A method and system for optimizing the scheduling of flood storage and detention areas

Abstract

The present application discloses a method and system for optimizing the scheduling of flood storage and detention areas, which involves reservoir scheduling and data processing technology, including: determining the flood storage and detention areas contained in the target basin and dividing them into multiple levels; using the Voronoi diagram algorithm to divide the flood storage and detention areas into adaptive grids of a set size; determining the grid data covered by any adaptive grid at a set time interval, and determining the corresponding grid attributes based on the grid data; constructing a flooding cost model for each adaptive grid; using an improved NSGA‑III algorithm to solve an optimization model with the goals of minimizing the total flooding cost, minimizing the maximum value of the super-safe flow of the river channel, and minimizing the water level deviation of the reservoir group; and outputting the optimal flood diversion control plan that meets the current moment according to the solution results. The method of the present application realizes quantitative decision-making on the scheduling of flood storage and detention areas through refined grid segmentation and dynamic scheduling cost evaluation.

Description

Technical Field

[0001] The present application relates to the field of reservoir scheduling and data processing technology, and in particular to a method and system for optimizing the scheduling of flood storage and detention areas. Background Art

[0002] As an important part of the river flood control system, the flood storage area can play an important role in reducing flood peaks, storing excess floods, and improving the overall flood resistance capacity of the basin through joint use with rivers, levees, reservoirs, etc. when the region is facing flood threats. On the other hand, it is also the home of the residents in the area. After more than 70 years of construction, my country has formed a relatively complete flood storage area system. With the increasing number of flood control projects in the basin, the increasing number of dispatchable project types, and the continuous expansion of dispatch objects and scales, the topological relationship of flood control projects has become increasingly complex. Especially when a basin-wide super-standard flood occurs, the timing and order of activation of the flood storage area group are particularly critical, which has put forward higher requirements for ensuring the safety of people's livelihood in the basin, optimizing and selecting flood control dispatching plans, and evaluating the benefits of flood control and disaster reduction.

[0003] Since flood storage and detention areas geographically overlap with the above-mentioned artificial wetlands, transformed wasteland, and converted forest land, flood discharge may be necessary when floods arrive, thus causing damage to production, economy, and even people's lives and property in the region. Therefore, the use of flood storage and detention areas is very important for river flood scheduling and management, which is necessary to ensure timely flood discharge when floods arrive without affecting the overall flood control situation, and to minimize the losses in the region as much as possible. Summary of the invention

[0004] The embodiments of the present application provide a method and system for optimizing the scheduling of a flood storage and detention area, which are used to achieve quantitative decision-making on the scheduling of a flood storage and detention area through refined grid segmentation and dynamic scheduling cost evaluation.

[0005] The present application embodiment provides a method for optimizing the scheduling of a flood storage area, including: Determine the flood storage areas included in the target basin and divide them into multiple levels according to the geographical elevation of each flood storage area; For any level of flood storage and detention area, the Voronoi diagram algorithm is used to divide the flood storage and detention area into adaptive grids of set size; According to the set time interval, the grid data covered by any adaptive grid is determined, and the corresponding grid attributes are determined based on the grid data, wherein the dynamic crop economic sub-weight of the flood storage area is introduced into the grid attributes; According to the grid parameters of each adaptive grid and the water level data at the current moment, the weight coefficient of each grid is adjusted through the fuzzy logic controller to construct the flooding cost model of each adaptive grid; The improved NSGA-III algorithm is used to solve the optimization model with the objectives of minimizing the total flooding cost, the maximum value of the river channel super-safe flow, and the water level deviation of the reservoir group; According to the solution results, the optimal flood diversion control plan that meets the current moment is output.

[0006] Optionally, also include: Establishing a three-dimensional model of the target area and displaying the optimal flood diversion control plan according to the output; and, The affected area under the optimal flood diversion control scheme is displayed according to the adaptive grids at each current level.

[0007] Optionally, using a Voronoi diagram algorithm to divide the flood storage area into adaptive grids of a set size includes: Obtaining topographic parameters of the flood storage area at any level; The flood storage area is divided into adaptive grids of set sizes by using the Voronoi diagram algorithm, and the adaptive grids are adaptively adjusted according to the terrain parameters so that the coverage areas of the adaptive grids are similar.

