CN117372629A - Reservoir visual data supervision control system and method based on digital twinning - Google Patents
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Abstract
The invention relates to the technical field of data processing, and discloses a reservoir visual data supervision and control system and method based on digital twinning. According to the invention, q control points are arranged around the reservoir, a three-dimensional laser shooting device is arranged on each control point, and the reservoir and the surrounding environment of the reservoir are shot in real time and modeled in real time according to shot image data by the arranged three-dimensional laser shooting devices; meanwhile, analyzing the shot image through an SAR image segmentation algorithm, calculating the current reservoir area, and estimating the reservoir capacity through the current reservoir area and the constructed three-dimensional model; meanwhile, calculating the reservoir permeability in a time period based on a reservoir permeability calculation mode and combining reservoir data monitored in real time; and predicting the reservoir water level condition through a digital twin technology based on the calculated data and weather conditions. According to the invention, the reservoir management and control is realized by means of real-time monitoring and digital twin prediction, and the reservoir management and control efficiency is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a reservoir visual data supervision and control system and method based on digital twinning.
Background
Along with the development of hydrologic informatization, numerous reservoir monitoring systems emerge, and the analysis and management of hydrologic data are gradually realized. Because most of the reservoir monitoring methods adopt traditional reservoir monitoring methods, namely, by arranging hydrologic monitoring stations and adopting professional instruments to measure parameters such as water quantity, water environment and the like, the measurement results are accurate, but the defects of complex monitoring process, unstable information transmission, large equipment loss and the like of a large area exist, and the requirements of rapidly acquiring reservoir parameters are more and more difficult to meet. Therefore, a high-efficiency reservoir monitoring means needs to be found to acquire dynamic changes of the reservoir in time.
The prior art CN109710672B monitors the reservoir in real time by installing different devices and a multilayer management system, but the existing monitoring device has the defects of complex monitoring process and unstable information transmission in a large area, so that the error of the monitoring information can be caused, and the actual requirement can not be met.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a reservoir visual data supervision control system and method based on digital twinning, which have the advantages of simple monitoring process, stable information transmission and the like and solve the problem of rapid acquisition of reservoir parameters.
In order to solve the technical problem of rapid acquisition of reservoir parameters, the invention provides the following technical scheme:
the embodiment discloses a reservoir data supervision and control method, which specifically comprises the following steps:
s1, setting q control points around a reservoir, and installing a three-dimensional laser shooting device on each control point;
s2, shooting the reservoir and the surrounding environment of the reservoir based on three-dimensional laser shooting devices installed on each control point, and constructing a three-dimensional model of the reservoir in real time;
s3, calculating the current water level area of the reservoir through an SAR image segmentation algorithm based on reservoir image data shot in real time, wherein the water level area is the surface area of the water under the current water level of the reservoir;
s4, estimating reservoir capacity based on the current reservoir water level area and the established three-dimensional model of the reservoir;
s5, estimating the reservoir permeability in unit time in real time based on a reservoir permeability calculation mode and combining reservoir data monitored in real time; predicting the change condition of the reservoir water level based on the established three-dimensional model of the reservoir, the permeation quantity of the reservoir in unit time and the weather condition of the reservoir capacity;
and S6, corresponding measures are taken to manage the reservoir based on the water level change condition of the reservoir.
Preferably, the step of setting q control points around the reservoir and installing a three-dimensional laser photographing device on each control point includes:
s11, numbering q control points based on the set control points 1 ，q 2 ，……，q q ；
S12, using control point q 1 For origin of coordinates, according to q 1 The coordinates establish a three-dimensional control coordinate system and are based on the control point q 1 Determining coordinates of other control points;
s13, storing the coordinates of each control point in a database of the system.
Preferably, the calculating the current water level area of the reservoir by using the SAR image segmentation algorithm, wherein the water level area is the surface area of the water under the current water level of the reservoir, comprises the following steps:
s31, separating an image target from a background through a fuzzy average algorithm;
s32, optimizing the separated image targets.
