Debris flow risk monitoring method, device and equipment and readable storage medium

Abstract

The invention provides a debris flow risk monitoring method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining a first image of a debris flow monitoring region at different moments in a preset time period, the first image comprising a hillside where debris flow has occurred; acquiring weather information in a preset time period; calculating a first risk score of debris flow based on all the first images, and calculating a second risk score of debris flow based on all the first images and weather information within a preset time period; and calculating a third risk score according to the first risk score and the second risk score, when the third risk score is greater than a preset score threshold, obtaining weather information in a future time period, and performing early warning of different degrees according to the weather information in the future time period and the third risk score. Compared with risk assessment only from a single angle, the method provided by the invention can better reflect the risk degree of debris flow on the hillside.

Description

Technical Field

[0001] The present invention relates to the technical field of debris flow monitoring, and in particular to a debris flow risk monitoring method, device, equipment and readable storage medium. Background Art

[0002] As a common geological disaster, debris flow has a strong impact energy, which can instantly destroy houses, villages, factories and other facilities, drown people and livestock, and even raise riverbeds, change topography, and cause a large number of casualties, traffic interruptions and infrastructure paralysis. Therefore, it is very necessary to monitor the risk of debris flow. At present, when monitoring some hillsides, the monitoring dimension is relatively single, which cannot well reflect the risk of debris flow. Summary of the invention

[0003] The object of the present invention is to provide a debris flow risk monitoring method, device, equipment and readable storage medium to improve the above-mentioned problems.

[0004] In order to achieve the above objectives, the present application provides the following technical solutions:

[0005] In a first aspect, an embodiment of the present application provides a method for monitoring debris flow risk, the method comprising: Acquire a first image of a debris flow monitoring area at different times within a preset time period, wherein the first image includes a hillside where a debris flow has occurred; and acquire weather information within the preset time period; A first risk score for mudslide is calculated based on all the first images, and a second risk score for mudslide is calculated based on all the first images and the weather information in the preset time period. A third risk score is calculated based on the first risk score and the second risk score. When the third risk score is greater than a preset score threshold, weather information in a future time period is obtained, and different degrees of early warning are issued based on the weather information in the future time period and the third risk score.

[0006] In a second aspect, an embodiment of the present application provides a debris flow risk monitoring device, comprising: An acquisition module, used to acquire a first image of the debris flow monitoring area at different times within a preset period of time, wherein the first image includes a hillside where the debris flow has occurred; and to acquire weather information within the preset period of time; The early warning module is used to calculate a first risk score for the occurrence of mudslides based on all the first images, and to calculate a second risk score for the occurrence of mudslides based on all the first images and the weather information within the preset time period; to calculate a third risk score based on the first risk score and the second risk score, and when the third risk score is greater than a preset score threshold, to obtain weather information within a future time period, and to issue different degrees of early warning based on the weather information within the future time period and the third risk score.

[0007] In a third aspect, an embodiment of the present application provides a debris flow risk monitoring device, the device comprising a memory and a processor. The memory is used to store a computer program; the processor is used to implement the steps of the debris flow risk monitoring method when executing the computer program.

[0008] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned debris flow risk monitoring method are implemented.

[0009] The beneficial effects of the present invention are:

[0010] In the present invention, from the perspective of data acquisition, the present invention only needs to acquire the image of the hillside, and the data acquisition is simple and easy; after acquisition, firstly, the central position information of the hillside at different times is calculated according to the images at different times, and the displacement change information within the preset time period is calculated according to the central position information, and the first risk score is calculated according to the displacement change information; then, the shape of the image at different times is predicted to predict the risk category it belongs to, and finally the second risk score is calculated according to the risk category at different times; finally, different degrees of early warning are carried out according to the first risk score, the second risk score and future weather information. In the present invention, the acquisition of the original data is simple and easy, and the risk assessment is carried out from the two perspectives of the displacement and shape of the hillside. Compared with the risk assessment from only a single perspective, the method in the present invention can better reflect the risk level of mudslides on the hillside.

[0011] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 is a schematic flow chart of a debris flow risk monitoring method according to an embodiment of the present invention; Figure 2 is a schematic structural diagram of a debris flow risk monitoring device according to an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of the debris flow risk monitoring equipment described in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0015] It should be noted that similar reference numerals or letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0016] Example 1

[0017] like Figure 1 As shown, this embodiment provides a debris flow risk monitoring method, which includes step S1 and step S2.

