Establishment method of rapid blocking model for dike breach

Abstract

The invention relates to the technical field of rapid dike rescue, in particular to a method for establishing a rapid blocking model for a dike breach, and the method comprises the steps: obtaining a water flow trend vector to determine a feature side slope, obtaining a side slope profile curve of the feature side slope, and determining a burst trend sub-vector based on a deformation feature point to determine a burst trend characterization vector; determining a breach deformation offset parameter and a breach deformation expansion parameter according to the comparison condition of the real-time burst trend sub-vector and the burst trend characterization vector so as to judge whether a breach contour prediction abnormal risk exists or not, determining a prediction error category of the breach contour loss in response to the existence of the breach contour prediction abnormal risk, and determining the breach contour loss prediction error category. According to the method and the device, the blocking model is established, and the acquisition optimization mode established by the model is determined, so that when the embankment breach appears, the deviation of the blocking model establishment is rapidly identified according to the actual form of the slope at the embankment breach, the acquisition optimization mode established by the model is adaptively adjusted, and the reliability of the rapid blocking model is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of rapid embankment rescue, and in particular to a method for establishing a rapid plugging model for embankment breaches. Background Art

[0002] Levees are crucial infrastructure for flood control. Due to the impact of global climate change, extreme rainstorms and frequent floods exceeding designated levels pose serious security threats to levees. Once a levee breach occurs, floodwaters will overflow unchecked, severely threatening the safety of people and property and causing long-term negative impacts on the ecological environment and socio-economic development. Therefore, rapid sealing of breaches is essential to minimize the damage. However, current levee breach sealing methods rely heavily on field experience, employing traditional methods such as riprap, sandbagging, and shipwreck diversion. These methods are inefficient under complex flow conditions with high velocities and large flows, making it difficult to quickly and effectively control breach development. Furthermore, they lack scientific theoretical foundations and precise model guidance, and rely heavily on empirical experience, which can easily lead to resource waste or failure in sealing. To improve the efficiency and success rate of levee breach sealing and reduce disaster losses, it is necessary to develop a model method that can quickly and accurately simulate the dynamic evolution of breaches and optimize sealing strategies based on real-time monitoring data. However, in the process of establishing a rapid sealing model, the accuracy of breach characteristic data collection directly affects the reliability of the model. Therefore, improving the accuracy of breach characteristic data collection is an urgent technical issue to be addressed.

[0003] For example, the Chinese patent authorization announcement number is: CN118395902B. The invention discloses a method for establishing a three-parameter selection model for dike breach sunken ships, including the following steps, proposing a principle for selecting blocking ships and a comprehensive model for selecting barges with three parameters; the three parameters include barge length, barge draft and barge anchor holding power; based on the breach head and water flow velocity, a breach scale classification model, a barge length selection model, a barge draft selection model, and a barge anchor holding power selection model are established to determine the anchor holding power safety factor. According to the complexity of the dike breach problem and the particularity of the blocking operation, the invention combines the breach length, water flow velocity, breach water depth and other parameters to calculate the length, draft, hull height, number of anchors, anchor weight and anchoring position of the blocking ship, thereby achieving accurate selection of dike breach blocking ships.

[0004] The following problems also exist in the prior art:

[0005] The existing technology does not take into account that when a levee breach occurs, the slope morphology on both sides of the breach will change. It is difficult to obtain comprehensive and reliable breach feature data using a single breach feature collection method. When a levee breach occurs, the existing technology cannot quickly identify the deviation in the construction of the plugging model based on the actual morphology of the slope at the levee breach, and cannot adaptively adjust the collection optimization method for model establishment, affecting the reliability of the rapid plugging model. Summary of the Invention

[0006] To this end, the present invention provides a method for establishing a rapid plugging model for a levee breach, so as to overcome the problems in the prior art of failing to quickly identify the deviation in the construction of the plugging model according to the actual shape of the slope at the levee breach when a levee breach occurs, failing to adaptively adjust the acquisition optimization method for model establishment, and affecting the reliability of the rapid plugging model.

[0007] To achieve the above-mentioned object, the present invention provides a method for establishing a rapid plugging model for a dike breach, comprising:

[0008] Obtaining a water flow trend vector of a dike breach, and determining a characteristic slope based on the water flow trend vector;

[0009] Acquire surface contour images of the characteristic slope at several moments in a preset acquisition cycle, extract slope contour curves based on the surface contour images, and divide the slope contour curves into several characteristic contour segments;

[0010] Determine a collapse tendency sub-vector based on the deformation feature points of the characteristic contour segment, and determine a collapse tendency characterization vector of the characteristic contour segment based on a plurality of collapse tendency sub-vectors;

[0011] Obtaining a real-time breach trend subvector for each characteristic contour segment, and determining breach deformation offset parameters and breach deformation expansion parameters based on a comparison between the real-time breach trend subvector and the breach trend characterization vector, to determine whether there is a breach contour prediction anomaly risk and to determine the prediction error category of the breach contour loss;

[0012] Determining an acquisition optimization method for model establishment based on the prediction error category, the acquisition optimization method includes adjusting the number of acquisition points within the characteristic sub-contour segment based on the contour morphology matching coefficient of the characteristic sub-contour segment at adjacent moments, or adjusting the time interval for data acquisition based on the breach deformation offset parameter or the breach deformation expansion parameter;

[0013] The number of divisions of the characteristic sub-contour segment is determined according to the water flow trend vector and the real-time collapse trend sub-vector of the characteristic contour segment.