[0008] Optionally, determining the grid data covered by any adaptive grid includes: Identify the facilities, crops, and personnel information included in any one of the adaptive grids, and determine the types of facilities and crops; The grid data of any one of the adaptive grids is determined according to the flood loss bases configured in advance for each facility type, crop type and personnel.

[0009] Optionally, determining corresponding grid attributes based on grid data includes: Assigning dynamic crop economic sub-weights to each crop according to the current moment, and assigning loss sub-weights to facilities in any adaptive grid according to the possible flooding height; The facilities, crops and personnel in any of the adaptive grids are accumulated under the corresponding weights to determine the corresponding grid attributes.

[0010] Optionally, the weight coefficient of each grid is adjusted by a fuzzy logic controller to construct a flooding cost model of each adaptive grid including: in, Represents an adaptive grid i The flooding cost at time t is, , , is the dynamic weight coefficient adjusted by the fuzzy logic controller, is the number of hours of flooding, is the evacuation time of people in the grid, For adaptive mesh i The number of facilities within For adaptive mesh i The water storage capacity at time t is For adaptive mesh i The maximum water storage capacity, is the weight function of the crop at time t.

[0011] Optionally, the optimization model with the objectives of minimizing the total flooding cost, minimizing the maximum value of the river channel super-safe flow, and minimizing the water level deviation of the reservoir group satisfies: Among them, E represents the minimization optimization model, Indicates the current flow of the river. represents the safe flow threshold of the river, Indicates the total number of grids divided into flood storage and detention areas, represents the current water storage height of reservoir j, represents the dispatching target water level of reservoir j, and M represents the total number of reservoirs in the flood storage area.

[0012] Optionally, the following constraints are also established: in, represents the water storage capacity of the i-th grid, represents the direct inflow of grid i, represents the outflow of grid i, represents the amount of water diverted from the adjacent grid k to the grid i, represents the set of adjacent grids of grid i, represents the grid set belonging to the j-th level flood storage area, represents the maximum flood diversion ratio of j-level flood storage areas, It indicates the total flood storage capacity of the flood storage area.

[0013] Optionally, the improved NSGA-III algorithm is used to solve the optimization model with the objectives of minimizing the total flooding cost, minimizing the maximum value of the river channel super-safe flow, and minimizing the water level deviation of the reservoir group, including: In the population initialization stage, the similarity between the current rainfall intensity and the historical data is calculated, and the historical solutions with similarity greater than a preset threshold are selected as the initial population to perform the solution.

[0014] The embodiment of the present application also proposes a flood storage and detention area optimization scheduling system, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the flood storage and detention area optimization scheduling method as described above are implemented.

[0015] The flood storage and detention area is divided into adaptive grids of set size through the Voronoi diagram algorithm to achieve refined grid segmentation, and dynamic scheduling cost evaluation is realized through the minimization optimization model, thereby achieving quantitative decision-making for flood storage and detention area scheduling.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 The basic process diagram of the flood storage and detention area optimization scheduling method of this embodiment is shown in FIG. DETAILED DESCRIPTION

[0018] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0019] The present application embodiment proposes a method for optimizing the scheduling of flood storage and detention areas, such as Figure 1 As shown, including: In step S101, the flood storage and detention areas included in the target basin are determined, and divided into multiple levels according to the geographical elevation of the location of each flood storage and detention area. In a specific example, a certain basin can be used as the scheduling optimization object. In some examples, a certain basin may contain multiple flood storage and detention areas along the basin. In this scenario, in the embodiment of the present application, the flood storage and detention areas are divided into levels according to the geographical elevation of the location of the flood storage and detention areas, and the flood storage and detention areas with similar high levels are divided into the first level. In a specific example, the first level can be divided into only one flood storage and detention area according to the geographical elevation or position relationship setting.

[0020] In step S102, for any level of flood storage and detention area, the Voronoi diagram algorithm is used to divide the flood storage and detention area into adaptive grids of a set size. In some examples, the size of the adaptive grid can be set according to actual needs, for example, 500m*500m or 1km*1km can be a grid, and the specific grid size is set according to actual conditions.

