An abnormality early warning method, device, equipment and medium in a water conservancy project

Abstract

The application relates to an abnormality early warning method, device, equipment and medium in a water conservancy project, and relates to the field of water conservancy construction. The method comprises the following steps: acquiring images of a mountain body and a water surface intersection at multiple time points, soil humidity values of multiple positions on the mountain body, and water level height values at each time point; determining the contour of the mountain body and the water surface intersection based on the images at each time point; determining the similarity of the contour at each time point and a preset contour; comparing the contour at each time point and the preset contour to obtain a contour section group of the intersection that has changed; determining the disaster risk value of the mountain body at each time point based on the contour section group at each time point, the soil humidity values of multiple positions on the mountain body above the contour section group, the slope and the similarity; determining the probability of the mountain body about to have a disaster based on the disaster risk value at each time point and the similarity; and if a preset probability threshold is reached, an alarm is given. The application can more accurately determine the possibility of the mountain body beside a water channel in water conservancy construction having a disaster.

Description

Technical Field

[0001] This application relates to the field of water conservancy construction, and in particular to an anomaly early warning method, device, equipment and medium in water conservancy projects. Background Technology

[0002] With advancements in water conservancy construction technology, people are now able to carry out water conservancy projects in increasingly complex scenarios. Some construction sites are located in rivers and waterways. When these construction sites are adjacent to mountains, it is crucial to be constantly vigilant for disasters such as landslides, mudslides, and rockfalls. Currently, weather forecasts and other meteorological methods are commonly used to predict the likelihood of landslides or other disasters over a period of time. However, this method is not very accurate and does not take into account the actual conditions of the mountains themselves. Therefore, how to more accurately determine the possibility of disasters occurring near waterways during water conservancy construction has become a significant challenge. Summary of the Invention

[0003] In order to more accurately determine the possibility of disasters occurring on mountainsides beside waterways during water conservancy construction, this application provides an anomaly early warning method, device, equipment, and medium for water conservancy projects.

[0004] Firstly, this application provides an anomaly early warning method in water conservancy projects, employing the following technical solution: An anomaly early warning method in water conservancy projects includes: Acquire target images at the junction of the mountain and the water surface at multiple time points within a preset time period, soil moisture values ​​at multiple locations on the mountain, and water level height values ​​at each time point; The target contour at the junction of the mountain and the water surface is determined based on the target image at each time point, and the similarity between the target contour at each time point and the corresponding preset contour is determined. The preset contour is the initial contour at the junction of the mountain and the water surface under the water level height value corresponding to each time point. The contour at each time point is compared with the corresponding preset contour to obtain at least one target contour segment group where the boundary at each time point changes. The target contour segment group includes two contour segments that are inconsistent at the same position in the contour and the corresponding preset contour. Based on the position of the target contour segment group at each time point, multiple soil moisture values ​​above the target contour segment and the slope at the location of each soil moisture value are determined. The disaster risk value of the mountain at each time point is determined based on the target contour segment group at each time point, the soil moisture value of multiple locations on the mountain above the target contour segment group at each time point, the slope of the location of each soil moisture value, and the similarity. The probability of an impending disaster on the mountain is determined based on the disaster risk value and similarity at each time point. If the probability reaches a preset probability threshold, an alarm message will be output.

[0005] By employing the above technical solutions, acquiring target images facilitates subsequent analysis of landslides, localized mudslides, and soil erosion at the mountain-water interface, thereby reducing the likelihood of future mountain disasters. Obtaining soil moisture values ​​at multiple locations on the mountain helps determine the soil moisture levels at various points within the mountain itself, as soil moisture levels influence the likelihood of mountain disasters. Acquiring water level values ​​at multiple time points allows for accurate determination of the standard conditions at the mountain-water interface under different water level values, i.e., the initial contour. Based on the target image at each time point, the target contour at the mountain-water interface at each time point is determined. The target contour represents the state of the mountain-water interface at each time point. The similarity between the target contour and the preset contour corresponding to the water level value at each time point is calculated. The similarity represents the difference between the target contour and the preset contour under the standard normal state. By comparing the preset contour corresponding to the water level value at each time point, the state at each time point can be determined. The change in the target contour segment group at the boundary indicates the presence of small-scale local disasters such as landslides or collapses. Based on the location of the target contour segment group, multiple soil moisture values ​​of the mountain above the target contour segment group and the slope of each soil moisture value collection location are determined. The target contour segment group, the soil moisture value above the target contour segment group, the slope, and the similarity are all key factors affecting the degree of disaster risk of the mountain at each time point. Therefore, based on the above four factors, the disaster risk value of the mountain at each time point can be comprehensively and accurately determined. Then, based on the disaster risk value and similarity of each time point, the accurate probability of an impending disaster on the mountain can be obtained through comprehensive analysis. A preset probability threshold serves as the dividing point for excessively high probabilities. If the preset probability threshold is reached, it indicates that the possibility of an impending disaster on the mountain is relatively high, and an alarm message is output to provide timely reminders. By considering the changes at the boundary between the mountain and the water surface, the soil moisture value of the mountain, and the slope, the probability of a mountain disaster can be judged more accurately.

[0006] In another possible implementation, the soil moisture value at each location includes sub-moisture values ​​at multiple depths within the mountain below each location. The determination of the mountain's disaster risk value at each time point based on the target contour segment group at each time point, the multiple soil moisture values ​​above the target contour segment group at each time point, the slope of the location of each soil moisture value, and similarity includes: Determine the area of ​​the region formed between two contour segments in the target contour segment group at each time point, as well as the maximum width between the two contour segments; Determine the first number of target contour segments at each time point and the total length of all target contour segments, and determine the ratio of the total length to the preset length, wherein the preset length is the length at the junction of the mountain and the water surface within the preset range; The first sub-risk value for each location is determined based on multiple sub-humidity values ​​and the slope. The second sub-risk value for a disaster on the mountain above each target contour segment group is determined based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, and the first sub-risk value of each location above each target contour segment group. The disaster risk value of the mountain at each time point is determined based on the first number of target contour segments at each time point, the second sub-risk value of the mountain above each target contour segment group, the ratio of the total length to the preset length, and the similarity.

[0007] In another possible implementation, determining the first sub-risk value for each location based on multiple sub-humidity values ​​and slope includes: The multiple sub-humidity values ​​at each location are mapped to a preset rectangular coordinate system to obtain a scatter plot; A first fitting function is determined based on the start and end points of the scatter plot, and a second fitting function is determined based on all the scatter points in the scatter plot. Determine the first slope of the first fitting function and the second slope of the second fitting function; Determine the average humidity value of multiple sub-humidity values ​​at each location; Determine the absolute value of the difference between the first slope and the second slope, and determine the sum of the slopes of the first slope and the second slope; Determine a first ratio between the sum of the slopes and the absolute value of the difference; The first sub-risk value for each location is determined based on the average humidity and the first ratio of the sum of the slopes to the absolute value of the difference.

[0008] In another possible implementation, determining the second sub-risk value for a landslide disaster above each target contour segment group based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, and the first sub-risk value at each location above each target contour segment group includes: Determine the target location in the mountain above each target contour segment group, where the target location is the position where the first sub-risk value reaches a preset threshold. Determine a second number of target locations in the mountain above each target contour segment group, and determine a second ratio of the second number to the total number of soil moisture value collection locations in the mountain above each target contour segment; Determine the difference between the first sub-risk value of each target position above each target contour segment group and the preset threshold, and determine the average difference of the differences corresponding to all target positions above each target contour segment group; The second sub-risk value for a disaster occurring on the mountain above each target contour segment group is determined based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, the second ratio, and the average of the differences.

[0009] In another possible implementation, the determination of the disaster risk value of the mountain at each time point based on the first number of target contour segment groups at each time point, the second sub-risk value of the mountain above each target contour segment group, the ratio of the total length to the preset length, and the similarity includes: Determine the sum of the second sub-risk values ​​of the second sub-risk value for the mountain above all target contour segments at each time point; The disaster risk value of the mountain at each time point is determined based on the first number of target contour segments, the sum of the second sub-risk values, the ratio of the total length to the preset length, and the similarity.

