deformation segments). The spatial spacing of the optical fiber sampling points is set to d. When d=0.5, each window contains N=10 / d=20 sampling points.

[0093] S22: Calculate the variance of the fiber optic sensor data within each window;

[0094] Specifically, for the i-th window (covering the fiber optic interval [i, i+N-1]), the variance of the fiber optic sensor data is... The calculation formula is as follows:

[0095]

[0096] In the formula, Let N be the variance of the fiber optic sensor data in the i-th window, and N be the number of sampling points within the window. The average strain within the window. , This represents the original strain data for the k-th sampling point.

[0097] S23: Select windows with variances greater than or equal to the first threshold from the variances of fiber optic sensor data within several windows as abnormal windows;

[0098] Specifically, if the variance of the fiber optic sensor data for a certain window is greater than or equal to the first threshold, the fiber optic interval corresponding to that window is determined to be an "internal deformation anomaly segment". For consecutive anomaly windows, they are merged into a single consecutive anomaly segment, and their start / end positions (corresponding to the spatial coordinates of the fiber optic cable) and maximum variance (reflecting the severity of slope deformation) are recorded.

[0099] Specifically, the magnitude of the first threshold directly determines the sensitivity of identifying "deformation anomaly segments." The magnitude of the first threshold is related to slope lithology, slope gradient, slope height, historical deformation data, monitoring accuracy requirements, and environmental interference levels. The slope lithology includes loose rock slopes and hard rock slopes. For loose rock slopes (such as sandstone and mudstone), the rock mass structure is loose, and small deformations easily accumulate; the threshold should ideally be set to 5 με (i.e., strain). For hard rock slopes (such as granite and limestone), the rock mass integrity is good, and strain changes are more significant when deformation begins; the threshold can be set to 3 με. The steeper the slope and the higher the slope height, the greater the self-weight stress of the slope, and the strain threshold for deformation triggering can be appropriately reduced (e.g., the threshold for loose rock slopes can be adjusted from 5 με to 4 με). For gentle or low slopes, the deformation sensitivity is low, and the threshold can be appropriately increased (e.g., the threshold for loose rock slopes can be set to 6 με). The historical deformation data is used when the target slope has historical slip records. The first threshold can be derived backward from the strain variance at the time of historical deformation (e.g., if the variance of a slope during historical slip is 8με, the threshold can be set to 6με, leaving a margin for early warning). When there is no historical data, the threshold can be determined through on-site calibration experiments (e.g., artificially simulating small-scale deformation and measuring the corresponding strain variance). The monitoring accuracy requirements include "early warning" (capturing the deformation initiation stage), where the threshold should be lowered (e.g., 4με for loose rock slopes), but a certain false alarm rate must be tolerated; "accurate identification of active slip zones," where the threshold should be higher (e.g., 6με for loose rock slopes) to reduce false alarms but may delay early warning. The environmental interference level requires an appropriate increase in the threshold (e.g., increasing by 1με-2με) when there is frequent blasting and strong vibration interference in open-pit mines to avoid misjudging interference signals as deformation; in areas with weak environmental interference (e.g., non-blasting areas), the threshold can be lowered to improve the sensitivity of deformation identification.

[0100] S24: Output the fiber optic sensor coordinates and fiber optic sensor data corresponding to the abnormal window as the deformation abnormal segment.

[0101] Specifically, the abnormal deformation segment includes the coordinates corresponding to the abnormality and the strain at the abnormality.

[0102] Specifically, the deformation anomaly distribution map inside the slope is generated by combining the location of the fiber optic cable (drilling depth and surface orientation), and the depth (e.g., 5m-8m inside the borehole), orientation (e.g., 200m-250m along the slope dip) and strain amplitude of the deformation anomaly segment are marked.

[0103] S3: Acquire drone data and LiDAR data;

[0104] Specifically, the drone data includes drone image data and lidar data.

[0105] Specifically, the UAV image data and lidar data are acquired by the UAV, and the UAV's flight plan is based on "zonal monitoring" (each unit ≤ 0.5km²). The UAV receives differential signals from ground base stations to improve positioning accuracy (UAV positioning is corrected using GCPs).

