Reservoir data matrix management platform and method based on artificial intelligence

Abstract

The application discloses a reservoir data matrix management platform and method based on artificial intelligence, relates to the technical field of reservoir data management, and comprises a reservoir three-dimensional simulation module, a reservoir data analysis module, a water quality factor index prediction module, a warning module, a water supply and diversion scheduling plan generation module and a reservoir management module; the reservoir three-dimensional simulation module is used for constructing a real-time three-dimensional simulation model of a target reservoir; the reservoir data analysis module is used for generating real-time water supply and diversion scheduling characteristics and basin flow characteristics of the target reservoir in each basin; the water quality factor index prediction module is used for predicting real-time water quality factor indexes of each basin of the target reservoir; and the warning module selectively sends a warning signal according to real-time water supply and diversion scheduling warning values of each basin of the target reservoir. Through mutual support among the modules, the application realizes matrix management of the target reservoir, and further guarantees safe operation of the target reservoir.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of reservoir data management, and particularly relates to a reservoir data matrix management platform and method based on artificial intelligence. BACKGROUND

[0002] The reservoir matrix operation management system is a system that adopts a matrix management framework and utilizes technologies such as digital twinning, Internet of Things, cloud computing, big data and artificial intelligence to achieve comprehensive, systematic, accurate and efficient reservoir operation management. Through the management mode of horizontal connection and vertical connection, the system enables mutual coordination and mutual support between various management departments, and ensures the safe operation of the reservoir and the full play of the benefits.

[0003] At present, the reservoir management platform monitors the reservoir data in real time. However, due to the lack of connection between the modules of the management platform and the large area of the reservoir, the monitoring data cannot be processed in a short time, so the dynamic change information of the reservoir data cannot be grasped in a short time. In addition, the reservoir data at different positions are affected by environmental factors and have different trends, so when considering the reservoir early warning decision conditions, the reservoir data at different positions need to be considered, which increases the data analysis amount. In addition, the generation of the existing reservoir water supply and diversion scheduling plan is based on the reservoir storage variable condition, and cannot prevent floods, that is, cannot guarantee the safe operation of the reservoir. SUMMARY

[0004] The present application aims to provide a reservoir data matrix management platform and method based on artificial intelligence to solve the problems in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a reservoir data matrix management platform based on artificial intelligence, the platform comprising a reservoir three-dimensional simulation module, a reservoir data analysis module, a water quality factor index prediction module, an early warning module, a water supply and diversion scheduling plan generation module and a reservoir management module.

[0006] The reservoir three-dimensional simulation module establishes a three-dimensional model of the target reservoir by oblique photography of the target reservoir position, and combines a BIM model, GIS coordinate data and monitoring data to construct a real-time three-dimensional simulation model of the target reservoir. The monitoring data includes rainwater condition data, water level data and water flow data of the target reservoir at each monitoring point.

[0007] The reservoir data analysis module generates real-time water supply and diversion scheduling characteristics and flow characteristics of each watershed of the target reservoir according to the change of the gradient index in each watershed of the target reservoir.

[0008] The water quality factor index prediction module predicts real-time water quality factor indexes of each watershed of the target reservoir according to watershed flow characteristics and rainwater condition data of the target reservoir in each watershed;

[0009] The early warning module selectively sends early warning signals according to real-time water supply and diversion scheduling early warning values of each watershed of the target reservoir;

[0010] The water supply and diversion scheduling plan generation module analyzes scheduling relationships between the target reservoir and each object based on object relationships of the target reservoir and water quality factor indexes of each watershed, and generates a water supply and diversion scheduling plan of the target reservoir in combination with early warning signals sent by the early warning module;

[0011] The reservoir matrix module manages water supply and diversion scheduling of the target reservoir according to the water supply and diversion scheduling plan.

[0012] Further, the reservoir data analysis module includes a watershed division unit, a watershed flow characteristic generation unit, and a water supply and diversion scheduling characteristic generation unit;

[0013] The watershed division unit divides the target reservoir into watersheds according to distribution of each monitoring point of the target reservoir, obtains a plurality of watersheds based on a division result, each watershed contains only one monitoring point and there is no regional intersection between randomly two watersheds, a number corresponding to the watershed is the same as a number of the monitoring point contained by the watershed, wherein i=1, 2, …, n represents a number corresponding to each monitoring point of the target reservoir, and n represents a total number of monitoring points of the target reservoir;

[0014] The gradient index is used to divide the target reservoir into watersheds, so that water flow data change trends in the same watershed are consistent, the water flow data includes water flow speed, water flow, dissolved oxygen content in water, and errors caused by inconsistent water flow data change trends are avoided, and analysis accuracy of reservoir data of the target reservoir is improved;

[0015] The watershed flow characteristic generation unit determines a horizontal flow coefficient w i(t-d→t) and a vertical flow coefficient r i(t-d→t) of the i-th watershed of the target reservoir in a [t-d, t] time period according to a real-time three-dimensional simulation model of the target reservoir, and generates a watershed flow characteristic N it of the i-th watershed of the target reservoir at the t time point based on a determination result, N it = (w i(t-d→t) , r i(t-d→t) ), wherein d represents a collection interval of monitoring data, and t represents a real-time time;

[0016] The water supply and diversion scheduling characteristic generation unit determines a water level type g i(t-d→t)And the water supply scheduling early warning value k i(t-d→t) Determination is made, based on the determination result, the target reservoir i basin at t time of water supply scheduling characteristics M it , M it =(g i(t-d→t) 、k i(t-d→t) )。

[0017] According to the flow characteristics of the target reservoir basin and the water supply scheduling characteristics, the water reservoir data difference between the two banks is considered in the early warning decision condition, which is beneficial to realize the early warning of the reservoir dam.