[0010] In another possible implementation, determining the probability of an impending disaster on the mountain based on the disaster risk value and similarity at each time point includes: Determine the target average value of disaster risk at all time points and the disaster risk value at the most recent time point; The disaster risk value at each time point is mapped to a preset rectangular coordinate system, and a third fitting function is determined based on the disaster risk values ​​at all time points; Determine the trend of the third fitting function, where the trend includes increasing, decreasing, and remaining unchanged; If the trend of change is upward, then the slope of the third fitting function and the similarity at the most recent time point are determined, the third ratio of the slope to the similarity at the most recent time point is determined, and the probability that the mountain will soon be affected by a disaster is determined based on the third ratio, the target average value and the disaster risk value at the most recent time point. If the trend of change is decreasing or unchanged, the probability of an impending disaster on the mountain is determined based on the target average value and the disaster risk value at the most recent time point.

[0011] In another possible implementation, the method further includes: Determine the area where each target contour segment group is located at the junction of the mountain and the water surface at each time point; By comparing the regions corresponding to all target contour segments at all time points, the overlapping regions where at least two target contour segments overlap are obtained. Determine the third number of target contour segment groups for each overlapping region, and the average of the second sub-risk values ​​for all target contour segment groups at each overlapping location; The risk value of each overlapping region is determined based on the third number of target contour segment groups in each overlapping region and the average of the second sub-risk values ​​of all target contour segment groups. Output the location of each overlapping region and its corresponding risk value.

[0012] Secondly, this application provides an anomaly early warning device for water conservancy projects, which adopts the following technical solution: An anomaly early warning device for water conservancy projects includes: The data acquisition module is used to acquire target images at the junction of the mountain and the water surface at multiple time points within a preset time period, soil moisture values ​​at multiple locations on the mountain, and water level height values ​​corresponding to each time point. The similarity calculation module is used to determine the target outline at the junction of the mountain and the water surface based on the target image at each time point, and to determine the similarity between the target outline at each time point and the corresponding preset outline. The preset outline is the initial outline at the junction of the mountain and the water surface under the water level height value corresponding to each time point. The first comparison module is used to compare the contour at each time point with the corresponding preset contour to obtain at least one target contour segment group that changes at the boundary of each time point. The target contour segment group includes two contour segments that do not have the same position in the contour and the corresponding preset contour. The first determining module is used to determine multiple soil moisture values ​​above the target contour segment and the slope of the location of each soil moisture value based on the position of the target contour segment group at each time point. The second determination module is used to determine the disaster risk value of the mountain at each time point based on the target contour segment group at each time point, the soil moisture value of multiple locations on the mountain above the target contour segment group at each time point, the slope of the location of each soil moisture value, and the similarity. The probability determination module is used to determine the probability that a disaster will occur on the mountain based on the disaster risk value and similarity at each time point; The first output module is used to output alarm information when the probability reaches a preset probability threshold.

[0013] By adopting the above technical solution, the data acquisition module obtains target images to facilitate subsequent analysis of collapses, local landslides, and erosion at the junction of the mountain and the water surface, thereby reducing the likelihood of subsequent mountain disasters. The data acquisition module also obtains soil moisture values ​​at multiple locations on the mountain to understand the soil moisture conditions at various points within the mountain, as soil moisture values ​​influence the likelihood of mountain disasters. Furthermore, the data acquisition module obtains water level values ​​at multiple time points to accurately determine the standard conditions at the junction of the mountain and the water surface under different water level values, i.e., the initial contour. The similarity calculation module determines the target contour at the junction of the mountain and the water surface at each time point based on the target image at each time point. The target contour represents the state of the junction at each time point. The similarity calculation module calculates the similarity between the target contour and the preset contour corresponding to the water level value at each time point. The similarity represents the difference between the target contour and the preset contour under standard normal conditions. Finally, the first comparison module compares the target contour with the preset contour corresponding to the water level value at each time point, thereby determining... The system identifies target contour segments whose boundaries change at each time point. Changes at the boundaries indicate the presence of small-scale localized disasters such as landslides or collapses. The first determination module determines multiple soil moisture values ​​of the mountain above the target contour segments and the slope of each soil moisture value collection location based on the location of the target contour segments. The target contour segments, the soil moisture values ​​above the target contour segments, the slope, and the similarity are all key factors affecting the degree of disaster risk of the mountain at each time point. Therefore, the second determination module can comprehensively and accurately determine the disaster risk value of the mountain at each time point based on the above four factors. Then, the probability determination module can comprehensively analyze the disaster risk value and similarity of each time point to obtain an accurate probability of an impending disaster. A preset probability threshold serves as the dividing point for excessively high probabilities. If the preset probability threshold is reached, it indicates that the possibility of an impending disaster is relatively high. The first output module outputs alarm information to provide timely reminders. By considering the changes at the boundary between the mountain and the water surface, the soil moisture value of the mountain, and the slope, the system can more accurately determine the probability of a mountain disaster.

[0014] In another possible implementation, the soil moisture value at each location includes sub-moisture values ​​at multiple depths within the mountain below each location. When determining the disaster risk value of the mountain at each time point based on the target contour segment group at each time point, the multiple soil moisture values ​​above the target contour segment group at each time point, the slope of the location of each soil moisture value, and similarity, the second determining module is specifically used for: Determine the area of ​​the region formed between two contour segments in the target contour segment group at each time point, as well as the maximum width between the two contour segments; Determine the first number of target contour segments at each time point and the total length of all target contour segments, and determine the ratio of the total length to the preset length, wherein the preset length is the length at the junction of the mountain and the water surface within the preset range; The first sub-risk value for each location is determined based on multiple sub-humidity values ​​and the slope. The second sub-risk value for a disaster on the mountain above each target contour segment group is determined based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, and the first sub-risk value of each location above each target contour segment group. The disaster risk value of the mountain at each time point is determined based on the first number of target contour segments at each time point, the second sub-risk value of the mountain above each target contour segment group, the ratio of the total length to the preset length, and the similarity.

[0015] In another possible implementation, when the second determining module determines the first sub-risk value for each location based on multiple sub-humidity values ​​and the slope, it is specifically used for: The multiple sub-humidity values ​​at each location are mapped to a preset rectangular coordinate system to obtain a scatter plot; A first fitting function is determined based on the start and end points of the scatter plot, and a second fitting function is determined based on all the scatter points in the scatter plot. Determine the first slope of the first fitting function and the second slope of the second fitting function; Determine the average humidity value of multiple sub-humidity values ​​at each location; Determine the absolute value of the difference between the first slope and the second slope, and determine the sum of the slopes of the first slope and the second slope; Determine a first ratio between the sum of the slopes and the absolute value of the difference; The first sub-risk value for each location is determined based on the average humidity and the first ratio of the sum of the slopes to the absolute value of the difference.

[0016] In another possible implementation, when the second determining module determines the second sub-risk value for a landslide above each target contour segment group based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, and the first sub-risk value at each location above each target contour segment group, it is specifically used for: Determine the target location in the mountain above each target contour segment group, where the target location is the position where the first sub-risk value reaches a preset threshold. Determine a second number of target locations in the mountain above each target contour segment group, and determine a second ratio of the second number to the total number of soil moisture value collection locations in the mountain above each target contour segment; Determine the difference between the first sub-risk value of each target position above each target contour segment group and the preset threshold, and determine the average difference of the differences corresponding to all target positions above each target contour segment group; The second sub-risk value for a disaster occurring on the mountain above each target contour segment group is determined based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, the second ratio, and the average of the differences.

[0017] In another possible implementation, when the second determining module determines the disaster risk value of the mountain at each time point based on the first number of target contour segment groups at each time point, the second sub-risk value of the mountain above each target contour segment group, the ratio of the total length to the preset length, and the similarity, it is specifically used for: Determine the sum of the second sub-risk values ​​of the second sub-risk value for the mountain above all target contour segments at each time point; The disaster risk value of the mountain at each time point is determined based on the first number of target contour segments, the sum of the second sub-risk values, the ratio of the total length to the preset length, and the similarity.

[0018] In another possible implementation, when determining the probability of an impending disaster on the mountain based on the disaster risk value and similarity at each time point, the probability determination module is specifically used for: Determine the target average value of disaster risk at all time points and the disaster risk value at the most recent time point; The disaster risk value at each time point is mapped to a preset rectangular coordinate system, and a third fitting function is determined based on the disaster risk values ​​at all time points; Determine the trend of the third fitting function, where the trend includes increasing, decreasing, and remaining unchanged; If the trend of change is upward, then the slope of the third fitting function and the similarity at the most recent time point are determined, the third ratio of the slope to the similarity at the most recent time point is determined, and the probability that the mountain will soon be affected by a disaster is determined based on the third ratio, the target average value and the disaster risk value at the most recent time point. If the trend of change is decreasing or unchanged, the probability of an impending disaster on the mountain is determined based on the target average value and the disaster risk value at the most recent time point.