[0106] S4: Establish a digital altitude difference model based on the UAV data;

[0107] Specifically, S4 includes the following steps:

[0108] S41: Acquire UAV data from two adjacent sampling periods;

[0109] S42: Establish two digital surface models or two digital terrain models based on UAV data from two adjacent sampling periods;

[0110] Specifically, in S41, the UAV data includes UAV image data and LiDAR data. The digital surface model is built based on the LiDAR data, using GreenValleyLiDAR360 (GreenValleyLiDAR360 is a LiDAR data processing platform developed by GreenValley International, mainly used for point cloud data acquisition, processing and analysis, supporting 3D modeling, digital twins and other applications) to build a digital terrain model to monitor the vertical displacement of bare land / slope rock mass (excluding interference from vegetation, temporary buildings, etc.). The digital terrain model is built based on the UAV image data, which is generated using Agisoft Metashape (Agisoft Metashape (formerly PhotoScan) is a professional photogrammetry software mainly used to generate 3D models, orthophotos and point cloud data from images) to monitor the vertical displacement of all objects on the surface (including vegetation, mineral deposits, and buildings) (such as changes in slope surface rock mass + mineral deposits).

[0111] Specifically, in S42, select the model to be built according to the needs of the monitoring location. If it is necessary to monitor the vertical displacement of all objects on the ground (including vegetation, mineral deposits, and buildings) (such as changes in slope surface rock mass + mineral deposits), select the digital surface model. If it is necessary to monitor the vertical displacement of bare land / slope rock mass (excluding interference from vegetation, temporary buildings, etc.), select the digital terrain model.

[0112] S43: Preprocess two digital surface models or two digital terrain models;

[0113] Specifically, the preprocessing includes coordinate system one, spatial registration, and clipping filtering. Two digital surface models or two digital terrain models are established by selecting different sampling times; therefore, the model with the earlier sampling time is established first, and the model with the later sampling time is established second. Coordinate system one ensures that the coordinate systems of the models are all set with the ground base station as the origin, the X-axis parallel to the slope direction, the Y-axis parallel to the slope dip, and the Z-axis perpendicular to the ground surface. When there are differences in the coordinate systems, a seven-parameter coordinate transformation (using the known coordinates of the ground base station to calculate the transformation parameters) is used to unify them. After transformation, the planar error is ≤2cm.

[0114] Specifically, the spatial registration uses the first model as a reference to register the second model. Several fixed control points (preferably more than or equal to ten) are selected from the two sets of UAV data. These fixed control points include, but are not limited to, the tops of anti-slide piles, monitoring piers, or hard pavement markers—points that are displacement-free and easily identifiable. The ArcGIS "Spatial Registration" tool or Agisoft Metashape's "Point Cloud Registration" function is used to align the two sets of data using these control points. After registration, the root mean square error (RMSE) is ≤3cm (to avoid false displacements caused by registration errors).

[0115] Specifically, the cropping process involves cropping the two DSM / DTM phases using slope boundary vector files (such as Shp format), retaining only the monitored area (excluding irrelevant areas outside the mining area) to ensure complete spatial consistency between the two phases of data. Filtering: Gaussian filtering (3×3 pixel window size) is used to remove small noise (such as single-pixel elevation fluctuations, mostly due to image matching errors or mine dust interference), retaining elevation changes ≥3cm (the minimum effective displacement threshold for open-pit mine slope slippage). The two phases are digital surface models or digital terrain models with different monitoring cycles (e.g., once daily).

[0116] S44: A digital height difference model is obtained by differential calculation based on two preprocessed digital surface models or two digital terrain models.

[0117] Specifically, for the two digital surface models or two digital terrain models after two preprocessing phases, the earlier phase is designated as DSM1 / DTM1, and the later phase as DSM2 / DTM2. The elevation difference is calculated pixel by pixel to generate a digital height difference model (DoD).

[0118] DoD(x,y)=DSM2(x,y)-DSM1(x,y);

[0119] If DoD(x,y)>0: it means that the ground surface at this pixel is vertically uplifted (such as mineral piles, which need to be removed later).

[0120] If DoD(x,y)<0: it indicates that the ground surface at this pixel is vertically subsiding (a typical characteristic of slope slip).

[0121] If DoD(x,y)≈0: it indicates that there is no obvious vertical displacement (stable region).