[0018] Step S1, obtaining a first image of a debris flow monitoring area at different times within a preset period of time, wherein the first image includes a hillside where a debris flow has occurred; and obtaining weather information within the preset period of time; In this step, the end time of the preset time period may be the current time, and the time length of the preset time period may be 5 minutes, 3 minutes, 2 minutes, 1 minute, etc. The specific time length may be customized according to the needs; wherein, the debris flow monitoring area includes the hillside where the debris flow has occurred; at the same time, within the preset time period, the first image may be acquired at intervals of the same time length, that is, the time length between the acquisition moments of any two adjacent first images is the same;

[0019] Step S2: Calculate a first risk score for debris flow based on all the first images, and calculate a second risk score for debris flow based on all the first images and the weather information in the preset time period; calculate a third risk score based on the first risk score and the second risk score, and when the third risk score is greater than a preset score threshold, obtain weather information in a future time period, and issue different degrees of early warning based on the weather information in the future time period and the third risk score.

[0020] In this step, the specific implementation steps of calculating the first risk score of debris flow based on all the first images include step S21; Step S21, calculating the central position information of the hillside in the first image, the calculation method comprising recording two adjacent first images as a third image and a fourth image, respectively, the acquisition time corresponding to the third image is earlier than that of the fourth image; filtering the third image and the fourth image respectively, wherein the three primary color values ​​in the image are filtered out, and a fifth image and a sixth image are obtained after filtering respectively; the hillside in the fourth image is segmented based on the fifth image and the sixth image, and the central position information of the hillside in the fourth image is calculated according to the segmented hillside image, and the risk degree of debris flow is calculated according to the calculated central position information, and the first risk score is calculated according to the risk degree.

[0021] In this step, considering that the hillside may slide when a debris flow occurs, the change in displacement is determined based on the center position of the hillside at different times within a period of time, and then the score is calculated based on the change in displacement; at the same time, in order to obtain accurate center position information, the first image is segmented, the hillside therein is segmented and then its center position information is calculated; wherein, in this step, the center position information of the hillside in the fourth image is calculated based on the segmented hillside image, the risk degree of the debris flow is calculated based on the calculated center position information, and the specific implementation steps of calculating the first risk score based on the risk degree include steps S211 and S212; Step S211, subtract the grayscale value of each pixel in the fifth image from the grayscale value of each pixel in the sixth image to obtain a seventh image; perform adaptive histogram equalization on the seventh image, wherein the image is divided into 8×8 sub-blocks, and histogram equalization is performed in each sub-block, and the contrast slope is limited to ≤3; then perform adaptive threshold binarization, wherein the Otsu algorithm is used to automatically determine the optimal threshold; then perform corrosion filtering and dilation processing in sequence to obtain an eighth image; determine the contour information of the hillside in the eighth image, and calculate the center position information of the hillside in the fourth image based on the contour information; correct the center position information, and after the correction, all the calculated center position information is sequentially displayed on the two-dimensional coordinate axis in chronological order. In this step, the center position information of the hillside in the fourth image can be obtained according to this calculation method. If the center position information of the hillside in the third image is to be calculated, it can be calculated according to the first image collected at the previous moment according to the same calculation method, and then the center position information of the hillside at each moment in the preset time period can be obtained; at the same time, in this step, considering that the deformation of the hillside is a dynamic change process, that is, the area of ​​the hillside at the previous moment and the next moment may be different, the center position information of the hillside at different moments is calculated respectively; The processed eighth image usually presents the outline of the hillside, so the minimum circumscribed rectangle can be used as the outline information of the hillside in the eighth image, and the center point coordinates of the minimum circumscribed rectangle are used as the center position information of the hillside in the fourth image, and then it is corrected; then, the horizontal and vertical coordinate axes are established, and the corresponding center position information at different times is reflected on the coordinate axes in the form of points; Among them, the correction is made according to the following formula; X c1 (t)=x c (t)+w noise (t)+w light (t)+w shape (t)+w res (t)+w morph (t), y c1 (t)=y c (t)+w noise (t)+w light (t)+w shape (t)+w res (t)+w morph (t), w noise (t) = k noise (1 / SNR(t)), wlight (t) = k light ΔL(t), w shape (t) = k shape (ΔA(t) / A(t)), w res (t) = k res (1-R(t) / R base ), w morph (t) = k morph ((K erode +K dilate ) / 2), In the formula, x c (t) is the horizontal coordinate of the center position at time t before correction, y c (t) is the ordinate of the center position at time t before correction; x c1 (t) is the horizontal coordinate of the center position at time t after correction, y c1 (t) is the ordinate of the center position at time t after correction; w noise (t) is the noise correction factor, k noise is the weight coefficient of noise influence (taken as 0.2), SNR(t) is the signal-to-noise ratio of the image at time t; w light (t) Light correction factor, k light is the weight coefficient of the illumination effect (taken as 0.4), ΔL(t) is the brightness change of the image at time t, and the brightness change is obtained by subtracting the brightness of the image at time t-1 from the brightness of the image at time t; w shape (t) is the shape correction factor, k shape is the weight coefficient of shape influence (taken as 0.5), ΔA(t) is the change of contour area at time t, the change of contour area is obtained by subtracting the contour area of ​​the image at time t-1 from the contour area of ​​the image at time t, A(t) is the current contour area, and the area of ​​the minimum circumscribed rectangle is taken as the contour area; w res (t) is the resolution correction factor, k res is the weight coefficient of resolution influence (taken as 0.08), R(t) is the resolution of the image at time t, R base is the reference resolution, and the nominal resolution of the camera that captures the first image is used as the reference resolution; morph (t) is the morphological correction factor, k morph is the weight coefficient of morphological influence (taken as 0.3), K erode is the size of the corrosion kernel, which is 3×3, K dilate is the size of the dilation kernel, which is 5×5.