[0019] In another possible implementation, the anomaly early warning device in a water conservancy project further includes: The region determination module is used to determine the region where each target contour segment group is located at the junction of the mountain and the water surface at each time point; The second comparison module is used to compare the regions corresponding to all target contour segments at all time points to obtain the overlapping region where at least two target contour segments overlap. The third determination module is used to determine the third number of target contour segment groups in each overlapping region, and the average of the second sub-risk values ​​of all target contour segment groups at each overlapping location. The fourth determination module is used to determine the risk value of each overlapping region based on the third number of target contour segment groups in each overlapping region and the average of the second sub-risk values ​​of all target contour segment groups. The second output module is used to output the location of each overlapping area and its corresponding risk value.

[0020] Thirdly, this application provides an electronic device that adopts the following technical solution: An electronic device comprising: At least one processor; Memory; At least one application, wherein the application is stored in memory and configured to be executed by at least one processor, the at least one configuration being for: executing an anomaly early warning method in a hydraulic engineering project as shown in any possible implementation of the first aspect.

[0021] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium, when the computer program is executed in a computer, causes the computer to perform an anomaly early warning method in a water conservancy project as described in any one of the first aspects.

[0022] In summary, this application includes at least one of the following beneficial technical effects: Acquiring target images facilitates subsequent analysis of landslides, localized mudslides, and erosion at the mountain-water interface, thereby reducing the likelihood of future mountain disasters. Obtaining soil moisture values ​​at multiple locations on the mountain helps determine the soil moisture levels at various points within the mountain itself, as soil moisture values ​​influence the probability of mountain disasters. Acquiring water level values ​​at multiple time points allows for accurate determination of the standard conditions at the mountain-water interface under different water level values, i.e., the initial contour. Based on the target image at each time point, the target contour at the mountain-water interface at each time point is determined. The target contour represents the state of the mountain-water interface at each time point. The similarity between the target contour and the preset contour corresponding to the water level value at each time point is calculated. The similarity represents the difference between the target contour and the preset contour under the standard normal state. Comparison with the preset contour corresponding to the water level value at each time point allows for the determination of the potential disasters occurring at the interface at each time point. The changing target contour segments, with changes at their boundaries, indicate the presence of small-scale localized disasters such as landslides or collapses. Based on the location of the target contour segments, multiple soil moisture values ​​of the mountain above them, along with the slope of each soil moisture value collection point, are determined. The target contour segments, the soil moisture values ​​above them, the slope, and the similarity are all key factors influencing the degree of disaster risk at each time point. Therefore, by comprehensively and accurately determining the disaster risk value of the mountain at each time point based on these four factors, and then analyzing the disaster risk value and similarity at each time point, an accurate probability of an impending disaster can be obtained. A preset probability threshold serves as the dividing point for excessively high probabilities. If the preset probability threshold is reached, it indicates a high probability of an impending disaster, and an alarm message is output for timely notification. By considering changes at the boundary between the mountain and the water surface, the soil moisture value of the mountain, and the slope, the probability of a disaster can be more accurately determined. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating an anomaly early warning method in a water conservancy project according to an embodiment of this application.

[0024] Figure 2 This is a schematic diagram of the structure of an abnormal early warning device in a water conservancy project according to an embodiment of this application.

[0025] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0026] The present application will be further described in detail below with reference to the accompanying drawings.

[0027] After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0030] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0031] This application provides an anomaly early warning method in water conservancy projects, executed by an electronic device. This electronic device can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application does not impose any limitations on this. Figure 1 As shown, the method includes steps S101, S102, S103, S104, and S105, wherein, S101, acquire target images of the junction between the mountain and the water surface at multiple time points within a preset time period, soil moisture values ​​at multiple locations on the mountain, and water level height values ​​corresponding to each time point.

[0032] In this embodiment of the application, the preset time period can be the past three days, the past seven days, or the past half month, etc., which is set by the staff into the electronic device according to the needs. The target images can be captured by cameras set on the water surface or mountain, or by drones taking aerial photos every day. The electronic device is connected to the camera or drone via wires or wirelessly to obtain target images at multiple time points. Multiple time points can be the same time point every day within the preset time period. Since the water flow will impact the interface between the mountain and the water surface, it may cause small landslides and other disasters. Target images at multiple time points facilitate subsequent analysis of changes in the mountain at the water surface interface, thereby determining the probability of mountain disasters.

[0033] Staff can pre-install multiple soil moisture sensors on the mountainside, arranged in a grid pattern to collect soil moisture values ​​from multiple locations more evenly and comprehensively. Higher soil moisture values ​​indicate a looser surface layer, making the mountain more prone to landslides and mudslides. Electronic equipment is connected to these soil moisture sensors via wires to obtain these values, facilitating subsequent analysis of the probability of mountain disasters. Similarly, staff can install water level sensors on the water surface to collect water level values ​​at corresponding time points. Electronic equipment is connected to the water level sensors via wires to obtain water level values, allowing for the filtering of water level values ​​at each specific time point.

[0034] S102, determine the target contour at the junction of the mountain and the water surface based on the target image at each time point, and determine the similarity between the target contour at each time point and the corresponding preset contour.

[0035] The preset contour is the initial contour at the junction of the mountain and the water surface at the water level height value corresponding to each time point.

[0036] In this embodiment, the electronic device can input the target image at each time point into a trained network model for contour detection to obtain the target contour at the junction of the mountain and the water surface at each time point. The network model can be a convolutional neural network model. The electronic device can also determine the target contour at the junction of the mountain and the water surface at each time point through steps such as denoising, grayscale transformation, and edge detection. Since the contour, direction, and curvature of the junction of the mountain and the water surface differ when the water level is different, the staff collects images of the junction of the mountain and the water surface in advance at different water levels when no disaster has occurred. Then, the electronic device performs steps such as denoising, grayscale transformation, and contour detection on the images to obtain the initial contour of the junction of the mountain and the water surface at different water levels and stores the initial contours at different water levels. Since the water level values ​​corresponding to different time points are different, the electronic device finds the corresponding preset contour based on the water level value at each time point. The electronic equipment calculates the similarity between the target contour and the corresponding preset contour at each time point, thereby determining the difference between the contour at the junction of the mountain and the water surface at each time point and the contour when no disaster has occurred. The greater the difference, the looser the mountain is, and the more prone it is to disaster, which could cause personal injury or property damage and affect water conservancy construction. The electronic equipment can determine the similarity by calculating the Euclidean distance and cosine similarity between the target contour and the preset contour at each time point.

[0037] S103, compare the contour of each time point with the corresponding preset contour to obtain at least one target contour segment group that changes at the boundary of each time point.

[0038] The target contour segment group includes two contour segments whose positions in the contour and the corresponding preset contour are inconsistent.

[0039] In the embodiments of this application, the electronic device performs an overlap mapping comparison between the contour of each time point and the corresponding preset contour, removes the contour segments that completely overlap, that is, removes the contour segments that have not been affected by the disaster, and obtains at least one target contour segment group where the boundary changes at each time point. Since the boundary usually includes the range of a river channel, the disaster may occur at different locations at the boundary, so the number of target contour segment groups is at least one.

[0040] S104, based on the position of the target contour segment group at each time point, determines multiple soil moisture values ​​above the target contour segment and the slope of the location of each soil moisture value.

[0041] In this embodiment, the electronic device can determine multiple soil moisture sensors at different heights above the target contour segment group based on its position at each time point. The soil moisture value above the target contour segment group characterizes the looseness and firmness of the soil in the upper part of the mountain. The presence of the target contour segment group indicates that a disaster has occurred at the boundary, thus suggesting a high probability of a disaster occurring in the upper part of the mountain at these locations. The multiple soil moisture values ​​above the target contour segment group facilitate subsequent analysis of the probability of a disaster. An angle sensor can be integrated into each soil moisture sensor to collect the slope of the mountain at each soil moisture value collection location. A steeper slope indicates a greater risk of disaster. Therefore, the soil moisture values ​​above the target contour segment group and the slope of each soil moisture value collection location facilitate accurate subsequent analysis of the probability of a disaster.