[0122] S5: Generate a three-dimensional displacement distribution map of the slope surface based on the UAV data and the digital height difference model;

[0123] Specifically, S5 includes the following steps:

[0124] S51: Calculate the vertical displacement of each pixel based on the digital height difference model;

[0125] Specifically, in S51, invalid values ​​in the digital height difference model are first removed, such as cloud cover, water bodies, and uncovered areas, with pixel values ​​of NoData. Then, based on fixed control points, vertical changes caused by non-slip are removed, and areas such as ore piles, temporary buildings, and withered vegetation are identified (e.g., the DoD values ​​in ore pile areas are mostly positive and discrete, with no corresponding internal strain), and marked as invalid displacement areas.

[0126] Specifically, when calculating the vertical displacement of each pixel, the value of each effective pixel in the digital height difference model is the vertical displacement of that position (unit: m). For example, DoD=-0.08m means that the point has sunk vertically by 8cm (in the direction of slope slip).

[0127] Regional displacement statistics: For the effective area of ​​the slope rock mass (after removing invalid areas), use ArcGIS zoning statistics tools to calculate the average vertical displacement (e.g., an average subsidence of 10cm in a high-risk area) and the maximum vertical displacement (e.g., a maximum subsidence of 15cm near the slip surface).

[0128] S52: Perform geometric correction and image enhancement on the UAV data to obtain enhanced data;

[0129] Specifically, the drone data includes two sets of drone data collected at different time periods. The two sets of drone data have the same resolution (e.g., 2cm / px, the same drone flight altitude, and the same drone model), similar lighting conditions (e.g., both collected between 9:00-10:00 to reduce shadow interference), and ≥80% overlap (to ensure a sufficient number of feature points).

[0130] Specifically, the geometric correction utilizes the ground base station's GCPs to perform geometric correction on the two phases of images, eliminating image shifts caused by lens distortion and flight attitude errors, with a corrected planar accuracy of ≤3cm.

[0131] Specifically, the image enhancement uses Photoshop for basic enhancement and OpenCV for batch processing, which is suitable for multiple image scenarios. It avoids low-contrast images caused by open-pit mine dust, and performs histogram equalization (enhancing the difference between light and dark) and Gaussian smoothing (removing noise) to ensure that features such as rock cracks and anti-slide piles are clear.

[0132] S53: The enhanced data and the enhanced data from the previous moment are processed using a feature algorithm to obtain several matching feature points. The enhanced data is obtained after geometric correction and influence enhancement, while the enhanced data from the previous moment refers to the image data collected during the previous UAV flight and processed using the same geometric correction and influence enhancement. The geometric correction utilizes ground base station GCPs to perform geometric correction on the two images, eliminating image shifts caused by lens distortion and flight attitude errors. The corrected planar accuracy is ≤3cm. Specifically, the image enhancement uses Photoshop for basic enhancement and OpenCV for batch processing, suitable for multiple image scenarios, avoiding low-contrast images caused by open-pit mine dust, and performs histogram equalization (enhancing brightness differences) and Gaussian smoothing (removing noise) to ensure clear features such as rock cracks and anti-slide piles.

[0133] Specifically, the feature algorithm includes SIFT (Scale Invariant Feature Transform) or SURF (Speed-Up Robust Feature Transform), loading images after two preprocessing phases (Img1 for the first phase and Img2 for the second phase).

[0134] Call the cv2.SIFT_create() function to detect keypoints and corresponding descriptors (used for feature matching) in the two images.

[0135] Feature point density is controlled to be ≥5 feature points per square meter (ensuring coverage of key slope areas). Then, the FLANN matcher (Fast Nearest Neighbor Search algorithm) or a brute-force matcher (BFMatcher) is used to match the descriptors of the two image periods to obtain initial matching pairs. Filtering criteria: a matching distance threshold (e.g., 0.7, retaining only highly similar matching pairs and discarding low-quality matches).

[0136] S54: Calculate the x-axis pixel displacement and y-axis pixel displacement of each pair of matched feature points;

[0137] Specifically, for each valid matching pair, let its pixel coordinates in the earlier image Img1 be (x1, y1) and its pixel coordinates in the later image Img2 be (x2, y2), then the pixel displacement is: Δ x像素 =x2-x1Δ y像素 =y2-y1

[0138] The sign of pixel displacement: reflects the direction of displacement (e.g., pixel > indicates displacement along the positive X-axis).

[0139] S55: The slope direction displacement and slope dip displacement are calculated based on the x-axis pixel displacement and the y-axis pixel displacement.

