Reservoir dam leakage monitoring method and system and medium

Abstract

The invention relates to the technical field of dam safety, in particular to a reservoir dam leakage monitoring method and system and a medium. Calculating leakage risk weights and comparing the leakage risk weights with a preset threshold value to obtain leakage data acquisition points; real-time leakage abnormal indexes are calculated according to the leakage data of the collection points, comprehensive leakage abnormal indexes are obtained by combining the real-time leakage abnormal indexes of the multiple time nodes, and a leakage abnormal index function is further obtained; if the function value is greater than a preset leakage abnormity index threshold value, dividing the coordinate points corresponding to the function value into leakage risk areas; auxiliary data are obtained, whether the leakage risk area is a leakage area or not is judged, and if the leakage risk area is the leakage area, early warning is carried out; and finally, establishing a visual platform to display data related to the leakage problem of the gravity dam. By designing the leakage risk weight and the comprehensive leakage abnormal index, the real-time monitoring efficiency of the leakage problem of the gravity dam is improved.

Description

Technical Field

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

[0002] A reservoir is an artificial water storage facility that stores and regulates water flow for the purpose of water supply, irrigation, flood control, power generation or ecological environment protection. The main structures of a reservoir include dams, water inlets, flood discharge outlets and water transfer facilities. The dam is an important part of the reservoir, used to block and store water flow. Its types mainly include gravity dams, earth dams, rockfill dams and arch dams.

[0003] Gravity dams are usually built of concrete or stone masonry, and rely on their own weight to resist water pressure. They are suitable for wider river valley terrain. Gravity dams have a strong structure, high stability, and good impermeability, making them suitable for reservoirs with higher water levels. Although the structure of a gravity dam is relatively strong, the joints of the dam are relatively weak. In long-term use, if anti-seepage treatment is not done well, leakage often occurs, causing serious safety problems.

[0004] In order to avoid potential safety hazards and ensure the safety of reservoirs and surrounding areas, it is necessary to enhance the supervision and early warning capabilities of gravity dam leakage safety issues and improve the efficiency of supervision of leakage safety issues at the joints of gravity dams, especially at the intersections of the joints.

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

[0006] The purpose of the present invention is to provide a monitoring method, system and medium for reservoir dam leakage. First, by identifying the intersection of joints on the gravity dam and calculating the leakage risk weight, leakage data collection points are selected to improve the supervision efficiency of gravity dam leakage problems; 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 area where leakage problems may exist, and the auxiliary data is combined to determine whether there is a leakage problem, thereby improving the accuracy of leakage problem identification; finally, through real-time monitoring of the early warning and visualization platform, the supervision efficiency of gravity dam leakage problems, especially leakage safety problems at joint intersections, is improved.

[0007] To achieve the above object, the present invention provides the following technical solutions: A method for monitoring leakage of a reservoir dam, comprising: Acquire and process the remote sensing image data of the gravity dam to obtain the joint intersection point; acquire and process the historical data of the joint intersection point to obtain the leakage risk weight; if the leakage risk weight is greater than the preset leakage risk threshold, the joint intersection point is the leakage data collection point; Further, the seam intersection includes: 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.

[0008] Furthermore, 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.

[0009] Acquire the leakage data of the leakage data collection point and process it to obtain a real-time leakage anomaly index; obtain the real-time leakage anomaly 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; Furthermore, 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.

[0010] Furthermore, 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, .

[0011] Furthermore, 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, it is divided into a leakage risk area.

[0012] Obtaining the leakage anomaly index function, 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; Further, 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.

[0013] Furthermore, 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.

[0014] 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.

[0015] The present invention also provides a monitoring system for reservoir dam leakage, comprising: The collection point selection module obtains and processes the remote sensing image data of the gravity dam to obtain the joint intersection point; obtains and processes the historical data of the joint intersection point to obtain the leakage risk weight; if the leakage risk weight is greater than the preset leakage risk threshold, the joint 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 anomaly index; obtains the real-time leakage anomaly 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 leakage abnormality index function, and if the function value is greater than a preset leakage abnormality index threshold, divides the coordinate point corresponding to the function value 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 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.

[0016] 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.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 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.

[0018] 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.

