A monitoring method, system and medium for leakage of reservoir dams

Abstract

The present invention relates to the technical field of dam safety, and specifically provides a method, system and medium for monitoring the leakage of a reservoir dam. First, remote sensing images of a gravity dam are processed to obtain joint intersection points; the leakage risk weights are calculated and compared with a preset threshold to obtain leakage data collection points; then, real-time leakage anomaly indicators are calculated based on the leakage data at the collection points, and combined with the real-time leakage anomaly indicators at multiple time nodes to obtain a comprehensive leakage anomaly indicator, and further obtain a leakage anomaly indicator function; if the function value is greater than the preset leakage anomaly indicator threshold, the coordinate points corresponding to the function value are divided into leakage risk areas; auxiliary data is obtained and it is determined whether the leakage risk area is a leakage area, and if it is the leakage area, a warning is given; finally, a visualization platform is established to display the data related to the leakage problem of the gravity dam. By designing the leakage risk weight and the comprehensive leakage anomaly indicator, the present invention improves the real-time monitoring efficiency of the leakage problem of the gravity dam.

Description

Technical Field

[0001] The present invention relates to the technical field of dam safety, and particularly to a method, a system and a medium for monitoring the leakage of a reservoir dam. Background Technique

[0002] A reservoir is an artificially constructed water storage facility that stores and regulates water flow for purposes such as water supply, irrigation, flood control, power generation, or ecological environment protection. The main structures of a reservoir include a dam, an intake, a flood discharge outlet, and a water conveyance facility. Among them, the dam is an important part of the reservoir, which is used to block and store water flow. The main types of dams include gravity dams, earth dams, rockfill dams, and arch dams, etc.

[0003] Gravity dams are usually built with concrete or masonry, and resist water pressure by their own weight. They are suitable for relatively wide river valley terrains. Gravity dams have a strong structure, high stability, and good impermeability, and are suitable for reservoirs with relatively high water levels. Although the structure of gravity dams is relatively strong, the joints where the dams are built are relatively weak areas. During long-term use, if anti-seepage treatment is not properly carried out, leakage often occurs, causing serious safety problems.

[0004] In order to avoid potential safety hazards and ensure the safety of the reservoir and the surrounding areas, it is necessary to enhance the supervision and early warning capabilities for the leakage safety problems of gravity dams, and improve the supervision efficiency for the leakage safety problems at the joints of gravity dams, especially at the intersection points of the joints.

[0005] Therefore, a method, a system and a medium for monitoring the leakage of a reservoir dam are proposed. Summary of the Invention

[0006] The purpose of the present invention is to provide a method, a system and a medium for monitoring the leakage of a reservoir dam. First, by identifying the intersection points of the joints on the gravity dam and calculating the leakage risk weights, the leakage data collection points are selected to improve the supervision efficiency for the leakage problems of the gravity dam; then, the comprehensive leakage anomaly index of the leakage data collection points is calculated, and the leakage anomaly index function is fitted to further divide the areas where leakage problems may exist, and combined with auxiliary data to determine whether there is a leakage problem, improving the accuracy rate of identifying leakage problems; finally, through the real-time monitoring of the early warning and visualization platform, the supervision efficiency for the leakage problems of the gravity dam, especially the leakage safety problems at the intersection points of the joints, is improved.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] A method for monitoring the leakage of a reservoir dam, including:

[0009] Obtain remote sensing image data of the gravity dam and process it to obtain the seam intersection points; obtain the historical data of the seam intersection points and process it to obtain the leakage risk weight; if the leakage risk weight is greater than the preset leakage risk threshold, then the seam intersection point is a leakage data collection point;

[0010] Further, the seam intersection points include:

[0011] Obtain the remote sensing image of the gravity dam and process it to obtain the grayscale image of the gravity dam; obtain the grayscale image of the gravity dam and perform image enhancement to obtain the enhanced grayscale image of the gravity dam; obtain the enhanced grayscale image of the gravity dam and identify the seam intersection points according to the edge detection algorithm.