[0140] Specifically, in S55, the x-axis pixel displacement and y-axis pixel displacement are converted into actual horizontal displacements (i.e., slope strike displacement and slope dip displacement, in meters) using the ground resolution (GSD) of the UAV imagery. The formula is as follows: Δ X实际 =Δ x像素 ×GSDΔ Y实际 =Δ y像素 ×GSD.

[0141] Ground resolution is obtained by calculating the UAV flight parameters using the following formula:

[0142] GSD=

[0143] S56: Establish a three-dimensional displacement distribution map of the slope surface based on the vertical displacement, the slope direction displacement, and the slope dip displacement.

[0144] Specifically, the maximum horizontal displacement area (e.g., maximum displacement of 12cm in the Y direction, corresponding to slope slip) and the stable area (displacement <3cm) are marked in the two-dimensional map. Then, the data is superimposed with the DoD vertical displacement results to obtain a three-dimensional displacement distribution map of the slope surface.

[0145] S6: Perform spatiotemporal matching of the three-dimensional displacement distribution map and deformation anomaly segments on the slope surface to obtain an anomaly correlation map;

[0146] Specifically, S6 includes the following steps:

[0147] S61: Use GPS timestamps to unify the sampling time of fiber optic sensor data and UAV data;

[0148] Specifically, in S61, the fiber optic sensing data (sampling interval 1s) is aligned with the UAV monitoring data (acquisition time accurate to the second) by using GPS timestamps to ensure that the internal strain and surface displacement data correspond at the same moment.

[0149] S62: Establish a three-dimensional coordinate system for the slope with the ground base station as the origin, the slope direction as the X-axis, the slope dip as the Y-axis, and the vertical direction of the ground surface as the Z-axis.

[0150] S63: Spatially correlate the three-dimensional displacement distribution map of the slope surface and the abnormal deformation segment in the three-dimensional coordinate system of the slope to obtain an abnormal correlation map.

[0151] Specifically, in S63, the three-dimensional coordinate system of the slope (with the ground base station as the origin, the X-axis parallel to the slope direction, the Y-axis parallel to the slope dip, and the Z-axis perpendicular to the ground surface) spatially correlates the spatial coordinates of the deformation anomaly segment corresponding to the optical fiber (e.g., borehole number 3, depth 6m, coordinates X=1200m, Y=800m, Z=350m) with the coordinates of the surface deformation zone corresponding to the UAV (e.g., X=1198m-1202m, Y=798m-802m, Z=350m-360m) to determine the corresponding area of ​​internal anomaly-surface deformation.

[0152] S7: Generate monitoring results based on the anomaly correlation diagram.

[0153] Specifically, S7 includes the following steps:

[0154] The current anomaly correlation diagram is compared with the previous anomaly correlation diagram. Areas with strain amplitudes greater than or equal to the second threshold and three-dimensional displacements greater than or equal to the third threshold are marked as slip risk areas and an alarm is issued.

[0155] Potential slip zones are defined as those with strain amplitudes greater than or equal to the second threshold and three-dimensional displacements less than the third threshold.

[0156] The strain amplitude is less than the second threshold and the three-dimensional displacement is greater than or equal to the third threshold, which are marked as non-slip regions;

[0157] Areas with strain amplitudes less than the second threshold and three-dimensional displacements less than the third threshold are designated as safe zones.

[0158] Specifically, one practical application of the monitoring method in S7 is as follows: If the strain amplitude of an "internal deformation anomaly segment" in a certain area is >8με (the core monitoring object of open-pit mine slopes is the shear deformation inside the rock mass. For loose sandstone slopes (compressive strength 15MPa-20MPa, elastic modulus 2GPa-5GPa), laboratory calibration shows that when the strain is <5με, the rock mass is in the elastic deformation stage; when the strain is 5με-8με, the rock mass enters the initial stage of plastic deformation; when the strain is >8με, the internal cracks of the rock mass are connected, and the slip surface is basically formed. After temperature correction and vibration denoising, the error of distributed optical fiber (DAS) is <1με. Setting 8με as the threshold is much larger than the monitoring error (safety redundancy of more than 7 times), which can avoid misjudging noise as deformation anomaly and ensure the reliability of identification.), and the three-dimensional displacement of the corresponding surface area is >5 (The surface displacement of the slope exhibits three stages as internal deformation develops: "slow-accelerated-sudden sliding": displacement < 3cm: corresponds to internal elastic deformation (stable stage); displacement 3cm-5cm: corresponds to the initial stage of internal plastic deformation; displacement > 5cm: at this point, the internal slip surface has been transmitted to the surface, and the surface rock mass has entered the overall sliding stage. The three-dimensional displacement monitoring accuracy of the UAV is: horizontal ±3cm, vertical ±5cm. Setting 5cm as the threshold, just exceeding the maximum error in the vertical direction (to avoid misjudgment due to insufficient accuracy), while ensuring that it is a "real and significant sliding signal"), is then determined as a "sliding risk zone"; if there is only abnormal internal strain but no surface displacement, it is determined as a "potential sliding zone" (requiring more intensive monitoring); if there is only surface displacement but no internal strain, investigate whether it is a non-sliding factor (such as changes in the ore pile).