[0018] Further, the specific method of the basin division unit for dividing the basin of the target reservoir is: in the real-time three-dimensional simulation model of the target reservoir, taking the i monitoring point as the center, the horizontal distance j×u as the water level height collection scale, determining the target position point and the matching target position point of the i monitoring point, collecting the water level height of each target position point and each matching target position point of the target reservoir, calculating the gradient index of the target position point j-1 and the matching target position point j respectively, and the ratio between the water level height difference value between the target position point j and the target position point j-1 and the water level height difference value between the matching target position point j-1 and the matching target position point j and u.

[0019] Randomly select the water level height collection scale s, if the difference between the gradient index of the target position point corresponding to the selected water level height collection scale s and the gradient index of the matching target position point corresponding to the selected water level height collection scale s is within the error range, and the difference between the gradient index of the target position point corresponding to the water level height collection scale s+u and the gradient index of the matching target position point corresponding to the water level height collection scale s+u is not within the error range, then the i monitoring point is taken as the center, s is taken as the radius to construct a spherical region, and the intersection region of the spherical region and the target reservoir is taken as the basin region corresponding to the i monitoring point, j=1,2,…,m represents the numbering of each target position point or matching target position point according to the distance value from the monitoring point from small to large, m represents the total number of numbering, and u represents the distance value.

[0020] Further, the specific method of the basin flow characteristic generation unit for determining the transverse flow coefficient and the longitudinal flow coefficient is:

[0021] In the i basin, the average value W (j×u)i(t-d→t)Calculating the difference Q between the average gradient index of the target position point corresponding to the water level height collection scale j x u in the time period [t-d, t] and the average gradient index of the matching target position point in the time period [t-d, t] (j×u)i(t-d→t) Calculating all Q for j = 1 to j = m (j×u)i(t-d→t) Summation processing to obtain Q' (j×u)i(t-d→t) , the transverse flow coefficient w of the i-th watershed of the target reservoir in the time period [t-d, t] i(t-d→t) =[W (j×u)i(t-d→t) +Q´ (j×u)i(t-d→t) / m] / [W (j×u)z(t-d→t) +Q´ (j×u)z(t-d→t) / m], where z represents the number corresponding to the nearest opposite monitoring point of the i-th monitoring point in the upstream area of the i-th monitoring point;

[0022] The longitudinal flow coefficient r of the i-th watershed of the target reservoir in the time period [t-d, t] i(t-d→t) =[W (j×u)i(t-d→t) +Q´ (j×u)i(t-d→t) / m] / [W (j×u)v(t-d→t) +Q´ (j×u)v(t-d→t) / m], where v represents the number corresponding to the nearest same side monitoring point of the i-th monitoring point in the upstream area of the i-th monitoring point.

[0023] Further, the specific method for determining the supply water scheduling warning value by the supply water scheduling feature generation unit is:

[0024] According to the water level type of the i-th watershed of the target reservoir in the time period [t-d, t], the water level type includes dead water level, check flood level and normal storage level, the first supply water scheduling warning value Y it of the i-th watershed of the target reservoir at time t is determined, and according to the watershed flow characteristics of the i-th watershed of the target reservoir at time t, the second supply water scheduling warning value X it of the i-th watershed of the target reservoir at time t is determined, X it =(w i(t-d→t) ×sinβ ziv +r i(t-d→t) )×(1-Y it )×f pi(t-d→t) , where β ziv represents the included angle between the line connecting the z-th monitoring point and the i-th monitoring point and the line connecting the v-th monitoring point and the i-th monitoring point, p = 1, 2, 3, when p = 1, the water level type is dead water level, when p = 2, the water level type is check flood level, and when p = 3, the water level type is normal storage level, f pi(t-d→t) = 1 or f pi(t-d→t) = -1, when the water level type is dead water level and K ijt -Kij(t-d) <0, the water level type is normal storage water level or checking flood level and K´ ijt -K´ ij(t-d) ≥0, f pi(t-d→t) =1, when the water level type is dead water level and K ijt -K ij(t-d) ≥0, the water level type is normal storage water level or checking flood level and K´ ijt -K´ ij(t-d) <0, f pi(t-d→t) =-1, K ijt , K´ ijt respectively represent the water level height value of the target position point j in the i-th basin of the target reservoir at t time, K ij(t-d) , K´ ij(t-d) respectively represent the water level height value of the target position point j in the i-th basin of the target reservoir at t-d time.

[0025] The sum value between X it and Y it is calculated to obtain the water supply scheduling early warning value k i(t-d→t) of the i-th basin of the target reservoir in the [t-d, t] time period.

[0026] Further, the specific method for the water quality factor index prediction module to predict the real-time water quality factor index of each basin of the target reservoir is as follows:

[0027] According to the real-time three-dimensional simulation model of the target reservoir, the average rainfall J i(t-d→t) and the average rainfall intensity G i(t-d→t) of the i-th basin of the target reservoir in the [t-d, t] time period are determined, and e is taken as the base number, the average rainfall J i(t-d→t) and the average rainfall intensity G i(t-d→t) are respectively used to construct exponential functions P1 and P2, P1=1-exp(-J i(t-d→t) ), P2=1-exp(-G i(t-d→t) ), wherein e=2.73, and the water quality factor index of the i-th basin of the target reservoir at t time is calculated according to S it =a1×P1+a2×P2+a3×{1-exp[-(w i(t-d→t) +r i(t-d→t) ×cosβ ziv )]}, wherein a1, a2 and a3 all represent proportional coefficients and a1+a2+a3=1, the water quality factor index calculated based on the above method can consider the influence of water turbidity, dissolved oxygen content in water and nutrients in water on the calculation result, and the rainfall condition can affect the turbidity of water.

