Optimization and Regulation System and Method for Basin Water Project Control and Operation Oriented to Smart Water Conservancy

Abstract

The present invention discloses a system and method for optimizing the operation and control of basin water projects for intelligent water conservancy. The control system includes a basin topology intelligent processor, a fine simulator for reservoir and dam control plans, a simulator for reservoir and dam operation plans, a simulator for flood flow in flood storage and detention areas, a multi-objective evaluator for operation plans, and an intelligent optimizer for project operation and control. The basin topology intelligent processor is used to intelligently construct the sets of optimized operation and control projects, upstream operation and control projects, and downstream operation and control projects for the basin reservoir project group and the dam project group. By introducing the basin topology intelligent processor, the present invention realizes the intelligent construction of the partition sets and domination relationships of basin water projects, laying a foundation for parallel computing; and combines the fine simulator for reservoir and dam control plans, the simulator for operation plans, and the simulator for flood flow in flood storage and detention areas to construct a multi-level and high-precision simulation system for water projects and water flow processes.

Description

Technical Field

[0001] The present invention relates to an optimized regulation system and method for the operation control of basin water projects for intelligent water conservancy, belonging to the fields of water conservancy and hydropower engineering and water environmental protection. Background Art

[0002] In basin management, the joint regulation of water project groups such as reservoirs and sluices is considered the core means to improve comprehensive benefits such as flood control, water supply, and power generation. However, due to the complex decision-making process of the optimized regulation of basin water project groups involving multiple objectives (such as flood control safety and water supply guarantee) and multiple constraints (such as water volume balance and project storage capacity limitation), traditional scheduling methods often show limitations of low computational efficiency and poor optimization effect when facing large-scale, high-dimensional, and strongly non-linear problems. These problems make it difficult for traditional methods to meet the requirements of rapid response and accurate decision-making in modern basin management.

[0003] The preview and pre-plan functions need to efficiently simulate and optimize the regulation plans of basin water project groups in a short time to cope with complex and changeable water conditions. However, there are still deficiencies in multiple key links in the existing technologies. For example, the processing of the topological relationship of water project groups in the basin relies on manual analysis, lacking intelligent and automated means to cope with the influence of recent human activities in the basin; the refined simulation ability is limited and cannot fully reflect the dynamic interaction between water flow and project regulation; in addition, in terms of computational efficiency, there is a lack of effective parallel computing support, resulting in too long time consumption for the optimization of large-scale regulation plans. These technical bottlenecks limit the rapid response ability of basin water project groups under extreme hydrological events and the full play of comprehensive benefits. Summary of the Invention

[0004] Object of the Invention: To overcome the deficiencies in the existing technologies, the present invention provides an optimized regulation system and method for the operation control of basin water projects for intelligent water conservancy. By introducing a basin topology intelligent processor, the intelligent construction of the partition set and dominance relationship of basin water projects is realized, laying a foundation for parallel computing; combined with a fine simulator for reservoir and sluice control pre-plans, a scheduling pre-plan simulator, and a water flow simulator for flood detention and retention spaces, a multi-level and high-precision simulation system of water projects and water flow processes is constructed.

[0005] Technical Solution: To solve the above technical problems, the optimized regulation system for the operation control of basin water projects for intelligent water conservancy of the present invention includes a basin topology intelligent processor, a fine simulator for reservoir and sluice control pre-plans, a reservoir and sluice scheduling pre-plan simulator, a water flow simulator for flood detention and retention spaces, a multi-objective evaluator for scheduling pre-plans, and an intelligent optimizer for project regulation;

[0006] The described basin topology intelligent processor is used to intelligently construct the neighborhood domination matrix D1 and the global domination matrix D2 of the reservoir and sluice project group in the basin, analyze and obtain the upstream control operation project set S1, the optimized regulation project set S2, the downstream control operation project set S3, and the domination camps within the sets, and support the parallel computing of the simulation analysis of the basin project group; it is used to obtain the specified scheduling methods and parameters of each reservoir and sluice project in the basin, and obtain the inflow of each project and its evolution lag time statement.

[0007] The described reservoir and sluice control plan fine simulator calculates the synthetic inflow process of the project according to the specified control operation project inflow and its evolution lag time statement.

[0008] The described reservoir and sluice scheduling plan simulator participates in the calculation of the synthetic inflow process of the project according to the specified optimized regulation project inflow and its evolution lag time statement.

[0009] The described flood routing and storage space water flow simulator identifies the most downstream project in the basin reservoir and sluice projects that completely does not dominate other projects through the global domination matrix D2. Facing the downstream flood routing and storage space of the most downstream project, with the calculated outflow of the most downstream project as the upstream incoming water boundary, based on the river channel confluence hydrology method or the hydrodynamic method, considering the influence of flood diversion in the flood storage and detention area, pumping station drainage, and inter-basin confluence factors, it conducts time-step simulation of the continuous water flow calculation in the flood routing and storage space, outputs the hydraulic element process of the control section in the flood routing and storage space within the calculation time domain, and quantitatively determines the violation amount of the flood routing and storage space constraints corresponding to the current upstream incoming water boundary. The involved constraint conditions include: hydraulic element range constraint, hydraulic element amplitude change constraint, hydraulic element rate of change constraint, hydraulic element threshold damage depth constraint, and hydraulic element threshold damage duration constraint.

[0010] The described scheduling plan multi-objective evaluator conducts multi-objective evaluation for the current optimized regulation project scheduling plan.

[0011] The described project regulation intelligent optimizer consists of three parts: a plan population initialization unit, a plan population quantitative evaluation unit, and a plan population heuristic evolution unit; among them, the plan population initialization unit randomly generates a two-dimensional matrix. Each row of the matrix represents a scheduling plan for all the projects participating in the optimized regulation, that is, the upstream water level or the downstream discharge process of each project within the calculation time domain. The matrix is formed by combining multiple scheduling plans in columns, which is the plan population; the described plan population quantitative evaluation unit assigns a quantitative priority to each scheduling plan using an adaptive constraint handling method based on the weighted objective deviation and the total constraint violation amount of each scheduling plan in the population; the described plan population heuristic evolution unit iteratively updates and evolves the plan population using a heuristic evolution algorithm based on the quantitative priorities of each scheduling plan.

[0012] Preferably, the basin topology intelligent processor constructs a basin project neighborhood domination matrix D1, where D1 is a two-dimensional square matrix with dimensions of M + N, M is the number of reservoir projects, N is the number of sluice projects, and the value of D1ij is 1, indicating that the outflow of project i directly flows into the adjacent downstream project j, that is, project i affects and dominates project j; the value is -1, indicating that project i directly receives the outflow of the adjacent upstream project j, that is, project i is affected and dominated by project j; the value is 0, indicating that there is no mutual influence and domination relationship between project i and project j. Based on the neighborhood domination matrix D1, matrix analysis is carried out to obtain the global domination matrix D2. D2 is a two-dimensional square matrix with dimensions of M + N. The value of D2ij is 1, indicating that the outflow of project i directly or indirectly flows into the downstream project j, that is, project i affects and dominates project j; the value is -1, indicating that project i directly or indirectly receives the outflow of the upstream project j, that is, project i is affected and dominated by project j; the value is 0, indicating that there is no mutual influence and domination relationship between project i and project j.

[0013] A regulation method for the above-mentioned optimized regulation system of basin water project control and operation for intelligent water conservancy includes the following steps:

[0014] Step 1: Apply the above-mentioned basin topology intelligent processor to obtain the designated scheduling methods and parameters of each reservoir and sluice project in the basin, the inflow of each project and its evolution lag time statement, construct the basin project neighborhood domination matrix D1 and the global domination matrix D2, divide the basin reservoir and sluice group into S1, S2 and S3. S1 is the set of upstream control and operation projects, S2 is the set of optimized regulation projects, and S3 is the set of downstream control and operation projects. Use the fast non-dominated sorting algorithm to obtain multiple domination camps within each project set, where the outflow of the projects contained in the upper camp is used as the inflow of the projects contained in the lower camp. For all projects belonging to the same camp, there is no intersection between their outflows and inflows. Record the CPU time at the start of Step 1.

[0015] Step 2: Based on the above-mentioned fine simulator for reservoir and sluice control plans, for the set of upstream control and operation projects S1, carry out project simulation calculations for each domination camp one by one: First, calculate the current project's combined inflow process according to the inflow and its evolution lag time statement, and then apply the mathematical models of various control and operation modes of different types of projects, and combine the current project's control parameters to perform continuous time-step simulations, and output the upstream water level and calculated outflow process of the current project within the calculation time domain.

[0016] Step 3: Based on the above-mentioned intelligent optimizer for project regulation, use the pre-plan population initialization unit to randomly generate a pre-plan population, that is, a two-dimensional matrix where each row represents a scheduling pre-plan for all projects participating in the optimized regulation. The scheduling pre-plan is the upstream water level or the downstream discharge process of the project within the calculation time domain.