[0022] Step S212, connecting the center position information corresponding to different moments, and calculating the first information between two adjacent line segments after the connection, wherein one of the two adjacent line segments close to the ordinate is extended, and the sine value of the angle between the extended line segment and the other line segment is recorded as the first information; calculating the first-order difference of two adjacent first information to obtain the second information; calculating the standard deviation of all the second information, comparing and analyzing the standard deviation with a preset standard deviation threshold range to obtain the risk level of the slope, and calculating the first risk score based on the risk level.

[0023] In this step, the center position information is represented in the form of points and then connected. The sine value of the angle between two adjacent line segments can be calculated, and then the standard deviation can be calculated, wherein the calculated standard deviation can be understood as the discrete feature of all first-order differences, and the changes in the angles of multiple displacement directions are determined by the discrete features; at the same time, in this step, the preset annotation difference threshold can be customized according to user needs; after setting, it can be compared with the calculated standard deviation, specifically: if the standard deviation is less than the lower limit of the standard deviation threshold range, the risk level is level one; if it is greater than or equal to the lower limit of the standard deviation threshold range and less than or equal to the upper limit of the standard deviation threshold range, the risk level is level two; if the standard deviation is greater than the upper limit of the standard deviation threshold range, the risk level is level three; different risk levels correspond to different first risk scores, for example, a risk level of level one corresponds to 10 points, a risk level of level two corresponds to 20 points, and a risk level of level three corresponds to 30 points, which can be customized according to user needs; Through the calculation method in this step, the corresponding risk score can be reflected by monitoring the position change of the downhill slope at different times. In addition to monitoring from the perspective of location information, this embodiment also monitors from the perspective of the shape of the slope. Specifically, in step S2, the specific implementation steps of calculating the second risk score of the occurrence of debris flow based on all the first images and the weather information within the preset time period include steps S22 and S23; Step S22, obtaining multiple historical first images, annotating each first image, and obtaining an annotated historical first image, wherein the annotated information includes three risk categories and the true probability of each risk category, and the three risk categories are level one risk, level two risk, and level three risk; taking the risk category with the highest true probability corresponding to each first image as the target risk category; and extracting feature information of the annotated historical first image; In this step, when labeling, the three risk categories of level one risk, level two risk and level three risk are labeled in sequence. At the same time, when labeling, the staff can label the risk category according to the shape of the hillside in the first historical image and some factors existing in the hillside (such as cracks, bulges, white water flow, etc.); in this way, the risk of mudslide on the hillside can be calculated from the perspective of the shape of the hillside; at the same time, the first historical image is the first image obtained at a historical time; Step S23, clustering all the annotated historical first images based on the feature information to obtain multiple clusters; for each cluster, counting the number of annotated historical first images with a target risk category of level one risk to obtain a first value, counting the number of annotated historical first images with a target risk category of level two risk to obtain a second value, counting the number of annotated historical first images with a target risk category of level three risk to obtain a third value, and counting the number of annotated historical first images contained in each cluster to obtain a fourth value; recording the ratio of the maximum value among the first value, the second value, and the third value to the fourth value as a fifth value; screening all the annotated historical first images according to the fifth value, training the model using the remaining annotated historical first images after screening to obtain a risk identification model, and obtaining the second risk score based on the risk identification model.