[0014] Further, the slope to which the water flow trend vector points is determined as the characteristic slope;

[0015] The direction of the water flow vector is the direction of the water flow upstream of the dike breach.

[0016] Furthermore, the process of determining the collapse tendency subvector includes:

[0017] Calculating the vertical distances between a plurality of points on the characteristic contour segment and the reference line segment, and determining the point with the maximum vertical distance as the deformation characteristic point;

[0018] The collapse tendency sub-vector is constructed with the midpoint of the reference line segment as the vector starting point and the deformation feature point as the vector end point;

[0019] The characteristic contour segment is divided along the width of the embankment, and the reference line segment is a line connecting two end points of the characteristic contour segment.

[0020] Furthermore, determining the collapse tendency characterization vector includes:

[0021] The collapse tendency sub-vectors of a single characteristic contour segment at several moments are obtained within the preset acquisition cycle, and a vector obtained by adding the collapse tendency sub-vectors at several moments is determined as the collapse tendency characterization vector of the characteristic contour segment.

[0022] Furthermore, determining the breach deformation offset parameter and the breach deformation expansion parameter includes:

[0023] Acquire a real-time collapse tendency subvector and a collapse tendency characterization vector of the characteristic contour segment, and determine the vector angle between the real-time collapse tendency subvector and the collapse tendency characterization vector as the collapse deformation offset parameter;

[0024] The vector modulus difference between the real-time collapse tendency sub-vector and the collapse tendency characterization vector is determined as the breach deformation expansion parameter.

[0025] Furthermore, the process of determining whether there is an abnormal risk of breach contour prediction includes:

[0026] If the breach deformation offset parameter and breach deformation expansion parameter of the characteristic contour segment meet the abnormality judgment conditions, it is determined that there is an abnormal risk in the breach contour prediction;

[0027] The abnormality determination condition is that the breach deformation offset parameter exceeds a preset breach deformation offset reference value, or the breach deformation expansion parameter exceeds a preset breach deformation expansion parameter reference value.

[0028] Furthermore, the process of determining the prediction error category of the breach contour loss includes,

[0029] If the rupture deformation offset parameter and the rupture deformation expansion parameter of the characteristic contour segment meet the first prediction error category judgment condition, then the prediction error category of the rupture contour loss is determined to be the first prediction error category;

[0030] If the rupture deformation offset parameter and the rupture deformation expansion parameter of the characteristic contour segment do not meet the first prediction error category judgment condition, the prediction error category of the rupture contour loss is determined to be the second prediction error category;

[0031] The first prediction error category determination condition is that the breach deformation offset parameter exceeds a preset breach deformation offset reference value, and the breach deformation expansion parameter exceeds a preset breach deformation expansion parameter reference value.

[0032] Furthermore, the acquisition optimization methods for model establishment are determined to include:

[0033] If the prediction error category is the first prediction error category, determining that the acquisition optimization method is to adjust the number of acquisition points in the characteristic sub-contour segment according to the contour morphology matching coefficient of the characteristic sub-contour segment at adjacent moments;

[0034] If the prediction error category is the second prediction error category, the acquisition optimization method is determined to be adjusting the time interval for data acquisition according to the breach deformation offset parameter or the breach deformation expansion parameter.

[0035] Furthermore, the process of determining the number of divisions of the characteristic sub-contour segments and the number of acquisition points includes:

[0036] The characteristic contour segment is divided into a plurality of characteristic sub-contour segments, wherein the number of the characteristic sub-contour segments is negatively correlated with the vector angle between the water flow trend vector and the real-time breach trend sub-vector;

[0037] The number of acquisition points in the characteristic sub-contour segment is positively correlated with the contour morphology matching coefficient;

[0038] The contour morphology matching coefficient is the variance of the contour morphology coincidence of the characteristic sub-contour segments at adjacent moments.

[0039] Furthermore, if the breach deformation offset parameter exceeds a preset breach deformation offset parameter reference value, the time interval for data acquisition is negatively correlated with the breach deformation offset parameter;

[0040] If the breach deformation expansion parameter exceeds a preset breach deformation expansion parameter reference value, the time interval for data acquisition is negatively correlated with the breach deformation expansion parameter.

[0041] Compared with the prior art, the present invention has the following advantages: in response to the occurrence of a dike breach, the present invention obtains a water flow trend vector, determines a characteristic slope based on the water flow trend vector, obtains surface contour images of the characteristic slope at several moments in a preset acquisition cycle, extracts a slope contour curve based on the surface contour image, divides the slope contour curve into several characteristic contour segments, determines a collapse trend sub-vector based on the deformation feature points of the characteristic contour segment, determines a collapse trend characterization vector of the characteristic contour segment based on the several collapse trend sub-vectors, obtains a real-time collapse trend sub-vector of each characteristic contour segment, and calculates the collapse trend characterization vector of the characteristic contour segment based on the real-time collapse trend sub-vector. The breach deformation offset parameters and breach deformation expansion parameters are determined by comparing the breach trend sub-vector with the breach trend characterization vector to determine whether there is a risk of abnormal breach contour prediction. In response to the existence of abnormal breach contour prediction risk, the prediction error category of the breach contour loss is determined, and the acquisition optimization method for model establishment is determined according to the prediction error category. Furthermore, when a levee breach occurs, the deviation in the construction of the plugging model can be quickly identified according to the actual morphology of the slope at the levee breach, and the acquisition optimization method for model establishment can be adaptively adjusted to improve the reliability of the rapid plugging model.