[0017] Step 4: Construct an empty historical plan library to store the evaluated optimized regulation project scheduling plans, as well as the corresponding reservoir sluice project and flood storage and detention space simulation results, weighted target deviation amounts, and total constraint violation amounts;

[0018] Step 5: For each scheduling plan of the optimized regulation project in the current plan population, if an evaluated scheduling plan can be located in the historical plan library, directly retrieve the reservoir sluice project and flood storage and detention space simulation results, weighted target deviation amounts, and total constraint violation amounts; otherwise, conduct simulation calculations to obtain the reservoir sluice project and flood storage and detention space simulation results, weighted target deviation amounts, and total constraint violation amounts, and add them to the historical plan library;

[0019] Step 6: Use the plan population quantitative evaluation unit of the engineering regulation intelligent optimizer to assign a quantitative priority to each scheduling plan in the population based on the weighted target deviation amounts and total constraint violation amounts of the scheduling plans in the population, using an adaptive constraint handling method;

[0020] The specific calculation method is as follows:

[0021] ;

[0022] where, is the priority of a scheduling plan in the population, is the weighted target deviation amount of this scheduling plan, is the total constraint violation amount of this scheduling plan, is the proportion of feasible plans with a total constraint violation amount of 0 in the population, is the deviation of each scheduling target of the optimized regulation project and flood storage and detention space is the weight coefficient, is the violation amount of each constraint of the optimized regulation project and flood storage and detention space;

[0023] Step 7: Use the plan population heuristic evolution unit of the engineering regulation intelligent optimizer to update the optimized regulation project scheduling plan population based on the quantitative priorities of the scheduling plans, and read the CPU time at this moment ;

[0024] Repeat steps 5 to 7 until the exit condition is met. The exit conditions include that the total calculation time reaches the preset upper limit, or the number of iterative updates reaches the preset upper limit, or one or more of them. Calculate the total calculation time : ; The calculation method of the number of iterative updates is: the number of repeated executions of steps 5 to 7; after exiting, output the optimized regulation project scheduling plan with the highest priority during the iterative calculation process.

[0025] Preferably, the method for applying the fast non-dominated sorting algorithm to matrices D1 and D2 is as follows:

[0026] A11. If no project in the basin participates in the optimized regulation project, all projects belong to the upstream control operation project set S1; if at least one project in the basin participates in the optimized regulation project, the optimized regulation project set S2 is not empty; if a control operation project appears within the scope of two optimized regulation projects, the combined set of projects participating in the optimized regulation is a subset of the optimized regulation project set S2.

[0027] A12. If the optimized regulation project set S2 is not empty, starting from all projects participating in the optimized regulation, continuously locate adjacent downstream projects according to matrices D1 and D2 and store them in the combined set of S2 and S3. After obtaining the combined set of S2 and S3, use the element exclusion method to obtain the upstream control operation project set S1.

[0028] A13. According to the neighborhood domination matrix D1 and the global domination matrix D2, determine the reservoir and sluice projects that do not dominate other projects at all, do not belong to the upstream control operation project set S1, and are designated to carry out control operations. Starting from these reservoir and sluice projects, continuously locate adjacent upstream projects and store them in the downstream control operation project set S3. The condition that the adjacent upstream projects should meet is that they do not belong to the upstream control operation project set S1 and are designated to carry out control operations. If there are no adjacent upstream projects that meet the conditions during the location process, the location ends.

[0029] A14. On the basis of obtaining the upstream control operation project set S1 and the downstream control operation project set S3, use the element exclusion method to obtain the optimized regulation project set S2.

[0030] A15. For sets S1, S2, and S3 respectively, extract the corresponding rows and columns of matrices D1 and D2 as new temporary domination matrices, and apply the fast non-dominated sorting algorithm to divide each project set into different domination camps.

[0031] Preferably, in step 5, the simulation calculation includes the following steps:

[0032] Step 5.1: Optimize and control each engineering camp in engineering set S2 one by one and conduct engineering type judgment and simulation calculation in the following manner: If the current project is a control application project, based on the fine simulator of the reservoir sluice control plan, calculate the current project's synthetic inflow process according to the inflow and its evolution lag statement, apply the mathematical models of various control application modes of different types of projects, and conduct continuous time-step simulation in combination with the current project's control parameters, and output the upstream water level of the current project and the calculated outflow process within the calculation time domain; If the current project is an optimization and control project, based on the reservoir sluice operation plan simulator, calculate the current project's synthetic inflow process according to the inflow and its evolution lag statement, based on the current project's operation plan, conduct continuous time-step simulation in combination with the optimization parameters, and output the upstream water level of the current project, the calculated inflow, and the calculated outflow process within the calculation time domain, and quantify the constraint violation amounts of each project at each calculation time step;

[0033] Step 5.2: Based on the fine simulator of the reservoir sluice control plan, for the downstream control application engineering set S3, each engineering camp conducts simulation calculations for each project one by one: First, calculate the current project's synthetic inflow process according to the inflow and its evolution lag statement, and then apply the mathematical models of various control application modes of different types of projects, and conduct continuous time-step simulation in combination with the current project's control parameters, and output the upstream water level of the current project and the calculated outflow process within the calculation time domain;

[0034] Step 5.3: If the constraint violation amounts of each project at each calculation time step in Step 5.1 are all 0, based on the flow simulator in the flood storage and detention space, use the calculated outflow process of the most downstream project in the basin as the upstream incoming water boundary, and conduct continuous time-step simulation of the flow in the flood storage and detention space, and output the hydraulic element process of the control section in the flood storage and detention space within the calculation time domain, and quantitatively determine the constraint violation amount in the flood storage and detention space; Otherwise, directly assign a very large value to the constraint violation amount in the flood storage and detention space;

[0035] Step 5.4: Based on the multi-objective evaluator of the operation plan, conduct multi-objective evaluation for the current optimization and control project operation plan. On the basis of quantitatively determining the constraint violation amount of the optimization and control project and the constraint violation amount in the flood storage and detention space, quantitatively determine the deviation of each scheduling objective of the optimization and control project and the flood storage and detention space, and then comprehensively determine the weighted objective deviation amount and the total constraint violation amount;

[0036] Step 5.5: Add the simulation results of the reservoir sluice projects and the flood storage and detention space, the weighted objective deviation amount, and the total constraint violation amount of the current simulated evaluation operation plan to the historical operation plan library.

[0037] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0038] (1) Intelligent topology processing: Intelligently divide the engineering set and the dominant camp through the dominance matrix to improve the efficiency and accuracy of topology processing, laying a foundation for parallel simulation calculations;

[0039] (2) Refined and efficient simulation: Use mathematical models of various control modes to accurately simulate the changes in engineering water levels and flows and the downstream flow process;

[0040] (3) Intelligent optimization and rapid response: Combine adaptive constraint processing and heuristic algorithms to quickly search for optimal plans and accelerate the response through parallel computing. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is the structural diagram of the basin water project control operation optimization and regulation system for the intelligent water conservancy of the present invention;

[0042] Figure 2 is the flow chart of the calculation acceleration method for the optimization and regulation of the control operation of the basin water project group of the present invention;

[0043] Figure 3 is the flow chart of step 5 of the present invention. SPECIFIC EMBODIMENTS

[0044] The present invention will be further described below with reference to the accompanying drawings.

[0045] Example 1: In the present invention, consider a basin containing 10 projects: 6 reservoirs (R1, R2, R3, R4, R5, R6, M = 6) and 4 sluice dams (G1, G2, G3, G4, N = 4), with a total of M + N = 10. The connection relationship of the projects is as follows:

[0046] The outflow of reservoir R1 flows into R2;

[0047] The outflow of reservoir R2 flows into R3;

[0048] The outflows of reservoirs R3 and R4 jointly flow into R5;

[0049] The outflows of reservoirs R5 and R6 jointly flow into sluice dam G1;

[0050] The outflows of sluice dams G1 and G2 jointly flow into sluice dam G3;

[0051] The outflows of sluice dams G3 and G4 jointly flow into the downstream river;

[0052] The dimensions of matrices D1 and D2 are 10×10.

[0053] First, construct the neighborhood dominance matrix D1:

[0054] The neighborhood dominance matrix D1 represents the direct influence relationship between projects: D1[i,j]=1 indicates that the out - flow of project i directly converges into the adjacent downstream project j, that is, i influences j; D1[i,j]= - 1 indicates that i receives the out - flow of j, that is, i is influenced by j; D1[i,j]=0 indicates that there is no direct influence, and the diagonal element D1[i,i]=0 because a project does not influence itself.