[0024] In this step, considering that all the annotated historical first images may contain noise samples, which may affect the subsequent training of the model, data is screened in this step; wherein, all the annotated historical first images are screened according to the fifth value, and the model is trained using the remaining annotated historical first images after the screening to obtain a risk identification model, and the specific implementation steps of obtaining the second risk score based on the risk identification model include steps S231 and S232; Step S231, compare the fifth numerical value with the preset first numerical threshold value, if the fifth numerical value is greater than the first numerical threshold value, analyze whether the target risk categories corresponding to all the annotated historical first images in such clusters are the same, if they are the same, take all the annotated historical first images in such clusters as target samples, if they are not the same, find the maximum value among the first numerical value, the second numerical value, and the third numerical value, and take all the annotated historical first images corresponding to the target risk category corresponding to the maximum value as target samples; compare the fifth numerical value with the preset first numerical threshold value, if the fifth numerical value is less than or equal to the first numerical threshold value, count the number of annotated historical first images to obtain a sixth numerical value, record the ratio of the fourth numerical value to the sixth numerical value as a seventh numerical value, compare the seventh numerical value with the preset second numerical threshold value, if the seventh numerical value is greater than the second numerical threshold value, take all the annotated historical first images in such clusters as target samples; In this step, taking all the annotated historical first images corresponding to the target risk category corresponding to the maximum value as the target sample can be understood as follows: if the target risk category corresponding to the maximum value is the first-level risk, then taking the annotated historical first image belonging to the first-level risk in such a cluster as the target sample; Meanwhile, in this step, if the seventh value is less than or equal to the second value threshold, all the annotated historical first images in such clusters are discarded as target samples, so it is not reflected in the above steps; the method in step S23 and step S231 can screen all the annotated historical first images, remove noise samples, improve the quality of samples, and thus facilitate training the model and improve the accuracy of the model; Step S232, use the target samples obtained after screening to train the model, wherein, during the training process, for each annotated historical first image, each annotated historical first image is first input into the convolution layer for convolution operation, and then normalization, nonlinear activation, and pooling are performed in sequence after the convolution operation to obtain a first calculation result, which is a feature map after pooling. The training process is completed based on the first calculation result to obtain a risk identification model, and the second risk score is obtained based on the risk identification model.

[0025] In this step, the specific implementation steps of completing the training process based on the first calculation result to obtain the risk identification model, and obtaining the second risk score based on the risk identification model include step S2321 and step S2322; Step S2321: multiply the first calculation result by a three-dimensional preset matrix to obtain a three-dimensional feature matrix, wherein the three values ​​in the three-dimensional feature matrix correspond to the predicted probabilities of three different risk categories respectively; calculate the difference between the three predicted probabilities and the three true probabilities by formula (1), wherein formula (1) includes: , In formula (1), is the difference size, is the weight coefficient of the ith risk category, is the true probability of the i-th risk category, and the generation time is ; is the predicted probability of the i-th risk category, generated at ; , , The time when the difference calculation starts; is the time decay rate of the true probability, which is taken as 0.05; is the time decay rate of the predicted probability, which is set to 0.1.

[0026] In the above formula, and You can also take 0.1 at the same time; the above formula more accurately reflects the impact of data timeliness on risk assessment by independently calculating the time decay of each parameter, thereby making the calculated difference more accurate.

[0027] In this step, the first risk category, the second risk category, and the third risk category may correspond to level one risk, level two risk, and level three risk, respectively; Multiplying the first calculation result with the three-dimensional preset matrix to obtain a three-dimensional feature matrix can be understood as follows: multiplying the first calculation result with the three-dimensional preset matrix can map the features in the pooled feature map to three dimensions, and the three values ​​in the final three-dimensional feature matrix correspond to the predicted probabilities of the three risk categories one by one, and then the predicted probabilities of the three risk categories can be obtained. For example, the first value corresponds to the predicted probability of the first-level risk, the second value corresponds to the predicted probability of the second-level risk, and the third value corresponds to the predicted probability of the third-level risk. In addition, the initial value in the preset matrix can be randomly set, and the value in the preset matrix is ​​continuously adjusted through training and learning; Step S2322, analyzing the difference size, if the difference size is greater than the preset difference threshold, adjusting the parameters in the convolution layer, the parameters in the normalization layer and the values ​​in the matrix, and continuing the training after the adjustment, when the difference size is less than the preset difference threshold, stopping the training, and using the model at the time of stopping the training as the risk identification model; inputting each of the first images into the risk identification model, outputting multiple risk categories corresponding to each of the first images and the probability of each risk category, sorting all of the risk categories in order from high to low according to the probability of each risk category, and obtaining sorted risk categories; using the sorted risk categories as the final risk categories corresponding to each of the first images; and calculating the second risk score for the occurrence of debris flow according to the final risk category corresponding to each of the first images and the weather information within the preset time period.