[0028] Further, the early warning module judges whether the supply and diversion water scheduling early warning value of the target reservoir i watershed at t time is greater than the early warning threshold value, if yes, sends an early warning signal to the supply and diversion water scheduling plan generation module, the early warning signal is the water level value and the number i of the target reservoir i watershed at t time, if not, no need to send an early warning signal to the supply and diversion water scheduling plan generation module.

[0029] According to the supply and diversion water scheduling early warning value, the comprehensive perception of the safety situation of the target reservoir and the accurate prediction of the risk hidden danger are realized, which is beneficial to provide decision support for the supply and diversion water scheduling of the target reservoir.

[0030] Further, the supply and diversion water scheduling plan generation module includes a scheduling object construction unit, a scheduling relationship analysis unit and a supply and diversion water scheduling plan generation unit.

[0031] The scheduling object construction unit sorts out the historical supply and diversion water system topological relationship diagram of the target reservoir, obtains the supply and diversion water information of the target reservoir, the supply and diversion water information includes the reservoir name, the reservoir supply and diversion water type and the reservoir supply and diversion water quantity, based on the supply and diversion water information, the first scheduling object set and the second scheduling object set are obtained, the first scheduling object set stores the reservoir name of the reservoir with the water supply quantity greater than 0, and the second scheduling object set stores the reservoir name of the reservoir with the water diversion quantity greater than 0;

[0032] The scheduling relationship analysis unit determines the scheduling object of the i watershed according to the water level type of the target reservoir i watershed at t time and the received early warning signal;

[0033] The supply and diversion water scheduling plan generation unit determines the supply and diversion water scheduling sequence of each watershed of the target reservoir according to the received early warning signal, the supply and diversion water scheduling sequence of each watershed is the sequence of the number of each watershed transmitted to the supply and diversion water scheduling plan generation unit, in combination with the scheduling object of each watershed of the target reservoir, the supply and diversion water scheduling plan of the target reservoir is generated.

[0034] According to the early warning situation of each watershed of the target reservoir and the water quality change situation of each watershed, the supply and diversion water scheduling object of each watershed is determined, which can realize accurate flood control processing of the target reservoir, and also can ensure that the supply and diversion water result will not affect the water quality of the corresponding reservoir.

[0035] Further, the specific method for determining the scheduling object of the i watershed by the scheduling relationship analysis unit is:

[0036] When the water level type of the i-th basin is a dead water level and the number i exists in the early warning signal received at the t time, the scheduling object type of the i-th basin is a second scheduling object, when the water level type is a check flood level and the number i exists in the early warning signal received at the t time, the scheduling object type of the i-th basin is a first scheduling object, the scheduling objects of the i-th basin are screened according to the water quality factor index of the i-th basin, and the specific screening method is: judging whether the water quality factor index of the i-th basin can reach the minimum water quality factor index requirement of each scheduling object corresponding to the determined scheduling object type, retaining the scheduling object corresponding to the minimum water quality factor index that can be reached, and eliminating the scheduling object corresponding to the minimum water quality factor index that cannot be reached;

[0037] The shortest distance value between the i-th basin and each scheduling object screened and retained is determined, and based on the determination result, the scheduling objects of the i-th basin are determined.

[0038] A reservoir data matrix management method based on artificial intelligence, the method comprises:

[0039] S10: a three-dimensional model of the target reservoir is established by oblique photography on the target reservoir position, and a real-time three-dimensional simulation model of the target reservoir is constructed by combining a BIM model, GIS coordinate data and monitoring data, the monitoring data including rainwater condition data, water level data and water flow data of the target reservoir at each monitoring point;

[0040] S20: according to the change of the gradient index in each basin of the target reservoir, real-time water supply and diversion scheduling characteristics and basin flow characteristics of the target reservoir in each basin are generated;

[0041] S30: according to the basin flow characteristics and the rainwater condition data of the target reservoir in each basin, the real-time water quality factor index of each basin of the target reservoir is predicted;

[0042] S40: according to the real-time water supply and diversion scheduling early warning value of each basin of the target reservoir, a selective early warning signal is sent out;

[0043] S50: based on the object relationship constructed by the target reservoir and the water quality factor index of each basin, the scheduling relationship between the target reservoir and each object is analyzed, and a water supply and diversion scheduling plan of the target reservoir is generated in combination with the early warning signal sent out by the early warning module;

[0044] S60: according to the water supply and diversion scheduling plan, the target reservoir is managed for water supply and diversion.

[0045] Compared with the prior art, the beneficial effects of the present application are:

[0046] 1. The present invention searches for the real-time basin flow characteristics and water supply and diversion scheduling characteristics of the target reservoir based on the real-time three-dimensional simulation model of the target reservoir. The search process can not only analyze the horizontal and vertical connections of each basin, but also eliminate the errors caused by inconsistent trends in water flow data, which is conducive to accurate analysis of the target reservoir data. The search results can be used to grasp various factors upstream and downstream of the reservoir, based on which full coverage, full-factor, all-weather and full-cycle management of the reservoir can be achieved.