[0159] Specifically, the monitoring results can also be used for quantitative modeling. Based on the coupled data, an internal strain-surface displacement correlation model can be established (e.g., ε=k×u, where ε is the internal strain, u is the vertical displacement of the surface, and k is the correlation coefficient, calibrated by field measurement, e.g., k≈0.2με / cm for loose rock slopes). This enables the calculation of surface displacement from known internal strain or the calculation of internal deformation depth from known surface displacement.

[0160] Specifically, the slip risk zone is divided according to the monitoring results, and the division criteria are shown in Table 1.

[0161] Table 1 Risk Area Classification Table

[0162]

[0163] Specifically, measures to implement emergency response in high-risk areas and prevent the spread of the landslide include:

[0164] (1) Monitoring measures: encrypted monitoring + real-time surveillance.

[0165] Monitoring frequency: The sampling rate of distributed optical fiber (DAS) is increased to 10kHz, and the drone flies twice a day (at 9:00 and 15:00 respectively, avoiding the blasting period).

[0166] Data processing: The edge server starts the "priority computing" mode, and the data processing cycle is compressed to 30 minutes / time. The ground control station is assigned to a dedicated person to monitor it 24 hours a day and generate a "displacement trend report" every hour.

[0167] (2) Engineering measures: Immediately suspend operations + emergency reinforcement.

[0168] Operational control: Notify the mine dispatch center within 10 minutes to suspend mining and transportation operations below and within 50m of the high-risk area, and evacuate all personnel and equipment (such as mining trucks and excavators).

[0169] Temporary reinforcement: Complete emergency reinforcement of "anchor bolts + shotcrete" within 24 hours (anchor bolt length ≥ 8m, spacing 1.5m, covering the surface of high-risk areas), with a focus on reinforcing the weak layer area at the toe of the slope (to prevent the slip surface from penetrating).

[0170] Drainage measures: Clean up drainage ditches around high-risk areas and add temporary drainage ditches (slope of 3‰) to prevent rainwater from seeping in and softening the rock mass (loose rock slopes are prone to increase strain by 3με-5με when exposed to water).

[0171] Specifically, measures to strengthen monitoring and control the development of deformation in medium-risk areas include:

[0172] (1) Monitoring measures: increase frequency + trend analysis.

[0173] Monitoring frequency: Distributed fiber optic sampling rate is maintained at 5kHz, and the drone flies once every 2 days (increased to once a day in rainy weather).

[0174] Data processing: Generate an "Internal-Surface Deformation Coupling Report" daily, focusing on analyzing whether the anomalous segment extends into the weak layer and whether the displacement is accelerating;

[0175] Special investigation: Scan the surface cracks once a week using LiDAR and compare the changes (cracks with a width > 5 mm should be marked as "key concern sections").

[0176] (2) Engineering measures: restricting operations + hazard investigation.

[0177] Work control: Limit the intensity of work below high-risk areas (daily work time ≤ 6 hours), and prohibit heavy equipment (load > 50t) from passing within 20m of the surrounding area;

[0178] Hazard investigation: Inspect the anti-slide piles and monitoring piers around high-risk areas once a week (if the anti-slide piles are found to be tilted >2°, reinforce them immediately), and clear loose rocks (to prevent them from falling and causing secondary disasters).

[0179] Lithological monitoring: Take rock samples (5m-8m deep) from high-risk areas every 10 days to test the water content (if the water content is >15%, drainage needs to be strengthened).

[0180] Specifically, for low-risk areas, routine monitoring and measures to prevent the emergence of deformities include:

[0181] (1) Monitoring measures: standard frequency + regular review.