[0019] 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

[0020] Figure 1 A flow chart of a method for monitoring reservoir dam leakage provided in Embodiment 1 of the present invention; Figure 2 is a flow chart for calculating leakage risk weight in the first embodiment of the present invention; Figure 3 A schematic diagram of the structure of a reservoir dam leakage monitoring system provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Embodiment 1 A water conservancy engineering team introduced a reservoir dam leakage monitoring method provided by the present invention when monitoring the gravity dam, so as to improve the monitoring efficiency of the gravity dam and prevent and identify potential leakage problems. The process is as follows: Figure 1 As shown, the specific implementation is as follows: First, remote sensing image data of the gravity dam is obtained and processed to obtain joint intersection points; historical data of the joint intersection points is obtained and processed to obtain leakage risk weights. If the leakage risk weight is greater than a preset leakage risk threshold, the joint intersection points are leakage data collection points; Further, the seam intersection includes: Acquire a remote sensing image of the gravity dam and process it to obtain a grayscale image of the gravity dam; acquire the grayscale image of the gravity dam and perform image enhancement to obtain an enhanced grayscale image of the gravity dam; acquire the enhanced grayscale image of the gravity dam and identify the joint intersection and the seam width of the joint intersection according to an edge detection algorithm.

[0023] The intersection points of the joints on the dam are where leakage often occurs. By graying, enhancing and edge detecting the remote sensing images of the gravity dam, the intersection points of the joints can be automatically and efficiently identified, providing data support for the selection of subsequent leakage data collection points.

[0024] Furthermore, the leakage data collection points include: The calculation process of the leakage risk weight is as follows: Figure 2 As shown, firstly, the historical data of the joint intersection is obtained, and the leakage risk weight of the joint intersection is calculated, wherein the historical data includes the position weight, seam width, historical leakage risk value and deformation degree of the joint intersection; Furthermore, 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; Furthermore, 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 larger; if the joint intersection point is at the dam top, the value of the position weight is smaller.

[0025] Furthermore, the seam width change rate at the seam intersection is It refers to obtaining the seam width of the intersection of the joint collected this time and the last time, the seam width includes the horizontal seam width and the vertical seam width, calculating the change value of the data collected twice, and obtaining the horizontal seam width change rate and longitudinal slit width variation rate , and the slit width change rate is obtained .

[0026] Furthermore, if the seam intersection has a leakage risk in historical monitoring, the historical leakage risk value is 1, otherwise it is 0.

[0027] Furthermore, the deformation degree of the seam intersection is measured by the change of image data collected at different time nodes.

[0028] 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 joint intersections are obtained and processed 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. C1, C3 and C4 in the table are marked as leakage data collection points.

[0029] Table 1. Leakage risk weights for some joint intersections

[0030] By comprehensively considering a series of data such as the location of the joint intersection, the rate of change of the joint width, the historical leakage risk value and the degree of deformation, the status of the joint intersection can be evaluated in multiple dimensions, effectively improving the accuracy of the 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 screened out as leakage data collection points to ensure the targetedness and efficiency of data collection.

[0031] Acquire the leakage data of the leakage data collection point and process it to obtain a real-time leakage anomaly index; obtain the real-time leakage anomaly 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; Furthermore, the comprehensive leakage abnormality index includes: Acquire and process the leakage data of the leakage data collection point, wherein the leakage data includes temperature data, 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; Furthermore, the calculation formula of the real-time leakage anomaly index is: ; in, Indicates real-time leakage abnormality indicators, Indicates the humidity at the point where leakage data is collected The weight coefficient of Represents the weight coefficient of the temperature data at the leakage data collection point, represents the temperature at the leakage data collection point, Indicates the historical number of leakage data collection points The temperature data collected Indicates the number of temperature acquisitions. Represents the weight coefficient of the water quality data on the other side of the leakage data collection point, Indicates conductivity The weight coefficient of Indicates water turbidity The weight coefficient of represents the weight coefficient of pressure error, represents the pressure on the other side of the leakage data collection point, Indicates the standard pressure on the other side of the leakage data collection point, Indicates the water flow rate on the other side of the leakage data collection point The weight coefficient of .

[0032] Furthermore, the temperature data refers to the temperature currently collected at the leakage data collection point and the past The temperatures collected are processed and used to measure the temperature changes at the leakage data collection points.

[0033] Furthermore, the water quality data refers to a water quality collection device installed on the other side of the leakage data collection point, that is, the side where the gravity dam is in contact with the water, for obtaining water quality data. The water quality data includes the conductivity and turbidity of the water at the corresponding position. The turbidity of the water refers to the degree of turbidity, because the water flow is unstable and the water quality is turbid at the location where the leakage problem occurs.

[0034] Furthermore, the pressure error refers to that a pressure acquisition device is installed on the side of the gravity dam in contact with water, and real-time pressure data and safety pressure data are acquired and the error is calculated to obtain the pressure error.