[0047] 2. The present invention analyzes the changes in water quality in each river basin based on the rainfall data and flow characteristics of each river basin. There is no need to make complex considerations on factors affecting water quality. Combined with the early warning situation of each river basin, it can determine the real-time water supply and diversion scheduling objects of each river basin, and provide support for scientific flood control scheduling of flood control departments. That is, it can achieve accurate flood control treatment of target reservoirs, and can also ensure that the water supply and diversion results will not affect the water quality of the corresponding reservoir, and realize the forecast, early warning, rehearsal and plan of flood control in the entire reservoir area.

[0048] 3. The present invention realizes comprehensive perception of the safety situation of the target reservoir and accurate prediction of potential risks based on the water supply and diversion scheduling warning value, which is conducive to providing decision support for the water supply and diversion scheduling of the target reservoir.

[0049] 4. The present invention realizes matrix management of the target reservoir through mutual support between modules, thereby ensuring the safe operation of the target reservoir. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Fig. 1 This is a schematic diagram of the working principle structure of an artificial intelligence-based reservoir data matrix management platform of the present invention;

[0051] Fig. 2 This is a workflow diagram of an artificial intelligence-based reservoir data matrix management method of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0053] Example: Figs. 1-2As shown, the present invention provides a reservoir data matrix management platform and method technical solution based on artificial intelligence, a reservoir data matrix management platform based on artificial intelligence, the platform includes a reservoir three-dimensional simulation module, a reservoir data analysis module, a water quality factor index prediction module, an early warning module, a water supply and diversion scheduling plan generation module and a reservoir management module;

[0054] The reservoir 3D simulation module establishes a 3D model of the target reservoir by taking oblique photography of the target reservoir location. It then combines the BIM model, GIS coordinate data, and monitoring data to build a real-time 3D simulation model of the target reservoir. The monitoring data includes rainfall data, water level data, and water flow data at each monitoring point of the target reservoir.

[0055] The reservoir data analysis module generates the real-time water supply and diversion scheduling characteristics and basin flow characteristics of the target reservoir in each basin based on the changes in the gradient index in each basin of the target reservoir;

[0056] The reservoir data analysis module includes a watershed division unit, a watershed flow characteristics generation unit, and a water supply and diversion scheduling characteristics generation unit;

[0057] The watershed division unit divides the watershed of the target reservoir according to the distribution of each monitoring point of the target reservoir. The specific division method is as follows: in the real-time three-dimensional simulation model of the target reservoir, with the i-th monitoring point as the center and the horizontal distance j×u as the water level height collection scale, the target location point and the matching target location point of the i-th monitoring point are determined. The shortest distance between each target location point or each matching target location point and the coastline of the target reservoir are equal. The connection line constructed by each target location point and the i-th monitoring point extends from the i-th monitoring point to the upstream of the target reservoir. The shortest distance between two adjacent target location points is u. The connection line constructed by each matching target location point and the i-th monitoring point extends from the i-th monitoring point to the downstream of the target reservoir. The shortest distance between two adjacent matching target location points is u. The water level height of the target reservoir at each target location point and each matching target location point is collected. According to H j(j-1) =G j(j-1) / u calculates the gradient index of the target position point j-1, according to H´ (j-1)j =G´ (j-1)j / u calculates the gradient index of the matching target position point j, where G j(j-1) 、G´ (j-1)j They represent the water level difference between target position point j and target position point j-1, and the water level difference between matching target position point j-1 and matching target position point j respectively;

[0058] The water level collection scale s is randomly selected. If the difference between the gradient index of the target position point corresponding to the selected water level collection scale s and the gradient index of the matching target position point corresponding to the selected water level collection scale s is within the error range, and the difference between the gradient index of the target position point corresponding to the water level collection scale s+u and the gradient index of the matching target position point corresponding to the water level collection scale s+u is not within the error range, then the i-th monitoring point is taken as the center, and s is taken as the radius to construct a spherical region. The intersection region of the target reservoir is taken as the watershed region corresponding to the i-th monitoring point. Based on the division result, a plurality of watersheds are obtained, each watershed contains only one monitoring point, and there is no regional intersection between randomly two watersheds. The number of the watershed is the same as the number of the monitoring point contained in the watershed, wherein i=1, 2, …, n represents the number of the monitoring point corresponding to the target reservoir, n represents the total number of the monitoring points of the target reservoir, j=1, 2, …, m represents the number of the target position point or the matching target position point in the order from small to large according to the distance value of the target position point or the matching target position point from the monitoring point, m represents the total number of the number, u represents the distance value, and the number of the target position point with the same shortest distance from the i-th monitoring point is the same as the number of the matching target position point;

[0059] The watershed flow characteristic generation unit determines the horizontal flow coefficient w i(t-d→t) and the vertical flow coefficient r i(t-d→t) of the i-th watershed of the target reservoir in the [t-d, t] time period according to the real-time three-dimensional simulation model of the target reservoir, and the specific method is as follows:

[0060] In the i-th watershed, the average value W (j×u)i(t-d→t) of the gradient index of the target position point corresponding to the water level collection scale j×u in the [t-d, t] time period and the average value of the gradient index of the matching target position point corresponding to the water level collection scale j×u in the [t-d, t] time period is calculated. The difference Q (j×u)i(t-d→t) between the average value of the gradient index of the target position point corresponding to the water level collection scale j×u in the [t-d, t] time period and the average value of the gradient index of the matching target position point corresponding to the water level collection scale j×u in the [t-d, t] time period is calculated. All Q (j×u)i(t-d→t) from j=1 to j=m are summed to obtain Q´ (j×u)i(t-d→t) , and the horizontal flow coefficient w i(t-d→t) of the i-th watershed of the target reservoir in the [t-d, t] time period is calculated as follows: (j×u)i(t-d→t) +Q´ (j×u)i(t-d→t) / m] / [W (j×u)z(t-d→t) +Q´ (j×u)z(t-d→t) / m], where z represents the number of the opposite-side monitoring point that is closest to the i-th monitoring point in the upstream area of ​​the i-th monitoring point. When the i-th monitoring point is located on the left / right side of the target reservoir and the z-th monitoring point is located on the right / left side of the target reservoir, the z-th monitoring point and the i-th monitoring point are called opposite-side monitoring points.