[0055] Analyze the connection relationship:

[0056] R1 converges into R2: D1[R1,R2]=1, D1[R2,R1]= - 1;

[0057] R2 converges into R3: D1[R2,R3]=1, D1[R3,R2]= - 1;

[0058] R3 and R4 converge into R5: D1[R3,R5]=1, D1[R4,R5]=1, D1[R5,R3]= - 1, D1[R5,R4]= - 1;

[0059] R5 and R6 converge into G1: D1[R5,G1]=1, D1[R6,G1]=1, D1[G1,R5]= - 1, D1[G1,R6]= - 1;

[0060] G1 and G2 converge into G3: D1[G1,G3]=1, D1[G2,G3]=1, D1[G3,G1]= - 1, D1[G3,G2]= - 1.

[0061] The neighborhood dominance matrix D1 is as follows: ;

[0062] Subsequently, construct the global dominance matrix D2:

[0063] The global dominance matrix D2 reflects the direct or indirect influence relationship through matrix analysis (such as transitive closure) based on D1: D2[i,j]=1 indicates that the out - flow of i directly or indirectly converges into j, that is, i influences j; D2[i,j]= - 1 indicates that i receives the out - flow of j, that is, i is influenced by j; D2[i,j]=0 indicates no influence. The analysis is as follows:

[0064] The influence of R1: It directly influences R2, influences R3 through R2, influences R5 through R3, influences G1 through R5, and influences G3 through G1. Therefore, D2[R1,R2]=1, D2[R1,R3]=1, D2[R1,R5]=1, D2[R1,G1]=1, D2[R1,G3]=1;

[0065] The influence of R2: directly affects R3, affects R5 through R3, affects G1 through R5, and affects G3 through G1. Therefore, D2[R2, R3]=1, D2[R2, R5]=1, D2[R2, G1]=1, D2[R2, G3]=1;

[0066] The influence of R3: directly affects R5, affects G1 through R5, and affects G3 through G1. Therefore, D2[R3, R5]=1, D2[R3, G1]=1, D2[R3, G3]=1;

[0067] The influence of R4: directly affects R5, affects G1 through R5, and affects G3 through G1. Therefore, D2[R4, R5]=1, D2[R4, G1]=1, D2[R4, G3]=1;

[0068] The influence of R5: directly affects G1 and affects G3 through G1. Therefore, D2[R5, G1]=1, D2[R5, G3]=1;

[0069] The influence of R6: directly affects G1 and affects G3 through G1. Therefore, D2[R6, G1]=1, D2[R6, G3]=1;

[0070] The influence of G1: directly affects G3. Therefore, D2[G1, G3]=1;

[0071] The influence of G2: directly affects G3. Therefore, D2[G2, G3]=1;

[0072] Global domination matrix D2: .

[0073] Subsequently, assuming that reservoirs R5, R6 and sluice dam G1 participate in the optimal operation, and other projects are under control operation, the method of applying the fast non-dominated sorting algorithm to matrices D1 and D2 is further illustrated.

[0074] Among them, the fast non-dominated sorting algorithm is implemented as follows:

[0075] The fast non-dominated sorting algorithm (Fast Nondominated Sorting Algorithm) is an algorithm derived from multi-objective optimization. The main steps of fast non-dominated sorting are as follows:

[0076] ①Initialization: For each reservoir and sluice project, initialize two parameters: the number of water projects n(p) that dominate this water project, and the set S(p) of water projects dominated by this water project.

[0077] ②Non-dominated level identification: Find all water projects with n(p)=0, that is, these water projects are not dominated by any other water projects. These water projects form a camp of water projects, and these water projects are marked as the current layer (the first layer) camp.

[0078] ③ Camp expansion: For each water project p in the current camp, consider the set of water projects S(p) it dominates. For each water project q in S(p), decrement the domination number n(q) of q by 1. When n(q) reaches 0, it means that the water project q is not dominated by other water projects outside the currently identified camp, so q belongs to the next camp level. Collect all newly identified non-dominated water projects to form a new non-dominated camp level, and then continue to repeat this process until all water projects are classified into camp levels.

[0079] (1) Determine whether S2 is empty

[0080] If at least one project participates in the optimal regulation, then S2 is not empty. R5, R6, and G1 are projects for optimal regulation, so S2 is not empty. Initially, S2 = {R5, R6, G1}. There is no need to consider the situation of "control operation projects appearing within the scope of two optimal regulation projects" (there are no control operation projects between the projects in the initial S2).

[0081] Other projects (R1, R2, R3, R4, G2, G3, G4) are to be assigned.

[0082] (2) Locate the union of S2 and S3 to determine S1

[0083] Starting from the optimal regulation projects (R5, R6, G1) in S2, locate the adjacent downstream projects according to matrix D2:

[0084] R5: D2[R5, G1] = 1, D2[R5, G3] = 1, affecting G1 and G3.

[0085] R6: D2[R6, G1] = 1, D2[R6, G3] = 1, affecting G1 and G3.

[0086] G1: D2[G1, G3] = 1, affecting G3. The downstream project is G3 (G1 is already included in S2), deposit it into the union of S2 and S3, and the current union is {R5, R6, G1, G3}. Use the element exclusion method to obtain S1: The universal set {R1, R2, R3, R4, R5, R6, G1, G2, G3, G4} minus {R5, R6, G1, G3}, resulting in S1 = {R1, R2, R3, R4, G2, G4}.

[0087] (3) Determine S3

[0088] Identify the projects that do not dominate other projects at all according to D2 (D2[i, j] ≠ 1 for all j ≠ i), and do not belong to S1 and are for control operation:

[0089] Row of G3: D2[G3, j] = 0 (for j ≠ G3), does not dominate other projects at all.

[0090] For row G4: D2[G4, j] = 0 (for all j), which does not dominate other processes at all.

[0091] Other processes (R1, R2, R3, R4, R5, R6, G1, G2) have positive values in D2, so they are excluded. Check S1 = {R1, R2, R3, R4, G2, G4}: G4 is in S1, so it is excluded; G3 is not in S1 and is for control operation, which meets the conditions. Starting from G3, locate the adjacent upstream process (D2[G3, i] = -1, and i is not in S1 and is for control operation):

[0092] D2[G3, R1] = -1, and R1 is in S1, so it is excluded.

[0093] D2[G3, R2] = -1, and R2 is in S1, so it is excluded.

[0094] D2[G3, R3] = -1, and R3 is in S1, so it is excluded.

[0095] D2[G3, R4] = -1, and R4 is in S1, so it is excluded.

[0096] D2[G3, R5] = -1, and R5 is for optimized regulation, so it is excluded.

[0097] D2[G3, R6] = -1, and R6 is for optimized regulation, so it is excluded.

[0098] D2[G3, G1] = -1, and G1 is for optimized regulation, so it is excluded.

[0099] D2[G3, G2] = -1, and G2 is in S1, so it is excluded. There is no upstream process that meets the conditions, and the location ends. Therefore, S3 = {G3}.

[0100] (4)Determine S2

[0101] Based on S1 = {R1, R2, R3, R4, G2, G4} and S3 = {G3}, use the element exclusion method: The universal set {R1, R2, R3, R4, R5, R6, G1, G2, G3, G4} minus S1 ∪ S3 = {R1, R2, R3, R4, G2, G4, G3}, resulting in S2 = {R5, R6, G1}, which is consistent with the initial judgment.

[0102] (5)Divide the domination camps

[0103] Apply the fast non - domination sorting algorithm to S1, S2, and S3 respectively, based on the corresponding rows and columns of D2:

[0104] S1 = {R1, R2, R3, R4, G2, G4}: Extract the rows and columns of R1, R2, R3, R4, G2, G4 in D2:

[0105] Fast non - dominated sorting:

[0106] Projects that are completely not dominated by other projects (D2[i,j]≠ - 1 for all j≠i):

[0107] The first camp: C1 = {R1, R4, G2, G4} (completely not dominated by other projects within S1).

[0108] The second camp: C2 = {R2}.

[0109] The third camp: C3 = {R3}.

[0110] S2 = {R5, R6, G1}: Extract the R5, R6, G1 rows and columns of D2:

[0111] Fast non - dominated sorting:

[0112] R5 and R6 dominate G1 (D2[R5,G1]=D2[R6,G1]=1), and they do not dominate each other (D2[R5,R6]=D2[R6,R5]=0), and the two are grouped into the first camp C1 = {R5, R6}.

[0113] After removing R5 and R6, there is only one project G1, which is grouped into the second camp C2 = {G1}.

[0114] The first camp of S2 is C1 = {R5, R6}, and the second camp is C2 = {G1}.

[0115] S3 = {G3}: There is only one project, and the first camp is C1 = {G3}.