[0042] S105. Based on the target contour segment group at each time point, the soil moisture values ​​at multiple locations on the mountain above the target contour segment group at each time point, the slope at the location of each soil moisture value, and the similarity, determine the disaster risk value of the mountain at each time point.

[0043] In summary, for the embodiments of this application, the specific attribute characteristics of the target contour segment group at each time point, the soil moisture values ​​at multiple locations above the target contour segment group at each time point, the slope at the location of each soil moisture value, and the similarity between the target contour at each time point and the corresponding preset contour are all key factors characterizing the possibility of a mountain disaster at each time point. Therefore, the electronic device can accurately determine the disaster risk value of the mountain at each time point by comprehensively analyzing the above four factors, such as the target contour segment group at each time point.

[0044] S106, based on the disaster risk value and similarity at each time point, determine the probability that a disaster will occur on the mountain.

[0045] In this embodiment, the similarity at each time point is the difference between the actual boundary contour and the standard contour, directly reflecting the degree of disaster at the boundary at each time point. The disaster risk value at each time point is the probability of a disaster occurring on the mountain at each time point, determined comprehensively based on multiple factors of the boundary and the mountain in step S105. Therefore, the electronic device can accurately determine the probability of an impending disaster on the mountain by comprehensively analyzing the disaster risk value at each time point and the similarity value, which directly reflects the degree of disaster at the boundary at each time point.

[0046] S107, If the probability reaches the preset probability threshold, an alarm message will be output.

[0047] In this embodiment, the preset probability threshold is the dividing point where the probability is too high. The preset probability threshold can be 75%. The electronic device compares the determined probability with the preset probability threshold. If the preset probability threshold of 75% is reached, it indicates that there is a high probability that a landslide is about to occur. The electronic device outputs an alarm message, such as sending a text message to the mobile phones or other terminal devices of relevant personnel saying "There is a high risk of a landslide and a disaster is about to occur. Please be careful." Alternatively, it can send an alarm to the host computer, buzzer indicator lights, or other relevant devices at the monitoring station where relevant personnel are on duty, so that relevant personnel can be informed in a timely manner that there is a high probability of a landslide. If the preset probability threshold is not reached, the electronic device does not output an alarm message.

[0048] One possible implementation of this application embodiment is that the soil moisture value at each location includes sub-moisture values ​​at multiple depths within the mountain below each location. In step S105, the disaster risk value of the mountain at each time point is determined based on the target contour segment group at each time point, the multiple soil moisture values ​​above the target contour segment group at each time point, the slope at the location of each soil moisture value, and similarity. Specifically, this includes steps S1051 (not shown in the figure), S1052 (not shown in the figure), S1053 (not shown in the figure), S1054 (not shown in the figure), and S1055 (not shown in the figure). S1051, determine the area of ​​the region formed between two contour segments in the target contour segment group at each time point, and the maximum width between the two contour segments.

[0049] In this embodiment of the application, a closed region is formed between two contour segments in the target contour segment group. The electronic device counts the number of pixels within the closed region to obtain its area, or maps the closed region onto a mesh map for area calculation. A larger closed region indicates a higher severity of minor disasters such as landslides, and a greater risk of disasters occurring on the upper mountain. The electronic device calculates the distance from each position on the two contour segments to the same position on the opposite contour segment, sorts the distances to determine the maximum distance, and thus determines the maximum width between the two contour segments. A larger maximum width indicates more landslides or other disasters causing erosion, a higher degree of minor disasters, and a greater risk of disasters occurring on the upper mountain.

[0050] S1052, determine the first number of target contour segment groups at each time point and the total length of all target contour segments, and determine the ratio of the total length to the preset length.

[0051] The preset length is the length at the junction of the mountain and the water surface within the preset range.

[0052] In this embodiment of the application, there may be more than one target contour segment group at each time point. Target contour segment groups may exist at different locations at the boundary. Therefore, the electronic device counts the target contour segments at each time point to obtain a first number of target contour segments at each time point. A larger first number indicates that more minor disasters occur on the mountain at each time point, and the risk of a mountain disaster is higher. The electronic device calculates the length of the boundary covered by each target contour segment group at each time point, and then sums the lengths of all target contour segment groups to obtain the total length. The electronic device divides the total length by a preset length to obtain the proportion. A larger proportion indicates that a larger area at the boundary experiences minor disasters, and thus a higher risk of a mountain disaster.

[0053] S1053, determine the first sub-risk value for each location based on multiple sub-humidity values ​​and slope for each location.

[0054] In this embodiment, each soil moisture measurement point extends deep into the mountainside, allowing for the collection of sub-moisture values ​​at different depths using soil moisture sensors at varying depths. Workers can attach multiple soil moisture sensors to different positions on a long pole, then extend the pole deep into the mountainside to collect multiple sub-moisture values ​​at each measurement location. Since the surface soil moisture value is lower due to factors such as sunlight exposure, collecting sub-moisture values ​​from within the mountainside improves the accuracy of assessing the risk of subsequent mountain disasters.

[0055] Staff can also attach angle sensors to one end of a long pole located on the mountain surface to collect the slope at each collection point. The greater the slope and the higher the multiple sub-humidity values, the greater the likelihood of a disaster occurring at that collection point. Therefore, electronic equipment comprehensively analyzes the multiple sub-humidity values ​​and slope at each humidity collection point to accurately determine the first sub-risk value for each soil moisture value collection point.

[0056] S1054, based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, and the first sub-risk value of each location above each target contour segment group, determine the second sub-risk value of the mountain above each target contour segment group where a disaster occurs.

[0057] In the embodiments of this application, the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, and the first sub-risk value of each soil moisture value collection location above each target contour segment group are key factors characterizing the risk of disasters occurring on the mountain above each target contour segment group. Therefore, the electronic device can accurately determine the second sub-risk value of the risk of disasters occurring on the mountain above each target contour segment group by comprehensively analyzing the above three factors.

[0058] S1055, the disaster risk value of the mountain at each time point is determined based on the first number of target contour segments at each time point, the second sub-risk value of the mountain above each target contour segment group, the ratio of the total length to the preset length, and the similarity.

[0059] In summary, for the embodiments of this application, the first number of target contour segments at each time point, the second sub-risk value of the mountain above, the ratio of the total length to the preset length, and the similarity are all key factors affecting the disaster risk of the mountain at each time point. Therefore, the electronic device can accurately determine the disaster risk value of the mountain at each time point by comprehensively analyzing the above four factors.

[0060] One possible implementation of this application embodiment involves determining a first sub-risk value for each location in step S1053 based on multiple sub-humidity values ​​and the slope. This specifically includes steps one, two, three, four, five, six, and seven. Step 1: Map multiple sub-humidity values ​​at each location to a preset rectangular coordinate system to obtain a scatter plot.

[0061] In this embodiment of the application, the horizontal axis of the preset rectangular coordinate system represents the depth of each humidity collection location from the surface to different interior locations, and the vertical axis represents the soil moisture value. The electronic device maps multiple sub-humidity values ​​of each soil moisture value collection location to the preset rectangular coordinate system according to their respective depths to obtain a scatter plot. The scatter plot represents the change of humidity with depth at each soil moisture value collection location.

[0062] Step 2: Determine the first fitting function based on the starting and ending points of the scatter plot, and determine the second fitting function based on all the scatter points in the scatter plot.

[0063] In this embodiment of the application, the electronic device obtains a linear function, namely the first fitting function, by drawing a straight line through the starting and ending points of the scatter plot. The first fitting function is a linear function fitted based on the surface humidity value and the deepest humidity value. The electronic device obtains a second fitting function by fitting the scatter plot using the least squares method, or by inputting the scatter plot into the Origin plugin for linear fitting. The second fitting function is a linear function obtained by comprehensively considering all scatter points.

[0064] Step 3: Determine the first slope of the first fitting function and the second slope of the second fitting function.

[0065] In this embodiment of the application, the electronic device determines a first slope of a first fitting function and a second slope of a second fitting function. A larger first slope indicates a greater humidity difference between the starting point (surface) and the ending point (deepest point), indicating increasingly higher humidity inside the mountain and a greater likelihood of disaster at that humidity sampling location. A larger second slope indicates that, overall, humidity increases significantly with depth. A larger second slope also indicates that the greater the depth, the wetter the humidity, resulting in higher overall humidity inside the mountain and a greater likelihood of disaster at that humidity sampling location.