[0012] Further, the leakage data collection points include:

[0013] Obtain the historical data of the seam intersection points and calculate the leakage risk weight of the seam intersection points; the historical data includes the position weight, seam width, historical leakage risk value and deformation degree of the seam intersection points;

[0014] The calculation formula of the leakage risk weight is:

[0015] ;

[0016] Wherein, represents the leakage risk weight of the seam intersection point, represents the position weight of the seam intersection point of the weight coefficient, represents the seam width change rate of the seam intersection point of the weight coefficient, represents the historical leakage risk value of the seam intersection point of the weight coefficient, represents the weight coefficient of the deformation degree of the seam intersection point, represents time nodes, represents the grayscale image of the seam intersection point, represents the th collection of the grayscale image on the pixel value of the pixel at the place, represents the number of pixel points of the grayscale image;

[0017] If the leakage risk weight of the seam intersection point is greater than the preset leakage risk threshold, then mark the seam intersection point as the leakage data collection point;

[0018] Obtain all the seam intersection points and process them to obtain a set of leakage data collection points.

[0019] Obtain the leakage data of the leakage data acquisition point and process it to obtain the real-time leakage anomaly index; obtain the real-time leakage anomaly index at the real-time leakage anomaly index and The real-time leakage anomaly indexes at historical time nodes are processed to obtain the comprehensive leakage anomaly index; obtain the comprehensive leakage anomaly indexes of all the leakage data acquisition points and perform function fitting to obtain the leakage anomaly index function;

[0020] Further, the real-time leakage anomaly index includes:

[0021] Obtain the leakage data of the leakage data acquisition point, where the leakage data includes the temperature, humidity, water quality data, pressure, and water flow rate of the leakage data acquisition point, and the water quality data includes conductivity and water quality turbidity;

[0022] Obtain the leakage data and perform weighted calculation to obtain the real-time leakage anomaly index of the leakage data acquisition point.

[0023] Further, the calculation formula of the comprehensive leakage anomaly index is:

[0024] ;

[0025] where, represents the comprehensive leakage anomaly index, represents historical time nodes, represents the real-time leakage anomaly index collected at the historical time node, is the weight coefficient of .

[0026] Further, the leakage anomaly index function includes:

[0027] Obtain the positions of all the leakage data acquisition points and establish a coordinate system;

[0028] Obtain the comprehensive leakage anomaly index and perform cubic spline interpolation fitting to obtain the leakage anomaly index function; if the function value of the leakage anomaly index function is greater than the preset leakage anomaly index threshold, it is classified as a leakage risk area.

[0029] Obtain the leakage anomaly index function. If the function value is greater than the preset leakage anomaly index threshold, the coordinate point corresponding to the function value is classified as a leakage risk area;

[0030] Obtain auxiliary data, and judge whether the leakage risk area is a leakage area according to the auxiliary data. If it is the leakage area, perform leakage warning;

[0031] Further, determining whether the leakage risk area is a leakage area according to the auxiliary data includes:

[0032] The auxiliary data includes water level change data and maintenance data;

[0033] Obtain the water level change data and process it to obtain the water level change amount. If the water level change amount is greater than the preset water level change threshold, then the leakage risk area is a leakage area;

[0034] Obtain the maintenance data of the leakage risk area and process it to obtain the historical maintenance index. If the historical maintenance index is greater than the preset maintenance index threshold, then the leakage risk area is a leakage area.

[0035] Further, the leakage warning further includes:

[0036] When warning, display the location of the leakage area, and set a response duration according to the distance between the leakage area and the monitoring center. If the leakage warning fails to be processed within the response duration, then issue a secondary warning.

[0037] Establish a visualization platform to display the comprehensive leakage anomaly index and the leakage area of all the leakage data collection points; the visualization platform receives the real-time touch information of the user and records the interval time. If the interval time is greater than the preset interval time threshold, then issue an unattended monitoring warning.

[0038] The present invention also provides a monitoring system for reservoir dam leakage, including:

[0039] A collection point selection module, which obtains the remote sensing image data of the gravity dam and processes it to obtain the joint intersection points; obtains the historical data of the joint intersection points and processes it to obtain the leakage risk weight. If the leakage risk weight is greater than the preset leakage risk threshold, then the joint intersection points are leakage data collection points;

[0040] A leakage data analysis module, which obtains the leakage data of the leakage data collection points and processes it to obtain the real-time leakage anomaly index; obtains the real-time leakage anomaly index at the real-time leakage anomaly index of historical time nodes and processes it to obtain the comprehensive leakage anomaly index; obtains the comprehensive leakage anomaly index of all the leakage data collection points and performs function fitting to obtain the leakage anomaly index function;

[0041] A leakage judgment module, which obtains the leakage anomaly index function. If the function value is greater than the preset leakage anomaly index threshold, then divides the coordinate points corresponding to the function value into leakage risk areas;

[0042] An auxiliary judgment module obtains auxiliary data and judges whether the leakage risk area is a leakage area according to the auxiliary data, and if it is the leakage area, a leakage warning is issued;

[0043] The leakage monitoring module establishes a visualization platform to display the comprehensive leakage anomaly indicators and the leakage area of ​​all the leakage data collection points; the visualization platform receives the user's real-time touch information and records the interval time. If the interval time is greater than the preset interval time threshold, an unmanned monitoring warning is issued.