[0116] Example 2: This example focuses on a small watershed in the middle and lower reaches of the Yangtze River in China. Watershed background information: This watershed is located in the humid region of southern China, with a watershed area of 8900 km², an average annual precipitation of 1020 mm, the flood season concentrated from May to October, and the dry season from November to April of the following year. The watershed has 4 reservoirs (JJ, DSH, ZH, LYD) and 3 sluice dams (DC, JZ, HS), which together form the watershed's water conservancy regulation system: The outflow of the leading reservoir JJ converges into the reservoir DSH after a 10 - hour evolution lag time; The outflow of the reservoir DSH (6 - hour evolution lag time) and the outflow of the reservoir ZH (8 - hour evolution lag time) jointly converge into the sluice dam DC; The outflow of the sluice dam DC (4 - hour evolution lag time) converges into the sluice dam JZ; The outflow of the sluice dam JZ (3 - hour evolution lag time) converges into the sluice dam HS, and the outflow of the reservoir LYD directly converges into the sluice dam HS after a 10 - hour evolution lag time; The outflow of the sluice dam HS finally enters the downstream river channel.

[0117] I. Watershed Topology Intelligent Processor

[0118] (1) Engineering information collection and collation

[0119] The designated scheduling methods and parameters of each reservoir and sluice project in the basin. Among them, reservoirs JJ, ZH, and LYD are control operation projects, and the control operation parameter of reservoir JJ is the target control water level of 200 meters; reservoirs DSH and sluice DC are optimization regulation projects, and the expected upstream water level of reservoir DSH is 150 meters. At the same time, obtain the inflow of each project and its evolution lag time statement. For example, the inflow of reservoir DSH includes the outflow of adjacent upstream reservoir JJ and the inflow from the inter-basin, and the water flow of reservoir JJ's outflow converges into reservoir DSH after 10 hours.

[0120] (2) Construct the domination matrix

[0121] Construct the neighborhood domination matrix D1. Based on the direct water flow influence between projects, if the outflow of project i directly converges into downstream project j, then D1[i,j]=1; if project i directly receives the outflow of upstream project j, then D1[i,j]=-1; if there is no direct water flow influence relationship between two projects, D1[i,j]=0. For example, from reservoir JJ to reservoir DSH, D1[reservoir JJ, reservoir DSH]=1; reservoir DSH receives the outflow of reservoir JJ, D1[reservoir DSH, reservoir JJ]=-1; there is no direct connection between reservoir JJ and reservoir ZH, D1[reservoir JJ, reservoir ZH]=0.

[0122] On the basis of the neighborhood domination matrix D1, construct the global domination matrix D2 by analyzing the indirect influence relationship. Taking reservoir JJ as an example, its water flow not only directly affects reservoir DSH, but also indirectly affects sluice DC, sluice JZ, and sluice HS through reservoir DSH. Therefore, in D2, D2[reservoir JJ, reservoir DSH]=1, D2[reservoir JJ, sluice DC]=1, D2[reservoir JJ, sluice JZ]=1, D2[reservoir JJ, sluice HS]=1. By analogy, determine the values of all elements in D2.

[0123] (3) Project set and domination camp division

[0124] Use the fast non-dominated sorting algorithm to perform set division and camp construction for projects based on matrices D1 and D2. Reservoir DSH and sluice DC participating in optimization regulation form the optimization regulation project set S2; upstream control operation reservoirs JJ, ZH, and LYD form the upstream control operation project set S1; downstream control operation sluices JZ and HS form the downstream control operation project set S3.

[0125] Further divide the projects within each set into camps according to the influence relationship. In S1, there is no mutual influence among reservoirs JJ, ZH, and LYD, and they jointly form the first camp: {reservoir JJ, reservoir ZH, reservoir LYD}; in S2, reservoir DSH affects dam DC. Reservoir DSH is the first-level camp: {reservoir DSH}, and dam DC is the second-level camp: {dam DC}; in S3, dam JZ affects dam HS. Dam JZ is the first-level camp: {dam JZ}, and dam HS is the second-level camp: {dam HS}. In this way, the spatial relationship between each project set and the influence relationship of the dominant camps are clarified. The outflow of the projects contained in the superior camp is declared as the inflow of the projects contained in the inferior camp, and there is no intersection between the outflows and inflows of all projects belonging to the same camp, which is convenient for subsequent parallel simulation calculations in the dominant camps.

[0126] II. Fine Simulator for Reservoir and Dam Control Plan

[0127] (I) Application of Reservoir Project Control Mode

[0128] Reservoirs JJ, ZH, and LYD all adopt the control mode of water level in front of the dam. Taking reservoir JJ as an example: Reservoir JJ adopts the control mode of water level in front of the dam with the goal of maintaining a specific water level (the target water level is 200 m, and the allowable fluctuation range is 198 - 202 m). When receiving the confluence from the basin boundary to the current project (the evolution lag time is 0 h), calculate the synthetic inflow process of the project according to the inflow and its evolution lag time. In the subsequent calculation process, carry out the calculation with the water balance equation and adopt the static storage capacity regulation calculation method. Give priority to ensuring the minimum discharge for power generation, and adjust the remaining excess water through flood discharge facilities. By continuously trying different discharge amounts, ensure that the water level at the end of the period is within the allowable range and meet the requirements such as the upstream water level constraint of the project, the water level range constraint of each water diversion and pumping facility, the discharge capacity range constraint of each water diversion and pumping facility, and the power generation range constraint. The initial scheduling water level is 202.3 m, the inflow is 200 cubic meters per second, and after calculation, it is determined that the discharge is 220 cubic meters per second (the tail water for power generation is 25 cubic meters per second, and the flood discharge is 195 cubic meters per second), and the water level at the end of the period reaches 202 m, meeting the water level requirements.

[0129] (II) Application of Dam Project Control Mode

[0130] Both the sluice dams JZ and HS adopt the upstream water level control. Taking the sluice dam JZ as an example: The sluice dam JZ adopts the upstream water level control mode, and the goal is to maintain the upstream water level at about 50 meters (the allowable fluctuation range is 48 - 52 meters) to ensure the domestic, industrial and irrigation water intake requirements of the upstream. If the incoming flow exceeds the maximum discharge capacity of 1400 cubic meters per second, the gate opening will be gradually increased to ensure that the water level does not exceed 52 meters. This process applies the mathematical model of the upstream water level control mode, with the water balance equation as the core, considering the upstream water level constraints of the project, the water level range constraints of the water diversion and pumping facilities, the discharge capacity range constraints of the water diversion and pumping facilities, etc.

[0131] III. Simulator for the reservoir sluice dam operation plan

[0132] (I) Optimization of the setting of regulation project parameters

[0133] The expected upstream water level of the reservoir DSH is subject to the constraints of the flood limit water level of 150 meters, the flood control high water level of 160 meters, and the upper limit of the power generation tail water flow of 30 cubic meters per second; the expected upstream water level of the sluice dam DC is 80 meters, the discharge capacity range is between 1100 - 1500 cubic meters per second, and the upper limit of the upstream water level of the sluice is the design flood level of 85 meters.

[0134] (II) Simulation calculation process

[0135] For the reservoir DSH and the sluice dam DC, first calculate the synthetic inflow process of the project according to the inflow and its evolution lag time statement. Then, for the specified project participating in the optimization regulation, based on the operation plan (in this example, the upstream water level process of the reservoir DSH within the calculation time domain and the discharge process of the sluice dam DC), combined with the optimization parameters, with the water balance equation as the core, adopt the static storage capacity regulation calculation method to conduct continuous time step simulation. During the simulation process, output the upstream water level of the project, the calculated inflow, the calculated outflow process, and quantify the constraint violation amounts of each project at each calculation time step, involving the upstream water level constraints of the project, the water level range constraints of the water diversion and pumping facilities, the discharge capacity range of the water diversion and pumping facilities, the power generation range constraints, etc.

[0136] IV. Simulator for the flood flow in the flood storage and detention space

[0137] (I) Determination of boundary conditions

[0138] Through the global domination matrix, it is determined that the sluice dam HS is the most downstream project, and its outflow serves as the upstream water inflow boundary of the downstream river channel of the project.

[0139] (II) Simulation process

[0140] The hydrodynamic method of river confluence is used to simulate the water flow in the downstream river. Considering factors such as confluence in the interval, hydraulic elements such as the water level and flow velocity at the flood control control section CH station of the downstream river are calculated. During the simulation process, constraint conditions of hydraulic elements are set, such as the water level at the control section cannot exceed the guaranteed water level, and the water level change rate cannot exceed 2 meters per hour. If the evaluated hydraulic elements exceed these constraint ranges, the depth and duration of constraint violations are recorded for subsequent evaluation.

[0141] V. Multi-objective Evaluator for Scheduling Plans

[0142] (I) Calculation Dimensions of Target Deviation

[0143] Optimize the scheduling objectives and deviations of regulation projects: Calculate the average deviation of the water level in the calculation period, that is, the average deviation of the storage water level in each period from the expected upstream water level parameters; Statistically calculate the proportion of the water level deviation duration, that is, the proportion of the number of periods when the actual water level exceeds the allowable range to the total number of simulated periods; Determine the deviation of the final regulated water level of the project, that is, the deviation of the storage water level at the end of the simulation from the expected upstream water level parameters.