[0066] Step 4: Determine the average humidity of the multiple sub-humidity values ​​at each location.

[0067] In the embodiments of this application, the electronic device calculates the average humidity of each humidity collection location by using an average value calculation formula for the sub-humidity values ​​at all depths of each location. The average humidity value is used to characterize the overall humidity level of each humidity collection location. The larger the average humidity value, the greater the risk of disaster at the humidity collection location.

[0068] Step 5: Determine the absolute value of the difference between the first slope and the second slope, and determine the sum of the slopes of the first slope and the second slope.

[0069] In this embodiment, the electronic device subtracts the second slope from the first slope to obtain the difference, and then takes the absolute value of the difference to obtain the absolute value of the difference. The smaller the absolute value of the difference, the closer the first slope and the second slope are, the greater the increase in humidity with depth at the humidity collection point, and the greater the probability of a disaster. The electronic device sums the first slope and the second slope to obtain the total slope. The larger the total slope, the greater the increase in humidity with depth, the higher the internal humidity, and the greater the probability of a disaster.

[0070] Step six: Determine the first ratio of the sum of slopes to the absolute value of the difference.

[0071] In the embodiments of this application, the electronic device uses the sum of the slopes divided by the absolute value of the difference to obtain the first ratio. The sum of the slopes is the numerator, and the absolute value of the difference is the denominator. Therefore, the larger the first ratio, the greater the possibility of a disaster occurring on the mountain at the soil moisture value collection point.

[0072] Step 7: Determine the first sub-risk value for each location based on the average humidity and the first ratio of the sum of slopes to the absolute value of the difference.

[0073] In summary, for the embodiments of this application, the average humidity and the first ratio are key factors affecting the risk of disaster at each humidity collection location above each target contour segment group. Therefore, the staff sets corresponding weights for the average humidity and the first ratio and stores them in the electronic device. The electronic device normalizes the average humidity and the first ratio according to a preset normalization formula to eliminate the influence of dimensions. The electronic device calls the corresponding weights to perform weighted calculations on the normalized values ​​to accurately determine the first sub-risk value of each location. Using the average humidity and the first ratio to determine the first sub-risk value of each humidity collection location is more accurate.

[0074] One possible implementation of this application embodiment is that, in step S1054, a second sub-risk value for a landslide disaster above each target contour segment group is determined based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, and the first sub-risk value at each location above each target contour segment group. This specifically includes steps Sa (not shown in the figure), Sb (not shown in the figure), Sc (not shown in the figure), and Sd (not shown in the figure). Sa, determine the target location in the mountain above each target contour segment group.

[0075] The target location is the position where the first sub-risk value reaches a preset threshold.

[0076] In this embodiment of the application, a preset threshold is used as the dividing point where the first sub-risk value is too high. The electronic device compares the first sub-risk value of each humidity collection position above each target contour segment group with the preset threshold to determine the target position that has reached the preset threshold.

[0077] Sb, determine the second number of target locations in the mountain above each target contour segment group, and determine the second ratio of the second number to the total number of soil moisture value collection locations in the mountain above each target contour segment.

[0078] In this embodiment of the application, the electronic device counts the target locations on the mountain above each target contour segment group to obtain a second quantity. The larger the second quantity, the more locations above each target contour segment group have a higher risk of disaster, and the greater the risk of disaster above the target contour segment. The electronic device divides the second quantity by the total number of locations where soil moisture values ​​are collected above each target contour segment to obtain a second ratio. The larger the second ratio, the greater the proportion of locations with a high risk of disaster, and the greater the risk of disaster.

[0079] Sc determines the difference between the first sub-risk value and the preset threshold for each target position above each target contour segment group, and determines the average difference of the differences corresponding to all target positions above each target contour segment group.

[0080] In this embodiment of the application, the electronic device subtracts a preset threshold from the first sub-risk value of each target position above each target contour segment group to obtain the difference. The larger the difference, the greater the deviation from the preset threshold, and the greater the possibility of a disaster. The electronic device averages the differences corresponding to all target positions above each target contour segment group to obtain the average difference. The average difference represents the overall level of the difference between all target positions above each target contour segment group and the preset threshold. The larger the average difference, the greater the risk of a disaster.

[0081] Sd is a second sub-risk value for the occurrence of a disaster on the mountain above each target contour segment group, based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, the second ratio, and the average of the differences.

[0082] In this embodiment, the area of ​​the region formed by each target contour segment group, the maximum width between two contour segments, the second ratio, and the average difference are all key factors characterizing the risk of a disaster occurring on the mountain above each target contour segment group. Therefore, the staff assigns corresponding weights to the four factors (area, maximum width, etc.) and stores them in the electronic device. The electronic device normalizes these four factors according to a preset normalization formula to eliminate the influence of dimensions. The electronic device then uses the corresponding weights to perform a weighted calculation on the normalized values ​​to obtain the second sub-risk value for a disaster occurring on the mountain above each target contour segment. Determining the second sub-risk value by comprehensively considering these four factors is more accurate. If no target location exists, the electronic device determines the second sub-risk value for a disaster occurring on the mountain above each target contour segment group solely based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, and the average of the first sub-risk values ​​from all humidity collection locations above each target contour segment group, through normalization and weighted calculation.

[0083] One possible implementation of this application embodiment is that step S1055 determines the disaster risk value of the mountain at each time point based on the first number of target contour segment groups at each time point, the second sub-risk value of the mountain above each target contour segment group, the ratio of the total length to the preset length, and the similarity. Specifically, this includes steps S1 (not shown in the figure) and S2 (not shown in the figure). S1 is the sum of the second sub-risk values ​​of the second sub-risk value for the mountain above all target contour segments at each time point.

[0084] S2, based on the first number of target contour segments, the sum of the second sub-risk values, the ratio of the total length to the preset length, and the similarity of each time point, determine the disaster risk value of the mountain at each time point.

[0085] In this embodiment, the electronic device sums the second sub-risk values ​​of all target contour segments at each time point to obtain the total second sub-risk value. The larger the total second sub-risk value, the greater the risk of a mountain disaster at each time point. In summary, the first quantity, the total second sub-risk value, the proportion, and the similarity are all key factors affecting the probability of a mountain disaster at each time point. Therefore, the staff pre-sets the weights of the above four factors, including the first quantity and the total second sub-risk value, and stores them in the electronic device. The electronic device normalizes the above four factors, including the first quantity and the total second sub-risk value, and then uses the corresponding weights to perform a weighted calculation on the normalized values ​​to obtain the disaster risk value of the probability of a mountain disaster at each time point. Determining the disaster risk value of a mountain disaster at each time point by comprehensively considering the above four factors, including the first quantity, is more accurate.

[0086] One possible implementation of this application embodiment is that step S106, which determines the probability of an impending mountain disaster based on the disaster risk value and similarity at each time point, specifically includes steps S1061 (not shown in the figure), S1062 (not shown in the figure), S1063 (not shown in the figure), S1064 (not shown in the figure), and S1065 (not shown in the figure), wherein... S1061, determine the target average value of disaster risk values ​​at all time points and the disaster risk value at the most recent time point.

[0087] In this embodiment of the application, the electronic device calculates the target average value by averaging the disaster risk values ​​at all time points. The target average value represents the probability of a mountain disaster occurring within a preset time period from an overall perspective. The disaster risk value at the most recent time point represents the probability of a disaster occurring on the mountain closest to the current time, directly affecting the probability of an impending mountain disaster.

[0088] S1062, map the disaster risk value at each time point to a preset rectangular coordinate system and determine the third fitting function based on the disaster risk values ​​at all time points.

[0089] In this embodiment of the application, the horizontal axis of the preset rectangular coordinate system represents the time point, and the vertical axis represents the disaster risk value. The electronic device maps the disaster risk value at each time point to the preset rectangular coordinate system and performs linear fitting using the Origin plugin to obtain a third fitting function. The third fitting function serves as a characteristic function of the change in disaster risk value over time.

[0090] S1063, determine the changing trend of the third fitting function.

[0091] The trends of change include increasing, decreasing, and remaining unchanged.

[0092] In the embodiments of this application, the electronic device obtains the changing trend of the third fitting function by determining the slope of the third fitting function. A positive slope indicates an increasing trend, a negative slope indicates a decreasing trend, and a slope of 0 indicates no change in trend.