[0021] In step S103, the grid data covered by any adaptive grid is determined according to the set time interval, and the corresponding grid attributes are determined based on the grid data, wherein the dynamic crop economic sub-weight of the flood storage area is introduced into the grid attribute. In a specific example, the embodiment of the present application determines the corresponding grid attributes by quantifying the grid data covered by the adaptive grid. For example, for agricultural crops and cash crops, a certain economic sub-weight can be given to the crop according to the current time, thereby characterizing the value attribute of the crop.

[0022] In step S104, according to the grid parameters of each adaptive grid and the water level data at the current moment, the weight coefficient of each grid is adjusted by a fuzzy logic controller to construct a flooding cost model of each adaptive grid.

[0023] In step S105, an improved NSGA-III algorithm is used to solve an optimization model with the objectives of minimizing the total flooding cost, minimizing the maximum value of the super-safe flow in the river channel, and minimizing the water level deviation of the reservoir group. In the specific example, a historical flood case library can be introduced to accelerate convergence through transfer learning.

[0024] In step S106, the optimal flood diversion control scheme that meets the current situation is output according to the solution result. In a specific example, the solution result can be mapped to gate control schemes at various levels to form a flood diversion control strategy, and the best solution is selected from different solutions as the final optimal flood diversion control scheme.

[0025] The method of the embodiment of the present application divides the flood storage and detention area into adaptive grids of a set size through a Voronoi diagram algorithm to achieve refined grid segmentation, and realizes dynamic scheduling cost evaluation through a minimization optimization model, thereby achieving quantitative decision-making for flood storage and detention area scheduling.

[0026] In some embodiments, the method further includes: establishing a three-dimensional model of the target area and displaying the output optimal flood diversion control solution; and The affected area under the optimal flood diversion control scheme is displayed according to the adaptive grids at each current level. In a specific example, the three-dimensional model of the target area can be established by obtaining high-precision DEM data of the flood storage area through drone aerial survey to construct a three-dimensional model of the target area. Based on the constructed three-dimensional model, the output optimal flood diversion control scheme is displayed or demonstrated, and further in the process of display or demonstration, the influence of the adaptive grids at each level according to the flood diversion control scheme is presented, so that users can intuitively judge the possible flood coverage area through the presentation, so as to specify relevant strategies.

[0027] In some embodiments, using the Voronoi diagram algorithm to divide the flood storage area into adaptive grids of a set size includes: The topographic parameters of the flood storage and detention areas at any level can be obtained, specifically, based on the three-dimensional model of the target area or watershed constructed above, to obtain the topographic parameters of each flood storage and detention area covered by the watershed.

[0028] The flood storage area is divided into adaptive grids of set sizes by using the Voronoi diagram algorithm, and the adaptive grids are adaptively adjusted according to the terrain parameters so that the coverage areas of the adaptive grids are similar.

[0029] In some embodiments, determining the mesh data covered by any adaptive mesh includes: Identify the facilities, crops, and personnel information included in any of the adaptive grids, and determine the types of facilities and crops. For example, in some examples, facilities may include buildings, public infrastructure, etc., crops may be local cash crops, agricultural crops, etc., and personnel may include permanent residents in the grid or an estimated resident population.

[0030] The grid data of any adaptive grid is determined according to the flood loss bases configured in advance for each facility type, crop type and personnel. Specifically, the flood bases are configured in advance for the facility types and crop types in the coverage area, and the corresponding evacuation bases are configured for the personnel, and the flood base + the evacuation base form the flood loss base.

[0031] In some embodiments, determining corresponding mesh attributes based on the mesh data includes: Assigning dynamic crop economic sub-weights to each crop according to the current moment, and assigning loss sub-weights to facilities in any adaptive grid according to the possible flooding height; The facilities, crops, and personnel in any of the adaptive grids are accumulated under the corresponding weights to determine the corresponding grid attributes. That is, in a specific example, the grid attributes of any adaptive grid include the accumulated values ​​used to characterize the flooding loss, and the grid attributes may also include the actual area, elevation, and other information of the grid.