[0044] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, any step in a method for monitoring leakage of a reservoir dam is implemented.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. First, the seam width and seam intersection of the seam area are accurately identified through image enhancement and edge detection algorithms. Then, the leakage risk weight of the seam intersection is comprehensively calculated by combining the position weight, the deformation degree of the seam intersection and the historical leakage risk value. It is then compared with the preset leakage risk threshold to accurately identify the leakage data collection point, which is conducive to the targeted deployment of sensors and data collection in places where leakage may exist, thereby improving the supervision accuracy of leakage problems.

[0047] 2. By combining the humidity, temperature, water quality, deformation and other data of the leakage data collection point, the real-time leakage anomaly index is comprehensively calculated, which can timely reflect the latest leakage changes of the leakage data collection point; by giving higher weights to newer data through attenuation weighting method, and considering historical measurement data at the same time, the long-term change trend of the leakage problem can be captured, and the errors that may be caused by short-term abnormal fluctuations in real-time data can be reduced.

[0048] 3. By fitting the leakage anomaly indicator function and performing segmentation, areas where leakage problems may exist can be accurately identified; external factors can be introduced to more comprehensively analyze leakage risks, assist in judging leakage problems, and avoid misjudgment; multiple warnings can be issued for areas where leakage problems may exist, strengthen risk warnings, and effectively prevent problems from being ignored or delayed; the visualization platform displays monitoring data in real time, and combines touch information recording with unmanned warning functions to achieve intelligent and interactive monitoring, ensure the continuity and effectiveness of monitoring, reduce the risks brought by human errors and monitoring interruptions, and improve supervision efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flow chart of a method for monitoring reservoir dam leakage provided in Embodiment 1 of the present invention;

[0050] Figure 2It is the flowchart for calculating the leakage risk weight in Embodiment 1 of the present invention;

[0051] Figure 3 It is the structural schematic diagram of a monitoring system for reservoir dam leakage provided in Embodiment 2 of the present invention. Specific embodiments

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] Embodiment 1

[0054] When a certain water conservancy engineering team monitors a gravity dam, it introduces a monitoring method for reservoir dam leakage provided by the present invention to improve the monitoring efficiency of the gravity dam and prevent and identify potential leakage problems. The process is as Figure 1 shown, and the specific implementation is as follows:

[0055] First, obtain and process the remote sensing image data of the gravity dam to obtain the joint intersection points; obtain and process the historical data of the joint intersection points to obtain the leakage risk weight. If the leakage risk weight is greater than the preset leakage risk threshold, the joint intersection point is a leakage data collection point;

[0056] Further, the joint intersection points include:

[0057] Obtain the remote sensing image of the gravity dam and process it to obtain the grayscale image of the gravity dam; obtain the grayscale image of the gravity dam and perform image enhancement to obtain the enhanced grayscale image of the gravity dam; obtain the enhanced grayscale image of the gravity dam and identify the joint intersection points and the joint widths of the joint intersection points according to the edge detection algorithm.

[0058] The joint intersection points on the dam are the places where leakage often occurs. By processing the remote sensing image of the gravity dam through grayscale conversion, image enhancement, and edge detection algorithm, the joint intersection points can be automatically and efficiently identified, providing data support for the selection of subsequent leakage data collection points.

[0059] Further, the leakage data collection points include:

[0060] The calculation process of the leakage risk weight is as Figure 2 shown. First, obtain the historical data of the joint intersection points and calculate the leakage risk weight of the joint intersection points. The historical data includes the position weight, joint width, historical leakage risk value, and deformation degree of the joint intersection points;

[0061] Further, the calculation formula for the leakage risk weight is as follows:

[0062] ;

[0063] Wherein, represents the leakage risk weight of the joint intersection point, represents the position weight of the joint intersection point of the weight coefficient, represents the seam width change rate of the joint intersection point of the weight coefficient, represents the historical leakage risk value of the joint intersection point of the weight coefficient, represents the weight coefficient of the deformation degree of the joint intersection point, represents time nodes, represents the grayscale image of the joint intersection point, represents the th acquisition of the grayscale value of the pixel at on the grayscale image, represents the number of pixel points of the grayscale image;

[0064] Further, the position weight of the joint intersection point represents the position of the joint intersection point in the entire gravity dam. If the joint intersection point is at the dam foundation, the value of the position weight is relatively large. If the position of the joint intersection point is at the dam crest, the value of the position weight is relatively small.