[0182] Monitoring frequency: Distributed fiber optic data is collected once a day (sampling rate 1kHz), and drones fly once a week;

[0183] Data processing: A "slope stability assessment report" is generated monthly, and compared with historical data (displacement change of <5cm in the past 3 months is considered stable);

[0184] Accuracy calibration: Calibrate the UAV positioning accuracy with GNSS every 2 weeks (ensure horizontal error ≤ 3cm).

[0185] (2) Engineering measures: daily maintenance + risk prevention.

[0186] Routine maintenance: Clean up loose soil and gravel around the ground fiber optic cable every month (to prevent crushing and damage), and check the ground base station signal (to ensure differential positioning is stable).

[0187] Vegetation management: Plant soil-stabilizing vegetation (such as alfalfa) on the top of slopes in low-risk areas to prevent topsoil from collapsing (vegetation roots can enhance the cohesion of topsoil and reduce strain fluctuations).

[0188] Blasting control: Blasting operations must be carried out far away from low-risk areas (distance ≥100m), and the intensity of blasting vibration must be controlled (particle vibration velocity <10cm / s, to avoid inducing abnormal strain).

[0189] Example 2

[0190] like Figure 2 As shown, the present invention provides a land-air dual-mode slope monitoring device based on fiber optic data, which includes:

[0191] The fiber optic data acquisition module is used to acquire fiber optic sensor data and fiber optic sensor coordinates within the slope area.

[0192] The strain anomaly calculation module calculates the deformation anomaly segment based on the fiber optic sensor data and the fiber optic sensor coordinates using the sliding window variance analysis method.

[0193] The drone module is used to acquire drone image data and LiDAR data;

[0194] The modeling module is used to establish a digital altitude difference model based on the UAV image data and the lidar data;

[0195] The displacement anomaly calculation module is used to generate a three-dimensional displacement distribution map of the slope surface based on the UAV image data and the digital height difference model.

[0196] The correlation module is used to perform spatiotemporal matching of the three-dimensional displacement distribution map and deformation anomaly segment of the slope surface to obtain the anomaly correlation map;

[0197] The output module is used to generate monitoring results based on the anomaly correlation diagram.

[0198] Specifically, the fiber optic data acquisition module employs distributed acoustic wave sensing and distributed temperature sensing. The distributed acoustic wave sensing has a sampling rate of 1kHz-10kHz, a spatial resolution of 0.5m-1m, and a strain resolution ≤1με. The distributed temperature sensing has a temperature range of -50℃-150℃, an accuracy of ±0.5℃, and a spatial resolution of 1m. The optical fiber uses mining-grade armored cable: a PE outer layer + stainless steel armor, with a tensile strength ≥10kN and resistance to mining grease corrosion.

[0199] Specifically, the drone module uses a DJI M350 RTK, equipped with a Zenmuse P1 camera (45MP) + L1 LiDAR (1550nm wavelength, point cloud density ≥200 points / cm²). The drone flies in a zigzag pattern (altitude 80-150m, speed 5m / s, overlap rate 85% / 75%) to acquire high-resolution images and LiDAR point clouds. The drone module also includes a supporting ground base station, which uses a Huace T7Pro GNSS receiver, supporting multiple systems including BeiDou / GLONASS / GPS, with static positioning accuracy of ±2.5mm +0.5ppm horizontally and ±5mm +1ppm vertically. The ground base station provides a high-precision positioning reference for the area, correcting the drone's RTK positioning error (especially in areas with electromagnetic interference). It also includes a mining ground control station, which uses an IP67-protected industrial flat panel, pre-installed with monitoring software, and supports real-time data linkage. The mining ground control station is used to implement pilot commands and autonomous obstacle avoidance, focusing on monitoring slip zones.

[0200] Specifically, let's take a slope in an open-pit coal mine (55° slope, 1000m long, 300m high) as an example for equipment deployment:

[0201] Fiber optic cable laying:

[0202] Drilling layout: One monitoring borehole is laid every 200m along the slope, for a total of 5 boreholes, with a drilling depth of 20m and a diameter of 100mm; a Φ50mm PVC sleeve is placed in each borehole, and an armored sensor optical cable (DAS / DTS dual mode) is inserted into the sleeve. The sleeve is fixed by grouting (cement grout water-cement ratio 1:1) between the sleeve and the borehole wall.