[0093] S1064, if the trend is upward, then determine the slope of the third fitting function and the similarity of the most recent time point, determine the third ratio of the slope to the similarity of the most recent time point, and determine the probability of an impending disaster on the mountain based on the third ratio, the target average value, and the disaster risk value of the most recent time point.

[0094] In this embodiment of the application, if the trend is upward, it indicates that the risk of a landslide disaster increases over a preset time period. Therefore, the electronic device determines the slope of the third fitting function. The larger the slope, the greater the increase in the probability of a landslide disaster, and the greater the risk of an impending disaster. The electronic device determines the similarity at the most recent time point, that is, the difference between the actual boundary contour closest to the current time and the standard boundary contour where no disaster has occurred. The larger the similarity, the smaller the difference, and the lower the risk of an impending disaster. The electronic device divides the slope of the third fitting function by the similarity at the most recent time point to obtain a third ratio, with the slope as the numerator and the similarity at the most recent time point as the denominator. Therefore, the larger the third ratio, the greater the risk of an impending landslide disaster.

[0095] In summary, the third ratio, the target average, and the disaster risk value at the most recent time point are all key factors influencing the probability of an impending landslide. Therefore, staff assign corresponding weights to the third ratio, the target average, and the disaster risk value at the most recent time point, and store these weights in the electronic device. The electronic device normalizes these three values, and then uses their respective weights to perform a weighted calculation to obtain a score, which represents the probability of a disaster. Staff can set a preset function to calculate the probability based on this score and store it in the electronic device. The electronic device can then substitute the determined score into the preset function to calculate the probability of an impending landslide.

[0096] S1065, if the trend is decreasing or unchanged, then the probability of an impending disaster on the mountain is determined based on the target average value and the disaster risk value at the most recent time point.

[0097] In this embodiment of the application, if the risk of a mountain disaster decreases or remains unchanged within a preset time period, it means that the probability of a mountain disaster is about to occur is only related to the disaster risk value at the most recent time point and the target average value of the overall probability of a mountain disaster within the preset time period. Therefore, the staff sets the weights corresponding to the target average value and the disaster risk value at the most recent time point. The electronic device calls the corresponding weights to perform a weighted calculation on the target average value and the disaster risk value at the most recent time point to obtain a score. Then, the electronic device substitutes this score into the preset function used to calculate the probability to obtain the probability of a mountain disaster.

[0098] In one possible implementation of this application embodiment, after step S107, steps S108 (not shown in the figure), S109 (not shown in the figure), S110 (not shown in the figure), S111 (not shown in the figure), and S112 (not shown in the figure) are further included, wherein, S108, determine the area where each target contour segment group is located at the junction of the mountain and the water surface at each time point.

[0099] In this embodiment of the application, the electronic device can determine the start and end positions of each target contour segment group at each time point. Based on the start and end positions, the region of each target contour segment group at the junction of the mountain and the water surface can be determined. The electronic device can map the preset contour at the junction of the mountain and the water surface to a Cartesian coordinate system, and then map each target contour segment group at each time point to the corresponding position in the preset contour in the Cartesian coordinate system, thereby obtaining the start and end position coordinates of each target contour segment group, and thus obtaining the region of each target contour segment group.

[0100] S109, compare the regions corresponding to all target contour segments at all time points to obtain the overlapping regions where at least two target contour segments overlap.

[0101] In the embodiments of this application, the electronic device compares the regions of all target contour segments at all time points to determine the overlapping region where at least two target contour segment groups overlap. The overlapping region is the region where at least two time points within a preset time period are both minor disasters.

[0102] S110, determine the third number of target contour segment groups for each overlapping region, and the average of the second sub-risk values ​​of all target contour segment groups at each overlapping location.

[0103] In the embodiments of this application, the electronic device counts the target contour segment groups that form each overlapping region to obtain a third number of target contour segment groups for each overlapping region. After the electronic device determines the target contour segment groups that form each overlapping region, it can determine the second sub-risk value of each target contour segment group that forms each overlapping region. The electronic device averages the second sub-risk values ​​of all target contour segment groups for each overlapping region to obtain the average value of all second sub-risk values ​​for each overlapping region.

[0104] S111, the risk value of each overlapping region is determined based on the third number of target contour segment groups in each overlapping region and the average of the second sub-risk values ​​of all target contour segment groups.

[0105] In this embodiment of the application, the greater the number of target contour segment groups forming each overlapping region, the more frequent the occurrence of minor disasters within a preset time period, and the looser the upper mountain, the greater the likelihood of a disaster occurring on the mountain above the overlapping region. The greater the average value of the second sub-risk value of all target contour segment groups in each overlapping region, the greater the risk of a disaster occurring on the mountain above the overlapping region. In summary, both the third quantity and the average value of the second sub-risk value affect the magnitude of the risk of an impending disaster on the mountain above each overlapping region. Therefore, the staff assigns corresponding weights to the third quantity and the average value of the second sub-risk value and stores them in the electronic device. The electronic device normalizes the third quantity of each overlapping region and the average value of the second sub-risk value of all target contour segment groups to eliminate the influence of different dimensions. Then, the electronic device calls the corresponding weights to perform a weighted calculation on the normalized values ​​to obtain a risk value characterizing the magnitude of the risk of a disaster occurring on the mountain above each overlapping region.

[0106] S112, output the location of each overlapping region and its corresponding risk value.

[0107] In this embodiment of the application, the electronic device can map each overlapping area to a preset image or model at the junction of the mountain and the water surface. Specifically, the electronic device can select or fill the preset image or model according to the position of each overlapping area. Then, the electronic device maps the risk value representing the risk of disaster on the mountain above each overlapping area to the corresponding position of each overlapping area in the preset image or model. Then, the electronic device displays the preset image or model marked with overlapping areas and risk values ​​through a display device such as a display screen, or sends the preset image marked with overlapping areas and risk values ​​to the mobile phones or other terminal devices of relevant personnel. This makes it easier for staff to know more clearly the location at the junction of the mountain and the water surface where the probability of disaster is high, and thus facilitates subsequent corresponding processing.

[0108] The above embodiments describe an anomaly early warning method in water conservancy projects from the perspective of method flow. The following embodiments describe an anomaly early warning device 20 in water conservancy projects from the perspective of virtual modules or virtual units. For details, please refer to the following embodiments.

[0109] This application provides an anomaly early warning device 20 for water conservancy projects, such as... Figure 2 As shown, an anomaly early warning device 20 in a water conservancy project may specifically include: The data acquisition module 201 is used to acquire target images at the junction of the mountain and the water surface at multiple time points within a preset time period, soil moisture values ​​at multiple locations on the mountain, and water level height values ​​corresponding to each time point. The similarity calculation module 202 is used to determine the target contour at the junction of the mountain and the water surface based on the target image at each time point, and to determine the similarity between the target contour at each time point and the corresponding preset contour. The preset contour is the initial contour at the junction of the mountain and the water surface under the water level height value corresponding to each time point. The first comparison module 203 is used to compare the contour at each time point with the corresponding preset contour to obtain at least one target contour segment group where the boundary at each time point changes. The target contour segment group includes two contour segments that are inconsistent at the same position in the contour and the corresponding preset contour. The first determining module 204 is used to determine multiple soil moisture values ​​above the target contour segment and the slope of the location of each soil moisture value based on the position of the target contour segment group at each time point. The second determining module 205 is used to determine the disaster risk value of the mountain at each time point based on the target contour segment group at each time point, the soil moisture value of multiple locations on the mountain above the target contour segment group at each time point, the slope of the location of each soil moisture value, and the similarity. The probability determination module 206 is used to determine the probability of an impending disaster on the mountain based on the disaster risk value and similarity at each time point. The first output module 207 is used to output alarm information when the probability reaches a preset probability threshold.