[0065] Further, the seam width change rate of the joint intersection point refers to obtaining the seam width of the joint intersection point in this acquisition and the previous acquisition. The seam width includes the transverse seam width and the longitudinal seam width. Calculate the change value of the data collected twice to obtain the transverse seam width change rate and the longitudinal seam width change rate , and obtain the seam width change rate .

[0066] Further, if there is a leakage risk in the historical monitoring of the joint intersection, the historical leakage risk value is 1, otherwise it is 0.

[0067] Further, the deformation degree of the joint intersection point is measured by the change of the image data collected at different time nodes.

[0068] If the leakage risk weight of the joint intersection point is greater than the preset leakage risk threshold, the joint intersection point is marked as the leakage data collection point;

[0069] Obtain all joint intersections and process them to obtain a set of leakage data collection points. Table 1 shows the leakage risk weights of some joint intersections. When the leakage risk weight is greater than 0.8, the joint intersection is marked as a leakage data collection point. In the table, C1, C3, and C4 are marked as leakage data collection points.

[0070] Table 1. Leakage risk weights of some joint intersections

[0071]

[0072] By comprehensively considering a series of data such as the position of the joint intersection, the seam width change rate, the historical leakage risk value, and the deformation degree, the state of the joint intersection can be evaluated in multiple dimensions, effectively improving the accuracy of leakage risk assessment; then, by calculating the leakage risk weight and comparing it with the preset threshold, the joint intersections with higher leakage risks are selected as leakage data collection points to ensure the pertinence and efficiency of data collection.

[0073] Obtain the leakage data of the leakage data collection points and process it to obtain real-time leakage anomaly indicators; obtain the real-time leakage anomaly indicators at the real-time leakage anomaly indicators at the historical time nodes and process them to obtain comprehensive leakage anomaly indicators; obtain the comprehensive leakage anomaly indicators of all the leakage data collection points and perform function fitting to obtain a leakage anomaly indicator function;

[0074] Furthermore, the comprehensive leakage anomaly indicator includes:

[0075] Obtain the leakage data of the leakage data collection points and process it. The leakage data includes the temperature data, humidity, water quality data, pressure, and water flow rate of the leakage data collection points. The water quality data includes conductivity and water quality turbidity;

[0076] Furthermore, the calculation formula for the real-time leakage anomaly indicator is:

[0077] ;

[0078] where represents the real-time leakage anomaly indicator, represents the humidity of the leakage data collection point weight coefficient, represents the weight coefficient of the temperature data of the leakage data collection point, represents the temperature of the leakage data collection point, represents the historical th collection of temperature data at the leakage data collection point, represents the number of temperature collections, represents the weight coefficient of the water quality data on the other side of the leakage data collection point, Represents the conductivity weight coefficient of Represents the water turbidity weight coefficient of Represents the weight coefficient of the pressure error Represents the pressure on the other side of the leakage data collection point Represents the standard pressure on the other side of the leakage data collection point Represents the water flow velocity on the other side of the leakage data collection point weight coefficient of

[0079] Further, the temperature data refers to obtaining the currently collected temperature and the temperature collected in the past times at the leakage data collection point and processing them to measure the temperature change of the leakage data collection point

[0080] Further, the water quality data refers to installing a water quality collection device on the other side of the leakage data collection point, that is, the side where the gravity dam contacts the water, to obtain water quality data. The water quality data includes the conductivity and water turbidity of the water at the corresponding position. The water turbidity refers to the degree of turbidity. Since the water flow is unstable at the position where there is a leakage problem, the water quality is turbid

[0081] Further, the pressure error refers to installing a pressure collection device on the side where the gravity dam contacts the water, obtaining the real-time collected pressure data and the safe pressure data and calculating the error to obtain the pressure error

[0082] Further, the water flow velocity refers to installing a flow velocity collection device on the other side of the leakage data collection point, that is, the side where the gravity dam contacts the water, to collect the water flow velocity. If there is a leakage problem, the water flow velocity is abnormal

[0083] By comprehensively considering various data such as the temperature, humidity, water quality, pressure, and water flow velocity of the leakage data collection point, it comprehensively reflects the state of the leakage data collection point, avoids the monitoring blind area caused by a single data source, realizes the real-time monitoring and accurate evaluation of the leakage data collection point, and is conducive to quickly locating the leakage area subsequently

[0084] Further, the calculation formula of the comprehensive leakage anomaly index is

[0085] ;

[0086] Among them, Represents the comprehensive leakage anomaly index Represents historical time nodes Represents the past real-time leakage anomaly index collected at the historical time node The weight coefficient, represents the attenuation factor, where, . As shown in Table 2, the comprehensive leakage anomaly indicators of some leakage data collection points are presented.