[0203] Surface deployment: Along the slope direction, lay one surface optical cable at the top, middle and bottom of the slope, with a total length of 1500m and a burial depth of 0.5m. Lay a 20cm wide stainless steel cover plate on top, and connect both ends of the optical cable to the DAS / DTS host (placed in the mine explosion-proof cabinet).

[0204] Deployment of drones and ground base stations:

[0205] Drone: DJI M350RTK was selected, equipped with Zenmuse P1 camera and L1LiDAR, mining anti-interference antenna was installed, and RTK and INS dual-mode navigation were debugged;

[0206] Ground base station: Deploy Huace T7ProGNSS base stations in flat (unobstructed) areas near the slope, set the sampling rate to 1Hz, and send differential signals to the UAV;

[0207] Ground control station: Deployed in the mine dispatch center, equipped with custom monitoring software, and connected to the DAS / DTS host and drones via a 4G private network.

[0208] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-described land-air dual-mode slope monitoring methods based on fiber optic data.

[0209] The present invention also provides an electronic device. The electronic device of this invention includes: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the land-air dual-modal slope monitoring method based on fiber optic data provided by the present invention. (See below for reference.) Figure 3 This illustrates a schematic diagram of the structure of a computer system 800 suitable for implementing embodiments of the present invention in an electronic device. For example... Figure 3As shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 802 or programs loaded from storage section 808 into random access memory (RAM) 803. The RAM 803 also stores various programs and data required for the operation of the computer system 800. The CPU 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0210] The following components are connected to I / O interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to I / O interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 810 as needed so that computer programs read from it can be installed into storage section 808 as needed.

[0211] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A land-air dual-mode slope monitoring method based on optical fiber data, characterized by, The method comprises the following steps: Step S1, obtaining fiber sensor data and fiber sensor coordinates in a slope area; Step S2, calculating a deformation anomaly segment by using a sliding window variance analysis method according to the fiber sensor data and the fiber sensor coordinates; Step S3, obtaining unmanned aerial vehicle data; Step S4, establishing a digital height difference model according to the unmanned aerial vehicle data obtained in step S3; Step S5, generating a three-dimensional displacement distribution map of a slope surface according to the unmanned aerial vehicle data obtained in step S3 and the digital height difference model obtained in step S4; Step S6, performing space-time matching on the three-dimensional displacement distribution map of the slope surface obtained in step S5 and the deformation anomaly segment to obtain an anomaly correlation map; Step S7, generating a slope monitoring result according to the anomaly correlation map obtained in step S6; The step S4 of establishing a digital height difference model according to unmanned aerial vehicle data comprises the following steps: Step S41, obtaining unmanned aerial vehicle data of two adjacent sampling periods; Step S42, establishing two digital surface models or two digital terrain models according to the unmanned aerial vehicle data of the two adjacent sampling periods obtained in step S41; Step S43, preprocessing the two digital surface models or the two digital terrain models obtained in step S42; Step S44, calculating a digital height difference model by using a difference method according to the two preprocessed digital surface models or the two preprocessed digital terrain models obtained in step S43, taking two preprocessed digital surface models or two preprocessed digital terrain models as DSM1 / DTM1 and DSM2 / DTM2, calculating a height difference pixel by pixel, and generating a digital height difference model DoD. 2.The land-air dual-mode slope monitoring method based on optical fiber data according to claim 1, characterized in that, The step of obtaining fiber sensor data and fiber sensor coordinates in a slope area comprises the following steps: Step S11, obtaining fiber sensor original data and fiber sensor coordinates; The fiber sensor original data obtained in step S11 comprises temperature data and strain data; Step S12, removing noise by using a wavelet threshold and removing high-frequency interference from the strain data obtained in step S11 to obtain first data; Step S13, correcting the first data obtained in step S12 according to the temperature data obtained in step S11 to obtain fiber sensor data. 3.The land-air dual-mode slope monitoring method based on optical fiber data according to claim 1, characterized in that, The step of calculating a deformation anomaly segment by using a sliding window variance analysis method according to the fiber sensor data and the fiber sensor coordinates comprises the following steps: Step S21, dividing the fiber sensor data into a plurality of windows according to the fiber sensor coordinates obtained in step S1; Step S22, calculating a variance of the fiber sensor data in each window; Step S23, selecting a window with a variance greater than or equal to a first threshold value from the variances of the fiber sensor data in the plurality of windows obtained in step S22 as an abnormal window; Step S24, outputting the fiber sensor coordinates and the fiber sensor data corresponding to the abnormal window obtained in step S23 as a deformation anomaly segment.

