Real-time ice collapse monitoring and early warning method

Abstract

The invention discloses a real-time monitoring ice collapse early warning method. The method comprises the following steps: acquiring a glacier field image based on an image acquisition device; processing the glacier field image, and extracting an ice collapse range of the monitored area; based on the extracted ice collapse range of the monitoring area, ice collapse event occurrence risk prediction is carried out; and carrying out early warning grade division according to an ice collapse event occurrence risk prediction result. According to the invention, ice collapse can be effectively and timely identified.

Description

Technical Field

[0001] The invention belongs to the technical field of ice avalanche monitoring, and in particular relates to a method for real-time monitoring and early warning of ice avalanches. Background Art

[0002] As the global climate warms, the frequency of glacier surges is increasing rapidly. Ice avalanches caused by glacier surges can lead to blocked rivers, damaged roads, and destroyed bridges in the downstream areas of glaciers, seriously threatening human life and property. Therefore, how to quickly and accurately identify and extract the range of ice avalanches has become one of the important topics of research and concern. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention proposes a method for real-time monitoring and early warning of ice avalanches, which can effectively and timely identify ice avalanches.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for real-time monitoring of ice avalanche warning comprises the following steps:

[0006] Acquire glacier on-site images based on image acquisition equipment;

[0007] Processing the glacier on-site images to extract the ice avalanche range in the monitoring area;

[0008] Based on the extracted ice avalanche range in the monitoring area, the risk of ice avalanche events is predicted;

[0009] The warning levels are divided according to the risk prediction results of ice avalanche events.

[0010] Preferably, the method for acquiring glacier on-site images based on an image acquisition device includes:

[0011] Two high-definition infrared cameras are installed on the drone in a complementary and symmetrical manner on the left and right. When working, they shoot along the monitored area to obtain high-definition on-site images of the glacier.

[0012] Preferably, before extracting the ice avalanche range in the monitoring area, the method also includes: cleaning the glacier site images: removing duplicates and outliers, supplementing missing values ​​using linear interpolation methods, and unifying the time resolution of the data.

[0013] Preferably, the method of processing the glacier site image to extract the ice avalanche range in the monitoring area includes:

[0014] Adopting object-oriented classification method, the spectral characteristics of ground objects and thematic attribute characteristics of objects are selected;

[0015] Based on the spectral characteristics of the ground objects and the thematic attribute characteristics of the objects, rule-based fuzzy classification method and nearest neighbor classification method are used in combination to establish ice avalanche disaster identification rules;

[0016] Based on the ice avalanche hazard identification rules, the ice avalanche range in the monitoring area is extracted.

[0017] Preferably, the method of selecting the spectral characteristics of ground objects and thematic attribute characteristics of objects by using an object-oriented classification method includes:

[0018] Select the red, green, blue, near infrared and two short-wave infrared bands of the high-definition glacier on-site images to participate in image segmentation and generate image objects;

[0019] At a large scale, the first layer segmentation and classification is performed based on the thematic vector data extracted from the coverage to determine the scope of land and non-land;

[0020] The non-ground covered area is segmented again to generate image objects for ice and water classification in the lake;

[0021] Based on the characteristic differences between ice and water in spectral and morphological features, the image features of ice and water in the lake are identified respectively.

[0022] Preferably, based on the ground feature spectral characteristics and the object thematic attribute characteristics, the method of establishing the ice avalanche disaster identification rules by comprehensively using the rule-based fuzzy classification method and the nearest neighbor classification method includes:

[0023] Based on the image features of ice and water, rule-based fuzzy classification and nearest neighbor classification are used to merge adjacent trivial objects in the same category into large image objects after classification, and the ice avalanche disaster recognition rules are established.

[0024] Preferably, the method for predicting the risk of ice avalanche events based on the extracted ice avalanche range in the monitoring area includes:

[0025] Determining a threshold value of positional displacement of ice in a lake within a preset time period, and a threshold value of level change of lake water within the preset time period;

[0026] Real-time detection of the position of ice in the lake, real-time detection of the level of lake water, and real-time measurement of rainfall;

[0027] Calculate the position displacement of ice in the lake within the preset time period, calculate the change in the level of lake water within the preset time period, and calculate the rainfall within the preset time period;

[0028] If the difference between the change in the lake water level within the preset time period and the rainfall within the preset time period is greater than the liquid level change threshold within the preset time period, and the position offset within the preset time period is greater than the position offset threshold within the preset time period, then the risk of an ice avalanche event is determined.