Preferably, the separation of the image target and the background by the fuzzy average algorithm comprises:
setting the number of total pixel points of an image as N, dividing the total pixel points into A types, wherein a is the center point of each type, and [ mu ] ij The expression point j belongs to the i-th class, and the objective function expression (1) and the constraint condition expression (2) express:
（1），
(2) Wherein m is a fuzzy index, a i Represents the center point of class i, x j Represents the sample point of the acquisition, | (x) j -a i )|| 2 Representing the point j to the i-th class center point a i J represents an objective function;
performing iterative clustering operation on the similar pixels to finish the separation of the image target and the background;
the iterative clustering operation for the similar pixels comprises:
calculating the distance from the random point in the class to the class center point based on the initially set center point of each class;
continuously adjusting the center point of each class according to the constraint condition, continuously iterating until the center points of all classes are not changed, and stopping iterating;
further, the classification of the pixel points in the image is completed by searching the minimum value of the objective function corresponding to each sample point, so that the separation of the image target and the background is realized;
clustering center a updated based on clustering algorithm i And clustering matrix [ mu ] ij As shown in formula (3), formula (4):
（3），
（4），
wherein a is k A center point representing a kth class; d (x) j a i ) Representing the point j to the i-th class center point a i Distance, d (x) j a k ) Representing the point j to the kth class center point a k Is a distance of (3).
Preferably, the estimating reservoir capacity based on the current reservoir water level area and the established three-dimensional model of the reservoir includes:
setting longitude and latitude of two points on the earth as C (lat 1, lng 1) and D (lat 2, lng 2), wherein the radius of the earth is R, and the distance between the two points is DL;
（9），
setting longitude and latitude coordinates of four points corresponding to the image as C (lat 1, lng 1), D (lat 2, lng 2), E (lat 3, lng 3), F (lat 4, lng 4), and calculating the distance between the CDs as DL according to a distance formula between the two points 1 The distance between DE is DL 2 The method comprises the steps of carrying out a first treatment on the surface of the The actual area S of the reservoir area for the image 1 As shown in formula (10):
（10），
the size of the image is set to be h multiplied by w, and the corresponding area size is S 2 The actual corresponding size P of each pixel point on the image is obtained according to the ratio of the size on the image to the actual corresponding area, and the area S on the image 2 And the actual size P is shown in the formula (11) and the formula (12):
（11），
（12），
the reservoir area S is the actual size represented by the number of the pixel points of the water area multiplied by each pixel point, and the calculation formula is shown in the formula (13):
（13），
wherein sum represents the number of pixel points in a water area, and S represents the area of a reservoir;
calculating the reservoir capacity through a prismatic table formula;
prismatic table formula:
（14），
wherein S is i Represents the area measured by the contour line of the ith layer, S i-1 Represents the area measured by a layer of ascending line on the ith layer, and Deltah represents the equal-altitude distance, V i The calculated storage capacity of the ith layer contour line is shown.