[0061] The longitudinal flow coefficient r of the target reservoir's basin i in the time period [td,t] i(t-d→t) =[W (j×u)i(t-d→t) +Q´ (j×u)i(t-d→t) / m] / [W (j×u)v(t-d→t) +Q´ (j×u)v(t-d→t) / m], where v represents the number of the monitoring point on the same side of the upstream area of ​​the i-th monitoring point, which is closest to the i-th monitoring point. When the i-th monitoring point and the v-th monitoring point are both located on the left or right side of the target reservoir, the v-th monitoring point and the i-th monitoring point are called i-side monitoring points.

[0062] Based on the determination results, the basin flow characteristics N of the target reservoir basin i at time t are generated. it , N it =(w i(t-d→t) ,r i(t-d→t) ), where d represents the collection interval of monitoring data, and t represents the real time;

[0063] The water supply and diversion scheduling feature generation unit generates the water level type g of the target reservoir's i-th basin in the [td, t] time period based on the real-time three-dimensional simulation model of the target reservoir. i(t-d→t) and water supply and diversion scheduling warning value k i(t-d→t) To determine, the specific method is:

[0064] According to the water level type of the target reservoir's i-th basin in the [td, t] time period, the water level type includes dead water level, check flood level and normal storage level. The first water supply and diversion scheduling warning value Y of the target reservoir's i-th basin at time t is it The first water supply and diversion scheduling warning value is set by humans according to the water level type. According to the flow characteristics of the target reservoir's i-th basin at time t, the second water supply and diversion scheduling warning value X of the target reservoir's i-th basin at time t is determined. it To confirm, X it =(w i(t-d→t) ×sinβ ziv +r i(t-d→t) )×(1-Y it )×f pi(t-d→t) , where β zivIt represents the angle between the line connecting the zth monitoring point and the ith monitoring point and the line connecting the vth monitoring point and the ith monitoring point, p=1,2,3. When p=1, it means the water level type is dead water level; when p=2, it means the water level type is check flood level; when p=3, it means the water level type is normal water level. pi(t-d→t) =1 or f pi(t-d→t) =-1, when the water level type is dead water level and K ijt -K ij(t-d) <0, water level type is normal water level or check flood level and K´ ijt -K´ ij(t-d) When ≥0, f pi(t-d→t) =1, when the water level type is dead water level and K ijt -K ij(t-d) ≥0, water level type is normal water level or check flood level and K´ ijt -K´ ij(t-d) When f pi(t-d→t) =-1;

[0065] To X it With Y it The sum of the values ​​is calculated to obtain the water supply and diversion scheduling warning value k of the target reservoir's i-th basin in the [td, t] time period. i(t-d→t) ;

[0066] Based on the determination results, the water supply and diversion scheduling characteristics M of the target reservoir's basin i at time t are generated. it , M it =(g i(t-d→t) 、k i(t-d→t) );

[0067] The water quality factor index prediction module predicts the real-time water quality factor index of each basin of the target reservoir based on the flow characteristics of the basin and the rainfall data of the target reservoir in each basin;

[0068] The specific method for the water quality factor index prediction module to predict the real-time water quality factor index of each basin of the target reservoir is as follows:

[0069] According to the real-time three-dimensional simulation model of the target reservoir, the mean rainfall J in the i-th basin of the target reservoir in the [td, t] time period is calculated. i(t-d→t) and the mean rainfall intensity G i(t-d→t) To determine, with e as the base, the mean rainfall J i(t-d→t) and the mean rainfall intensity G i(t-d→t) Construct exponential functions P1 and P2 for the exponential respectively, P1=1-exp(-J i(t-d→t) ), P2=1-exp(-G i(t-d→t) ), e=2.73, according to S it=a1×P1+a2×P2+a3×{1-exp[-(w i(t-d→t) +r i(t-d→t) ×cosβ ziv )]} calculates the water quality factor index of the target reservoir at the i watershed at t, wherein a1, a2, a3 all represent proportional coefficients and a1+a2+a3=1;

[0070] The early warning module selectively sends an early warning signal according to the real-time water supply and diversion scheduling early warning value of each watershed of the target reservoir;

[0071] The early warning module determines whether the water supply and diversion scheduling early warning value of the i watershed of the target reservoir at t is greater than the early warning threshold value, if yes, sends an early warning signal to the water supply and diversion scheduling plan generation module, the early warning signal is the water level height value of the i watershed of the target reservoir at t and the number i, if not, no need to send an early warning signal to the water supply and diversion scheduling plan generation module;

[0072] The water supply and diversion scheduling plan generation module analyzes the scheduling relationship of the target reservoir and each object based on the object relationship constructed by the target reservoir and the water quality factor index of each watershed, and generates the water supply and diversion scheduling plan of the target reservoir in combination with the early warning signal sent by the early warning module;