[0144] Scheduling objectives and deviations of the downstream river: Calculate the target deviation of the hydraulic elements at the control section CH station, especially the depth and duration of the simulated water level exceeding the guaranteed water level.

[0145] (II) Priority and Weight Allocation Logic

[0146] The scheduling objectives are ranked in ascending order of priority as the average deviation of the reservoir in each period, the duration of the reservoir water level deviation, the deviation of the final regulated water level of the reservoir, and the deviation of the flood control safety objective of the river. Through the setting of priorities and weights, the weighted target deviation and the total constraint violation amount are comprehensively determined to conduct a multi-objective evaluation of the current optimized regulation project scheduling plan.

[0147] VI. Intelligent Optimizer for Project Regulation

[0148] (I) Details of Initializing the Plan Population

[0149] A large number of scheduling plans are randomly generated. For the reservoir DSH, the water level ranges from 140 to 160 meters, and a discrete reservoir scheduling water level process is generated at a 1-meter step; for the sluice dam DC, the discharge ranges from 1100 to 1500 cubic meters per second, and a discrete downstream discharge process is generated at a 50-cubic-meter-per-second step. These discrete value processes are combined to generate a basic plan, and then 10% of the time step values in the basic plan are randomly perturbed to finally form 500 initial plans. Each plan is stored in the form of a two-dimensional matrix, with rows representing different plans and columns representing the time course of the optimized regulation project.

[0150] (II) Adaptive Constraint Processing Stage

[0151] In the initial iteration stage, due to a large number of contingency plans for constraint violations, in accordance with the principles of the adaptive constraint handling method, contingency plans with smaller amounts of constraint violations are preferentially retained. Even if the objectives of these contingency plans deviate greatly, they are given a higher selection probability. In the middle and late stages of iteration, when the number of contingency plans that meet the constraints increases, a "goal priority" strategy is adopted. Calculate the weighted objective deviation of each contingency plan, retain the top 50% of the contingency plans, and maintain the diversity of the contingency plans through crowding degree calculation to prevent falling into local optimal solutions.

[0152] (III) Heuristic evolutionary operations

[0153] Evolve the contingency plans through crossover and mutation operations. The crossover operation randomly selects two high-quality contingency plans and exchanges the parameters in the middle period (for example, exchange the water level values of the reservoir DSH from the 2nd to the 4th hour), thereby generating new contingency plans and enabling the new contingency plans to inherit the advantages of both sides. The mutation operation fine-tunes the parameters of a certain period of a single project with a probability of 5% (for example, increase or decrease the discharge of the sluice dam DC by 10 cubic meters per second in a certain hour) to explore the uncovered parameter space. When the total calculation time exceeds 0.5 hours (the preset upper limit), stop the iteration and output the optimal scheduling contingency plan.

[0154] VII. Computational acceleration methods

[0155] (I) Optimization of parallel computing strategy

[0156] Use hardware resources for parallel computing. The reservoirs JJ, ZH, and LYD in the upstream control engineering set S1 have no mutual influence and jointly form the first camp. Therefore, for the reservoirs JJ, ZH, and LYD in S1, allocate 3 CPU cores for separate processing. Each project independently runs the fine simulator for the control contingency plan, reducing the single-project simulation time from 3 times in serial to 1 time in parallel. For the reservoirs DSH and the sluice dam DC in the optimized regulation engineering set S2, utilize the parallel computing ability of the GPU to simultaneously simulate the scheduling contingency plans, reducing the single-contingency plan calculation time from 2 times to 1 time. There is no domination relationship between projects in the same camp and they can be calculated independently. The upper and lower hierarchical camps are executed in the order of "calculate the upstream level first and the downstream level later" to ensure the correctness of the water flow evolution logic.

[0157] (II) Historical contingency plan library mechanism

[0158] Before starting the iterative optimization, establish a historical contingency plan library to store information such as contingency plan numbers, generation times, optimized regulation engineering parameters, simulation results (including the outputs of upstream control engineering, downstream control engineering, and downstream river channel simulation results), and evaluation results (weighted objective deviation, total constraint violation amount, etc.). Use the hash algorithm to encode the contingency plan parameters and achieve fast query through the Redis database. As the iteration progresses, the hit rate gradually increases, saving approximately 40% of the simulation time in total and effectively using the historical contingency plan library to accelerate the calculation.

[0159] Meanwhile, for the new scheduling plan for which simulation evaluation is carried out, the simulation evaluation results (including the target deviation amount and the total constraint violation amount) are added to the historical plan library.

[0160] (3) Invalid calculation avoidance rules

[0161] When a constraint violation occurs during the simulation of the optimization control project (such as the DSH water level of the reservoir exceeding 160 meters, or the DC discharge of the sluice dam exceeding 1500 cubic meters per second), the midstream and downstream simulation of the current plan is no longer carried out (skipping the simulation of the sluice dams JZ and HS and the downstream river channel simulation), and the constraint violation amount of the downstream river channel is directly assigned as a preset maximum value (such as 1000), and the reason for invalidity is recorded to avoid the repeated generation of similar invalid plans, saving a large amount of calculation time and effectively avoiding invalid calculations.

[0162] Such as Figures 1 to 3 As shown, a regulation method for a basin water project control and operation optimization regulation system for intelligent water conservancy includes the following steps:

[0163] Step 1: Apply the basin topology intelligent processor to obtain the specified scheduling methods and parameters of each reservoir and sluice dam project in the basin, the inflow of each project and its evolution lag time statement, construct the basin project neighborhood domination matrix D1 and the global domination matrix D2, divide the basin reservoir and sluice dam group into the upstream control and operation project set S1, the optimization regulation project set S2, and the downstream control and operation project set S3, and use the fast non-dominated sorting algorithm to obtain multiple domination camps within each project set, where the outflow of the projects in the upper-level camp is used as the inflow of the projects in the lower-level camp, and there is no intersection between the outflow and inflow of all projects belonging to the same camp;

[0164] The neighborhood domination matrix D1 is: ;

[0165] The global domination matrix D2 is: ;

[0166] The upstream control and operation project set S1: the reservoirs JJ, ZH, and LYD for upstream control and operation;

[0167] The optimization regulation project set S2: the reservoir DSH and the sluice dam DC participating in optimization regulation;

[0168] The downstream control and operation project set S3: the sluice dams JZ and HS for downstream control and operation;

[0169] Step 2: Based on the refined simulator for the reservoir sluice control plan, for the upstream control operation project set S1 (reservoirs JJ, ZH, LYD), conduct engineering simulation calculations for each dominant camp one by one: First, calculate the current project synthetic inflow process according to the inflow and its evolution lag time statement, and then apply the mathematical model of the reservoir project water level control operation mode in front of the dam, and conduct continuous time-step simulation in combination with the current project control parameters to output the upstream water level and calculated outflow process of the current project within the calculation time domain; after the simulation of the upstream control operation project set S1 is completed, it will not be recalculated in subsequent iterative optimizations as a measure to accelerate the calculation; conduct parallel calculations for the project groups within each dominant camp as a measure to accelerate the calculation;

[0170] Step 3: Based on the engineering regulation intelligent optimizer, use the pre-plan population initialization unit to randomly generate a pre-plan population, that is, each row represents a two-dimensional matrix of all the engineering dispatching pre-plans participating in the optimization regulation, and the dispatching pre-plan is the upstream water level or the downstream discharge process of the project within the calculation time domain;

[0171] For reservoir DSH, within the water level range of 140 - 160 meters, generate a discrete reservoir dispatching water level process with a 1-meter step; for sluice DC, within the discharge range of 1100 - 1500 cubic meters per second, generate a discrete downstream discharge process with a 50-cubic-meter-per-second step. Combine these discrete value processes to generate a basic pre-plan, and then randomly perturb 10% of the time-step values in the basic pre-plan to finally form 500 initial pre-plans. Each pre-plan is stored in the form of a two-dimensional matrix, with rows representing different pre-plans and columns representing the time course of the optimization regulation project.