[0029] Preferably, the method for dividing the warning level according to the risk prediction result of the ice avalanche event includes:

[0030] The finite element analysis software ABAQUS was used to perform calculations and analysis, and a model was established based on actual data. The water level of the glacial lake and the position of ice blocks were analyzed respectively, and the calculation results of the infiltration line of the numerical simulation were obtained.

[0031] It also includes deformation analysis under stress field and infiltration line analysis under seepage field, and processes the infiltration pressure and water level monitoring data to obtain the monitored infiltration line results;

[0032] Compare the calculated infiltration line results of numerical simulation with the infiltration line results after monitoring data processing to obtain the numerical simulation results;

[0033] The warning levels are divided according to the numerical simulation results.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention proposes a method for real-time monitoring and early warning of ice avalanches, which obtains glacier site images based on image acquisition equipment; processes the glacier site images to extract the range of ice avalanches in the monitoring area; predicts the risk of ice avalanches based on the extracted range of ice avalanches in the monitoring area; and divides the early warning levels according to the prediction results of the risk of ice avalanches. The present invention can effectively and timely identify ice avalanches. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. 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 creative labor.

[0037] Figure 1 It is a flow chart of a method for real-time monitoring and early warning of ice avalanches according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Embodiment 1

[0041] like Figure 1 As shown, the present invention provides a method for real-time monitoring of ice avalanche warning, comprising the following steps:

[0042] Acquire glacier on-site images based on image acquisition equipment;

[0043] Processing the glacier on-site images to extract the ice avalanche range in the monitoring area;

[0044] Based on the extracted ice avalanche range in the monitoring area, the risk of ice avalanche events is predicted;

[0045] The warning levels are divided according to the risk prediction results of ice avalanche events.

[0046] In this embodiment, the method for acquiring a glacier on-site image based on an image acquisition device includes:

[0047] Two high-definition infrared cameras are installed on the drone in a complementary and symmetrical manner on the left and right. When working, they shoot along the monitored area to obtain high-definition on-site images of the glacier.

[0048] In this embodiment, before extracting the ice avalanche range in the monitoring area, the process also includes: cleaning the glacier site images: removing duplicates and outliers, supplementing missing values ​​using linear interpolation, and unifying the time resolution of the data.

[0049] In this embodiment, the method of processing the glacier site image to extract the ice avalanche range in the monitoring area includes:

[0050] Adopting object-oriented classification method, the spectral characteristics of ground objects and thematic attribute characteristics of objects are selected;

[0051] Based on the spectral characteristics of the ground objects and the thematic attribute characteristics of the objects, rule-based fuzzy classification method and nearest neighbor classification method are used in combination to establish ice avalanche disaster identification rules;

[0052] Based on the ice avalanche hazard identification rules, the ice avalanche range in the monitoring area is extracted.

[0053] Among them, the method of selecting the spectral characteristics of ground objects and thematic attribute characteristics of objects by using the object-oriented classification method includes:

[0054] Select the red, green, blue, near infrared and two short-wave infrared bands of the high-definition glacier on-site images to participate in image segmentation and generate image objects;

[0055] At a large scale, the first layer segmentation and classification is performed based on the thematic vector data extracted from the coverage to determine the scope of land and non-land;

[0056] The non-ground covered area is segmented again to generate image objects for ice and water classification in the lake;

[0057] Based on the characteristic differences between ice and water in spectral and morphological features, the image features of ice and water in the lake are identified respectively.

[0058] Specifically, the segmentation scale of land is 500, the shape factor is 0.1, and the compactness is 0.5; the segmentation scale of non-land is 250, the shape factor is 0.1, and the compactness is 0.5.

[0059] The present invention selects the normalized difference ice and snow index (NDSI), normalized difference water index (NDWI), normalized difference vegetation index (ND-VI) and other features for classification. The feature values ​​and thresholds of each classification category are:

[0060] Ice in the lake: NDSI>-0.47, NDVI<0, NDWI<0.2;

[0061] Lake water: NDSI<0, NDVI<0, NDWI>0.1.