[0042] In particular, the present invention determines the characteristic slope based on the water flow trend vector. It can be understood that determining it as the characteristic slope can clarify the key parts of data collection. The slope pointed by the water flow trend vector is directly impacted by the upstream water flow, and its stability is very important for breach sealing. Detailed data collection in this area can improve the pertinence and efficiency of data collection. At the same time, since the characteristic slope is the part of the breach that is most significantly affected by the water flow, collecting data in this area can more directly reflect the key information of the interaction between the water flow and the slope. These data are of great significance for accurately establishing a rapid sealing model and simulating the breach evolution process. Higher relevance and value help improve the accuracy and reliability of the model. During the breach sealing process, as the water flow conditions and slope status change dynamically, the latest data of the characteristic slope can be obtained in a timely manner and fed back into the rapid sealing model to achieve dynamic updating and optimization of the model parameters, so that the model can better adapt to the actual situation and provide a more accurate basis for sealing decisions. The present invention determines the characteristic slope according to the water flow trend vector, and further, realizes the rapid identification of the key areas for the construction of the sealing model according to the actual shape of the slope at the embankment breach when a embankment breach occurs, thereby improving the reliability of the rapid sealing model.

[0043] In particular, the present invention determines the collapse tendency characterization vector of the characteristic contour segment based on several collapse tendency sub-vectors. It can be understood that by calculating the vertical distance between each point on the characteristic contour segment and the reference line segment to determine the deformation feature point, and constructing the collapse tendency sub-vector based on this, the local deformation information on the characteristic contour segment can be accurately captured. The collapse tendency sub-vector reflects the local deformation trend of the characteristic contour segment at a certain moment. By calculating the vector sum of the collapse tendency sub-vectors at several moments to obtain the collapse tendency characterization vector, the deformation information at different moments can be comprehensively considered, and the cumulative deformation trend of the characteristic contour segment over a period of time can be quantified. The present invention determines the collapse trend characterization vector of the characteristic contour segment based on several collapse trend sub-vectors, thereby realizing the rapid identification of the actual collapse trend of the embankment breach slope and improving the reliability of the rapid plugging model.

[0044] In particular, the present invention determines whether there is an abnormal risk in the prediction of the breach contour based on the breach deformation offset parameter and the breach deformation expansion parameter, and determines the prediction error category of the breach contour loss in response to the existence of the abnormal risk in the prediction of the breach contour. It can be understood that the real-time calculation of the breach deformation offset parameter and the breach deformation expansion parameter can accurately capture the difference between the real-time breach trend sub-vector of the characteristic contour segment and the breach trend characterization vector, thereby monitoring the dynamic changes of the breach. The breach deformation offset parameter and the breach deformation expansion parameter both reflect the changes in different aspects of the breach in morphology and development trend, which are helpful to timely discover the abnormal situation of the rapid plugging model. After determining that there is an abnormal risk in the prediction of the breach contour, the specific prediction error category is further determined, that is, It is distinguished whether both the breach deformation offset parameter and the breach deformation expansion parameter are abnormal, or only the breach deformation offset parameter is abnormal, or only the breach deformation expansion parameter is abnormal. By analyzing the abnormal risk and error category of the breach contour prediction, feedback information is provided to the rapid plugging model, and the model can be adjusted and optimized based on this information. The present invention determines whether there is an abnormal risk of breach contour prediction based on the breach deformation offset parameter and the breach deformation expansion parameter, and determines the prediction error category of the breach contour loss in response to the existence of the abnormal risk of breach contour prediction. Furthermore, it realizes the rapid identification of the deviation of the plugging model construction according to the actual morphology of the slope at the embankment breach, adaptively adjusts the acquisition optimization method of the model establishment, and improves the reliability of the rapid plugging model.

[0045] In particular, under the first prediction error category condition, the present invention adjusts the number of acquisition points in the characteristic sub-contour segment according to the contour morphological matching coefficient of the characteristic sub-contour segment at adjacent moments. It can be understood that the first prediction error category, that is, the breach deformation offset parameter exceeds the preset breach deformation offset reference value, and the breach deformation expansion parameter exceeds the preset breach deformation expansion parameter reference value. The characteristic contour segment is divided into several characteristic sub-contour segments, which can analyze the change of the slope in more detail. The first prediction error category characterizes that the contour change is more complex. Through subdivision, it can go deep into a smaller local range, which is helpful to obtain detailed changes. The variance of the contour morphological coincidence of the characteristic sub-contour segments at adjacent moments is calculated as the contour morphological matching coefficient to quantify the degree of morphological change of the characteristic sub-contour segment. The higher the contour morphological matching coefficient, the more The larger the variance, the more unstable the change of the contour morphology at adjacent moments, the more drastic the change in the area, and more acquisition points are needed to accurately capture these changes. Conversely, the smaller the contour morphology matching coefficient, the relatively stable the change, and the number of acquisition points can be appropriately reduced. Increasing the acquisition points in the area with drastic changes can more accurately obtain the deformation information of the area, avoid information loss due to insufficient acquisition points, and thus affect the reliability of model construction. Reducing the acquisition points in the area with stable changes can avoid resource waste, optimize the resource allocation of data acquisition, and make the acquisition work more efficient. Furthermore, it is possible to quickly identify the deviation of the plugging model construction according to the actual morphology of the slope at the embankment breach, adaptively adjust the acquisition optimization method of the model establishment, and improve the reliability of the rapid plugging model.