4. The land-air dual-mode slope monitoring method based on optical fiber data according to claim 1, characterized in that, The step of generating a three-dimensional displacement distribution map of a slope surface according to the unmanned aerial vehicle data and the digital height difference model comprises the following steps: Step S51, calculating the vertical displacement of each pixel according to the digital height difference model; Step S52, performing geometric correction and image enhancement on the UAV data to obtain enhanced data; Step S53, processing the enhanced data obtained in step S52 and the enhanced data of the previous moment using a feature algorithm to obtain a plurality of matching feature points; Step S54, calculating the x-axis pixel displacement and y-axis pixel displacement of each pair of matching feature points obtained in step S53; Step S55, calculating the slope strike displacement and slope tendency displacement according to the x-axis pixel displacement and y-axis pixel displacement obtained in step S54; Step S56, establishing a three-dimensional displacement distribution map of the slope surface according to the vertical displacement obtained in step S51, the slope strike displacement obtained in step S55, and the slope tendency displacement obtained in step S55.

5. The land-air dual-mode slope monitoring method based on optical fiber data according to claim 1, characterized in that, The time-space matching of the three-dimensional displacement distribution map of the slope surface and the deformation anomaly segment to obtain an anomaly correlation map includes the following steps: Step S61, using a GPS timestamp to unify the sampling time of the fiber sensor data obtained in step S1 and the UAV data obtained in step S3; Step S62, establishing a three-dimensional coordinate system of the slope with the ground base station as the origin, the slope strike as the X-axis, the slope tendency as the Y-axis, and the vertical direction of the surface as the Z-axis; Step S63, correlating the three-dimensional displacement distribution map of the slope surface obtained in step S5 and the deformation anomaly segment obtained in step S2 in the three-dimensional coordinate system of the slope to obtain an anomaly correlation map.

6. The land-air dual-mode slope monitoring method based on optical fiber data according to claim 1, characterized in that, The generation of a monitoring result according to the anomaly correlation map includes the following steps: Comparing the anomaly correlation map of the current moment obtained in step S6 with the anomaly correlation map of the previous moment, marking the areas with a strain amplitude greater than or equal to a second threshold value and a three-dimensional displacement greater than or equal to a third threshold value as a sliding risk area and issuing an alarm; Marking the areas with a strain amplitude greater than or equal to a second threshold value and a three-dimensional displacement less than a third threshold value as a potential sliding area; Marking the areas with a strain amplitude less than a second threshold value and a three-dimensional displacement greater than or equal to a third threshold value as a non-sliding area; Marking the areas with a strain amplitude less than a second threshold value and a three-dimensional displacement less than a third threshold value as a safe area.

7. A land-air dual-mode slope monitoring device based on optical fiber data, characterized in that, It includes: A fiber data acquisition module for acquiring fiber sensor data and fiber sensor coordinates in a slope area; A strain anomaly calculation module for calculating deformation anomaly segments using a sliding window variance analysis method based on the fiber sensor data and the fiber sensor coordinates; A UAV module for acquiring UAV data; A modeling module for establishing a digital height difference model based on the UAV data; A displacement anomaly calculation module for generating a three-dimensional displacement distribution map of the slope surface based on the UAV data and the digital height difference model; An association module for time-space matching of the three-dimensional displacement distribution map of the slope surface and the deformation anomaly segment to obtain an anomaly correlation map; An output module for generating a monitoring result according to the anomaly correlation map; Wherein, the digital height difference model is established based on the UAV data, specifically including: Acquiring UAV data of two adjacent sampling periods; According to the obtained unmanned aerial vehicle data of two adjacent sampling periods, two digital surface models or two digital terrain models are established; The obtained two digital surface models or two digital terrain models are preprocessed; According to the obtained two preprocessed digital surface models or two digital terrain models, a digital height difference model is obtained by difference calculation, two preprocessed digital surface models or two digital terrain models are set as DSM1 / DTM1 for the former period and DSM2 / DTM2 for the later period, the elevation difference is calculated pixel by pixel, and the digital height difference model DoD is generated.

8. An electronic device based on optical fiber data, characterized by Comprise: One or more processors; Storage devices for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement a land-air dual-mode slope monitoring method based on optical fiber data as claimed in any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement a land-air dual-mode slope monitoring method based on optical fiber data as claimed in any one of claims 1-6.

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