[0035] Furthermore, the water flow rate refers to a flow rate collection device installed on the other side of the leakage data collection point, that is, the side where the gravity dam contacts the water, for collecting the water flow rate. If there is a leakage problem, the water flow rate is abnormal.

[0036] By comprehensively considering various data such as temperature, humidity, water quality, pressure and water flow rate at the leakage data collection point, the status of the leakage data collection point is fully reflected, avoiding the monitoring blind spots caused by a single data source, and realizing real-time monitoring and accurate evaluation of the leakage data collection point, which is conducive to the subsequent rapid positioning of the leakage area.

[0037] Furthermore, the calculation formula of the comprehensive leakage anomaly index is: ; in, represents the comprehensive leakage abnormality index, express A historical time point, Indicates the past Real-time leakage anomaly indicators collected at historical time nodes The weight coefficient of represents the attenuation factor, where Table 2 shows the comprehensive leakage anomaly indicators of some leakage data collection points.

[0038] Table 2. Comprehensive leakage anomaly indicators of some leakage data collection points

[0039] By introducing real-time leakage anomaly indicators at historical time nodes and assigning weight coefficients combined with attenuation factors, we can make full use of historical data and real-time data to conduct a comprehensive assessment of leakage data collection points, reduce the misjudgment that may be caused by single real-time data, and improve the accuracy of leakage monitoring.

[0040] Furthermore, the humidity anomaly index function includes: The positions of all the leakage data collection points are obtained and a coordinate system is established, and then the comprehensive leakage anomaly index is obtained and cubic spline interpolation fitting is performed to obtain a leakage anomaly index function; if the function value of the leakage anomaly index function is greater than a preset leakage anomaly index threshold, it is divided into a leakage risk area.

[0041] Furthermore, the coordinate system is first constructed, and the position coordinates of each leakage data collection point are , the comprehensive leakage anomaly index corresponding to the location coordinates is .

[0042] Furthermore, when fitting the leakage anomaly index function, this embodiment uses interpolation functions such as cubic spline interpolation or radial basis function to construct the fitting function , and then calculate the error between the fitting function and the real data to measure the fitting result. The error calculation formula is: ; in, Indicates the error, Indicates the amount of data, Indicates Comprehensive leakage anomaly indicators of data, Indicated in coordinates The value of the fitted function at location.

[0043] Furthermore, sources of preset leakage anomaly index thresholds include typical index values ​​when leakage occurs in historical monitoring data, expert experience or model analysis results.

[0044] Furthermore, the coordinate positions in the fitting function that are greater than a preset leakage anomaly index threshold are obtained and mapped to the gravity dam to obtain positions where leakage risks may exist, namely, leakage risk areas.

[0045] Firstly, the comprehensive leakage anomaly index is fitted by cubic spline interpolation, which can more accurately describe the index changes between different leakage data collection points; the discrete leakage data collection point information is converted into a continuous leakage anomaly index function, which can comprehensively cover the monitoring area of ​​the entire gravity dam and identify more potential leakage risk areas.

[0046] 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; Further, 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.

[0047] Furthermore, the water level variation refers to obtaining the water level information collected daily and calculating the change in water level height to obtain the water level variation, which is used to measure the impact of changes in the water storage capacity of the entire reservoir on dam leakage.

[0048] Furthermore, the number of maintenance times and the maintenance area in the leakage risk area are counted and weighted to obtain the historical maintenance index.

[0049] By combining water level change data and maintenance data, the actual situation in leakage risk areas can be more comprehensively assessed. For example, a significant increase in water level change may indicate that the reservoir is releasing floodwater through the dam or that rainfall has increased, while high-frequency or large-scale maintenance records may indicate that the area has structural weaknesses. Comprehensive analysis of these data can significantly reduce the possibility of misjudgment and provide a solid basis for early warning.

[0050] Furthermore, the leakage warning also includes: The location of the leakage area is displayed during the early warning, and the response time is set according to the location of the leakage area and the distance under monitoring. If the staff fails to reach the leakage area and deal with the leakage problem within the response time, a secondary early warning is issued.

[0051] Increase the response time and secondary warning mechanism during the early warning. If the response time is exceeded, the secondary warning will be automatically triggered, further urging emergency personnel to take action as soon as possible to avoid the leakage problem from worsening, helping to ensure that the leakage area can be dealt with in a timely manner.

[0052] 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.

[0053] Furthermore, a visualization platform is provided in the monitoring center, and the visualization platform receives real-time touch information from the user, and the touch information includes the comprehensive leakage abnormality index of the leakage data collection point clicked and viewed by the user and the leakage area viewed by the user. If the visualization platform does not receive the real-time touch information from the user within a preset interval time threshold, an early warning of unattended monitoring is issued.