Preferably, the calculating the reservoir permeation quantity in the time period based on the reservoir permeation quantity calculation and combining the reservoir data monitored in real time comprises:
the method for estimating the reservoir permeability in real time based on the water balance formula comprises the following steps:
wherein V is Warehouse entry Representing the water quantity of the reservoir, and measuring by a hydrographic station of the reservoir in-storage river; v (V) Library descent The total precipitation amount of the reservoir surface is represented and is measured by a reservoir rainfall station; v (V) Warehouse out Representing the water yield of the reservoir, including the water discharge capacity of the reservoir, the industrial and agricultural water and the water regulating capacity, and monitoring by a hydrological station under a reservoir dam; v (V) Warehouse steam Representing the evaporation capacity of the water surface of the reservoir, and monitoring from water surface evaporation stations around the reservoir; v (V) Library tolerance Representing the change in reservoir capacity over a period of time, such as due to changes in natural environment;
setting a reservoir permeation quantity calculation formula for calculating the reservoir permeation quantity every half year, and comparing and correcting the real-time estimated reservoir permeation quantity based on the calculated reservoir permeation quantity;
reservoir penetration calculation formula:
wherein Q represents reservoir permeation quantity, Q i The permeation quantity of the reservoir subareas is represented, and Z represents the subarea number; k (K) i Represents the permeability coefficient of the water exchange layer of each zone, G i Representing the area of each area of the reservoir; deltaH i Representing the difference between the reservoir level and the groundwater level; l (L) i Representing the distances from the centroid of each zone to the water level observation well of the groundwater;
setting an error threshold value of the reservoir permeation quantity according to the calculated reservoir permeation quantity, continuing the estimation process when the difference value of the reservoir permeation quantity estimated in real time and the calculated reservoir permeation quantity is within an error range, and checking each item of data in the real-time estimation when the difference value of the reservoir permeation quantity estimated in real time and the calculated reservoir permeation quantity exceeds the error threshold value;
for the reservoir permeation quantity estimated in real time, when the reservoir permeation quantity exceeds the threshold value by setting the reservoir permeation quantity threshold value, judging that the reservoir is damaged by permeation flow, reporting the reservoir permeation quantity through alarm equipment, and arranging to take corresponding engineering remedial measures.
Preferably, the reasonable prediction of the reservoir water level change condition based on the reservoir real-time modeling, the reservoir permeation quantity estimated in real time, the reservoir capacity and the weather condition comprises the following steps:
based on the weather condition of real-time monitoring, the reservoir water-in quantity and reservoir water-out quantity are set to be unchanged by a digital twin technology, and when the weather changes, the reservoir water level change is predicted by the total amount of reservoir surface precipitation and the evaporation amount of the reservoir surface.
Preferably, a series of alarm devices are installed around the reservoir based on the reservoir contour line according to the reservoir capacity and the real-time estimated reservoir permeation quantity, when the water level monitored in real time reaches the alarm devices, the alarm devices report and remind the water level, and corresponding engineering remedial measures are arranged by reservoir staff.
The embodiment also discloses a reservoir visual data supervision and control system based on digital twinning, which specifically comprises: the system comprises a three-dimensional laser shooting device, alarm equipment, an analysis module, a database, a visualization module, a three-dimensional model construction module and a data twinning module;
the three-dimensional laser shooting device is used for shooting the reservoir and the surrounding environment of the reservoir in real time and transmitting the shot image data to the three-dimensional model building module;
the three-dimensional model construction module is used for carrying out real-time modeling according to the image data obtained by transmission, and simultaneously transmitting the three-dimensional model data constructed in real time to the twin module and the visualization module;
the database is used for storing measured data and collected hydrologic data;
the data twinning module is used for predicting the constructed three-dimensional model data according to the calculated data, the reservoir monitoring data and the three-dimensional model data constructed by the three-dimensional model construction module, and transmitting the prediction result to the visualization module;
the analysis module is used for analyzing the reservoir condition according to the data monitored in real time and the collected hydrologic data, and immediately generating an emergency signal to the alarm equipment when the analysis shows that an emergency situation occurs;
the visualization module is used for providing a visual interface to display the transmitted data;
the alarm device is used for reporting the received data in real time.
Compared with the prior art, the invention provides a reservoir visual data supervision and control system and method based on digital twinning, which have the following beneficial effects:
1. according to the invention, the control points are arranged in advance, the three-dimensional laser shooting device is arranged in the control points, the structural outline of the reservoir is considered to the greatest extent, and the real-time performance of monitoring and controlling the reservoir is improved in a mode of modeling shooting data in real time.
2. According to the SAR image segmentation method, the background and the target in the shot image are separated through the clustering algorithm and the mode of continuously adjusting the threshold value, so that the interference of the background on the target is reduced, and the accuracy and the effectiveness of target detection are ensured.