[0172] Step 4: Construct an empty historical pre-plan library to save the evaluated optimization regulation project dispatching pre-plans, as well as the corresponding simulation results of the reservoir sluice project and the flood storage and detention space, the weighted target deviation amount, and the total constraint violation amount;

[0173] Step 5: For each dispatching pre-plan of the optimization regulation project in the current pre-plan population, if an evaluated dispatching pre-plan can be located in the historical pre-plan library, directly retrieve the simulation results of the reservoir sluice project and the flood storage and detention space, the weighted target deviation amount, and the total constraint violation amount; otherwise, conduct simulation calculations to obtain the simulation results of the reservoir sluice project and the flood storage and detention space, the weighted target deviation amount, and the total constraint violation amount:

[0174] The simulation calculations include the following steps:

[0175] Step 5.1: All projects within the optimized regulation project set S2 are optimized regulation projects (reservoir DSH and sluice dam DC). Each dominant camp conducts project type judgment and simulation calculation in the following manner: For optimized regulation projects, based on the above-mentioned reservoir and sluice dam operation plan simulator, calculate the current project's synthetic inflow process according to the inflow and its evolution lag declaration. Based on the current project operation plan, combined with the optimization parameters, conduct continuous time-step simulation, and output the upstream water level, calculated inflow, and calculated outflow process of the current project within the calculation time domain, and quantify the constraint violation amount of each project at each calculation time step.

[0176] Step 5.2: Based on the above-mentioned detailed simulator for reservoir and sluice dam control plans, for the downstream control operation project set S3 (sluice dams JZ, HS), each dominant camp conducts simulation calculations for each project: First, calculate the current project's synthetic inflow process according to the inflow and its evolution lag declaration, and then apply the mathematical model of the water level control operation mode above the sluice dam project, combined with the current project control parameters, conduct continuous time-step simulation, and output the upstream water level and calculated outflow process of the current project within the calculation time domain.

[0177] Step 5.3: If the constraint violation amount of each project at each calculation time step in Step 5.1 is 0, based on the above-mentioned flood routing and storage space water flow simulator, with the calculated outflow process of the project at the most downstream of the basin as the upstream inflow boundary, conduct continuous time-step simulation of the flood routing and storage space water flow, and output the hydraulic element process of the control section of the flood routing and storage space within the calculation time domain, and quantitatively determine the constraint violation amount of the flood routing and storage space; otherwise, directly assign a maximum value of 1000 to the constraint violation amount of the flood routing and storage space.

[0178] Step 5.4: Based on the above-mentioned multi-objective evaluator for operation plans, conduct multi-objective evaluation for the current optimized regulation project operation plan. On the basis of quantitatively determining the constraint violation amount of the optimized regulation project and the constraint violation amount of the flood routing and storage space, quantitatively determine the deviation of each scheduling objective of the optimized regulation project and the flood routing and storage space, and then comprehensively determine the weighted objective deviation amount and the total constraint violation amount.

[0179] Step 5.5: Add the simulation results of the reservoir and sluice dam projects and the flood routing and storage space, the weighted objective deviation amount, and the total constraint violation amount of the current simulated evaluation operation plan to the historical plan library.

[0180] Step 6: Use the plan population quantitative evaluation unit of the above-mentioned project regulation intelligent optimizer to assign a quantitative priority to each operation plan in the population based on the weighted objective deviation amount and the total constraint violation amount of each operation plan in the population, and the specific calculation method is as follows:

[0181] ;

[0182] Among them, is the priority of a scheduling plan in the population, is the weighted target deviation of this plan, is the total constraint violation of this plan, is the proportion of feasible plans with a total constraint violation of 0 in the population; for a scheduling plan, is the deviation of each scheduling target of the optimized regulation project and the flood storage and detention space of the weight coefficient, is the violation of each constraint of the optimized regulation project and the flood storage and detention space. Through calculation, when the number of plans with a total constraint violation of 0 in the population is small, the plan with a smaller total constraint violation is preferred, and when the number of plans with a total constraint violation of 0 in the population is large, the plan with a smaller weighted target deviation is preferred.

[0183] Step 7: Use the plan population heuristic evolution unit of the engineering regulation intelligent optimizer to update the scheduling plan population of the optimized regulation project based on the quantitative priorities of each scheduling plan.

[0184] The plan population heuristic evolution unit is based on the quantitative priorities of the scheduling plans calculated in Step 6 P , and uses a heuristic evolution algorithm to update the population. Specifically, it is realized through three core operators of genetic algorithm selection, crossover, and mutation: First, the selection operator adopts the roulette wheel strategy of genetic algorithm, and calculates the probability of being selected according to the priority of each scheduling plan P (the higher the priority P , the greater the probability of being selected), and thus screens out the parent individuals from the current population to provide a basic sample for subsequent operations; then, use the genetic algorithm crossover operator to perform single-point crossover on the selected parent individuals, that is, randomly select a decision variable position as the crossover point, and exchange the gene segments of the two parent individuals after this point to generate offspring individuals carrying the characteristics of both parents, so as to inherit the advantages of the parents and introduce new combination possibilities; finally, use the genetic algorithm mutation operator to perturb the gene positions of the generated offspring individuals with a low probability (0.04). For each decision variable, if the random number is less than the mutation probability, the variable value is randomly adjusted to increase the population diversity with small changes and avoid falling into local optima; the entire evolution process updates the scheduling plan population by performing selection, crossover, and mutation operations, so that the population gradually evolves towards a better scheduling plan under the guidance of the priority P .

[0185] Repeat Step 5 to Step 7 until the exit condition is met: when the total calculation time exceeds 0.5 hours (the preset upper limit), stop the iteration and output the optimal scheduling plan. The total calculation time calculation method is: before Step 7 is executed and jumps to Step 5, calculate the total calculation time according to the following formula : , where is the CPU time when step 7 is completed, and P is the CPU time when step 1 starts to execute. After exiting, the optimization control project scheduling plan with the highest priority during the iterative calculation process is output ( P the calculation method is shown in step 6), that is, the optimal control plan.

[0186] In the present invention, the fine simulators of reservoir sluice control plans, the simulators of reservoir sluice scheduling plans, and the flow simulators in flood detention and storage spaces have exactly the same calculation time domain, and the calculation time steps are determined according to the simulation requirements of reservoir sluice projects and flood detention and storage spaces, and can be different.

[0187] In the present invention, an implementation of the mathematical model for the operation mode of controlling the water level in front of the dam of a reservoir project is as follows:

[0188] (1) Select the maximum discharge capacity curve. If the initial water level at the scheduling time period exceeds the maximum controlled discharge water level in the project scheduling regulations, then select the maximum sum of the discharge capacities of all spillways (open discharge line), otherwise determine the maximum controlled discharge flow curve (controlled discharge line) corresponding to different water levels according to the regulations. Secondly, select the minimum discharge capacity curve: if the maximum discharge capacity curve is the open discharge line, then the minimum discharge capacity curve is also the open discharge line, otherwise determine the minimum discharge capacity curve according to the regulations;

[0189] (2) Perform time interpolation according to the specified target water level control parameters to obtain the target control water level at the end of the current time period. Based on the water level in front of the dam at the beginning of the reservoir time period, judge the highest and lowest water levels in front of the dam at the end of the time period through the corresponding minimum and maximum discharge capacities, and obtain the reasonable range of the water level in front of the dam at the end of the time period. Select the value closest to the target water level at the end of the time period within the reasonable range as the water level at the end of the time period;

[0190] (3) When the water level at the beginning of the time period is lower than the crest elevation of the spillway / the elevation of the gate bottom slab, give priority to water storage, and the upper and lower limits of the water level at the end of the time period are the same;

[0191] (4) When the water level at the beginning of the time period is greater than or equal to the flood limit water level and less than the higher value of the flood limit water level / normal storage water level, consider the relationship between the incoming water in the current time period and the maximum discharge capacity corresponding to the initial water level. If the incoming water is large, then iteratively calculate a higher water level at the end of the time period, otherwise iteratively calculate a lower water level at the end of the time period, and thus obtain the lower limit of the water level at the end of the time period; when all the incoming water is stored without discharge, the water level at the end of the time period is obtained, and thus the upper limit of the water level at the end of the time period is obtained;

[0192] (5) When the water level at the beginning of the time period is greater than or equal to the higher value of the flood limit water level / normal storage water level, consider the relationship between the incoming water in the current time period and the maximum discharge capacity corresponding to the initial water level. If the incoming water is large, then iteratively calculate a higher water level at the end of the time period, otherwise iteratively calculate a lower water level at the end of the time period, and thus obtain the lower limit of the water level at the end of the time period; for the upper limit of the water level at the end of the time period, take the trial value of the water level at the end of the time period;

[0193] (6) For the calculated discharge during a time period, if the water levels at the beginning and end of the time period both meet the constraints of the power generation diversion water level range, it is preferentially determined as the outflow of the power generation tail water, and the remaining water volume is determined as the outflow of the flood discharge facilities; otherwise, all are determined as the outflow of the flood discharge facilities.

[0194] In the present invention, an implementation of the mathematical model for the open spill control operation mode of the reservoir project spillway gate is as follows:

[0195] (1) Select the discharge capacity curve. If the initial water level at the scheduling time period exceeds the maximum controlled discharge water level in the project scheduling regulations, then select the maximum sum of the flow capacities of all spillways (open spill line); otherwise, select the maximum flow capacity curve of the common spillway.