[0062] Among them, based on the spectral characteristics of the ground objects and the thematic attribute characteristics of the objects, the method of establishing the ice avalanche disaster identification rules by comprehensively using the rule-based fuzzy classification method and the nearest neighbor classification method includes:

[0063] Based on the image features of ice and water, rule-based fuzzy classification and nearest neighbor classification are used to merge adjacent trivial objects in the same category into large image objects after classification, and the ice avalanche disaster recognition rules are established.

[0064] In this embodiment, the method for predicting the risk of ice avalanche events based on the extracted ice avalanche range in the monitoring area includes:

[0065] Determine a position deviation threshold value △|SC| of ice in the lake within a preset time period T, and a level change threshold value △YH of lake water within the preset time period T;

[0066] The position of ice in the lake is detected in real time by using a positioning device, the level of lake water is detected in real time by using a level detection device, and the rainfall is measured in real time by using a rainfall measurement device;

[0067] Calculate the position offset △|AB| of the ice in the lake within the preset time period T, calculate the level change H1 of the lake water within the preset time period T, and calculate the rainfall H2 within the preset time period T;

[0068] If the difference between the lake water level change H1 in the preset time period T and the rainfall H2 in the preset time period T is greater than the level change threshold △YH in the preset time period, the effective level change △H of the ice lake in the time period T is calculated by the formula: △H=H1-H2; △H>△YH;

[0069] And if the position offset △|AB| within the preset time period T is greater than the position offset threshold △|SC| within the preset time period T, that is, △|AB|>△|SC|, then it is determined that there is a risk of an ice avalanche event.

[0070] In this embodiment, the method for dividing the warning level according to the ice avalanche event risk prediction result includes:

[0071] The finite element analysis software ABAQUS was used to perform calculations and analysis, and a model was established based on actual data. The water level of the glacial lake and the position of ice blocks were analyzed respectively, and the calculation results of the infiltration line of the numerical simulation were obtained.

[0072] It also includes deformation analysis under stress field and infiltration line analysis under seepage field, and processes the infiltration pressure and water level monitoring data to obtain the monitored infiltration line results;

[0073] Compare the calculated infiltration line results of numerical simulation with the infiltration line results after monitoring data processing to obtain the numerical simulation results;

[0074] The warning levels are divided according to the numerical simulation results.

[0075] Specifically, the position offset threshold △|SC| is determined according to the natural environment of the area where the ice lake is located. For example, the wind force and the flow of ice lake water will affect the movement speed of ice in the lake water. The position offset threshold △|SC| is the maximum distance that ice in the lake water can move within a certain time period T under normal local climatic conditions. If the movement distance of ice in the lake water is greater than the position offset threshold △|SC| within the time period T, it indicates that the environment has changed, which may be strong winds, floods or ice avalanches. Similarly, the liquid level change threshold △YH is the maximum value of the liquid level change of the ice lake under normal circumstances. If the liquid level change of the ice lake exceeds the liquid level change threshold △YH within the time period T, it indicates that ice avalanches may have occurred.

[0076] When an ice avalanche occurs, a large amount of ice cubes enter the ice lake, causing a strong impact on the lake water, and a surge is generated on the surface of the ice lake. The surge will push the ice cubes in the original lake to move quickly, thereby moving a longer distance in a shorter time. Therefore, if the position offset △|AB| of the ice cubes in the original lake in the time period T is greater than the position offset threshold △|SC|, it indicates that an ice avalanche may have occurred. However, strong winds may also cause the ice cubes in the original lake to move quickly, so the present invention uses the effective liquid level change △H of the ice lake in the time period T to make further judgments. When a large amount of ice cubes enter the ice lake during an ice avalanche, the liquid level of the ice lake will rise, so the liquid level change of the ice lake can also be used as a basis for judging ice avalanches. However, since heavy rainfall can also cause the liquid level of the ice lake to rise rapidly, in order to eliminate the rapid rise in the liquid level caused by heavy rainfall, the present invention also measures the rainfall in the time period T, and subtracts the rainfall H2 in the same time period from the liquid level change H1, which can eliminate the influence of the change in the height of the ice lake liquid level caused by rainfall, thereby ensuring the accuracy of the judgment.