3. According to the invention, the calculation of the reservoir area is realized by means of the ratio of the size of the shot image to the actually corresponding area, and the reservoir capacity on each layer of ascending line is accurately calculated by using a prismatic table formula, so that the accuracy of calculating the reservoir capacity is ensured.
4. According to the invention, the reservoir permeability is calculated by combining the water balance formula and the reservoir permeability calculation formula, and the reservoir water level change condition is reasonably predicted by a digital twin technology based on the reservoir real-time modeling, the real-time estimated reservoir permeability and the reservoir capacity weather condition, so that the rationality and the accuracy of reservoir water level prediction are ensured.
5. The invention realizes the early warning of the water level by installing the alarm equipment on different ascending lines, ensures the timely control of the water level of the reservoir and improves the efficiency of controlling the reservoir.
Drawings
Fig. 1 is a schematic structural diagram of a reservoir visual supervision control flow based on digital twinning.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment discloses a reservoir data supervision and control method, which specifically comprises the following steps:
s1, setting q control points around a reservoir, and installing a three-dimensional laser shooting device on each control point;
the steps of setting q control points around the reservoir and installing the three-dimensional laser shooting device on each control point comprise the following steps:
s11, numbering q control points based on the set control points 1 ，q 2 ，……，q q ；
S12, using control point q 1 For origin of coordinates, according to q 1 The coordinates establish a three-dimensional control coordinate system and are based on the control point q 1 Determining coordinates of other control points;
s13, storing the coordinates of each control point in a database of the system;
s2, shooting the reservoir and the surrounding environment of the reservoir based on three-dimensional laser shooting devices installed on each control point, and constructing a three-dimensional model of the reservoir in real time;
s3, calculating the current water level area of the reservoir through an SAR image segmentation algorithm based on reservoir image data shot in real time, wherein the water level area is the surface area of the water under the current water level of the reservoir;
further, the current reservoir water level area is calculated through the SAR image segmentation algorithm, the water level area is the water surface area under the current water level of the reservoir, and the method comprises the following steps:
s31, separating an image target from a background through a fuzzy average algorithm;
setting the number of total pixel points of an image as N, dividing the total pixel points into A types, wherein a is the center point of each type, and [ mu ] ij The expression point j belongs to the i-th class, and the objective function expression (1) and the constraint condition expression (2) express:
（1），
(2) Wherein m is a blur index, the blur index is a blur degree in a shooting image, and a i Represents the center point of class i, x j Represents the sample point of the acquisition, | (x) j -a i )|| 2 Representing the point j to the i-th class center point a i J represents an objective function;
further, iterative clustering operation is carried out on the similar pixels, and separation of the image target and the background is completed;
the iterative clustering operation for the similar pixels comprises:
calculating the distance from the random point in the class to the class center point based on the initially set center point of each class;
continuously adjusting the center point of each class according to the constraint condition, continuously iterating until the center points of all classes are not changed, and stopping iterating;
further, the classification of the pixel points in the image is completed by searching the minimum value of the objective function corresponding to each sample point, so that the separation of the image target and the background is realized;
further, a clustering center a updated based on clustering algorithm i And clustering matrix [ mu ] ij As shown in formula (3), formula (4):
（3），
(4) Wherein a is k A center point representing a kth class; d (x) j a i ) Representing the point j to the i-th class center point a i Distance, d (x) j a k ) Representing the point j to the kth class center point a k Is a distance of (2);
s32, optimizing the separated image targets;
image enhancement algorithms in the prior art may be employed, for example: model-free, model-based, and optimization strategy-based methods, etc.;
in order to realize the cooperation with the image separation algorithm, the scheme is preferable to automatically select a threshold value by using the gray scale characteristics of the image through a multi-threshold value Otsu algorithm to perform image segmentation, obtain a gray scale average value set of each uniform region, and take the gray scale average value set as the threshold value input of a blurring algorithm;