[0087] Table 2. Comprehensive Leakage Anomaly Indicators of Some Leakage Data Collection Points

[0088]

[0089] By introducing the real-time leakage anomaly indicators of historical time nodes and assigning the weight coefficients combined with the attenuation factor, it is possible to comprehensively evaluate the leakage data collection points by making full use of historical data and real-time data, reduce the misjudgment that may be brought by single real-time data, and improve the accuracy of leakage monitoring.

[0090] Furthermore, the humidity anomaly index function includes:

[0091] Obtain the positions of all the leakage data collection points and establish a coordinate system, then obtain the comprehensive leakage anomaly indicators and perform cubic spline interpolation fitting to obtain the leakage anomaly index function; if the function value of the leakage anomaly index function is greater than the preset leakage anomaly index threshold, it is classified as a leakage risk area.

[0092] Furthermore, first construct a coordinate system, and the position coordinates of each leakage data collection point are , and the comprehensive leakage anomaly indicator corresponding to the position coordinates is .

[0093] Furthermore, when fitting the leakage anomaly index function, in this embodiment, interpolation functions such as cubic spline interpolation or radial basis function are used to construct the fitting function , and then the error between the fitting function and the real data is calculated to measure the fitting result. The error calculation formula is:

[0094] ;

[0095] where, represents the error, represents the number of data, represents the th comprehensive leakage anomaly indicator of the data, represents the value of the fitting function at the coordinate position.

[0096] Furthermore, the sources of the preset leakage anomaly index threshold include the typical index values when leakage occurs in historical monitoring data, expert experience, or model analysis results.

[0097] Further, obtain the coordinate positions in the fitting function that are greater than the preset leakage anomaly index threshold, and map them onto the gravity dam to obtain the positions where leakage risks may exist, i.e., the leakage risk areas.

[0098] First, perform function fitting on the comprehensive leakage anomaly index through cubic spline interpolation, which can more accurately describe the index changes between different leakage data collection points; converting the discrete leakage data collection point information into a continuous leakage anomaly index function can comprehensively cover the monitoring area of the entire gravity dam and identify more potential leakage risk areas.

[0099] Obtain auxiliary data, and determine whether the leakage risk area is a leakage area based on the auxiliary data. If it is a leakage area, issue a leakage warning.

[0100] Further, determining whether the leakage risk area is a leakage area based on the auxiliary data includes:

[0101] The auxiliary data includes water level change data and maintenance data;

[0102] Obtain the water level change data and process it to obtain the water level change amount. If the water level change amount is greater than the preset water level change threshold, the leakage risk area is a leakage area;

[0103] Obtain the maintenance data of the leakage risk area and process it to obtain the historical maintenance index. If the historical maintenance index is greater than the preset maintenance index threshold, the leakage risk area is a leakage area.

[0104] Further, the water level change amount refers to obtaining the water level information collected every day and calculating the change in water level height to obtain the water level change amount, which is used to measure the impact of the change in the water storage volume of the entire reservoir on the leakage of the dam.

[0105] Further, count the number of maintenance times and the maintenance area within the leakage risk area, and perform weighted processing to obtain the historical maintenance index.

[0106] By combining the water level change data and the maintenance data, the actual situation of the leakage risk area can be more comprehensively evaluated. For example, a significant increase in the water level change amount may indicate that the reservoir is discharging flood through the dam or the rainfall has increased, while a high frequency or large area of maintenance records may indicate that there are structural weaknesses in this area; comprehensively analyzing these data can significantly reduce the possibility of misjudgment and provide a solid basis for early warning.

[0107] Further, the leakage warning also includes:

[0108] During early warning, display the location of the leakage area, and set a response duration according to the location of the leakage area and the distance in monitoring. If the staff fails to reach the leakage area and handle the leakage problem within the response duration, a secondary early warning is issued.

[0109] During early warning, increase the response duration and the secondary early warning mechanism. If the response duration is exceeded, the secondary early warning will be automatically triggered, further urging the emergency personnel to take actions as soon as possible, avoiding the deterioration of the leakage problem, and helping to ensure that the leakage area can be processed in time.

