Monitoring and early warning system and method for collapse of end of glacier entering lake

Abstract

The invention discloses a lake-entering glacier terminal collapse monitoring and early warning system, which comprises a data monitoring unit, a transmission unit, an analysis unit, a data judgment unit and an early warning unit, and is characterized in that the data monitoring unit comprises an equipment box, a wind driven generator, a monitoring probe and an overwater detector, the wind driven generator is arranged at the top of the equipment box, and the monitoring probe is arranged at the top of the equipment box; the monitoring probe is arranged on the equipment box and used for acquiring glacier surface image information, the overwater detector is arranged in a water area around a target glacier, the data judgment unit and the data transmission unit are arranged in the equipment box, and the detection unit is arranged on the water. According to the method, ice crack information is accurately extracted through the deep learning model, the glacier surface movement speed is accurately detected in combination with an optical image cross-correlation method, and a scientific basis is provided for glacier protection and disaster prevention and control.

Description

Technical Field

[0001] The present invention relates to the field of glacier monitoring technology, and in particular to a system and method for monitoring and warning collapse of the terminal of a glacier entering a lake. Background Art

[0002] As global warming intensifies, glacier melt and crevasses are becoming increasingly pronounced, leading to frequent secondary disasters such as ice avalanches and glacial lake outbursts, posing a serious threat to human life and property safety and regional ecological stability. The formation and evolution of glacial crevasses are driven by multiple factors, including temperature changes, glacier dynamics, and topographic stress distribution. Assessment of their hazards requires a multi-source data approach and a multidisciplinary approach.

[0003] Current technology uses high-resolution imagery (such as Landsat and Sentinel series) to extract the spatiotemporal distribution characteristics of cracks on the glacier surface. For example, Sentinel-2 data is used to monitor cracks in a valley in the Himalayas. Combined with a multispectral index-enhanced crack and edge detection algorithm, this method systematically quantifies the spatiotemporal evolution of glacier cracks. While this method can macroscopically reflect crack distribution trends, its microscopic dynamic processes, such as the crack tip expansion mechanism, are still imperfect due to limited resolution. Therefore, a system and method for monitoring and early warning of glacier collapse at the end of a lake is urgently needed. Summary of the Invention

[0004] A monitoring and early warning system for the collapse of the terminal glacier entering a lake comprises a data monitoring unit, a transmission unit, an analysis unit, a data judgment unit and an early warning unit.

[0005] Furthermore, the data monitoring unit includes an equipment box, a wind turbine, a monitoring probe and an aquatic detector. The wind turbine is arranged on the top of the equipment box, the monitoring probe is arranged on the equipment box for obtaining glacier surface image information, and the aquatic detector is arranged in the waters around the target glacier.

[0006] Furthermore, the data judgment unit and the data transmission unit are both arranged in the equipment box.

[0007] Furthermore, the detection unit is arranged on water.

[0008] A method for monitoring and warning collapse of the end of a glacier entering a lake, based on a system for monitoring and warning collapse of the end of a glacier entering a lake, comprises the following steps:

[0009] S1. A monitoring device is installed on the glacier surface to extract the glacier surface features, and an on-water detection device records the height of the water surge;

[0010] S2, the transmission unit uploads the glacier surface feature images monitored by the monitoring unit and the pre-processed images to the analysis unit;

[0011] S3, the analysis unit simulates and predicts based on the images of the transmission unit, and calculates the glacier cracks and glacier transport speed through the optical image cross-correlation method and the Trans-UNet model respectively;

[0012] S4, a data judgment unit, which determines whether to issue a warning based on the values ​​calculated by the analysis unit, the changes in the cracks and positions between the initial value and the current time period as different levels of warning thresholds; the surge caused by the ice front collapse and the displacement of the detector, where the surge height reaches the height of the lake dam as the warning threshold, and the displacement of the detector is based on the distance from the detector to the lake dam as the warning threshold;

[0013] S5. After receiving the alarm signal from the judgment unit, an early warning signal is sent to surrounding base stations and signal stations;

[0014] Further S1, the glacier surface features include: glacier crevasses, glacier shadows;

[0015] Further S2, the transmission unit preprocessing includes: noise reduction, sharpening, and normalization;

[0016] Further S3 also includes:

[0017] S3.1. Obtain glacier surface displacement information based on the glacier surface shadow image. The normalized cross-correlation algorithm is often used to monitor glacier velocity. Its basic principle is to obtain the relative positions of feature points on images at different times by calculating the image grayscale value, and then calculate the results of glacier movement using the following mathematical method:

[0018]

[0019] S3.2. Based on the acquired crack images on the glacier surface, accurate calculation of crack length, maximum width, and direction is achieved using differential geometry algorithms.