[0032] The traditional method of the original water level-storage capacity curve method only uses the linear relationship of V=f(H), ignoring the differences between grids, and the static economic evaluation does not consider dynamic factors such as crop growth cycle and evacuation time window. In some embodiments of the present application, the weight coefficient of each grid is adjusted by a fuzzy logic controller, and the flooding cost model of each adaptive grid is constructed, including: in, Represents an adaptive grid i The flooding cost at time t is, , , is the dynamic weight coefficient adjusted by the fuzzy logic controller, is the number of hours of flooding, is the evacuation time of people in the grid, For adaptive mesh i The number of facilities within For adaptive mesh i The water storage capacity at time t is For adaptive mesh i The maximum water storage capacity, is the weight function of the crop at time t.

[0033] Design of fuzzy controller with dynamic update , , Rules, for example: "If the crop maturity is > 80%, then increase Weight to 0.4.

[0034] In some embodiments, the optimization model with the objectives of minimizing the total flooding cost, minimizing the maximum value of the super-safe flow of the river channel, and minimizing the water level deviation of the reservoir group satisfies: Among them, E represents the minimization optimization model, Indicates the current flow of the river. represents the safe flow threshold of the river, Indicates the total number of grids divided into flood storage and detention areas, represents the current water storage height of reservoir j, represents the dispatching target water level of reservoir j, and M represents the total number of reservoirs in the flood storage area.

[0035] In some embodiments, the following constraints are also established: in, represents the water storage capacity of the i-th grid, represents the direct inflow of grid i, represents the outflow of grid i, represents the amount of water diverted from the adjacent grid k to the grid i, represents the set of adjacent grids of grid i, represents the grid set belonging to the j-th level flood storage area, represents the maximum flood diversion ratio of j-level flood storage areas, It indicates the total flood storage capacity of the flood storage area.

[0036] In some embodiments, the improved NSGA-III algorithm is used to solve the optimization model with the objectives of minimizing the total flooding cost, minimizing the maximum value of the river channel super-safe flow, and minimizing the water level deviation of the reservoir group, including: in the population initialization stage, calculating the similarity between the current rainfall intensity and the historical data, and selecting the historical solution with a similarity greater than a preset threshold as the initial population to perform the solution. For example, the similarity between the previous rainfall intensity and the historical data is defined to satisfy: in, is the current rainfall intensity, For cases in historical data, based on this, you can choose If there are historical cases with a value greater than 0.8, the historical case with the maximum similarity is selected as the initial population.

[0037] In the non-dominated sorting process, constraints on the fairness of water allocation across levels are added: .

[0038] In other examples, evacuation time constraints: when the flooding time is predicted Less than the grid evacuation threshold When the flood diversion priority of the grid is forced to be reduced by a certain percentage, for example, 50%.

[0039] The method of the present application solves the problem that regular grids cannot adapt to the terrain through dynamic grid generation of Voronoi diagrams, effectively improves the algorithm convergence speed through fuzzy control-improved NSGA-III, and realizes dynamic scheduling cost evaluation through the method of the present application, thereby achieving quantitative decision-making for flood storage and detention area scheduling.

[0040] The embodiment of the present application also proposes a flood storage and detention area optimization scheduling system, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the flood storage and detention area optimization scheduling method as described above are implemented.

[0041] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present disclosure with equivalent elements, modifications, omissions, combinations (e.g., various embodiments intersecting schemes), adaptations or changes. It is not limited to the examples described in this specification or during the implementation of this application, and its examples will be interpreted as non-exclusive.

[0042] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. For example, those of ordinary skill in the art may use other embodiments when reading the above description.

[0043] The above embodiments are merely exemplary embodiments of the present disclosure. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the essence and protection scope of the present disclosure, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present invention.

Claims

1. A method for optimizing the scheduling of flood storage and detention areas, characterized in that: include: Determine the flood storage areas included in the target basin and divide them into multiple levels according to the geographical elevation of each flood storage area; For any level of flood storage and detention area, the Voronoi diagram algorithm is used to divide the flood storage and detention area into adaptive grids of set size; According to the set time interval, the grid data covered by any adaptive grid is determined, and the corresponding grid attributes are determined based on the grid data, wherein the dynamic crop economic sub-weight of the flood storage area is introduced into the grid attributes; According to the grid parameters of each adaptive grid and the water level data at the current moment, the weight coefficient of each grid is adjusted through the fuzzy logic controller to construct the flooding cost model of each adaptive grid; The improved NSGA-III algorithm is used to solve the optimization model with the objectives of minimizing the total flooding cost, the maximum value of the river channel super-safe flow, and the water level deviation of the reservoir group; According to the solution results, the optimal flood diversion control plan that meets the current moment is output.