[0073] The water supply and diversion scheduling plan generation module includes a scheduling object construction unit, a scheduling relationship analysis unit and a water supply and diversion scheduling plan generation unit;

[0074] The scheduling object construction unit sorts out the historical water supply and diversion topological relationship diagram of the target reservoir to obtain the water supply and diversion information of the target reservoir, the water supply and diversion information includes the reservoir name, the reservoir water supply and diversion type and the reservoir water supply and diversion amount, and based on the water supply and diversion information, the first scheduling object set and the second scheduling object set are obtained, the first scheduling object set stores the reservoir name of which the water supply amount is greater than 0, and the second scheduling object set stores the reservoir name of which the water diversion amount is greater than 0;

[0075] The scheduling relationship analysis unit determines the scheduling object type of the i th basin according to the water level type of the i th basin of the target reservoir at time t and the received early warning signal, when the water level type of the i th basin is the dead water level and there is a number i in the received early warning signal at time t, the scheduling object type of the i th basin is the second scheduling object, when the water level type is the check flood level and there is a number i in the received early warning signal at time t, the scheduling object type of the i th basin is the first scheduling object, the scheduling object of the i th basin is screened according to the water quality factor index of the i th basin, and the specific screening method is: judging whether the water quality factor index of the i th basin can reach the minimum water quality factor index requirement of each scheduling object corresponding to the determined scheduling object type, retaining the scheduling object corresponding to the minimum water quality factor index that can be reached, and eliminating the scheduling object corresponding to the minimum water quality factor index that cannot be reached, determining the shortest distance value between the i th basin and each scheduling object screened and retained, and based on the determination result, determining the scheduling object of the i th basin, and the scheduling object of the i th basin is the scheduling object screened and retained corresponding to the shortest distance value;

[0076] The water supply scheduling plan generation unit determines the water supply scheduling order of each basin of the target reservoir according to the received early warning signal, and the water supply scheduling order of each basin is the order of the number of each basin transmitted to the water supply scheduling plan generation unit, and generates the water supply scheduling plan of the target reservoir in combination with the scheduling object of each basin of the target reservoir;

[0077] The reservoir management module manages the water supply and water diversion of the target reservoir according to the water supply scheduling plan.

[0078] A reservoir data matrix management method based on artificial intelligence, the method comprising:

[0079] S10: A three-dimensional model of the target reservoir is established by oblique photography of the location of the target reservoir, and a real-time three-dimensional simulation model of the target reservoir is constructed in combination with a BIM model, GIS coordinate data and monitoring data, the monitoring data including rainwater condition data, water level data and water flow data of the target reservoir at each monitoring point;

[0080] S20: Real-time water supply and water diversion scheduling characteristics and basin flow characteristics of the target reservoir in each basin are generated according to the change of gradient index in each basin of the target reservoir;

[0081] S30: Real-time water quality factor indexes of each basin of the target reservoir are predicted according to the basin flow characteristics and the rainwater condition data of the target reservoir in each basin;

[0082] S40: Early warning signals are selectively sent according to real-time water supply and water diversion scheduling early warning values of each basin of the target reservoir;

[0083] S50: based on the object relationship constructed by the target reservoir, and the water quality factor index of each watershed, the scheduling relationship between the target reservoir and each object is analyzed, and the early warning signal issued by the early warning module is combined to generate the supply water scheduling plan of the target reservoir;

[0084] S60: according to the supply water scheduling plan, the supply water scheduling management of the target reservoir is carried out.