[0020] Among them, the operation steps of glacier crack length are:

[0021] L1. Skeleton preprocessing: Perform morphological refinement and debranching on the input binary ice crack image to obtain a single-pixel width skeleton structure;

[0022] L2, node sampling: perform equidistant sampling along the main axis of the skeleton with a step size Δs (configurable parameter, the default value is 5 pixels) to obtain the ordered node sequence coordinates (x i ,y i ),i=1,2…,n;

[0023]

[0024] L3, differential accumulation: Calculate the Euclidean distance of adjacent nodes and accumulate them using the following formula, adding up the straight-line distance each time:

[0025]

[0026] Repeat the above steps until the distance between the last two points is calculated;

[0027] L4, precision optimization: Adopting adaptive step adjustment algorithm, the sampling step size is automatically reduced when the curvature change of adjacent nodes exceeds the threshold;

[0028] The maximum width of glacier cracks:

[0029] P1, skeleton orientation analysis, for each skeleton point (x i ,y i ),i=1,2…,n calculate the local tangent direction θ k , establish the normal equation:

[0030] y=tan(θ k +π / 2)(xx k )+y k

[0031] P2, edge detection, extending to both sides along the normal direction, respectively detecting the crack edge points (x ai ,y ai ),i=1,2,…,n; a,b=1,2,…,n;

[0032] P3. Width calculation: Calculate the crack width at each point using the following formula:

[0033]

[0034] P4, extreme value screening: traverse all skeleton nodes to perform the above calculations, and take the maximum value as the maximum crack width w max ;

[0035] w max =2maxw (i) +…+w (j)

[0036] P5. Repeat the above calculation steps until the crack width at the last point is calculated;

[0037] Steps for determining the direction of glacier crevasses:

[0038] T1. Segmentation processing: Divide the complete skeleton line into m approximate linear segments S according to the curvature characteristics. j ,j=1,2…,n

[0039] T2. Direction vector calculation: For each linear segment S j, take the first and last node coordinates to calculate the direction vector:

[0040]

[0041] T3. Angle conversion: Calculate the strike angle of each segment and convert it into the standard azimuth using the following formula:

[0042]

[0043] T4. Overall trend determination: j The sequence is statistically analyzed in a circular manner, and the weighted average value is taken as the main direction angle α of the crack. avg :

[0044]

[0045] Further, in S4, different levels of warning are applied when the initial values ​​of the displacement alarm thresholds of the length, width, and direction of the mid-glacier crevasse are compared with the current time period at 10%, 20%, 50%, and 100%, respectively;

[0046] The surge and displacement alarm thresholds caused by ice front collapse are different levels of warning when the comparison between the initial value and the current time period changes by 25%, 50%, 75%, and 100%.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention has developed a monitoring and early warning system and method for the collapse of the end of glaciers entering the lake. It accurately extracts ice crack information through a deep learning model and accurately detects the surface movement speed of the glacier by combining the optical image cross-correlation method, providing a scientific basis for glacier protection and disaster prevention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the description of the specific implementation methods. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 is a flow chart of the method of the present invention;

[0051] Figure 2 This is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] Example

[0054] like Figure 1-2 As shown, this embodiment provides a monitoring and early warning system for the collapse of the end of a glacier entering a lake, including: a data monitoring unit, a transmission unit, an analysis unit, a data judgment unit and an early warning unit.

[0055] Preferably, the data monitoring unit includes an equipment box, a wind turbine, a monitoring probe and an aquatic detector. The wind turbine is arranged on the top of the equipment box, the monitoring probe is arranged on the equipment box for obtaining glacier surface image information, and the aquatic detector is arranged in the waters around the target glacier.

[0056] Preferably, the transmission unit is used to perform noise reduction and sharpening on the data monitored by the data monitoring unit and then upload the data.

[0057] Preferably, the analysis unit can calculate the glacier movement speed and extract the glacier cracks based on the image processed by the transmission unit.

[0058] Preferably, the data judgment unit can bring in the glacier hazard evaluation index based on the value calculated by the analysis unit to determine whether to issue an early warning.

[0059] Preferably, after receiving the alarm signal from the judgment unit, the early warning unit sends an early warning signal to surrounding base stations and signal stations.

[0060] Preferably, the data judgment unit and the data transmission unit are both arranged in the equipment box, and the data judgment unit and the data transmission unit are both provided with electric energy by the wind turbine.

[0061] Preferably, the detection unit is arranged on the water, and the level of the surge is determined by the displacement of the detection unit caused by the surge.