2. The method for optimizing the scheduling of flood storage and detention areas according to claim 1, characterized in that: Also includes: Establishing a three-dimensional model of the target area and displaying the optimal flood diversion control plan according to the output; as well as, The affected area under the optimal flood diversion control scheme is displayed according to the adaptive grids at each current level.

3. The method for optimizing the scheduling of flood storage and detention areas according to claim 1, characterized in that: The Voronoi diagram algorithm is used to divide the flood storage area into adaptive grids of a set size, including: Obtaining topographic parameters of the flood storage area at any level; The flood storage area is divided into adaptive grids of set sizes by using the Voronoi diagram algorithm, and the adaptive grids are adaptively adjusted according to the terrain parameters so that the coverage areas of the adaptive grids are similar.

4. The method for optimizing the scheduling of flood storage and detention areas according to claim 3, characterized in that: Determine the mesh data covered by any adaptive mesh including: Identify the facilities, crops, and personnel information included in any one of the adaptive grids, and determine the types of facilities and crops; The grid data of any one of the adaptive grids is determined according to the flood loss bases configured in advance for each facility type, crop type and personnel.

5. The method for optimizing the scheduling of flood storage and detention areas according to claim 4, characterized in that: Determining the corresponding grid properties based on the grid data includes: Assigning dynamic crop economic sub-weights to each crop according to the current moment, and assigning loss sub-weights to facilities in any adaptive grid according to the possible flooding height; The facilities, crops and personnel in any of the adaptive grids are accumulated under the corresponding weights to determine the corresponding grid attributes.

6. The method for optimizing the scheduling of flood storage and detention areas according to claim 5, characterized in that: The weight coefficient of each grid is adjusted by the fuzzy logic controller, and the flooding cost model of each adaptive grid is constructed, including: in, Represents an adaptive grid i The flooding cost at time t is, , , is the dynamic weight coefficient adjusted by the fuzzy logic controller, is the number of hours of flooding, is the evacuation time of people in the grid, For adaptive mesh i The number of facilities within For adaptive mesh i The water storage capacity at time t is For adaptive mesh i The maximum water storage capacity, is the weight function of the crop at time t.

7. The method for optimizing the scheduling of flood storage and detention areas according to claim 6, characterized in that: The optimization model with the objectives of minimizing the total flooding cost, the maximum value of the river channel super-safe flow, and the water level deviation of the reservoir group meets the following requirements: Among them, E represents the minimization optimization model, Indicates the current flow of the river. represents the safe flow threshold of the river, Indicates the total number of grids divided into flood storage and detention areas, represents the current water storage height of reservoir j, represents the dispatching target water level of reservoir j, and M represents the total number of reservoirs in the flood storage area.

8. The method for optimizing the scheduling of flood storage and detention areas according to claim 7, characterized in that: It also includes establishing the following constraints: in, represents the water storage capacity of the i-th grid, represents the direct inflow of grid i, represents the outflow of grid i, represents the amount of water diverted from the adjacent grid k to the grid i, represents the set of adjacent grids of grid i, represents the grid set belonging to the j-th level flood storage area, represents the maximum flood diversion ratio of j-level flood storage areas, It indicates the total flood storage capacity of the flood storage area.

9. The method for optimizing the scheduling of flood storage and detention areas according to claim 8, characterized in that: The improved NSGA-III algorithm is used to solve the optimization model with the objectives of minimizing the total flooding cost, minimizing the maximum value of the river channel super-safe flow, and minimizing the water level deviation of the reservoir group, including: In the population initialization stage, the similarity between the current rainfall intensity and the historical data is calculated, and the historical solutions with similarity greater than a preset threshold are selected as the initial population to perform the solution.

10. A flood storage area optimization scheduling system, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the method for optimizing the scheduling of a flood storage and detention area as claimed in any one of claims 1 to 9 are implemented.

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