[0028] In this step, the parameters in the convolution layer and the parameters in the normalization layer may be the weight matrix of each convolution kernel in the convolution layer, the normalization coefficient, etc.; the specific implementation steps of calculating the second risk score of the occurrence of debris flow according to the final risk category corresponding to each of the first images and the weather information within the preset time period include steps S23221 and S23222; Step S23221, grouping the risk categories ranked first in the final risk categories corresponding to each of the first images to obtain a risk category set; counting the proportion of each risk category in the risk category set, and taking the risk category with the largest proportion as the risk category within the preset time period; obtaining the risk score within the preset time period according to the risk category and the preset correspondence table, wherein different risk categories in the correspondence table correspond to different risk scores; In this step, for example, if the risk category with the largest proportion is level 2 risk, then level 2 risk is used as the risk category within the preset period; then the risk score within the preset period can be obtained according to the corresponding table; Step S23222: Analyze the weather information within the preset time period, wherein if the weather information is cloudy or sunny, the risk score is used as the second risk score; otherwise, an impact factor is obtained, the impact factor is multiplied by the risk score, and the multiplication result is recorded as the second risk score.

[0029] In this step, it is considered that when it is cloudy or sunny, the weather will basically have no effect on the slope surface, but other weather such as rainy days, thunderstorms, and hail will further affect the slope surface. Therefore, it is necessary to obtain the impact factor at this time. The impact factor can be set by the staff according to the weather information. For example, the impact factor can be 1.2; In step S2, a third risk score is calculated according to the first risk score and the second risk score. When the third risk score is greater than a preset score threshold, weather information in a future time period is obtained. The specific implementation steps of providing different degrees of early warning according to the weather information in the future time period and the third risk score include step S24; Step S24: perform weighted summation on the first risk score and the second risk score according to their respective corresponding weights to obtain the third risk score; when the third risk score is greater than a preset score threshold, calculate the weather score for the future time period according to the weather information for the future time period and its preset score table, where different weather information in the score table corresponds to different weather scores; perform weighted summation on the third risk score and the weather score to obtain a fourth risk score, and perform different degrees of warning according to different fourth risk scores.

[0030] In this step, when the third risk score is less than or equal to the preset score threshold, no warning is required. Therefore, this embodiment only considers the case where it is greater than. In the case where it is greater than, if the future weather is bad, such as heavy rain, mudslides may occur. Therefore, the score of future weather needs to be added, and finally different degrees of warning are given according to the calculated fourth score. The starting time of the future period is the current time, and the length of the future period can be 30 minutes, 60 minutes, etc.; In this embodiment, from the perspective of data acquisition, this embodiment only needs to obtain the image of the hillside, and the data acquisition is simple and easy; after acquisition, firstly, the central position information of the hillside at different times is calculated according to the images at different times, and the displacement change information within the preset time period is calculated according to the central position information, and the first risk score is calculated according to the displacement change information; then, the shape of the image at different times is predicted to predict the risk category it belongs to, and finally the second risk score is calculated according to the risk category at different times; finally, different degrees of warning are made according to the first risk score, the second risk score and future weather information. In this embodiment, the acquisition of raw data is simple and easy, and risk assessment is performed from two perspectives of displacement and shape of the hillside. Compared with risk assessment from only a single perspective, the method in this embodiment can better reflect the risk level of mudslides on the hillside.

[0031] Example 2

[0032] like Figure 2 As shown, this embodiment provides a debris flow risk monitoring device, which includes an acquisition module 1 and an early warning module 2.

[0033] Acquisition module 1, used to acquire first images of the debris flow monitoring area at different times within a preset period of time, wherein the first image includes a hillside where the debris flow has occurred; and to acquire weather information within the preset period of time; The early warning module 2 is used to calculate a first risk score for the occurrence of debris flow based on all the first images, and to calculate a second risk score for the occurrence of debris flow based on all the first images and the weather information within the preset time period; a third risk score is calculated based on the first risk score and the second risk score, and when the third risk score is greater than a preset score threshold, weather information within a future time period is obtained, and early warnings of different degrees are issued based on the weather information within the future time period and the third risk score.