setting the gray level of the image as L and the number of pixel points with the gray level value of L as n l The number of the total pixel points is N, and after normalization processing, the ratio of each gray value isGray average value ∈of image>Sum of variances of->The method comprises the steps of carrying out a first treatment on the surface of the Setting the image to be s-type, wherein the probability of each type is represented by formula (5), the average value is represented by formula (6) and the variance is represented by formula (7):
（5），
（6），
（7），
wherein t is k As a threshold value for the segmentation,representing the probability that a pixel in class k satisfies a threshold, u k For the gray average value, sigma, of k-class images k Is the variance between k classes of images;
further, the multi-threshold inter-class variance is shown in equation (8):
（8），
further, the first four segmentation thresholds are taken out from a gray level average value set of segmentation results of the multi-threshold Otsu algorithm and used as constraint condition parts of the fuzzy clustering algorithm, and optimization of the separated image targets is achieved by updating values of the constraint condition parts of the fuzzy clustering algorithm;
s4, estimating reservoir capacity based on the current reservoir water level area and the established three-dimensional model of the reservoir;
setting longitude and latitude of two points on the earth as C (lat 1, lng 1) and D (lat 2, lng 2), wherein the radius of the earth is R, and the distance between the two points is DL;
（9），
setting longitude and latitude coordinates of four points corresponding to the image as C (lat 1, lng 1), D (lat 2, lng 2), E (lat 3, lng 3), F (lat 4, lng 4), and calculating the distance between the CDs as DL according to a distance formula between the two points 1 The distance between DE is DL 2 The method comprises the steps of carrying out a first treatment on the surface of the The actual area S of the reservoir area for the image 1 As shown in formula (10);
（10），
the size of the image is set to be h multiplied by w, and the corresponding area size is S 2 The actual corresponding size P of each pixel point on the image is obtained according to the ratio of the size on the image to the actual corresponding area, and the area S on the image 2 And the actual size P is shown in the formula (11) and the formula (12):
（11），
（12），
further, the reservoir area S is the actual size represented by the number of the pixel points in the water area multiplied by each pixel point, and the calculation formula is shown in formula (13):
（13），
wherein sum represents the number of pixel points in a water area, and S represents the area of a reservoir;
further, calculating the reservoir capacity through a prismatic table formula;
prismatic table formula:
（14），
wherein S is i Represents the area measured by the contour line of the ith layer, S i-1 Represents the area measured by a layer of ascending line on the ith layer, and Deltah represents the equal-altitude distance, V i Representing the calculated storage capacity of the ith layer contour line;
s5, estimating the reservoir permeability in unit time in real time based on a reservoir permeability calculation mode and combining reservoir data monitored in real time; predicting the change condition of the reservoir water level based on the established three-dimensional model of the reservoir, the permeation quantity of the reservoir in unit time, the reservoir capacity and the weather condition;
the method for estimating the reservoir permeability in real time based on the water balance formula comprises the following steps:
wherein V is Warehouse entry Representing the water quantity of the reservoir, and measuring by a hydrographic station of the reservoir in-storage river; v (V) Library descent The total precipitation amount of the reservoir surface is represented and is measured by a reservoir rainfall station; v (V) Warehouse out Representing the water yield of the reservoir, including the water discharge capacity of the reservoir, the industrial and agricultural water and the water regulating capacity, and monitoring by a hydrological station under a reservoir dam; v (V) Warehouse steam Representing the evaporation capacity of the water surface of the reservoir, and monitoring from water surface evaporation stations around the reservoir; v (V) Library tolerance Representing the change in reservoir capacity over a period of time, such as due to changes in natural environment;
further, reservoir permeation quantity is calculated through a reservoir permeation quantity calculation formula every half year, and comparison and correction are carried out on the real-time estimated reservoir permeation quantity based on the calculated reservoir permeation quantity;
reservoir penetration calculation formula:
wherein Q represents reservoir permeation quantity, Q i The permeation quantity of the reservoir subareas is represented, and Z represents the subarea number; k (K) i Represents the permeability coefficient of the water exchange layer of each zone, G i Representing the area of each area of the reservoir; deltaH i Representing the difference between the reservoir level and the groundwater level; l (L) i Representing the distances from the centroid of each zone to the water level observation well of the groundwater;
setting an error threshold value of the reservoir permeation quantity according to the calculated reservoir permeation quantity, continuing the estimation process when the difference value of the reservoir permeation quantity estimated in real time and the calculated reservoir permeation quantity is within an error range, and checking each item of data in the real-time estimation when the difference value of the reservoir permeation quantity estimated in real time and the calculated reservoir permeation quantity exceeds the error threshold value;
for the reservoir permeation quantity estimated in real time, judging that the reservoir is damaged by permeation flow when the reservoir permeation quantity exceeds a threshold value by setting the reservoir permeation quantity threshold value, reporting by alarm equipment, and arranging to take corresponding engineering remedial measures;
further, based on the weather condition monitored in real time, the reservoir water inflow and reservoir water outflow are set to be unchanged by a digital twin technology, and when the weather changes, the reservoir water level change is predicted by the total amount of reservoir surface precipitation and the reservoir water surface evaporation;
s6, corresponding measures are taken to manage the reservoir based on the water level change condition of the reservoir;
according to reservoir capacity and real-time estimated reservoir permeability, a series of alarm devices are installed around the reservoir based on a reservoir contour line, when the water level monitored in real time reaches the alarm devices, the alarm devices report and remind the water level, and corresponding engineering remedial measures are arranged by reservoir staff;
the embodiment also discloses a reservoir visual data supervision and control system based on digital twinning, which specifically comprises: the system comprises a three-dimensional laser shooting device, alarm equipment, an analysis module, a database, a visualization module, a three-dimensional model construction module and a data twinning module;
the three-dimensional laser shooting device is used for shooting the reservoir and the surrounding environment of the reservoir in real time and transmitting the shot image data to the three-dimensional model building module;
the three-dimensional model construction module is used for carrying out real-time modeling according to the image data obtained by transmission, and simultaneously transmitting the three-dimensional model data constructed in real time to the twin module and the visualization module;
the database is used for storing measured data and collected hydrologic data;
the data twinning module is used for predicting the constructed three-dimensional model data according to the calculated data, the reservoir monitoring data and the three-dimensional model data constructed by the three-dimensional model construction module, and transmitting the prediction result to the visualization module;
the analysis module is used for analyzing the reservoir condition according to the data monitored in real time and the collected hydrologic data, and immediately generating an emergency signal to the alarm equipment when the analysis shows that an emergency situation occurs;
the visualization module is used for providing a visual interface to display the transmitted data;
the alarm device is used for reporting the received data in real time.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. The reservoir data supervision and control method is characterized by comprising the following steps of:
s1, setting q control points around a reservoir, and installing a three-dimensional laser shooting device on each control point;
s2, shooting the reservoir and the surrounding environment of the reservoir based on three-dimensional laser shooting devices installed on each control point, and constructing a three-dimensional model of the reservoir in real time;
s3, calculating the current water level area of the reservoir through an SAR image segmentation algorithm based on reservoir image data shot in real time, wherein the water level area is the surface area of the water under the current water level of the reservoir;
s4, estimating reservoir capacity based on the current reservoir water level area and the established three-dimensional model of the reservoir;
s5, estimating the reservoir permeability in unit time in real time based on a reservoir permeability calculation mode and combining reservoir data monitored in real time; predicting the change condition of the reservoir water level based on the established three-dimensional model of the reservoir, the permeation quantity of the reservoir in unit time, the reservoir capacity and the weather condition;
and S6, corresponding measures are taken to manage the reservoir based on the water level change condition of the reservoir.