[0046] In particular, the present invention adjusts the time interval for data acquisition according to the breach deformation offset parameter or the breach deformation expansion parameter under the second prediction error category condition. It can be understood that the second prediction error category, namely, the breach deformation offset parameter exceeds the preset breach deformation offset reference value, or the breach deformation expansion parameter exceeds the preset breach deformation expansion parameter reference value. When a single parameter exceeds the preset value, by adjusting the time interval for data acquisition, data can be collected more frequently, thereby capturing the details and trends of breach changes in a timely manner. When the breach deformation offset parameter exceeds the preset breach deformation offset reference value, it indicates that the shape or direction of the breach has changed significantly, shortening the data acquisition time interval. The time interval can collect more data and improve the reliability of model construction. When the breach deformation expansion parameter exceeds the preset breach deformation expansion parameter reference value, the change in the breach exceeds the prediction range. Increasing the data collection frequency can allow the relevant model to better fit the breach change curve and more accurately predict the future development of the breach. Under the second prediction error category condition, the present invention adjusts the time interval for data acquisition according to the breach deformation offset parameter or the breach deformation expansion parameter, thereby realizing the rapid identification of the deviation in the construction of the plugging model according to the actual morphology of the slope at the embankment breach, adaptively adjusting the collection optimization method for model establishment, and improving the reliability of the rapid plugging model. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A diagram showing the steps of a method for establishing a rapid plugging model for a dike breach according to an embodiment of the present invention;

[0048] Figure 2 A logic flow chart for determining whether there is an abnormal risk in breach contour prediction according to an embodiment of the present invention;

[0049] Figure 3 A logic flow chart for determining the prediction error category of breach contour loss according to an embodiment of the present invention;

[0050] Figure 4 A logical flow chart for determining an acquisition optimization method for model establishment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0052] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0053] It should be noted that, in the description of the present invention, terms such as "upper", "lower", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0054] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted" and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0055] See also Figure 1 , which is a step diagram of a method for establishing a rapid plugging model for a dike breach according to an embodiment of the present invention. The method for establishing a rapid plugging model for a dike breach according to the present invention includes:

[0056] Step S100, obtaining a water flow trend vector of a dike breach, and determining a characteristic slope based on the water flow trend vector;

[0057] Specifically, the present invention does not impose any specific limitation on the method of obtaining the water flow direction vector. Preferably, it can be obtained through a flow meter. The flow meter is used to obtain multiple water flow velocities, and these velocity data are vector-synthesized to obtain the water flow direction vector. It will not be repeated here.

[0058] Step S200, acquiring surface contour images of the characteristic slope at several moments in a preset acquisition cycle, extracting a slope contour curve based on the surface contour images, and dividing the slope contour curve into several characteristic contour segments;

[0059] Specifically, the preset acquisition period can be set by those skilled in the art according to the accuracy requirements for constructing a rapid plugging model. The higher the accuracy requirement, the longer the preset acquisition period. The value range of the preset acquisition period can be [2, 8], and the interval unit is min. Preferably, the preset acquisition period can be 3 minutes, and the interval between several moments in the preset acquisition period can be 30 seconds. Surface contour images at 6 moments can be obtained in one preset acquisition period.

[0060] Specifically, the present invention does not impose any specific limitation on the method of obtaining characteristic contour segments. Preferably, a high-resolution camera can be mounted on an unmanned aerial vehicle to obtain a surface contour image, and an edge detection algorithm can be used to extract the slope contour curve, which is divided into several characteristic contour segments. This will not be repeated here.

[0061] Step S300, determining a collapse tendency sub-vector based on the deformation characteristic points of the characteristic contour segment, and determining a collapse tendency characterization vector of the characteristic contour segment based on a plurality of collapse tendency sub-vectors;

[0062] Step S400: obtaining a real-time breach trend sub-vector of each characteristic contour segment, and determining breach deformation offset parameters and breach deformation expansion parameters based on a comparison between the real-time breach trend sub-vector and the breach trend characterization vector, so as to determine whether there is a breach contour prediction abnormality risk and determine the prediction error category of the breach contour loss;

[0063] Step S500, determining an acquisition optimization method for model establishment according to the prediction error category;

[0064] The acquisition optimization method includes adjusting the number of acquisition points in the characteristic sub-contour segment according to the contour morphology matching coefficient of the characteristic sub-contour segment at adjacent moments, or adjusting the time interval of data acquisition according to the breach deformation offset parameter or the breach deformation expansion parameter;

[0065] The number of divisions of the characteristic sub-contour segment is determined according to the water flow trend vector and the real-time collapse trend sub-vector of the characteristic contour segment.

[0066] Specifically, the slope to which the water flow direction vector points is determined as the characteristic slope;

[0067] The direction of the water flow vector is the direction of the water flow upstream of the dike breach.

[0068] Specifically, the present invention determines the characteristic slope according to the water flow trend vector. It can be understood that the determination of the characteristic slope can clarify the key parts of data collection. The slope pointed by the water flow trend vector is directly impacted by the upstream water flow, and its stability is crucial to the breach sealing. Detailed data collection in this area can improve the pertinence and efficiency of data collection. At the same time, since the characteristic slope is the part of the breach that is most significantly affected by the water flow, collecting data in this area can more directly reflect the key information of the interaction between the water flow and the slope. These data are of great significance for accurately establishing a rapid sealing model and simulating the breach evolution process. It has higher relevance and value, which helps to improve the accuracy and reliability of the model. During the breach sealing process, as the water flow conditions and slope status change dynamically, the latest data of the characteristic slope can be obtained in time and fed back to the rapid sealing model to achieve dynamic updating and optimization of the model parameters, so that the model can better adapt to the actual situation and provide a more accurate basis for sealing decisions. The present invention determines the characteristic slope according to the water flow trend vector, and further, when a levee breach occurs, it can quickly identify the key areas for constructing the sealing model according to the actual shape of the slope at the levee breach, thereby improving the reliability of the rapid sealing model.