[0196] (2) If the water level at the beginning of the time period is less than the crest elevation of the spillway / bottom elevation of the gate, give priority to water storage.

[0197] (3) If the water level at the beginning of the time period is greater than or equal to the crest elevation of the spillway / bottom elevation of the gate, consider the relationship between the inflow during the current time period and the maximum discharge capacity corresponding to the initial water level. If the inflow is large, then based on the principle of water balance and the bisection method, iteratively calculate the higher water level at the end of the time period; otherwise, obtain the lower water level at the end of the time period.

[0198] (4) For the calculated discharge during a time period, if the water levels at the beginning and end of the time period both meet the constraints of the power generation diversion water level range, it is preferentially determined as the outflow of the power generation tail water, and the remaining water volume is determined as the outflow of the flood discharge facilities; otherwise, all are determined as the outflow of the flood discharge facilities.

[0199] In the present invention, an implementation of the mathematical model for the water level control operation mode above the gate of the sluice project is as follows:

[0200] (1) Perform time interpolation according to the specified target water level control parameters to obtain the target water level at the end of the current time period. Based on the water level in front of the sluice at the beginning of the time period of the sluice, judge the highest and lowest water levels in front of the sluice at the end of the time period through the minimum and maximum discharge capacities, and obtain the reasonable range of the water level in front of the sluice at the end of the time period. Subsequently, select the value closest to the target water level at the end of the time period within the reasonable range as the water level at the end of the time period.

[0201] (2) If the water level at the beginning of the time period is less than or equal to the flood limit water level + super elevation, give priority to water storage to obtain the upper limit of the water level in front of the sluice at the end of the time period; the sluice iteratively calculates the lower limit of the water level at the end of the time period with the maximum capacity for discharge.

[0202] (3) If the water level at the beginning of the time period is greater than the flood limit water level + super elevation, the sluice iteratively calculates the water level at the end of the time period with the maximum capacity for discharge, and this water level serves as both the upper limit and the lower limit.

[0203] (4) For the calculated discharge during a time period, if the water levels at the beginning and end of the time period both meet the constraints of the power generation diversion water level range, it is preferentially determined as the outflow of the power generation tail water, and the remaining water volume is determined as the outflow of the flood discharge facility; otherwise, it is all determined as the outflow of the flood discharge facility.

[0204] In the present invention, an implementation of the mathematical model for the gradually open flood discharge control operation mode of the sluice dam project is as follows:

[0205] (1) If any of the following conditions is satisfied, it is determined that the sluice dam at the current time step is in a fully open flood discharge state: 1) The inflow during the time period is greater than the maximum discharge capacity corresponding to the water level at the beginning of the time period; 2) The water level at the beginning of the time period is greater than the flood limit water level + safety margin; 3) The sluice dam in the previous time period was in an open flood discharge state.

[0206] (2) If the current time period is in a fully open flood discharge state, considering the relationship between the inflow during the current time period and the maximum discharge capacity corresponding to the initial water level, if the inflow is larger, the higher water level at the end of the time period is iteratively calculated based on the principle of water volume balance and the bisection method; otherwise, the lower water level at the end of the time period is obtained.

[0207] (3) If the current time period is still in a gradually open flood discharge state, the lower water level at the end of the time period is iteratively calculated. If the water level variation during the time period is less than or equal to the maximum allowable variation in the operation regulation, the sluice dam at the end of the time period is adjusted to a fully open flood discharge state; otherwise, the water level at the end of the time period is corrected as follows: the initial water level minus the maximum allowable variation.

[0208] (4) For the calculated discharge during a time period, if the water levels at the beginning and end of the time period both meet the constraints of the power generation diversion water level range, it is preferentially determined as the outflow of the power generation tail water, and the remaining water volume is determined as the outflow of the flood discharge facility; otherwise, it is all determined as the outflow of the flood discharge facility.

[0209] VIII. Industry Application Value

[0210] Topological analysis: Traditional methods rely on manual drawing analysis, which takes a long time and is difficult to adapt to engineering scheduling changes. Through matrix automatic construction and algorithm division, the present invention can complete topological analysis within 10 seconds and can dynamically adapt to scheduling changes.

[0211] Control simulation: Traditional single-project serial simulation takes 2 - 3 times as long for a single project and has a single mode. The present invention performs parallel computing according to the dominant camps, supports multiple control modes, and the overall efficiency is increased by more than 3 times, and the simulation flexibility is greatly improved.

[0212] Optimization iteration: Traditional methods rely on empirical trial calculations, which take more than 2 hours for 100 iterations and are prone to falling into local optima. The present invention combines intelligent evolution with an acceleration strategy, reducing the number of iterations by 40%, reflecting the advantages of the engineering regulation intelligent optimizer and the calculation acceleration method.

[0213] This embodiment details the operation process of each component and the application of the calculation acceleration method, and verifies the effectiveness of the system through actual engineering. Each component collaborates closely, and the calculation acceleration method significantly improves the calculation efficiency, providing strong support for the scientific regulation of the basin water project group, fully demonstrating the innovation and practicality of the present invention in the field of water conservancy projects.

[0214] By introducing a basin topology intelligent processor, the present invention realizes the intelligent construction of the partition set and dominance relationship of basin water projects, laying a foundation for parallel computing; combined with a fine simulator for reservoir and sluice control plans, a scheduling plan simulator, and a flow simulator for flood detention and retention spaces, a multi-level and high-precision simulation system for water projects and water flow processes is constructed; at the same time, the collaborative work of the multi-objective evaluator for scheduling plans and the intelligent optimizer for project regulation realizes the quantitative evaluation and intelligent optimization of regulation plans. In addition, the present invention innovatively proposes a calculation acceleration method, which significantly improves the system operation efficiency through parallel computing, efficient invocation of the historical plan library, and avoidance of invalid calculations.

[0215] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A basin water project control and operation optimization regulation system for intelligent water conservancy, characterized in that: It includes a basin topology intelligent processor, a fine simulator for reservoir and sluice control plans, a simulator for reservoir and sluice operation plans, a flow simulator for flood detention spaces, a multi-objective evaluator for operation plans, and an intelligent optimizer for project regulation and control; The basin topology intelligent processor is used to intelligently construct the neighborhood domination matrix D1 and the global domination matrix D2 of the basin reservoir and sluice project group, analyze and obtain the upstream control operation project set S1, the optimized regulation project set S2, the downstream control operation project set S3, and the domination camps within the sets, and support the parallel computing of the simulation analysis of the basin project group; it is used to obtain the specified operation modes and parameters of each reservoir and sluice project in the basin, and obtain the inflow of each project and its evolution delay time statement; The fine simulator for reservoir and sluice control plans calculates the synthetic inflow process of the project according to the specified control operation project inflow and its evolution delay time statement; The simulator for reservoir and sluice operation plans calculates the synthetic inflow process of the project according to the specified optimized regulation project inflow and its evolution delay time statement; The flow simulator for flood detention spaces identifies the most downstream project in the basin reservoir and sluice projects that does not dominate other projects through the global domination matrix D2. Facing the downstream flood detention space of the most downstream project, with the outflow calculated by the most downstream project as the upstream inflow boundary, based on the river channel confluence hydrology method or the hydrodynamic method, it conducts continuous time-step simulation of the flow in the flood detention space, outputs the hydraulic element process of the control section in the flood detention space within the calculation time domain, and quantitatively determines the constraint violation amount of the flood detention space corresponding to the current upstream inflow boundary. The involved constraint conditions include: hydraulic element range constraint, hydraulic element amplitude change constraint, hydraulic element rate of change constraint, hydraulic element threshold damage depth constraint, and hydraulic element threshold damage duration constraint; The multi-objective evaluator for operation plans conducts multi-objective evaluation on the current optimized regulation project operation plan; The intelligent optimizer for project regulation and control consists of three parts: a plan population initialization unit, a plan population quantitative evaluation unit, and a plan population heuristic evolution unit; among them, the plan population initialization unit randomly generates a two-dimensional matrix. Each row of the matrix represents a scheduling plan for all projects participating in the optimized regulation, that is, the upstream water level or the downstream discharge process of each project within the calculation time domain. The matrix is formed by combining multiple scheduling plans in columns, which is the plan population; the plan population quantitative evaluation unit assigns a quantitative priority to each scheduling plan using an adaptive constraint handling method based on the weighted target deviation amount and the total constraint violation amount of each scheduling plan in the population; the plan population heuristic evolution unit iteratively updates and evolves the plan population using a heuristic evolution algorithm based on the quantitative priorities of each scheduling plan.