[0062] A method for monitoring and warning collapse of the end of a glacier entering a lake, based on a system for monitoring and warning collapse of the end of a glacier entering a lake, comprises the following steps:

[0063] S1. A monitoring device is installed on the glacier surface to extract the glacier surface features, and an on-water detection device records the height of the water surge;

[0064] S2, the transmission unit uploads the glacier surface feature images monitored by the monitoring unit and the pre-processed images to the analysis unit;

[0065] S3, the analysis unit simulates and predicts based on the images of the transmission unit, and calculates the glacier cracks and glacier transport speed through the optical image cross-correlation method and the Trans-UNet model respectively;

[0066] S4, a data judgment unit, which determines whether to issue a warning based on the values ​​calculated by the analysis unit, the changes in the cracks and positions between the initial value and the current time period as different levels of warning thresholds; the surge caused by the ice front collapse and the displacement of the detector, where the surge height reaches the height of the lake dam as the warning threshold, and the displacement of the detector is based on the distance from the detector to the lake dam as the warning threshold;

[0067] S5. After receiving the alarm signal from the judgment unit, an early warning signal is sent to surrounding base stations and signal stations;

[0068] Further S1, glacier surface features include: glacier crevasses, glacier shadows;

[0069] Further S2, transmission unit preprocessing includes: noise reduction, sharpening, and normalization;

[0070] Further S3 also includes:

[0071] S3.1. Obtain glacier surface displacement information based on the glacier surface shadow image. The normalized cross-correlation algorithm is often used to monitor glacier velocity. Its basic principle is to obtain the relative positions of feature points on images at different times by calculating the image grayscale value, and then calculate the results of glacier movement using the following mathematical method:

[0072]

[0073] S3.2. Based on the acquired crack images on the glacier surface, accurate calculation of crack length, maximum width, and direction is achieved using differential geometry algorithms.

[0074] Among them, the operation steps of glacier crack length are:

[0075] L1. Skeleton preprocessing: Perform morphological refinement and debranching on the input binary ice crack image to obtain a single-pixel width skeleton structure;

[0076] L2, node sampling: perform equidistant sampling along the main axis of the skeleton with a step size Δs (configurable parameter, the default value is 5 pixels) to obtain the ordered node sequence coordinates (x i ,y i ),i=1,2…,n;

[0077]

[0078] L3, differential accumulation: Calculate the Euclidean distance of adjacent nodes and accumulate them using the following formula, adding up the straight-line distance each time:

[0079]

[0080] Repeat the above steps until the distance between the last two points is calculated;

[0081] L4, precision optimization: Adopting adaptive step adjustment algorithm, the sampling step size is automatically reduced when the curvature change of adjacent nodes exceeds the threshold;

[0082] The maximum width of glacier cracks:

[0083] P1, skeleton orientation analysis, for each skeleton point (x i ,y i ),i=1,2…,n calculate the local tangent direction θ k , establish the normal equation:

[0084] y=tan(θ k +π / 2)(xx k )+y k

[0085] P2, edge detection, extending to both sides along the normal direction, respectively detecting the crack edge points (x ai ,y ai ),i=1,2,…,n; a,b=1,2,…,n;

[0086] P3. Width calculation: Calculate the crack width at each point using the following formula:

[0087]

[0088] P4, extreme value screening: traverse all skeleton nodes to perform the above calculations, and take the maximum value as the maximum crack width w max ;

[0089] w max =2maxw (i) +…+w (j)

[0090] P5. Repeat the above calculation steps until the crack width at the last point is calculated;

[0091] Steps for determining the direction of glacier crevasses:

[0092] T1. Segmentation processing: Divide the complete skeleton line into m approximate linear segments S according to the curvature characteristics. j ,j=1,2…,n

[0093] T2. Direction vector calculation: For each linear segment S j, take the first and last node coordinates to calculate the direction vector:

[0094]

[0095] T3. Angle conversion: Calculate the strike angle of each segment and convert it into the standard azimuth using the following formula:

[0096]

[0097] T4. Overall trend determination: j The sequence is statistically analyzed in a circular manner, and the weighted average value is taken as the main direction angle α of the crack. avg :

[0098]

[0099] Further, in S4, different levels of warning are applied when the initial values ​​of the displacement alarm thresholds of the length, width, and direction of the mid-glacier crevasse are compared with the current time period at 10%, 20%, 50%, and 100%, respectively;

[0100] The surge and displacement alarm thresholds caused by ice front collapse are different levels of warning when the comparison between the initial value and the current time period changes by 25%, 50%, 75%, and 100%.

[0101] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A monitoring and early warning system for the collapse of the end of a glacier entering a lake, characterized by: include: Data monitoring unit, transmission unit, analysis unit, data judgment unit and early warning unit.