[0069] Specifically, it is understandable that the slope to which the flow trend vector points directly bears the impact and scouring force of the upstream water flow. The water flow in this area has a high flow velocity and concentrated energy, which will aggravate the scouring of the slope foot and soil erosion. At the same time, the high-speed water flow will also form a large dynamic water pressure and shear force on the slope surface, resulting in a decrease in the shear strength of the soil and an increase in the risk of landslides and collapses. The mud and other materials carried by the water flow will also have an abrasive effect on the slope, further destroying the slope structure. Therefore, under the joint action of multiple factors, the shape of the slope is more likely to change, and this slope is screened as a characteristic slope. Furthermore, when a levee breach occurs, the key area for constructing the blocking model can be quickly identified according to the actual shape of the slope at the levee breach, thereby improving the reliability of the rapid blocking model.

[0070] Specifically, the process of determining the collapse tendency subvector includes:

[0071] Calculating the vertical distances between a plurality of points on the characteristic contour segment and the reference line segment, and determining the point with the maximum vertical distance as the deformation characteristic point;

[0072] The collapse tendency sub-vector is constructed with the midpoint of the reference line segment as the vector starting point and the deformation feature point as the vector end point;

[0073] The characteristic contour segment is divided along the width of the embankment, and the reference line segment is a line connecting two end points of the characteristic contour segment.

[0074] Specifically, when calculating the vertical distances from several points on the characteristic contour segment to the reference line segment, several points are selected along the characteristic contour segment at a preset fixed interval. The preset fixed interval is the product of the length value of the characteristic contour segment and the fixed interval factor. The fixed interval factor can be set by those skilled in the art according to the accuracy requirements for constructing a rapid plugging model. The higher the accuracy requirement, the smaller the fixed interval factor. The value range of the fixed interval factor can be [0.01, 0.03]. Preferably, the fixed interval factor can be 0.02.

[0075] Specifically, the number of divisions of the characteristic contour segments is the product of the embankment width value and the division factor. The division factor can be set by technical personnel in this field according to the accuracy requirements of constructing a rapid plugging model. The higher the accuracy requirement, the smaller the division factor. The value range of the division factor can be [0.1, 0.3]. Preferably, the division factor can be 0.2.

[0076] Specifically, determining the collapse tendency characterization vector includes:

[0077] The collapse tendency sub-vectors of a single characteristic contour segment at several moments are obtained within the preset acquisition cycle, and a vector obtained by adding the collapse tendency sub-vectors at several moments is determined as the collapse tendency characterization vector of the characteristic contour segment.

[0078] Specifically, the preset acquisition period can be set by those skilled in the art according to the accuracy requirements for constructing a rapid plugging model. The higher the accuracy requirement, the longer the preset acquisition period. The value range of the preset acquisition period can be [2, 8], and the interval unit is min. Preferably, the preset acquisition period can be 3 minutes, and the interval between several moments in the preset acquisition period can be 30 seconds. Surface contour images at 6 moments can be obtained in one preset acquisition period.

[0079] Specifically, the present invention determines the collapse tendency characterization vector of the characteristic contour segment based on several collapse tendency sub-vectors. It can be understood that by calculating the vertical distance between each point on the characteristic contour segment and the reference line segment to determine the deformation feature point, and constructing the collapse tendency sub-vector based on this, the local deformation information on the characteristic contour segment can be accurately captured. The collapse tendency sub-vector reflects the local deformation trend of the characteristic contour segment at a certain moment. By calculating the vector sum of the collapse tendency sub-vectors at several moments to obtain the collapse tendency characterization vector, the deformation information at different moments can be comprehensively considered, and the cumulative deformation trend of the characteristic contour segment over a period of time can be analyzed. Quantification and direction characterization, when constructing a rapid plugging model, can more accurately simulate the development process of the breach, providing a more reliable basis for formulating a reasonable plugging strategy. At the same time, the collapse trend characterization vector integrates the collapse trend sub-vectors of multiple moments, and samples and integrates the deformation information of the characteristic slope multiple times, which can more accurately reflect the actual collapse trend of the slope and reduce the error and uncertainty of the data at a single moment. The present invention determines the collapse trend characterization vector of the characteristic contour segment based on several collapse trend sub-vectors, thereby realizing the rapid identification of the actual collapse trend of the embankment breach slope and improving the reliability of the rapid plugging model.

[0080] Specifically, it can be understood that when constructing a rapid plugging model, using this integrated and optimized breach trend characterization vector as an input parameter can enable the model to better fit the actual situation, improve the model's prediction accuracy for breach development, and thus enhance the model's reliability in guiding plugging operations.

[0081] Specifically, determining the breach deformation offset parameter and the breach deformation expansion parameter includes:

[0082] Acquire a real-time collapse tendency subvector and a collapse tendency characterization vector of the characteristic contour segment, and determine the vector angle between the real-time collapse tendency subvector and the collapse tendency characterization vector as the collapse deformation offset parameter;

[0083] The vector modulus difference between the real-time collapse tendency sub-vector and the collapse tendency characterization vector is determined as the breach deformation expansion parameter.

[0084] Specifically, the real-time collapse trend sub-vector is the collapse trend sub-vector of the characteristic contour segment obtained at the current moment.

[0085] Specifically, the vector modulus difference is the difference between the modulus of the real-time collapse tendency sub-vector and the modulus of the collapse tendency characterization vector.