[0110] This application discloses an anomaly early warning device 20 in a water conservancy project. The data acquisition module 201 acquires target images to facilitate subsequent analysis of conditions such as landslides, localized landslides, and soil erosion at the junction of the mountain and water surface, thereby reducing the likelihood of future mountain disasters. The data acquisition module 201 also acquires soil moisture values ​​at multiple locations on the mountain to determine the soil moisture levels at various points within the mountain, as soil moisture levels affect the likelihood of mountain disasters. Furthermore, the data acquisition module 201 acquires water level values ​​at multiple time points to accurately determine the standard conditions (i.e., initial contours) at the junction of the mountain and water surface under different water level values. A similarity calculation module 202 determines the target contour at the junction of the mountain and water surface at each time point based on the target image at each time point. The target contour represents the state of the junction at each time point. The similarity calculation module 202 calculates the similarity between the target contour and a preset contour corresponding to the water level value at each time point. The similarity represents the difference between the target contour and the preset contour under the standard normal state. Finally, a first comparison module 203 further compares the target contour with a preset contour corresponding to the water level value at each time point. By comparing the contours, the target contour segments that change at the boundary at each time point can be identified. Changes at the boundary indicate that there are small local disasters such as landslides or collapses in the mountain. The first determination module 204 determines multiple soil moisture values ​​of the mountain above the target contour segment group and the slope of each soil moisture value collection location based on the location of the target contour segment group. The target contour segment group, the soil moisture value above the target contour segment group, the slope, and the similarity are all key factors affecting the degree of disaster risk of the mountain at each time point. Therefore, the second determination module 205 can comprehensively and accurately determine the disaster risk value of the mountain at each time point based on the above four factors. Then, the probability determination module 206 can comprehensively analyze the disaster risk value and similarity of each time point to obtain an accurate probability of an impending disaster in the mountain. A preset probability threshold is used as the dividing point for excessively high probabilities. If the preset probability threshold is reached, it indicates that the possibility of an impending disaster in the mountain is relatively high. The first output module 207 outputs alarm information to achieve timely reminders. Based on the changes at the boundary between the mountain and the water surface, the soil moisture value of the mountain, the slope, and other factors, the probability of a mountain disaster can be judged more accurately.

[0111] In one possible implementation of this application embodiment, the soil moisture value at each location includes sub-moisture values ​​at multiple depths within the mountain below each location. When determining the disaster risk value of the mountain at each time point based on the target contour segment group at each time point, the multiple soil moisture values ​​above the target contour segment group at each time point, the slope of the location of each soil moisture value, and similarity, the second determining module 205 is specifically used for: Determine the area of ​​the region formed between two contour segments in the target contour segment group at each time point, as well as the maximum width between the two contour segments; Determine the first number of target contour segments at each time point and the total length of all target contour segments, and determine the ratio of the total length to the preset length, where the preset length is the length at the junction of the mountain and the water surface within the preset range. The first sub-risk value for each location is determined based on multiple sub-humidity values ​​and the slope. The second sub-risk value for a disaster on the mountain above each target contour segment group is determined based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, and the first sub-risk value of each location above each target contour segment group. The disaster risk value of the mountain at each time point is determined based on the first number of target contour segments at each time point, the second sub-risk value of the mountain above each target contour segment group, the ratio of the total length to the preset length, and the similarity.

[0112] In one possible implementation of this application embodiment, when the second determining module 205 determines the first sub-risk value for each location based on multiple sub-humidity values ​​and slope, it is specifically used for: Multiple sub-humidity values ​​at each location are mapped to a preset rectangular coordinate system to obtain a scatter plot; The first fitting function is determined based on the start and end points of the scatter plot, and the second fitting function is determined based on all the scatter points in the scatter plot. Determine the first slope of the first fitting function and the second slope of the second fitting function; Determine the average humidity of multiple sub-humidity values ​​at each location; Determine the absolute value of the difference between the first slope and the second slope, and determine the sum of the slopes of the first slope and the second slope; Determine the first ratio of the sum of slopes to the absolute value of the difference; The first sub-risk value for each location is determined based on the average humidity and the first ratio of the sum of slopes to the absolute value of the difference.

[0113] In one possible implementation of this application embodiment, when the second determining module 205 determines the second sub-risk value for a landslide disaster above each target contour segment group based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, and the first sub-risk value at each location above each target contour segment group, it is specifically used for: Determine the target location in the mountain above each target contour segment group. The target location is the position where the first sub-risk value reaches a preset threshold. Determine a second number of target locations in the mountain above each target contour segment group, and determine a second ratio of the second number to the total number of soil moisture value collection locations in the mountain above each target contour segment; Determine the difference between the first sub-risk value and the preset threshold for each target location above each target contour segment group, and determine the average difference of the differences corresponding to all target locations above each target contour segment group; The second sub-risk value for a disaster on the mountain above each target contour segment group is determined based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, the second ratio, and the average of the differences.

[0114] In one possible implementation of this application embodiment, when the second determining module 205 determines the disaster risk value of the mountain at each time point based on the first number of target contour segment groups at each time point, the second sub-risk value of the mountain above each target contour segment group, the ratio of the total length to the preset length, and the similarity, it is specifically used for: Determine the sum of the second sub-risk values ​​of the second sub-risk value for the mountain above all target contour segments at each time point; The disaster risk value of the mountain at each time point is determined based on the first number of target contour segments, the sum of the second sub-risk values, the ratio of the total length to the preset length, and the similarity.

[0115] In one possible implementation of this application embodiment, when determining the probability of an impending mountain disaster based on the disaster risk value and similarity at each time point, the probability determination module 206 is specifically used for: Determine the target average value of disaster risk at all time points and the disaster risk value at the most recent time point; The disaster risk value at each time point is mapped to a preset rectangular coordinate system, and a third fitting function is determined based on the disaster risk values ​​at all time points; Determine the trend of the third fitted function, including trends of increase, decrease, and no change; If the trend is upward, then determine the slope of the third fitting function and the similarity at the most recent time point, determine the third ratio of the slope to the similarity at the most recent time point, and determine the probability of an impending disaster on the mountain based on the third ratio, the target average value, and the disaster risk value at the most recent time point. If the trend is decreasing or unchanged, the probability of an impending disaster on the mountain is determined based on the target average value and the disaster risk value at the most recent time point.

[0116] In one possible implementation of this application embodiment, an anomaly early warning device 20 in a water conservancy project further includes: The region determination module is used to determine the region where each target contour segment group is located at the junction of the mountain and the water surface at each time point; The second comparison module is used to compare the regions corresponding to all target contour segments at all time points to obtain the overlapping region where at least two target contour segments overlap. The third determination module is used to determine the third number of target contour segment groups in each overlapping region, and the average of the second sub-risk values ​​of all target contour segment groups at each overlapping location. The fourth determination module is used to determine the risk value of each overlapping region based on the third number of target contour segment groups in each overlapping region and the average of the second sub-risk values ​​of all target contour segment groups. The second output module is used to output the location of each overlapping area and its corresponding risk value.

[0117] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the abnormal early warning device 20 in the water conservancy project described above can be referred to the corresponding process in the aforementioned method embodiments, and will not be repeated here.

[0118] This application provides an electronic device, such as... Figure 3 As shown, Figure 3 The illustrated electronic device 30 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 30 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 30 does not constitute a limitation on the embodiments of this application.

[0119] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0120] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0121] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0122] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0123] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0124] This application provides a computer-readable storage medium storing a computer program, which, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments. Compared with related technologies, the target image obtained in this application facilitates subsequent analysis of collapses, local landslides, and erosion at the junction of the mountain and the water surface, thereby facilitating the prediction of potential mountain disasters. Obtaining soil moisture values ​​at multiple locations on the mountain facilitates understanding the soil moisture conditions at various points on the mountain, as soil moisture conditions affect the likelihood of mountain disasters. Obtaining water level values ​​at multiple time points facilitates accurate determination of the standard conditions at the junction of the mountain and the water surface under different water level values, i.e., the initial contour. Based on the target image at each time point, the target contour at the junction of the mountain and the water surface at each time point is determined. The target contour represents the state of the junction of the mountain and the water surface at each time point. The similarity between the target contour and the preset contour corresponding to the water level value at each time point is calculated. The similarity represents the difference between the target contour and the preset contour under the standard normal state. Then, a comparison is made with the preset contour corresponding to the water level value at each time point, thereby determining the initial contour at each time point. The target contour segment group changes at the boundary. Changes at the boundary indicate that there is a small local disaster such as landslide or collapse on the mountain. Based on the location of the target contour segment group, multiple soil moisture values ​​of the mountain above the target contour segment group and the slope of each soil moisture value collection location are determined. The target contour segment group, the soil moisture value above the target contour segment group, the slope, and the similarity are all key factors affecting the degree of disaster risk of the mountain at each time point. Therefore, based on the above four factors, the disaster risk value of the mountain at each time point can be determined comprehensively and accurately. Then, based on the disaster risk value and similarity of each time point, the probability of an impending disaster on the mountain can be accurately analyzed. A preset probability threshold is set as the dividing point of excessively high probability. If the preset probability threshold is reached, it means that the possibility of an impending disaster on the mountain is relatively high, and an alarm message is output to achieve timely reminder. Based on the changes at the boundary between the mountain and the water surface, the soil moisture value of the mountain, the slope, and other factors, the probability of a mountain disaster can be judged more accurately.