[0085] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the appended claims rather than the above description, and all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. An artificial intelligence-based reservoir data matrix management platform, characterized by: The platform includes a reservoir three-dimensional simulation module, a reservoir data analysis module, a water quality factor index prediction module, an early warning module, a water supply and diversion scheduling plan generation module, and a reservoir management module; The reservoir 3D simulation module establishes a 3D model of the target reservoir by taking oblique photography of the target reservoir location, and constructs a real-time 3D simulation model of the target reservoir by combining the BIM model, GIS coordinate data and monitoring data. The monitoring data includes rainfall data, water level data and water flow data of the target reservoir at each monitoring point. The reservoir data analysis module generates the real-time water supply and diversion scheduling characteristics and basin flow characteristics of the target reservoir in each basin according to the changes in the gradient index in each basin of the target reservoir; The reservoir data analysis module includes a watershed division unit, a watershed flow characteristic generation unit, and a water supply and diversion scheduling characteristic generation unit; The watershed division unit divides the target reservoir into watersheds according to the distribution of each monitoring point of the target reservoir, and obtains a plurality of watersheds based on the division results. Each watershed contains only one monitoring point and there is no regional intersection between two random watersheds. The numbers corresponding to the watersheds are the same as the numbers of the monitoring points contained in the watersheds, wherein i=1,2,…,n represents the numbers corresponding to the monitoring points of the target reservoir, and n represents the total number of monitoring points in the target reservoir; The basin flow characteristic generation unit calculates the lateral flow coefficient w of the i-th basin of the target reservoir in the [td, t] time period according to the real-time three-dimensional simulation model of the target reservoir. i(t-d→t) and longitudinal flow coefficient r i(t-d→t) To determine, the specific method is: In the i-th basin, the mean value W between the mean gradient index of the target location point corresponding to the water level height acquisition scale j×u in the [td, t] time period and the mean gradient index of the matching target location point in the [td, t] time period is (j×u)i(t-d→t) Calculate the difference Q between the mean gradient index of the target location point corresponding to the water level height acquisition scale j×u in the [td, t] time period and the mean gradient index of the matching target location point in the [td, t] time period (j×u)i(t-d→t) Calculate all Q from j=1 to j=m (j×u)i(t-d→t) Perform the summation to get Q´ (j×u)i(t-d→t) , the lateral flow coefficient w of the target reservoir's basin i in the time period [td,t] i(t-d→t) =[W (j×u)i(t-d→t) +Q´ (j×u)i(t-d→t) / m] / [W (j×u)z(t-d→t) +Q´ (j×u)z(t-d→t) / m], where z represents the number of the monitoring point on the opposite side that is closest to the i-th monitoring point in the upstream area of ​​the i-th monitoring point; The longitudinal flow coefficient r of the target reservoir's basin i in the time period [td,t] i(t-d→t) =[W (j×u)i(t-d→t) +Q´ (j×u)i(t-d→t) / m] / [W (j×u)v(t-d→t) +Q´ (j×u)v(t-d→t) / m], where v represents the number of the monitoring point on the same side of the upstream area of ​​the i-th monitoring point that is closest to the i-th monitoring point; Based on the determination results, the basin flow characteristics N of the target reservoir basin i at time t are generated. it , N it =(w i(t-d→t) ,r i(t-d→t) ), where d represents the interval of monitoring data collection, t represents the real-time time, j = 1, 2, …, m represents the numbering of each target location point or each matching target location point in ascending order of the distance from the monitoring point to the target location point, m represents the total number, and u represents the distance value; The water supply and diversion scheduling feature generation unit generates the water level type g of the target reservoir's i-th basin in the [td, t] time period according to the real-time three-dimensional simulation model of the target reservoir. i(t-d→t) and water supply and diversion scheduling warning value k i(t-d→t) Determine and generate the water supply and diversion scheduling characteristics M of the target reservoir's i-th basin at time t based on the determination results it , M it =(g i(t-d→t) 、k i(t-d→t) ); The water quality factor index prediction module predicts the real-time water quality factor index of each watershed of the target reservoir based on the watershed flow characteristics and the rainfall data of the target reservoir in each watershed; The early warning module selectively issues an early warning signal based on the real-time water supply and diversion scheduling early warning value of each basin of the target reservoir; The water supply and diversion scheduling plan generation module analyzes the scheduling relationship between the target reservoir and each object based on the object relationship constructed by the target reservoir and the water quality factor index of each basin, and generates a water supply and diversion scheduling plan for the target reservoir in combination with the warning signal issued by the warning module; The reservoir management module performs water supply and diversion scheduling management on the target reservoir according to the water supply and diversion scheduling plan.

2. The artificial intelligence-based reservoir data matrix management platform according to claim 1, characterized in that: The specific method of the watershed division unit for dividing the target reservoir into watersheds is as follows: in a real-time three-dimensional simulation model of the target reservoir, with the i-th monitoring point as the center and the horizontal distance j×u as the water level height collection scale, the target position point and the matching target position point of the i-th monitoring point are determined, the water level height of the target reservoir at each target position point and each matching target position point is collected, and the water level height difference between the target position point j and the target position point j-1, and the water level height difference between the matching target position point j-1 and the matching target position point j are calculated with the ratio of u respectively to obtain the gradient index of the target position point j-1 and the matching target position point j; The water level height collection scale s is randomly selected. If the difference between the gradient index of the target location point corresponding to the selected water level height collection scale s and the gradient index of the matching target location point corresponding to the selected water level height collection scale s is within the error range, and the difference between the gradient index of the target location point corresponding to the water level height collection scale s+u and the gradient index of the matching target location point corresponding to the water level height collection scale s+u is not within the error range, then the spherical area constructed with the i-th monitoring point as the center and s as the radius is used, and the intersection area with the target reservoir is taken as the watershed area corresponding to the i-th monitoring point.

3. The artificial intelligence-based reservoir data matrix management platform according to claim 2, characterized in that: The specific method for the water supply and diversion scheduling feature generation unit to determine the water supply and diversion scheduling warning value is: According to the water level type of the target reservoir's i-th basin in the [td, t] time period, the water level type includes dead water level, check flood level and normal storage level. The first water supply and diversion scheduling warning value Y of the target reservoir's i-th basin at time t is it According to the flow characteristics of the target reservoir’s basin i at time t, the second water supply and diversion scheduling warning value X of the target reservoir’s basin i at time t is determined. it To confirm, X it =(w i(t-d→t) ×sinβ ziv +r i(t-d→t) )×(1-Y it )×f pi(t-d→t) , where β ziv It represents the angle between the line connecting the zth monitoring point and the ith monitoring point and the line connecting the vth monitoring point and the ith monitoring point, p=1,2,3. When p=1, it means the water level type is dead water level; when p=2, it means the water level type is check flood level; when p=3, it means the water level type is normal water level. pi(t-d→t) =1 or f pi(t-d→t) =-1, when the water level type is dead water level and K ijt -K ij(t-d) <0, water level type is normal water level or check flood level and K´ ijt -K´ ij(t-d) When ≥0, f pi(t-d→t) =1, when the water level type is dead water level and K ijt -K ij(t-d) ≥0, water level type is normal water level or check flood level and K´ ijt -K´ ij(t-d) When f pi(t-d→t) =-1, K ijt 、K´ ijt They represent the target location point j in the i-th basin of the target reservoir and the water level height value of the matching target location point j at time t, respectively. ij(t-d) 、K´ ij(t-d) They represent the target location point j in the i-th basin of the target reservoir and the water level height value of the matching target location point j at time td respectively; To X it With Y it The sum of the values ​​is calculated to obtain the water supply and diversion scheduling warning value k of the target reservoir's i-th basin in the [td, t] time period. i(t-d→t) .