2. The reservoir data supervision and control method according to claim 1, wherein the step of setting q control points around the reservoir and installing a three-dimensional laser shooting device on each control point comprises:
s11, numbering q control points based on the set control points 1 ，q 2 ，……，q q ；
S12, using control point q 1 For origin of coordinates, according to q 1 The coordinates establish a three-dimensional control coordinate system and are based on the control point q 1 Determining coordinates of other control points;
s13, storing the coordinates of each control point in a database of the system.
3. The method for supervising and controlling reservoir data according to claim 1, wherein the calculating the current water level area of the reservoir by the SAR image segmentation algorithm, the water level area being the surface area of the water under the current water level of the reservoir, comprises the steps of:
s31, separating an image target from a background through a fuzzy average algorithm;
s32, optimizing the separated image targets.
4. A reservoir data supervision and control method according to claim 3, wherein the separation of the image target and the background by the fuzzy average algorithm comprises:
setting the number of total pixel points of an image as N, dividing the total pixel points into A types, wherein a is the center point of each type, and [ mu ] ij The expression point j belongs to the i-th class, and the objective function 1 and constraint condition 2 are expressed as follows:
（1），
(2) Wherein m is a blur index, the blur index is a blur degree in a shooting image, and a i Represents the center point of class i, x j Represents the sample point of the acquisition, | (x) j -a i )|| 2 Representing the point j to the i-th class center point a i J represents an objective function;
performing iterative clustering operation on the similar pixels to finish the separation of the image target and the background;
the iterative clustering operation for the similar pixels comprises:
calculating the distance from the random point in the class to the class center point based on the initially set center point of each class;
continuously adjusting the center point of each class according to the constraint condition, continuously iterating until the center points of all classes are not changed, and stopping iterating;
the classification of the pixel points in the image is completed by searching the minimum value of the objective function corresponding to each sample point, so that the separation of the image target and the background is realized;
clustering center a updated based on clustering algorithm i And clustering matrix [ mu ] ij As shown in formula 3, formula 4:
（3），
(4) Wherein a is k A center point representing a kth class; d (x) j a i ) Representing the point j to the i-th class center point a i Distance, d (x) j a k ) Representing the point j to the kth class center point a k Is a distance of (3).
5. The method for supervising and controlling reservoir data according to claim 1, wherein the estimating reservoir capacity based on the current reservoir water level area and the established three-dimensional model of the reservoir comprises:
setting longitude and latitude of two points on the earth as C (lat 1, lng 1) and D (lat 2, lng 2), wherein the radius of the earth is R, and the distance between the two points is DL;
（9），
the longitude and latitude coordinates of four points forming a rectangle in the image are set as C (lat 1, lng 1), D (lat 2, lng 2), E (lat 3, lng 3), F (lat 4, lng 4), and the distance between the CDs can be calculated as DL according to a distance formula between the two points 1 The distance between DE is DL 2 The method comprises the steps of carrying out a first treatment on the surface of the Water for image pairActual area S of library region 1 As shown in equation 10:
（10），
the size of the image is set to be h multiplied by w, and the corresponding area size is S 2 The actual corresponding size P of each pixel point on the image is obtained according to the ratio of the size on the image to the actual corresponding area, and the area S on the image 2 And the actual size P is shown in formula 11, formula 12:
（11），
（12），
the reservoir area S is the actual size represented by the number of the pixel points of the water area multiplied by each pixel point, and the calculation formula is shown in formula 13:
（13），
wherein sum represents the number of pixel points in a water area, and S represents the area of a reservoir;
calculating the reservoir capacity through a prismatic table formula;
prismatic table formula:
（14），
wherein S is i Represents the area measured by the contour line of the ith layer, S i-1 Represents the area measured by a layer of ascending line on the ith layer, and Deltah represents the equal-altitude distance, V i The calculated storage capacity of the ith layer contour line is shown.