[0110] Establish a visualization platform to display the comprehensive leakage anomaly indicators and the leakage area of all the leakage data collection points; the visualization platform receives the real-time touch information of the user, and records the interval time. If the interval time is greater than the preset interval time threshold, an unattended monitoring early warning is issued.

[0111] Furthermore, a visualization platform is set up in the monitoring center. The visualization platform receives the real-time touch information of the user. The touch information includes the user clicking and viewing the comprehensive leakage anomaly indicators of the leakage data collection points and the user viewing the leakage area. If the visualization platform does not receive the real-time touch information of the user within the preset interval time threshold, an unattended monitoring early warning is issued.

[0112] A monitoring method for reservoir dam leakage provided by this embodiment first obtains the historical data of the joint intersection points and calculates the leakage risk weights, screens out the collection points with higher leakage possibilities as key monitoring points, improving the accuracy of leakage monitoring; then calculates the comprehensive leakage anomaly indicators of the leakage data collection points and fits the leakage anomaly indicator thresholds, achieving the precise positioning of the leakage risk area; then fuses the auxiliary data such as water level change data and historical repairs, further improving the reliability of leakage area judgment; finally, through real-time early warning and secondary early warning, as well as the information display of the visualization platform, the safety and reliability of gravity dam leakage problem monitoring are improved.

[0113] Embodiment 2

[0114] This embodiment provides a monitoring system for reservoir dam leakage, and the structure is as Figure 3 shown, and the specific implementation method is as follows:

[0115] A collection point selection module obtains the remote sensing image data of the gravity dam and processes it to obtain the joint intersection points; obtains the historical data of the joint intersection points and processes it to obtain the leakage risk weights; if the leakage risk weight is greater than the preset leakage risk threshold, the joint intersection point is a leakage data collection point.

[0116] Furthermore, the calculation formula of the leakage risk weight is:

[0117] ;

[0118] Among them, represents the leakage risk weight of the joint intersection point, represents the position weight of the joint intersection point as the weight coefficient, represents the seam width change rate of the joint intersection point as the weight coefficient, represents the historical leakage risk value of the joint intersection point as the weight coefficient, represents the weight coefficient of the deformation degree of the joint intersection point, represents time nodes, represents the grayscale image of the joint intersection point, represents the th time of acquisition of the grayscale value of the pixel point at on the grayscale image, represents the number of pixel points of the grayscale image;

[0119] As shown in Table 3, the leakage risk weights of some joint intersection points are shown. When the leakage risk weight is greater than 0.8, the joint intersection point is marked as a leakage data collection point. In the table, A1, A2, and A4 are leakage data collection points.

[0120] Table 3. Leakage risk weights of some joint intersection points

[0121]

[0122] The leakage data analysis module obtains the leakage data of the leakage data collection point and processes it to obtain a real-time leakage anomaly index; obtains the real-time leakage anomaly index of the real-time leakage anomaly index and historical time nodes and processes it to obtain a comprehensive leakage anomaly index;

[0123] Furthermore, the calculation formula of the comprehensive leakage anomaly index is:

[0124] ;

[0125] Among them, represents the comprehensive leakage anomaly index, represents historical time nodes, represents the weight coefficient of the real-time leakage anomaly index collected at the past th historical time node, represents the attenuation factor, ; represents the humidity of the leakage data collection point as the weight coefficient, The weight coefficient of the temperature data of the leakage data acquisition point The temperature of the leakage data acquisition point Indicates the historical th collected temperature data of the leakage data acquisition point Indicates the number of temperature acquisitions The weight coefficient of the water quality data on the other side of the leakage data acquisition point Indicates the conductivity of the weight coefficient Indicates the turbidity of the water quality of the weight coefficient The weight coefficient indicating the pressure error The pressure on the other side of the leakage data acquisition point The standard pressure on the other side of the leakage data acquisition point Indicates the water flow rate on the other side of the leakage data acquisition point of the weight coefficient. As shown in Table 4 are the comprehensive leakage anomaly indicators of some leakage data acquisition points

[0126] Obtain the comprehensive leakage anomaly indicators of all the leakage data acquisition points and perform function fitting to obtain a leakage anomaly indicator function ;

[0127] Table 4. Comprehensive leakage anomaly indicators of some leakage data acquisition points

[0128]

[0129] Furthermore, use score to measure the goodness of fit, and the calculation formula is:

[0130] ;

[0131] Among them, Indicates the number of data Indicates the th comprehensive leakage anomaly indicator of the data Indicates the value of the fitting function at the coordinate position Indicates the data mean of the comprehensive leakage anomaly indicator