[0034] In a specific implementation of the present disclosure, the early warning module 2 further includes a first calculation unit 21 .

[0035] The first calculation unit 21 is used to calculate the central position information of the hillside in the first image, and the calculation method includes recording two adjacent first images as a third image and a fourth image, respectively, and the acquisition time corresponding to the third image is earlier than that of the fourth image; filtering the third image and the fourth image respectively, wherein the three primary color values ​​in the image are filtered out, and the fifth image and the sixth image are obtained after filtering respectively; the hillside in the fourth image is segmented based on the fifth image and the sixth image, and the central position information of the hillside in the fourth image is calculated according to the segmented hillside image, and the risk degree of debris flow is calculated according to the calculated central position information, and the first risk score is calculated according to the risk degree.

[0036] In a specific implementation of the present disclosure, the first calculation unit 21 further includes a second calculation unit 211 and a third calculation unit 212 .

[0037] The second calculation unit 211 is used to subtract the grayscale value of each pixel in the fifth image from the grayscale value of each pixel in the sixth image to obtain a seventh image; perform adaptive histogram equalization processing on the seventh image, wherein the image is divided into 8×8 sub-blocks, and histogram equalization is performed in each sub-block, and the contrast slope is limited to ≤3; then perform adaptive threshold binarization processing, wherein the Otsu algorithm is used to automatically determine the optimal threshold; then perform corrosion filtering and dilation processing in sequence to obtain an eighth image; determine the contour information of the hillside in the eighth image, and calculate the center position information of the hillside in the fourth image according to the contour information; correct the center position information, and after the correction, all the calculated center position information is sequentially reflected on the two-dimensional coordinate axis according to the chronological order. The third calculation unit 212 is used to connect the central position information corresponding to each moment, and calculate the first information between two adjacent line segments after the connection, wherein a line segment close to the vertical coordinate of the two adjacent line segments is extended, and the sine value of the angle between the extended line segment and the other line segment is recorded as the first information; calculate the first-order difference of two adjacent first information to obtain the second information; calculate the standard deviation of all the second information, compare and analyze the standard deviation with a preset standard deviation threshold range to obtain the risk level of the slope, and calculate the first risk score based on the risk level.

[0038] In a specific implementation of the present disclosure, the early warning module 2 further includes an acquisition unit 22 and a training unit 23 .

[0039] The acquisition unit 22 is used to acquire multiple historical first images, annotate each first image, and obtain an annotated historical first image, wherein the annotated information includes three risk categories and the true probability of each risk category, wherein the three risk categories are level one risk, level two risk, and level three risk; the risk category with the highest true probability corresponding to each first image is used as the target risk category; and feature information of the annotated historical first image is extracted; The training unit 23 is used to cluster all the annotated historical first images based on the feature information to obtain multiple clusters; for each cluster, the number of annotated historical first images with a target risk category of level one risk is counted to obtain a first value, the number of annotated historical first images with a target risk category of level two risk is counted to obtain a second value, the number of annotated historical first images with a target risk category of level three risk is counted to obtain a third value, and the number of annotated historical first images contained in each cluster is counted to obtain a fourth value; the ratio of the maximum value among the first value, the second value, and the third value to the fourth value is recorded as a fifth value; all the annotated historical first images are screened according to the fifth value, the model is trained using the remaining annotated historical first images after the screening to obtain a risk identification model, and the second risk score is obtained based on the risk identification model.

[0040] It should be noted that, regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0041] Example 3

[0042] Corresponding to the above method embodiment, the embodiment of the present disclosure further provides a debris flow risk monitoring device. The debris flow risk monitoring device described below and the debris flow risk monitoring method described above can be referenced to each other.

[0043] Figure 3 FIG. 3 is a block diagram of a debris flow risk monitoring device 300 according to an exemplary embodiment. Figure 3 As shown, the debris flow risk monitoring device 300 may include: a processor 301 and a memory 302. The debris flow risk monitoring device 300 may also include one or more of a multimedia component 303, an I / O interface 304, and a communication component 305.