[0077] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for real-time monitoring of ice avalanche warning, characterized in that: The following steps are involved: Acquire glacier on-site images based on image acquisition equipment; Processing the glacier on-site images to extract the ice avalanche range in the monitoring area; Based on the extracted ice avalanche range in the monitoring area, the risk of ice avalanche events is predicted; The warning levels are divided according to the risk prediction results of ice avalanche events.

2. The method for real-time monitoring and early warning of ice avalanches according to claim 1 is characterized in that: The method of obtaining glacier on-site images based on image acquisition equipment includes: Two high-definition infrared cameras are installed on the drone in a complementary and symmetrical manner on the left and right. When working, they shoot along the monitored area to obtain high-definition on-site images of the glacier.

3. The method for real-time monitoring and early warning of ice avalanches according to claim 1 is characterized in that: Before extracting the range of ice avalanches in the monitoring area, the following steps are also included: cleaning the on-site images of the glacier: removing duplicates and outliers, using linear interpolation to supplement missing values, and unifying the time resolution of the data.

4. The method for real-time monitoring and early warning of ice avalanches according to claim 1 is characterized in that: The method of processing the glacier on-site image to extract the ice avalanche range in the monitoring area includes: Adopting object-oriented classification method, the spectral characteristics of ground objects and thematic attribute characteristics of objects are selected; Based on the spectral characteristics of the ground objects and the thematic attribute characteristics of the objects, rule-based fuzzy classification method and nearest neighbor classification method are used in combination to establish ice avalanche disaster identification rules; Based on the ice avalanche hazard identification rules, the ice avalanche range in the monitoring area is extracted.

5. The method for real-time monitoring and early warning of ice avalanches according to claim 4 is characterized in that: The method of selecting the spectral characteristics of ground objects and the thematic attribute characteristics of objects by using the object-oriented classification method includes: Select the red, green, blue, near infrared and two short-wave infrared bands of the high-definition glacier on-site images to participate in image segmentation and generate image objects; At a large scale, the first layer segmentation and classification is performed based on the thematic vector data extracted from the coverage to determine the scope of land and non-land; The non-ground covered area is segmented again to generate image objects for ice and water classification in the lake; Based on the characteristic differences between ice and water in spectral and morphological features, the image features of ice and water in the lake are identified respectively.

6. The method for real-time monitoring and early warning of ice avalanches according to claim 4 is characterized in that: Based on the spectral characteristics of the ground object and the thematic attribute characteristics of the object, the method of establishing the ice avalanche disaster identification rules by comprehensively using the rule-based fuzzy classification method and the nearest neighbor classification method includes: Based on the image features of ice and water, rule-based fuzzy classification and nearest neighbor classification are used to merge adjacent trivial objects in the same category into large image objects after classification, and the ice avalanche disaster recognition rules are established.

7. The method for real-time monitoring and early warning of ice avalanches according to claim 1 is characterized in that: Based on the extracted ice avalanche range in the monitoring area, the methods for predicting the risk of ice avalanche events include: Determining a threshold value of positional displacement of ice in a lake within a preset time period, and a threshold value of level change of lake water within the preset time period; Real-time detection of the position of ice in the lake, real-time detection of the level of lake water, and real-time measurement of rainfall; Calculate the position displacement of ice in the lake within the preset time period, calculate the change in the level of lake water within the preset time period, and calculate the rainfall within the preset time period; If the difference between the change in the lake water level within the preset time period and the rainfall within the preset time period is greater than the liquid level change threshold within the preset time period, and the position offset within the preset time period is greater than the position offset threshold within the preset time period, then the risk of an ice avalanche event is determined.

8. The method for real-time monitoring and early warning of ice avalanches according to claim 1 is characterized in that: The methods for dividing the warning level according to the risk prediction results of ice avalanche events include: The finite element analysis software ABAQUS was used to perform calculations and analysis, and a model was established based on actual data. The water level of the glacial lake and the position of ice blocks were analyzed respectively, and the calculation results of the infiltration line of the numerical simulation were obtained. It also includes deformation analysis under stress field and infiltration line analysis under seepage field, and processes the infiltration pressure and water level monitoring data to obtain the monitored infiltration line results; Compare the calculated infiltration line results of numerical simulation with the infiltration line results after monitoring data processing to obtain the numerical simulation results; The warning levels are divided according to the numerical simulation results.

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