[0086] See also Figure 2 As shown, it is a logic flow chart of determining whether there is an abnormal risk of rupture contour prediction according to an embodiment of the present invention. The process of determining whether there is an abnormal risk of rupture contour prediction includes:

[0087] If the breach deformation offset parameter and breach deformation expansion parameter of the characteristic contour segment meet the abnormality judgment conditions, it is determined that there is an abnormal risk in the breach contour prediction;

[0088] If the breach deformation offset parameter and breach deformation expansion parameter of the characteristic contour segment do not meet the abnormality judgment conditions, it is determined that there is no abnormal risk in the breach contour prediction;

[0089] The abnormality determination condition is that the breach deformation offset parameter exceeds a preset breach deformation offset reference value, or the breach deformation expansion parameter exceeds a preset breach deformation expansion parameter reference value.

[0090] Specifically, the preset breach deformation offset reference value is the product of the breach deformation offset average value and the offset factor. The breach deformation offset average value is the average value of several historical data under the same environment. The offset factor can be set by technical personnel in this field according to the accuracy requirements of constructing a rapid plugging model. The higher the accuracy requirement, the smaller the offset factor. The value range of the offset factor can be [0.3, 0.5]. Preferably, the offset factor can be 0.4.

[0091] Specifically, the preset reference value of the breach deformation expansion parameter is the product of the average value of the breach deformation expansion parameter and the deformation factor. The average value of the breach deformation expansion parameter is the average value of several historical data under the same environment. The deformation factor can be set by technical personnel in this field according to the accuracy requirements of constructing a rapid plugging model. The higher the accuracy requirement, the smaller the offset factor and the smaller the deformation factor. The value range of the deformation factor can be [0.3, 0.5]. Preferably, the deformation factor can be 0.4.

[0092] Specifically, the present invention determines whether there is an abnormal risk in the prediction of the breach contour based on the breach deformation offset parameter and the breach deformation expansion parameter, and determines the prediction error category of the breach contour loss in response to the existence of the abnormal risk in the prediction of the breach contour. It can be understood that the real-time calculation of the breach deformation offset parameter and the breach deformation expansion parameter can accurately capture the difference between the real-time breach trend sub-vector of the characteristic contour segment and the breach trend characterization vector, thereby monitoring the dynamic changes of the breach. The breach deformation offset parameter and the breach deformation expansion parameter both reflect the changes in different aspects of the breach in morphology and development trend, which are helpful to timely discover the abnormal situation of the rapid plugging model. After determining that there is an abnormal risk in the prediction of the breach contour, the specific prediction error category is further determined. That is, it is distinguished whether both the breach deformation offset parameter and the breach deformation expansion parameter are abnormal, or only the breach deformation offset parameter is abnormal, or only the breach deformation expansion parameter is abnormal. By analyzing the abnormal risk and error category of the breach contour prediction, feedback information is provided to the rapid plugging model, and the model can be adjusted and optimized based on this information. The present invention determines whether there is an abnormal risk of breach contour prediction based on the breach deformation offset parameter and the breach deformation expansion parameter, and determines the prediction error category of the breach contour loss in response to the existence of the abnormal risk of breach contour prediction. Furthermore, it realizes the rapid identification of the deviation of the plugging model construction according to the actual morphology of the slope at the embankment breach, adaptively adjusts the acquisition optimization method of the model establishment, and improves the reliability of the rapid plugging model.

[0093] See also Figure 3 As shown, it is a logic flow chart for determining the prediction error category of the breach contour loss according to an embodiment of the present invention. The process of determining the prediction error category of the breach contour loss includes:

[0094] If the rupture deformation offset parameter and the rupture deformation expansion parameter of the characteristic contour segment meet the first prediction error category judgment condition, then the prediction error category of the rupture contour loss is determined to be the first prediction error category;

[0095] If the rupture deformation offset parameter and the rupture deformation expansion parameter of the characteristic contour segment do not meet the first prediction error category judgment condition, the prediction error category of the rupture contour loss is determined to be the second prediction error category;

[0096] The first prediction error category determination condition is that the breach deformation offset parameter exceeds a preset breach deformation offset reference value, and the breach deformation expansion parameter exceeds a preset breach deformation expansion parameter reference value.

[0097] See also Figure 4 As shown, it is a logical flow chart of determining the acquisition optimization method for model establishment according to an embodiment of the present invention. The acquisition optimization method for determining the model establishment includes:

[0098] If the prediction error category is the first prediction error category, determining that the acquisition optimization method is to adjust the number of acquisition points in the characteristic sub-contour segment according to the contour morphology matching coefficient of the characteristic sub-contour segment at adjacent moments;

[0099] If the prediction error category is the second prediction error category, the acquisition optimization method is determined to be adjusting the time interval for data acquisition according to the breach deformation offset parameter or the breach deformation expansion parameter.

[0100] Specifically, the process of determining the number of divisions of the characteristic sub-contour segments and the number of acquisition points includes:

[0101] The characteristic contour segment is divided into a plurality of characteristic sub-contour segments, wherein the number of the characteristic sub-contour segments is negatively correlated with the vector angle between the water flow trend vector and the real-time breach trend sub-vector;

[0102] The number of acquisition points in the characteristic sub-contour segment is positively correlated with the contour morphology matching coefficient;

[0103] The contour morphology matching coefficient is the variance of the contour morphology coincidence of the characteristic sub-contour segments at adjacent moments.