2. The system for monitoring and warning collapse of glaciers entering lakes according to claim 1, characterized in that: The data monitoring unit includes an equipment box, a wind turbine, a monitoring probe and an aquatic detector. The wind turbine is arranged on the top of the equipment box. The monitoring probe is arranged on the equipment box for obtaining glacier surface image information. The aquatic detector is arranged in the waters around the target glacier.

3. The system for monitoring and warning collapse of glaciers entering lakes according to claim 2, characterized in that: The data judgment unit and the data transmission unit are both arranged in the equipment box.

4. The system for monitoring and warning collapse of the end of a glacier entering a lake according to claim 3 is characterized by: The detection unit is arranged on water.

5. A method for monitoring and early warning of glacier collapse at the end of a lake, characterized by: The method is implemented based on a system for monitoring and warning collapse of glaciers entering a lake according to claim 4, and comprises the following steps: S1. A monitoring device is installed on the glacier surface to extract the glacier surface features, and an on-water detection device records the height of the water surge; S2, the transmission unit uploads the glacier surface feature images monitored by the monitoring unit and the pre-processed images to the analysis unit; S3, the analysis unit simulates and predicts based on the images of the transmission unit, and calculates the glacier cracks and glacier transport speed through the optical image cross-correlation method and the Trans-UNet model respectively; S4, a data judgment unit, which determines whether to issue a warning based on the values ​​calculated by the analysis unit, the changes in the cracks and positions between the initial value and the current time period as different levels of warning thresholds; the surge caused by the ice front collapse and the displacement of the detector, where the surge height reaches the height of the lake dam as the warning threshold, and the displacement of the detector is based on the distance from the detector to the lake dam as the warning threshold; S5. After receiving the alarm signal from the judgment unit, an early warning signal is sent to surrounding base stations and signal stations; Further S1, the glacier surface features include: glacier crevasses, glacier shadows; Further S2, the transmission unit preprocessing includes: noise reduction, sharpening, and normalization; Further S3 also includes: S3.

1. Obtain glacier surface displacement information based on the glacier surface shadow image. The normalized cross-correlation algorithm is often used to monitor glacier velocity. Its basic principle is to obtain the relative positions of feature points on images at different times by calculating the image grayscale value, and then calculate the results of glacier movement using the following mathematical method: S3.

2. Based on the acquired crack images on the glacier surface, accurate calculation of crack length, maximum width, and direction is achieved using differential geometry algorithms. Among them, the operation steps of glacier crack length are: L1. Skeleton preprocessing: Perform morphological refinement and debranching on the input binary ice crack image to obtain a single-pixel width skeleton structure; L2, node sampling: perform equal-space sampling along the main axis of the skeleton with a step length of Δs to obtain the ordered node sequence coordinates (x i ,y i ),i=1,2…,n; L3, differential accumulation: Calculate the Euclidean distance of adjacent nodes and accumulate them using the following formula, adding up the straight-line distance each time: Repeat the above steps until the distance between the last two points is calculated; L4, precision optimization: Adopting adaptive step adjustment algorithm, the sampling step size is automatically reduced when the curvature change of adjacent nodes exceeds the threshold; The maximum width of glacier cracks: P1, skeleton orientation analysis, for each skeleton point (x i ,y i ),i=1,2…,n calculate the local tangent direction θ k , establish the normal equation: and tan(θ) k +π / 2)(xx k )+y k P2, edge detection, extending to both sides along the normal direction, respectively detecting the crack edge points (x ai ,y ai ),i=1,2,…,n; a,b=1,2,…,n; P3. Width calculation: Calculate the crack width at each point using the following formula: P4, extreme value screening: traverse all skeleton nodes to perform the above calculations, and take the maximum value as the maximum crack width w max ; In max =2maxw (i) +…+in (j) P5. Repeat the above calculation steps until the crack width at the last point is calculated; Steps for determining the direction of glacier crevasses: T1. Segmentation processing: Divide the complete skeleton line into m approximate linear segments S according to the curvature characteristics. j ,j=1,2…,n T2. Direction vector calculation: For each linear segment S j , take the first and last node coordinates to calculate the direction vector: T3. Angle conversion: Calculate the strike angle of each segment and convert it into the standard azimuth using the following formula: T4. Overall trend determination: j The sequence is statistically analyzed in a circular manner, and the weighted average value is taken as the main direction angle α of the crack. avg : Further, in S4, different levels of warning are applied when the initial values ​​of the displacement alarm thresholds of the length, width, and direction of the mid-glacier crevasse are compared with the current time period at 10%, 20%, 50%, and 100%, respectively; The surge and displacement alarm thresholds caused by ice front collapse are different levels of warning when the comparison between the initial value and the current time period changes by 25%, 50%, 75%, and 100%.

Images