[0044] The processor 301 is used to control the overall operation of the debris flow risk monitoring device 300 to complete all or part of the steps in the above-mentioned debris flow risk monitoring method. The memory 302 is used to store various types of data to support the operation of the debris flow risk monitoring device 300, and these data may include, for example, instructions for any application or method operating on the debris flow risk monitoring device 300, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 303 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 302 or sent via the communication component 305. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 304 provides an interface between the processor 301 and other interface modules, and the above-mentioned other interface modules can be a keyboard, a mouse, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 305 is used for wired or wireless communication between the debris flow risk monitoring device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 305 can include: Wi-Fi module, Bluetooth module, NFC module.

[0045] In an exemplary embodiment, the debris flow risk monitoring device 300 can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processor, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned debris flow risk monitoring method.

[0046] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned debris flow risk monitoring method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 302 including program instructions, and the above-mentioned program instructions can be executed by the processor 301 of the debris flow risk monitoring device 300 to complete the above-mentioned debris flow risk monitoring method.

[0047] Example 4

[0048] Corresponding to the above method embodiment, the embodiment of the present disclosure further provides a readable storage medium. The readable storage medium described below and the debris flow risk monitoring method described above can be referred to in correspondence with each other.

[0049] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the debris flow risk monitoring method of the above method embodiment.

[0050] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A debris flow risk monitoring method, characterized in that: include: Acquire a first image of a debris flow monitoring area at different times within a preset time period, wherein the first image includes a hillside where the debris flow has occurred; and obtaining weather information within the preset time period; Calculating a first risk score for a debris flow based on all the first images, and calculating a second risk score for a debris flow based on all the first images and weather information within the preset time period; A third risk score is calculated based on the first risk score and the second risk score. When the third risk score is greater than a preset score threshold, weather information in a future time period is obtained, and warnings of varying degrees are issued based on the weather information in the future time period and the third risk score.

2. The debris flow risk monitoring method according to claim 1, characterized in that: A first risk score of a debris flow is calculated based on all first images, including: Calculate the central position information of the hillside in the first image, the calculation method includes recording two adjacent first images as a third image and a fourth image, respectively, the acquisition time corresponding to the third image is earlier than that of the fourth image; filter the third image and the fourth image respectively, wherein the three primary color values ​​in the image are filtered out, and the fifth image and the sixth image are obtained after filtering respectively; segment the hillside in the fourth image based on the fifth image and the sixth image, calculate the central position information of the hillside in the fourth image according to the segmented hillside image, calculate the risk degree of debris flow according to the calculated central position information, and calculate the first risk score according to the risk degree.

3. The debris flow risk monitoring method according to claim 2, characterized in that: Calculating the center position information of the hillside in the fourth image according to the segmented hillside image, calculating the risk level of debris flow according to the calculated center position information, and calculating the first risk score according to the risk level, including: Subtract the grayscale value of each pixel in the fifth image from the grayscale value of each pixel in the sixth image to obtain a seventh image; perform adaptive histogram equalization on the seventh image, wherein the image is divided into 8×8 sub-blocks, and histogram equalization is performed in each sub-block, and the contrast slope is limited to ≤3; then perform adaptive threshold binarization, wherein the Otsu algorithm is used to automatically determine the optimal threshold; then perform corrosion filtering and dilation processing in sequence to obtain an eighth image; determine the contour information of the hillside in the eighth image, and calculate the center position information of the hillside in the fourth image based on the contour information; correct the center position information, and after the correction, all the calculated center position information is sequentially displayed on the two-dimensional coordinate axis in chronological order, and the corresponding center position information at different times; The center position information corresponding to different moments is connected, and after the connection, the first information between two adjacent line segments is calculated, wherein one of the two adjacent line segments close to the ordinate is extended, and the sine value of the angle between the extended line segment and the other line segment is recorded as the first information; the first-order difference of two adjacent first information is calculated to obtain the second information; the standard deviation of all the second information is calculated, and the standard deviation is compared and analyzed with the preset standard deviation threshold range to obtain the risk degree of the slope, and the first risk score is calculated according to the risk degree.

4. The debris flow risk monitoring method according to claim 1, characterized in that: Calculating a second risk score of debris flow based on all the first images and weather information within the preset time period includes: Acquire multiple historical first images, annotate each first image, and obtain an annotated historical first image, wherein the annotated information includes three risk categories and the true probability of each risk category, and the three risk categories are level one risk, level two risk, and level three risk; take the risk category with the highest true probability corresponding to each first image as the target risk category; extract feature information of the annotated historical first image; Based on the feature information, all the annotated historical first images are clustered to obtain multiple clusters; for each cluster, the number of annotated historical first images with a target risk category of level one risk is counted to obtain a first value, the number of annotated historical first images with a target risk category of level two risk is counted to obtain a second value, the number of annotated historical first images with a target risk category of level three risk is counted to obtain a third value, and the number of annotated historical first images contained in each cluster is counted to obtain a fourth value; the ratio of the maximum value among the first value, the second value, and the third value to the fourth value is recorded as a fifth value; all the annotated historical first images are screened according to the fifth value, the model is trained using the remaining annotated historical first images after the screening to obtain a risk identification model, and the second risk score is obtained based on the risk identification model.