4. The artificial intelligence-based reservoir data matrix management platform according to claim 3 is characterized by: The specific method for the water quality factor index prediction module to predict the real-time water quality factor index of each basin of the target reservoir is: According to the real-time three-dimensional simulation model of the target reservoir, the mean rainfall J in the i-th basin of the target reservoir in the [td, t] time period is calculated. i(t-d→t) and the mean rainfall intensity G i(t-d→t) To determine, with e as the base, the mean rainfall J i(t-d→t) and the mean rainfall intensity G i(t-d→t) Construct exponential functions P1 and P2 for the exponential respectively, P1=1-exp(-J i(t-d→t) ), P2=1-exp(-G i(t-d→t) ), where e=2.73, according to S it =a1×P1+a2×P2+a3×{1-exp[-(w i(t-d→t) +r i(t-d→t) ×cosβ ziv )]}The water quality factor index of the i-th basin of the target reservoir at time t is calculated, where a1, a2, and a3 all represent proportional coefficients and a1+a2+a3=1.

5. The artificial intelligence-based reservoir data matrix management platform according to claim 4 is characterized by: The early warning module determines whether the water supply and diversion scheduling early warning value of the i-th basin of the target reservoir at time t is greater than the early warning threshold. If so, a early warning signal is sent to the water supply and diversion scheduling plan generation module. The early warning signal is the water level height value and number i of the i-th basin of the target reservoir at time t. If not, there is no need to send a early warning signal to the water supply and diversion scheduling plan generation module.

6. The artificial intelligence-based reservoir data matrix management platform according to claim 5, characterized in that: The water supply and diversion scheduling plan generation module includes a scheduling object construction unit, a scheduling relationship analysis unit and a water supply and diversion scheduling plan generation unit; The scheduling object construction unit sorts out the historical water supply and diversion water system topology relationship diagram of the target reservoir to obtain water supply and diversion information of the target reservoir, the water supply and diversion information including the reservoir name, the reservoir water supply and diversion type and the reservoir water supply and diversion amount, and obtains a first scheduling object set and a second scheduling object set based on the water supply and diversion information, the first scheduling object set stores the names of reservoirs whose water supply amount is greater than 0, and the second scheduling object set stores the names of reservoirs whose water diversion amount is greater than 0; The scheduling relationship analysis unit determines the scheduling object of the i-th basin according to the water level type of the i-th basin of the target reservoir at time t and the situation of receiving the early warning signal; The water supply and diversion scheduling plan generation unit determines the water supply and diversion scheduling order of each river basin of the target reservoir based on the received early warning signal. The water supply and diversion scheduling order of each river basin is the order in which the river basin numbers are transmitted to the water supply and diversion scheduling plan generation unit. Combined with the scheduling objects of each river basin of the target reservoir, the water supply and diversion scheduling plan of the target reservoir is generated.

7. The artificial intelligence-based reservoir data matrix management platform according to claim 6, characterized in that: The specific method for the scheduling relationship analysis unit to determine the scheduling object of the i-th watershed is: When the water level type of the i-th basin is dead water level and the number i exists in the early warning signal received at time t, the scheduling object type of the i-th basin is the second scheduling object. When the water level type is the check flood level and the number i exists in the early warning signal received at time t, the scheduling object type of the i-th basin is the first scheduling object. The scheduling objects of the i-th basin are screened according to the water quality factor index of the i-th basin. The specific screening method is: determine whether the water quality factor index of the i-th basin can meet the minimum water quality factor index requirements of each scheduling object corresponding to the determined scheduling object type, retain the scheduling objects corresponding to the minimum water quality factor index that can be achieved, and eliminate the scheduling objects corresponding to the minimum water quality factor index that cannot be achieved; The shortest distance value between the i-th watershed and each scheduling object retained by screening is determined, and based on the determination result, the scheduling object of the i-th watershed is determined.

8. An artificial intelligence-based reservoir data matrix management method applied to the artificial intelligence-based reservoir data matrix management platform according to any one of claims 1 to 7, characterized in that: The method comprises: S10: Establish a three-dimensional model of the target reservoir by taking oblique photography of the target reservoir location. Combine the BIM model, GIS coordinate data, and monitoring data to build a real-time three-dimensional simulation model of the target reservoir. The monitoring data includes rainfall data, water level data, and water flow data at each monitoring point of the target reservoir. S20: Generate the real-time water supply and diversion scheduling characteristics and basin flow characteristics of the target reservoir in each basin according to the change of the gradient index in each basin of the target reservoir; S30: Based on the flow characteristics of the watershed and the rainfall data of the target reservoir in each watershed, the real-time water quality factor index of each watershed of the target reservoir is predicted; S40: selectively issuing warning signals based on the real-time water supply and diversion scheduling warning values ​​of each basin of the target reservoir; S50: Based on the object relationship constructed for the target reservoir and the water quality factor index of each basin, the scheduling relationship between the target reservoir and each object is analyzed, and the water supply and diversion scheduling plan of the target reservoir is generated in combination with the warning signal issued by the warning module; S60: Perform water supply and diversion scheduling management for the target reservoir according to the water supply and diversion scheduling plan.

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