[0125] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0126] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An anomaly early warning method in water conservancy projects, characterized in that, include: Acquire target images at the junction of the mountain and the water surface at multiple time points within a preset time period, soil moisture values ​​at multiple locations on the mountain, and water level height values ​​at each time point; The target contour at the junction of the mountain and the water surface is determined based on the target image at each time point, and the similarity between the target contour at each time point and the corresponding preset contour is determined. The preset contour is the initial contour at the junction of the mountain and the water surface under the water level height value corresponding to each time point. The contour at each time point is compared with the corresponding preset contour to obtain at least one target contour segment group where the boundary at each time point changes. The target contour segment group includes two contour segments that are inconsistent at the same position in the contour and the corresponding preset contour. Based on the position of the target contour segment group at each time point, multiple soil moisture values ​​above the target contour segment and the slope at the location of each soil moisture value are determined. The disaster risk value of the mountain at each time point is determined based on the target contour segment group at each time point, the soil moisture value of multiple locations on the mountain above the target contour segment group at each time point, the slope of the location of each soil moisture value, and the similarity. The probability of an impending disaster on the mountain is determined based on the disaster risk value and similarity at each time point. If the probability reaches a preset probability threshold, an alarm message will be output.

2. The anomaly early warning method in water conservancy projects according to claim 1, characterized in that, The soil moisture value at each location includes sub-moisture values ​​at multiple depths within the mountain below each location. The determination of the mountain's disaster risk value at each time point, based on the target contour segment group at each time point, multiple soil moisture values ​​above the target contour segment group at each time point, the slope of the location of each soil moisture value, and similarity, includes: Determine the area of ​​the region formed between two contour segments in the target contour segment group at each time point, as well as the maximum width between the two contour segments; Determine the first number of target contour segments at each time point and the total length of all target contour segments, and determine the ratio of the total length to the preset length, wherein the preset length is the length at the junction of the mountain and the water surface within the preset range; The first sub-risk value for each location is determined based on multiple sub-humidity values ​​and the slope. The second sub-risk value for a disaster on the mountain above each target contour segment group is determined based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, and the first sub-risk value of each location above each target contour segment group. The disaster risk value of the mountain at each time point is determined based on the first number of target contour segments at each time point, the second sub-risk value of the mountain above each target contour segment group, the ratio of the total length to the preset length, and the similarity.

3. The anomaly early warning method in water conservancy projects according to claim 2, characterized in that, The determination of the first sub-risk value for each location based on multiple sub-humidity values ​​and slope includes: The multiple sub-humidity values ​​at each location are mapped to a preset rectangular coordinate system to obtain a scatter plot; A first fitting function is determined based on the start and end points of the scatter plot, and a second fitting function is determined based on all the scatter points in the scatter plot. Determine the first slope of the first fitting function and the second slope of the second fitting function; Determine the average humidity value of multiple sub-humidity values ​​at each location; Determine the absolute value of the difference between the first slope and the second slope, and determine the sum of the slopes of the first slope and the second slope; Determine a first ratio between the sum of the slopes and the absolute value of the difference; The first sub-risk value for each location is determined based on the average humidity and the first ratio of the sum of the slopes to the absolute value of the difference.

4. The anomaly early warning method in water conservancy projects according to claim 2, characterized in that, The second sub-risk value for a landslide disaster above each target contour segment group is determined based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, and the first sub-risk value at each location above each target contour segment group. This includes: Determine the target location in the mountain above each target contour segment group, where the target location is the position where the first sub-risk value reaches a preset threshold. Determine a second number of target locations in the mountain above each target contour segment group, and determine a second ratio of the second number to the total number of soil moisture value collection locations in the mountain above each target contour segment; Determine the difference between the first sub-risk value of each target position above each target contour segment group and the preset threshold, and determine the average difference of the differences corresponding to all target positions above each target contour segment group; The second sub-risk value for a disaster occurring on the mountain above each target contour segment group is determined based on the area of ​​the region formed between two contour segments in each target contour segment group, the maximum width between the two contour segments, the second ratio, and the average of the differences.

5. The anomaly early warning method in water conservancy projects according to claim 2, characterized in that, The method of determining the disaster risk value of the mountain at each time point based on the first number of target contour segment groups at each time point, the second sub-risk value of the mountain above each target contour segment group, the ratio of the total length to the preset length, and the similarity includes: Determine the sum of the second sub-risk values ​​of the second sub-risk value for the mountain above all target contour segments at each time point; The disaster risk value of the mountain at each time point is determined based on the first number of target contour segments, the sum of the second sub-risk values, the ratio of the total length to the preset length, and the similarity.

6. The anomaly early warning method in water conservancy projects according to claim 1, characterized in that, The determination of the probability of an impending disaster on the mountain based on the disaster risk value and similarity at each time point includes: Determine the target average value of disaster risk at all time points and the disaster risk value at the most recent time point; The disaster risk value at each time point is mapped to a preset rectangular coordinate system, and a third fitting function is determined based on the disaster risk values ​​at all time points; Determine the trend of the third fitting function, where the trend includes increasing, decreasing, and remaining unchanged; If the trend of change is upward, then the slope of the third fitting function and the similarity at the most recent time point are determined, the third ratio of the slope to the similarity at the most recent time point is determined, and the probability that the mountain will soon be affected by a disaster is determined based on the third ratio, the target average value and the disaster risk value at the most recent time point. If the trend of change is decreasing or unchanged, the probability of an impending disaster on the mountain is determined based on the target average value and the disaster risk value at the most recent time point.

7. The anomaly early warning method in water conservancy projects according to claim 1, characterized in that, The method further includes: Determine the area where each target contour segment group is located at the junction of the mountain and the water surface at each time point; By comparing the regions corresponding to all target contour segments at all time points, the overlapping regions where at least two target contour segments overlap are obtained. Determine the third number of target contour segment groups for each overlapping region, and the average of the second sub-risk values ​​for all target contour segment groups at each overlapping location; The risk value of each overlapping region is determined based on the third number of target contour segment groups in each overlapping region and the average of the second sub-risk values ​​of all target contour segment groups. Output the location of each overlapping region and its corresponding risk value.

8. An anomaly early warning device for water conservancy projects, characterized in that, include: The data acquisition module is used to acquire target images at the junction of the mountain and the water surface at multiple time points within a preset time period, soil moisture values ​​at multiple locations on the mountain, and water level height values ​​corresponding to each time point. The similarity calculation module is used to determine the target outline at the junction of the mountain and the water surface based on the target image at each time point, and to determine the similarity between the target outline at each time point and the corresponding preset outline. The preset outline is the initial outline at the junction of the mountain and the water surface under the water level height value corresponding to each time point. The first comparison module is used to compare the contour at each time point with the corresponding preset contour to obtain at least one target contour segment group that changes at the boundary of each time point. The target contour segment group includes two contour segments that do not have the same position in the contour and the corresponding preset contour. The first determining module is used to determine multiple soil moisture values ​​above the target contour segment and the slope of the location of each soil moisture value based on the position of the target contour segment group at each time point. The second determination module is used to determine the disaster risk value of the mountain at each time point based on the target contour segment group at each time point, the soil moisture value of multiple locations on the mountain above the target contour segment group at each time point, the slope of the location of each soil moisture value, and the similarity. The probability determination module is used to determine the probability that a disaster will occur on the mountain based on the disaster risk value and similarity at each time point; The first output module is used to output alarm information when the probability reaches a preset probability threshold.

9. An electronic device, characterized in that, It includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in the memory and configured to be executed by the at least one processor, the at least one application being used to execute an anomaly early warning method in a water conservancy project according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed in the computer, the computer is instructed to perform an anomaly early warning method in a water conservancy project as described in any one of claims 1 to 7.

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