[0054] A method for monitoring reservoir dam leakage provided in this embodiment first obtains historical data of joint intersections and calculates leakage risk weights, thereby screening out collection points with higher leakage possibilities as key monitoring points, thereby improving the accuracy of leakage monitoring; then, by calculating the comprehensive leakage anomaly index of the leakage data collection points and fitting the leakage anomaly index threshold, accurate positioning of the leakage risk area is achieved; then, auxiliary data such as water level change data and historical maintenance are integrated to further improve the reliability of leakage area judgment; finally, through real-time warning and secondary warning, as well as information display on a visualization platform, the safety and reliability of gravity dam leakage problem monitoring are improved.

[0055] Embodiment 2 This embodiment provides a monitoring system for reservoir dam leakage, the structure of which is as follows: Figure 3 As shown, the specific implementation is as follows: The collection point selection module obtains and processes the remote sensing image data of the gravity dam to obtain the joint intersection point; obtains and processes the historical data of the joint intersection point to obtain the leakage risk weight; if the leakage risk weight is greater than the preset leakage risk threshold, the joint intersection point is the leakage data collection point.

[0056] Furthermore, 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; Table 3 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. A1, A2 and A4 in the table are leakage data collection points.

[0057] Table 3. Leakage risk weights for some joint intersections

[0058] The leakage data analysis module obtains and processes the leakage data of the leakage data collection point to obtain a real-time leakage anomaly index; obtains the real-time leakage anomaly index and The real-time leakage anomaly indicators of historical time nodes are collected and processed to obtain comprehensive leakage anomaly indicators; Furthermore, the calculation formula of the comprehensive leakage anomaly index is: ; in, Represents the comprehensive leakage abnormality index, express A historical time point, Indicates the past The weight coefficient of the real-time leakage anomaly index collected at each historical time node, represents the attenuation factor, ; Indicates the humidity at the point where leakage data is collected The weight coefficient of Represents the weight coefficient of the temperature data at the leakage data collection point, represents the temperature at the point where leakage data is collected, Indicates the historical number of leakage data collection points The temperature data collected Indicates the number of temperature acquisitions. Represents the weight coefficient of the water quality data on the other side of the leakage data collection point, Indicates conductivity The weight coefficient of Indicates water turbidity The weight coefficient of represents the weight coefficient of pressure error, represents the pressure on the other side of the leakage data collection point, Indicates the standard pressure on the other side of the leakage data collection point, Indicates the water flow rate on the other side of the leakage data collection point Table 4 shows the comprehensive leakage anomaly index of some leakage data collection points.

[0059] Obtain the comprehensive leakage anomaly index of all the leakage data collection points and perform function fitting to obtain the leakage anomaly index function ; Table 4. Comprehensive leakage anomaly indicators of some leakage data collection points

[0060] Furthermore, using The score measures the goodness of fit and is calculated as: ; in, Indicates the amount of data, Indicates Comprehensive leakage anomaly indicators of data, Indicated in coordinates The value of the fitted function at position, It represents the data mean of comprehensive leakage anomaly index.

[0061] A leakage judgment module obtains the leakage abnormality index function, and if the function value is greater than a preset leakage abnormality index threshold, divides the coordinate point corresponding to the function value 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; Further, 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.

[0062] Furthermore, 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 location of the leakage area and the distance under monitoring. If the leakage warning is not processed within the response time, a secondary warning is issued.

[0063] 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.

[0064] The present embodiment provides a reservoir dam leakage monitoring system, which first identifies the intersection of joints on the dam, and selects appropriate leakage data collection points according to the leakage risk weight, thereby improving the accuracy of leakage monitoring; then, by calculating the comprehensive leakage anomaly index of the leakage data collection point, the working status of the collection point is accurately evaluated, thereby improving the efficiency of leakage monitoring; subsequently, by obtaining the comprehensive leakage anomaly index of the leakage data collection point and fitting the leakage anomaly index function, it is helpful to accurately divide the area where leakage exists, improve the leakage supervision efficiency, and monitor the gravity dam leakage problem in real time through the early warning and visualization platform, thereby improving the supervision efficiency of leakage safety problems at the intersection of dam joints.

[0065] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that 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; Obtaining and processing historical data of the joint intersections to obtain leakage risk weights; 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 anomaly index; obtain the real-time leakage anomaly 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; 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; Obtaining and processing historical data of the joint intersections to obtain leakage risk weights; 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 anomaly index; obtains the real-time leakage anomaly 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 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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