[0104] Specifically, the preset acquisition period can be set by those skilled in the art according to the accuracy requirements for constructing a rapid plugging model. The higher the accuracy requirement, the longer the preset acquisition period. The value range of the preset acquisition period can be [2, 8], and the interval unit is min. Preferably, the preset acquisition period can be 3 minutes, and the interval between several moments in the preset acquisition period can be 30 seconds. Surface contour images at 6 moments can be obtained in one preset acquisition period.

[0105] Specifically, the degree of coincidence of contour morphology can be obtained based on pixel matching. By comparing image pixels, the number of pixels in the overlapping area is counted and divided by the number of pixels in the total area to obtain the degree of coincidence of contour morphology. This will not be described in detail here.

[0106] Specifically, under the first prediction error category condition, the present invention adjusts the number of acquisition points in the characteristic sub-contour segment according to the contour morphological matching coefficient of the characteristic sub-contour segment at adjacent moments. It can be understood that the first prediction error category, that is, the breach deformation offset parameter exceeds the preset breach deformation offset reference value, and the breach deformation expansion parameter exceeds the preset breach deformation expansion parameter reference value. The characteristic contour segment is divided into several characteristic sub-contour segments, which can analyze the changes of the slope in more detail. The first prediction error category characterizes that the contour changes are more complex. Through subdivision, it can go deep into a smaller local range, which is helpful to obtain detailed changes. The variance of the contour morphological coincidence of the characteristic sub-contour segments at adjacent moments is calculated as the contour morphological matching coefficient, so as to quantify the degree of morphological change of the characteristic sub-contour segment. The contour morphological matching coefficient The larger the coefficient is, the greater the variance is, the more unstable the changes in the contour morphology at adjacent moments are, the more drastic the changes in the area are, and more acquisition points are needed to accurately capture these changes. Conversely, the smaller the contour morphology matching coefficient is, the more stable the changes are, and the number of acquisition points can be appropriately reduced. Increasing the acquisition points in the area with drastic changes can more accurately obtain the deformation information of the area, avoid information loss due to insufficient acquisition points, and thus affect the reliability of model construction. Reducing the acquisition points in the area with stable changes can avoid resource waste, optimize the resource allocation of data acquisition, and make the acquisition work more efficient. Furthermore, it is possible to quickly identify the deviation of the plugging model construction according to the actual morphology of the slope at the embankment breach, adaptively adjust the acquisition optimization method of the model establishment, and improve the reliability of the rapid plugging model.

[0107] Specifically, the number of divisions of the characteristic sub-contour segment is determined based on the water flow trend vector and the real-time breach trend sub-vector of the characteristic contour segment. It can be understood that the number of divisions of the characteristic sub-contour segment is negatively correlated with the vector angle between the water flow trend vector and the real-time breach trend sub-vector. Water flow is an important factor affecting the development of slope breaches. The size of the angle reflects the degree of influence of water flow on the current breach trend. When the angle is small, it means that the driving effect of water flow on breach development is more concentrated, and the local area of the slope is more affected. At this time, increasing the number of divisions of the characteristic sub-contour segment can more finely monitor the areas greatly affected by water flow. On the contrary, when the angle is large, the effect of water flow is relatively dispersed, and the number of divisions can be appropriately reduced. This division method can adaptively adjust the monitoring accuracy according to the water flow conditions, so that the division is more in line with the actual breach development situation. Furthermore, it realizes the rapid identification of the deviation of the construction of the blocking model according to the actual morphology of the slope at the embankment breach, adaptively adjusts the acquisition optimization method of the model establishment, and improves the reliability of the rapid blocking model.

[0108] Specifically, if the breach deformation offset parameter exceeds a preset breach deformation offset parameter reference value, the time interval for data acquisition is negatively correlated with the breach deformation offset parameter;

[0109] If the breach deformation expansion parameter exceeds a preset breach deformation expansion parameter reference value, the time interval for data acquisition is negatively correlated with the breach deformation expansion parameter.

[0110] Specifically, the present invention adjusts the time interval for data acquisition according to the breach deformation offset parameter or the breach deformation expansion parameter under the second prediction error category condition. It can be understood that the second prediction error category, namely, the breach deformation offset parameter exceeds the preset breach deformation offset reference value, or the breach deformation expansion parameter exceeds the preset breach deformation expansion parameter reference value. When a single parameter exceeds the preset value, by adjusting the time interval for data acquisition, data can be collected more frequently, thereby capturing the details and trends of breach changes in a timely manner. When the breach deformation offset parameter exceeds the preset breach deformation offset reference value, it indicates that the shape or direction of the breach has changed significantly, shortening the data acquisition time interval. Taking the time interval can collect more data and improve the reliability of model construction. When the breach deformation expansion parameter exceeds the preset breach deformation expansion parameter reference value, the change in the breach exceeds the prediction range. Increasing the data collection frequency can allow the relevant model to better fit the breach change curve and more accurately predict the future development of the breach. Under the second prediction error category condition, the present invention adjusts the time interval for data acquisition according to the breach deformation offset parameter or the breach deformation expansion parameter, thereby realizing the rapid identification of the deviation in the construction of the plugging model according to the actual shape of the slope at the embankment breach, adaptively adjusting the collection optimization method for model establishment, and improving the reliability of the rapid plugging model.