2. The optimized regulation system for the operation control of basin water projects for intelligent water conservancy according to claim 1, characterized in that: The described basin topology intelligent processor constructs a neighborhood domination matrix D1 for basin projects. Here, D1 is a two-dimensional square matrix with a dimension of M + N, where M is the number of reservoir projects and N is the number of sluice and dam projects. The value of D1ij being 1 indicates that the outflow of project i directly flows into the adjacent downstream project j, that is, project i affects and dominates project j. The value of -1 indicates that project i directly receives the outflow of the adjacent upstream project j, that is, project i is affected and dominated by project j. The value of 0 indicates that there is no mutual influence and domination relationship between project i and project j. Based on the neighborhood domination matrix D1, matrix analysis is carried out to obtain the global domination matrix D2. D2 is a two-dimensional square matrix with a dimension of M + N. The value of D2ij being 1 indicates that the outflow of project i directly or indirectly flows into the downstream project j, that is, project i affects and dominates project j. The value of -1 indicates that project i directly or indirectly receives the outflow of the upstream project j, that is, project i is affected and dominated by project j. The value of 0 indicates that there is no mutual influence and domination relationship between project i and project j.

3. A regulation method for the optimized regulation system of basin water project control and operation for intelligent water conservancy as described in claim 1 or 2, characterized in that, It includes the following steps: Step 1: Apply the described basin topology intelligent processor to obtain the specified scheduling methods and parameters of each reservoir and sluice project in the basin, the inflow of each project and its evolution delay time statement, construct the basin project neighborhood dominance matrix D1 and the global dominance matrix D2, divide the basin reservoir and sluice group into S1, S2, and S3, where S1 is the set of upstream control operation projects, S2 is the set of optimized regulation projects, and S3 is the set of downstream control operation projects. Use the fast non-dominated sorting algorithm to obtain multiple dominant camps within each project set. The outflow of the projects in the upper camp is used as the inflow of the projects in the lower camp. For all projects belonging to the same camp, there is no intersection between their outflows and inflows. Record the CPU time at the start of Step 1 ; Step 2: Based on the above-mentioned reservoir and sluice control plan fine simulator, for the upstream control operation project set S1, project simulation calculations are carried out for each domination camp one by one: First, calculate the current project synthetic inflow process according to the inflow and its evolution lag time statement. Then, apply the mathematical models of various control operation modes of different types of projects, and combine the current project control parameters to perform continuous time-step simulations, and output the upstream water level of the current project and the calculated outflow process within the calculation time domain. Step 3: Based on the above-mentioned project regulation intelligent optimizer, use the pre-plan population initialization unit to randomly generate a pre-plan population, that is, a two-dimensional matrix where each row represents a scheduling plan for all projects participating in the optimized regulation project, and the scheduling plan is the upstream water level or the downstream discharge process of the project within the calculation time domain. Step 4: Construct an empty historical pre-plan library to save the evaluated optimized regulation project scheduling plans, as well as the corresponding simulation results of reservoir and sluice projects and flood storage and detention spaces, weighted target deviation amounts, and total constraint violation amounts. Step 5: For each scheduling plan of the optimized regulation project in the current pre-plan population, if an evaluated scheduling plan can be located in the historical pre-plan library, directly retrieve the simulation results of reservoir and sluice projects and flood storage and detention spaces, weighted target deviation amounts, and total constraint violation amounts; otherwise, carry out simulation calculations to obtain the simulation results of reservoir and sluice projects and flood storage and detention spaces, weighted target deviation amounts, and total constraint violation amounts, and add them to the historical pre-plan library. Step 6: Use the pre-plan population quantitative evaluation unit of the above-mentioned project regulation intelligent optimizer to assign a quantitative priority to each scheduling plan based on the weighted target deviation amount and total constraint violation amount of each scheduling plan in the population by using an adaptive constraint handling method. The specific calculation method is as follows: ； Among them, is the priority of a scheduling plan in the population, is the weighted target deviation of this scheduling plan, is the total constraint violation amount of this scheduling plan, is the proportion of feasible plans with a total constraint violation amount of 0 in the population, is the deviation of each scheduling target of the optimization regulation project and the flood storage and detention space of the weight coefficient, is the violation amount of each constraint of the optimization regulation project and the flood storage and detention space; Step 7: Adopt the pre - plan population heuristic evolution unit of the engineering regulation intelligent optimizer, update and optimize the engineering scheduling pre - plan population using the heuristic evolution algorithm based on the quantitative priorities of each scheduling pre - plan, and read the CPU time at this moment ; Repeat steps 5 to 7 until the exit condition is met. The exit conditions include that the total calculation time reaches the preset upper limit or the number of iterative updates reaches the preset upper limit. Calculate the total calculation time : ; The number of iterative updates is calculated as follows: the number of times steps 5 to 7 are repeatedly executed; After exiting, output the optimization and control engineering scheduling plan with the highest priority during the iterative calculation process.

4. The regulation method of the basin water project control and operation optimization regulation system for intelligent water conservancy according to claim 3, characterized in that: The method of applying the fast non-dominated sorting algorithm to the domination matrices D1 and D2 is as follows: A11. If no project in the basin participates in the optimized regulation project, all projects belong to the upstream control operation project set S1; if at least one project in the basin participates in the optimized regulation project, the optimized regulation project set S2 is not empty. If a control operation project appears within the scope of two optimization and regulation projects, the set of participating optimization and regulation projects is a subset of the optimization and regulation project set S2; A12. If the optimization and regulation project set S2 is not empty, starting from all participating optimization and regulation projects, continuously locate adjacent downstream projects according to matrices D1 and D2 and store them in the union of S2 and S3. After obtaining the union of S2 and S3, use the element exclusion method to obtain the upstream control operation project set S1; A13. According to matrices D1 and D2, determine the reservoir and dam projects that do not dominate other projects at all, do not belong to the upstream control operation project set S1, and are designated to carry out control operations. Starting from these reservoir and dam projects, continuously locate adjacent upstream projects and store them in the downstream control operation project set S3. The condition that the adjacent upstream projects should meet is that they do not belong to the upstream control operation project set S1 and are designated to carry out control operations. If there are no adjacent upstream projects that meet the conditions during the location process, the location ends; A14. On the basis of obtaining the upstream control operation project set S1 and the downstream control operation project set S3, use the element exclusion method to obtain the optimization and regulation project set S2; A15. For the sets S1, S2, and S3 respectively, extract the corresponding rows and columns of matrices D1 and D2 as new temporary domination matrices, and apply the fast non-dominated sorting algorithm to divide each project set into different domination camps.

5. The regulation method of the basin water project control operation optimization regulation system for intelligent water conservancy according to claim 4, characterized in that: In step 5, the simulation calculation includes the following steps: Step 5.1: For each domination camp in the optimization and regulation project set S2, carry out project type judgment and simulation calculation in the following way: If the current project is a control operation project, based on the fine simulator of the reservoir and dam control plan, calculate the synthetic inflow process of the current project according to the inflow and its evolution lag time statement, apply the mathematical models of various control operation modes of different types of projects, and perform continuous calculation time step simulation in combination with the control parameters of the current project, and output the upstream water level and calculated outflow process of the current project within the calculation time domain; If the current project is an optimization and regulation project, based on the reservoir and dam operation plan simulator, calculate the synthetic inflow process of the current project according to the inflow and its evolution lag time statement, based on the operation plan of the current project, perform continuous calculation time step simulation in combination with the optimization parameters, and output the upstream water level, calculated inflow, and calculated outflow process of the current project within the calculation time domain, and quantify the constraint violation amount of each project at each calculation time step; Step 5.2: Based on the fine simulator of the reservoir and dam control plan, for the downstream control operation project set S3, carry out simulation calculation for each project in each domination camp: First, calculate the synthetic inflow process of the current project according to the inflow and its evolution lag time statement, and then apply the mathematical models of various control operation modes of different types of projects, and perform continuous calculation time step simulation in combination with the control parameters of the current project, and output the upstream water level and calculated outflow process of the current project within the calculation time domain; Step 5.3: If the constraint violation amounts of all projects at each computational time step in Step 5.1 are all 0, based on the above flood detention space water flow simulator, with the calculated outflow process of the project at the most downstream of the basin as the upstream incoming water boundary, conduct continuous computational time step simulation of the flood detention space water flow, output the hydraulic element process of the control section of the flood detention space within the computational time domain, and quantitatively determine the constraint violation amount of the flood detention space; otherwise, directly assign a very large numerical value to the constraint violation amount of the flood detention space; Step 5.4: Based on the above multi-objective evaluator of the operation plan, conduct multi-objective evaluation for the current optimized regulation project operation plan. On the basis of quantitatively determining the constraint violation amount of the optimized regulation project and the constraint violation amount of the flood detention space, quantitatively determine the deviation of each scheduling objective of the optimized regulation project and the flood detention space, and then comprehensively determine the weighted objective deviation amount and the total constraint violation amount; Step 5.5: Add the simulation results of the reservoir sluice projects and the flood detention space, the weighted objective deviation amount and the total constraint violation amount of the current simulated and evaluated operation plan to the historical plan library.

Images