6. The reservoir data supervision and control method according to claim 1, wherein the reservoir permeability real-time estimation of the reservoir permeability in unit time based on the reservoir permeability calculation mode and in combination with the reservoir data monitored in real time comprises:
the method for estimating the reservoir permeability in real time based on the water balance formula comprises the following steps:
wherein V is Warehouse entry Representing the water quantity of the reservoir, and measuring by a hydrographic station of the reservoir in-storage river; v (V) Library descent The total precipitation amount of the reservoir surface is represented and is measured by a reservoir rainfall station; v (V) Warehouse out Representing the water yield of the reservoir, including the water discharge capacity of the reservoir, the industrial and agricultural water and the water regulating capacity, and monitoring by a hydrological station under a reservoir dam; v (V) Warehouse steam Representing the evaporation capacity of the water surface of the reservoir, and monitoring from water surface evaporation stations around the reservoir; v (V) Library tolerance Representing the change of reservoir capacity in a period of time;
setting a reservoir permeation quantity calculation formula for calculating the reservoir permeation quantity every half year, and comparing and correcting the real-time estimated reservoir permeation quantity based on the calculated reservoir permeation quantity;
reservoir penetration calculation formula:
wherein Q represents reservoir permeation quantity, Q i The permeation quantity of the reservoir subareas is represented, and Z represents the subarea number; k (K) i Represents the permeability coefficient of the water exchange layer of each zone, G i Representing the area of each area of the reservoir; deltaH i Representing the difference between the reservoir level and the groundwater level; l (L) i Representing the distances from the centroid of each zone to the water level observation well of the groundwater;
setting an error threshold value of the reservoir permeation quantity according to the calculated reservoir permeation quantity, continuing the estimation process when the difference value of the reservoir permeation quantity estimated in real time and the calculated reservoir permeation quantity is within an error range, and checking each item of data in the real-time estimation when the difference value of the reservoir permeation quantity estimated in real time and the calculated reservoir permeation quantity exceeds the error threshold value;
for the reservoir permeation quantity estimated in real time, when the reservoir permeation quantity exceeds the threshold value by setting the reservoir permeation quantity threshold value, judging that the reservoir is damaged by permeation flow, reporting the reservoir permeation quantity through alarm equipment, and arranging to take corresponding engineering remedial measures.
7. The reservoir data supervision and control method according to claim 1, wherein the predicting reservoir water level change conditions based on the established three-dimensional model of the reservoir, the permeation amount of the reservoir in unit time, the reservoir capacity and the weather conditions comprises:
based on the weather condition of real-time monitoring, the reservoir water-in quantity and reservoir water-out quantity are set to be unchanged by a digital twin technology, and when the weather changes, the reservoir water level change is predicted by the total amount of reservoir surface precipitation and the evaporation amount of the reservoir surface.
8. A digital twinning-based reservoir visual data supervisory control system implementing the reservoir data supervisory control method of any of claims 1-7, comprising: the system comprises a three-dimensional laser shooting device, alarm equipment, an analysis module, a database, a three-dimensional model construction module and a data twinning module;
the three-dimensional laser shooting device is used for shooting the reservoir and the surrounding environment of the reservoir in real time and transmitting the shot image data to the three-dimensional model building module;
the three-dimensional model construction module is used for carrying out real-time modeling according to the image data obtained by transmission, and simultaneously transmitting the three-dimensional model data constructed in real time to the twin module and the visualization module;
the database is used for storing measured data and collected hydrologic data;
the data twinning module is used for predicting the constructed three-dimensional model data according to the calculated data, the reservoir monitoring data and the three-dimensional model data constructed by the three-dimensional model construction module, and transmitting the prediction result to the visualization module;
the analysis module is used for analyzing the reservoir condition according to the data monitored in real time and the collected hydrologic data, and immediately generating an emergency signal to the alarm equipment when the analysis shows that an emergency situation occurs;
the alarm device is used for reporting the received data in real time.
9. The digital twinning-based reservoir visual data supervisory control system of claim 8, further comprising a visualization module for providing a visual interface for displaying the transmitted data.
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