[0132] Leakage judgment module, obtain the leakage anomaly indicator function, if the function value is greater than the preset leakage anomaly indicator threshold, then divide the coordinate point corresponding to the function value into the leakage risk area;

[0133] Auxiliary judgment module, obtain auxiliary data, and judge whether the leakage risk area is a leakage area according to the auxiliary data, if it is the leakage area, then issue a leakage warning;

[0134] Further, determining whether the leakage risk area is a leakage area according to the auxiliary data includes:

[0135] The auxiliary data includes water level change data and maintenance data;

[0136] Obtain the water level change data and process it to obtain the water level change amount. If the water level change amount is greater than the preset water level change threshold, then the leakage risk area is a leakage area;

[0137] Obtain the maintenance data of the leakage risk area and process it to obtain the historical maintenance index. If the historical maintenance index is greater than the preset maintenance index threshold, then the leakage risk area is a leakage area.

[0138] Further, the leakage warning further includes:

[0139] When warning, display the location of the leakage area, and set a response duration according to the location of the leakage area and the distance in the monitoring. If the leakage warning fails to be processed within the response duration, a secondary warning is issued.

[0140] The leakage monitoring module establishes a visualization platform to display the comprehensive leakage anomaly index and the leakage area of all the leakage data collection points; the visualization platform receives the real-time touch information of the user and records the interval time. If the interval time is greater than the preset interval time threshold, an unattended monitoring warning is issued.

[0141] A monitoring system for reservoir dam leakage provided by this embodiment first identifies the joint intersection points on the dam, and selects appropriate leakage data collection points according to the leakage risk weight, improving the accuracy of leakage monitoring; then, by calculating the comprehensive leakage anomaly index of the leakage data collection points, the working state of the collection points is accurately evaluated, improving the efficiency of leakage monitoring; subsequently, by obtaining the comprehensive leakage anomaly index of the leakage data collection points and fitting the leakage anomaly index function, it is beneficial to accurately divide the area where leakage exists, improving the efficiency of leakage supervision, and through warning and the visualization platform, the leakage problem of the gravity dam is monitored in real time, improving the supervision efficiency of the leakage safety problem of the dam joint intersection points.

[0142] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring leakage of a reservoir dam, characterized in that: include: Obtain and process remote sensing image data of gravity dam to obtain joint intersection points; Acquire and process historical data of the joint intersection to obtain a leakage risk weight, wherein the historical data includes a position weight, a seam width, a historical leakage risk value, and a deformation degree of the joint intersection; If the leakage risk weight is greater than the preset leakage risk threshold, the seam intersection point is the leakage data collection point; Acquire the leakage data of the leakage data collection point and process it to obtain a real-time leakage abnormality index, wherein the leakage data includes temperature data, humidity, water quality data, pressure and water flow rate at the leakage data collection point, and the water quality data includes conductivity and water turbidity; obtain the real-time leakage abnormality index and The real-time leakage anomaly index of each historical time node is processed to obtain a comprehensive leakage anomaly index; the comprehensive leakage anomaly index of all the leakage data collection points is obtained and function fitting is performed to obtain a leakage anomaly index function; Obtaining the coordinate point of the leakage anomaly index function and the function value of the coordinate point, and if the function value is greater than a preset leakage anomaly index threshold, dividing the coordinate point corresponding to the function value into a leakage risk area; Acquire auxiliary data, and determine whether the leakage risk area is a leakage area according to the auxiliary data, and if it is the leakage area, issue a leakage warning; the auxiliary data includes water level change data and maintenance data; A visualization platform is established to display the comprehensive leakage anomaly indicators and the leakage areas of all the leakage data collection points; the visualization platform receives the user's real-time touch information and records the interval time. If the interval time is greater than the preset interval time threshold, an unmanned monitoring warning is issued.

2. A method for monitoring reservoir dam leakage according to claim 1, characterized in that: The seam intersections include: A remote sensing image of the gravity dam is obtained and processed to obtain a grayscale image of the gravity dam; the grayscale image of the gravity dam is obtained and image enhancement is performed to obtain an enhanced grayscale image of the gravity dam; the enhanced grayscale image of the gravity dam is obtained and the seam intersection is identified according to an edge detection algorithm.