5. A debris flow risk monitoring device, characterized in that: include: An acquisition module, used to acquire a first image of the debris flow monitoring area at different times within a preset time period, wherein the first image includes a hillside where the debris flow has occurred; and obtaining weather information within the preset time period; An early warning module, configured to calculate a first risk score of a debris flow based on all the first images, and to calculate a second risk score of a debris flow based on all the first images and weather information within the preset time period; A third risk score is calculated based on the first risk score and the second risk score. When the third risk score is greater than a preset score threshold, weather information in a future time period is obtained, and warnings of varying degrees are issued based on the weather information in the future time period and the third risk score.

6. The debris flow risk monitoring device according to claim 5, characterized in that: Early warning module, including: A first calculation unit is used to calculate the central position information of the hillside in the first image, and the calculation method includes recording two adjacent first images as a third image and a fourth image, respectively, and the acquisition time corresponding to the third image is earlier than that of the fourth image; filtering the third image and the fourth image respectively, wherein the three primary color values ​​in the image are filtered out, and a fifth image and a sixth image are obtained after filtering respectively; the hillside in the fourth image is segmented based on the fifth image and the sixth image, and the central position information of the hillside in the fourth image is calculated according to the segmented hillside image, and the risk degree of debris flow is calculated according to the calculated central position information, and the first risk score is calculated according to the risk degree.

7. The debris flow risk monitoring device according to claim 6, characterized in that: The first computing unit comprises: The second calculation unit is used to subtract the grayscale value of each pixel in the fifth image from the grayscale value of each pixel in the sixth image to obtain a seventh image; perform adaptive histogram equalization processing on the seventh image, wherein the image is divided into 8×8 sub-blocks, and histogram equalization is performed in each sub-block, and the contrast slope is limited to ≤3; then perform adaptive threshold binarization processing, wherein the optimal threshold is automatically determined using the Otsu algorithm; then perform corrosion filtering and dilation processing in sequence to obtain an eighth image; determine the contour information of the hillside in the eighth image, and calculate the center position information of the hillside in the fourth image according to the contour information; correct the center position information, and after the correction, all the calculated center position information is sequentially reflected on the two-dimensional coordinate axis according to the chronological order. The third calculation unit is used to connect the central position information corresponding to different moments, and calculate the first information between two adjacent line segments after the connection, wherein a line segment close to the ordinate of the two adjacent line segments is extended, and the sine value of the angle between the line segment and the other line segment after the extension is recorded as the first information; calculate the first-order difference of two adjacent first information to obtain the second information; calculate the standard deviation of all the second information, compare and analyze the standard deviation with a preset standard deviation threshold range to obtain the risk level of the slope, and calculate the first risk score based on the risk level.

8. The debris flow risk monitoring device according to claim 5, characterized in that: Early warning module, including: an acquisition unit, used to acquire a plurality of historical first images, annotate each first image, and obtain an annotated historical first image, wherein the annotated information includes three risk categories and the true probability of each risk category, wherein the three risk categories are level one risk, level two risk, and level three risk; the risk category with the highest true probability corresponding to each first image is taken as the target risk category; and feature information of the annotated historical first image is extracted; A training unit is used to cluster all the annotated historical first images based on the feature information to obtain multiple clusters; for each cluster, count the number of annotated historical first images with a target risk category of level one risk to obtain a first value, count the number of annotated historical first images with a target risk category of level two risk to obtain a second value, count the number of annotated historical first images with a target risk category of level three risk to obtain a third value, and count the number of annotated historical first images contained in each cluster to obtain a fourth value; record the ratio of the maximum value among the first value, the second value, and the third value to the fourth value as a fifth value; screen all the annotated historical first images according to the fifth value, train the model using the annotated historical first images remaining after the screening to obtain a risk identification model, and obtain the second risk score based on the risk identification model.

9. A debris flow risk monitoring device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the debris flow risk monitoring method as described in any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the debris flow risk monitoring method according to any one of claims 1 to 4 are implemented.

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