[0111] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0112] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for establishing a rapid plugging model for dike breaches, characterized in that: include: Obtaining a water flow trend vector of a dike breach, and determining a characteristic slope based on the water flow trend vector; Acquire surface contour images of the characteristic slope at several moments in a preset acquisition cycle, extract slope contour curves based on the surface contour images, and divide the slope contour curves into several characteristic contour segments; Determine a collapse tendency sub-vector based on the deformation feature points of the characteristic contour segment, and determine a collapse tendency characterization vector of the characteristic contour segment based on a plurality of collapse tendency sub-vectors; Obtaining a real-time breach trend subvector for each characteristic contour segment, and determining breach deformation offset parameters and breach deformation expansion parameters based on a comparison between the real-time breach trend subvector and the breach trend characterization vector, to determine whether there is a breach contour prediction anomaly risk and to determine the prediction error category of the breach contour loss; Determining an acquisition optimization method for model establishment based on the prediction error category, the acquisition optimization method includes adjusting the number of acquisition points within the characteristic sub-contour segment based on the contour morphology matching coefficient of the characteristic sub-contour segment at adjacent moments, or adjusting the time interval for data acquisition based on the breach deformation offset parameter or the breach deformation expansion parameter; The number of divisions of the characteristic sub-contour segment is determined according to the water flow trend vector and the real-time collapse trend sub-vector of the characteristic contour segment.

2. The method for establishing a rapid plugging model for a dike breach according to claim 1, characterized in that: Determining the slope to which the water flow direction vector points as the characteristic slope; The direction of the water flow vector is the direction of the water flow upstream of the dike breach.

3. The method for establishing a rapid plugging model for a dike breach according to claim 2, characterized in that: The process of determining the subvector of the outage tendency includes: Calculating the vertical distances between a plurality of points on the characteristic contour segment and the reference line segment, and determining the point with the maximum vertical distance as the deformation characteristic point; The collapse tendency sub-vector is constructed with the midpoint of the reference line segment as the vector starting point and the deformation feature point as the vector end point; The characteristic contour segment is divided along the width of the embankment, and the reference line segment is a line connecting two end points of the characteristic contour segment.

4. The method for establishing a rapid plugging model for a dike breach according to claim 3, characterized in that: Determining the characterization vector of the collapse tendency includes: The collapse tendency sub-vectors of a single characteristic contour segment at several moments are obtained within the preset acquisition cycle, and a vector obtained by adding the collapse tendency sub-vectors at several moments is determined as the collapse tendency characterization vector of the characteristic contour segment.

5. The method for establishing a rapid plugging model for a dike breach according to claim 4, characterized in that: Determining the breach deformation offset parameter and the breach deformation expansion parameter includes: Acquire a real-time collapse tendency subvector and a collapse tendency characterization vector of the characteristic contour segment, and determine the vector angle between the real-time collapse tendency subvector and the collapse tendency characterization vector as the collapse deformation offset parameter; The vector modulus difference between the real-time collapse tendency sub-vector and the collapse tendency characterization vector is determined as the breach deformation expansion parameter.

6. The method for establishing a rapid plugging model for a dike breach according to claim 5, characterized in that: The process of determining whether there is a risk of abnormal breach profile prediction includes: If the breach deformation offset parameter and breach deformation expansion parameter of the characteristic contour segment meet the abnormality judgment conditions, it is determined that there is an abnormal risk in the breach contour prediction; The abnormality determination condition is that the breach deformation offset parameter exceeds a preset breach deformation offset reference value, or the breach deformation expansion parameter exceeds a preset breach deformation expansion parameter reference value.

7. The method for establishing a rapid plugging model for a dike breach according to claim 6, characterized in that: The process of determining the prediction error category of breach profile loss includes, If the rupture deformation offset parameter and the rupture deformation expansion parameter of the characteristic contour segment meet the first prediction error category judgment condition, then the prediction error category of the rupture contour loss is determined to be the first prediction error category; If the rupture deformation offset parameter and the rupture deformation expansion parameter of the characteristic contour segment do not meet the first prediction error category judgment condition, the prediction error category of the rupture contour loss is determined to be the second prediction error category; The first prediction error category determination condition is that the breach deformation offset parameter exceeds a preset breach deformation offset reference value, and the breach deformation expansion parameter exceeds a preset breach deformation expansion parameter reference value.

8. The method for establishing a rapid plugging model for a dike breach according to claim 7, characterized in that: Determine the acquisition optimization methods for model building, including: If the prediction error category is the first prediction error category, determining that the acquisition optimization method is to adjust the number of acquisition points in the characteristic sub-contour segment according to the contour morphology matching coefficient of the characteristic sub-contour segment at adjacent moments; If the prediction error category is the second prediction error category, the acquisition optimization method is determined to be adjusting the time interval for data acquisition according to the breach deformation offset parameter or the breach deformation expansion parameter.

9. The method for establishing a rapid plugging model for a dike breach according to claim 8, characterized in that: The process of determining the number of divisions of the characteristic sub-contour segments and the number of acquisition points includes: The characteristic contour segment is divided into a plurality of characteristic sub-contour segments, wherein the number of the characteristic sub-contour segments is negatively correlated with the vector angle between the water flow trend vector and the real-time breach trend sub-vector; The number of acquisition points in the characteristic sub-contour segment is positively correlated with the contour morphology matching coefficient; The contour morphology matching coefficient is the variance of the contour morphology coincidence of the characteristic sub-contour segments at adjacent moments.

10. The method for establishing a rapid plugging model for a dike breach according to claim 8, characterized in that: If the breach deformation offset parameter exceeds a preset breach deformation offset parameter reference value, the time interval for data acquisition is negatively correlated with the breach deformation offset parameter; If the breach deformation expansion parameter exceeds a preset breach deformation expansion parameter reference value, the time interval for data acquisition is negatively correlated with the breach deformation expansion parameter.

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