3. A method for monitoring reservoir dam leakage according to claim 1, characterized in that: The leakage data collection points include: Acquire historical data of the joint intersection and calculate the leakage risk weight of the joint intersection; the historical data includes the position weight, seam width, historical leakage risk value and deformation degree of the joint intersection; The calculation formula of the leakage risk weight is: in, represents the leakage risk weight of the joint intersection, Represents the position weight of the seam intersection The weight coefficient of Indicates the change rate of the seam width at the seam intersection The weight coefficient of Represents the historical leakage risk value of the joint intersection The weight coefficient of The weight coefficient representing the degree of deformation at the intersection of the seams, express Time nodes, A grayscale image representing the intersections of the seams, Indicates On the grayscale image collected The gray value of the pixel at Represents the number of pixels in a grayscale image; If the leakage risk weight of the joint intersection is greater than a preset leakage risk threshold, marking the joint intersection as the leakage data collection point; All the seam intersection points are acquired and processed to obtain a set of leakage data collection points.

4. A method for monitoring reservoir dam leakage according to claim 1, characterized in that: The real-time leakage anomaly indicators include: Acquire leakage data of the leakage data collection point, wherein the leakage data includes temperature, humidity, water quality data, pressure and water flow rate of the leakage data collection point, and the water quality data includes conductivity and water turbidity; The leakage data is acquired and weighted calculation is performed to obtain the real-time leakage anomaly index of the leakage data collection point.

5. A method for monitoring reservoir dam leakage according to claim 4, characterized in that: The calculation formula of the comprehensive leakage anomaly index is: in, represents the comprehensive leakage abnormality index, express A historical time point, Indicates Real-time leakage anomaly indicators collected at historical time nodes The weight coefficient of represents the attenuation factor, .

6. A method for monitoring reservoir dam leakage according to claim 1, characterized in that: The leakage anomaly index function includes: Obtaining the positions of all leakage data collection points and establishing a coordinate system; The comprehensive leakage anomaly index is obtained and cubic spline interpolation fitting is performed to obtain the leakage anomaly index function; if the function value of the leakage anomaly index function is greater than a preset leakage anomaly index threshold, the coordinate point corresponding to the function value is divided into a leakage risk area.

7. A method for monitoring reservoir dam leakage according to claim 1, characterized in that: Judging whether the leakage risk area is a leakage area according to the auxiliary data includes: The auxiliary data includes water level change data and maintenance data; The water level change data is acquired and processed to obtain a water level change amount. If the water level change amount is greater than a preset water level change threshold, the leakage risk area is a leakage area. The maintenance data of the leakage risk area is acquired and processed to obtain a historical maintenance index. If the historical maintenance index is greater than a preset maintenance index threshold, the leakage risk area is a leakage area.

8. A method for monitoring reservoir dam leakage according to claim 1, characterized in that: The leakage warning also includes: The location of the leakage area is displayed during the warning, and the response time is set according to the distance between the location of the leakage area and the monitoring center. If the leakage warning cannot be processed within the response time, a secondary warning is issued.

9. A monitoring system for reservoir dam leakage, characterized in that: include: The acquisition point selection module acquires and processes the remote sensing image data of the gravity dam to obtain the intersection points of the joints; Acquire and process historical data of the joint intersection to obtain a leakage risk weight, wherein the historical data includes a position weight, a seam width, a historical leakage risk value, and a deformation degree of the joint intersection; If the leakage risk weight is greater than the preset leakage risk threshold, the seam intersection point is the leakage data collection point; The leakage data analysis module obtains and processes the leakage data of the leakage data collection point to obtain a real-time leakage abnormality index, wherein the leakage data includes temperature data, humidity, water quality data, pressure and water flow rate at the leakage data collection point, and the water quality data includes conductivity and water turbidity; obtains the real-time leakage abnormality index and The real-time leakage anomaly index of each historical time node is processed to obtain a comprehensive leakage anomaly index; the comprehensive leakage anomaly index of all the leakage data collection points is obtained and function fitting is performed to obtain a leakage anomaly index function; A leakage judgment module obtains the coordinate point of the leakage abnormality index function and the function value of the coordinate point, and if the function value is greater than a preset leakage abnormality index threshold, the coordinate point corresponding to the function value is divided into a leakage risk area; An auxiliary judgment module obtains auxiliary data and judges whether the leakage risk area is a leakage area according to the auxiliary data, and if it is the leakage area, a leakage warning is issued; the auxiliary data includes water level change data and maintenance data; The leakage monitoring module establishes a visualization platform to display the comprehensive leakage anomaly indicators and the leakage area of ​​all the leakage data collection points; the visualization platform receives the user's real-time touch information and records the interval time. If the interval time is greater than the preset interval time threshold, an unmanned monitoring warning is issued.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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