Gate opening control method and device, storage medium and terminal

Abstract

The application discloses a gate dam opening degree control method and device, a storage medium and a terminal, relates to the technical field of data processing, can be used in the field of water conservancy, and mainly aims to solve the problem of low control accuracy of the gate dam of a complex water system. Mainly includes generating a plurality of initial opening degree control quantity combinations for each to-be-controlled gate station on each water system in the target water network region, and constructing a topological structure. For any initial opening degree control quantity combination, the properties of the corresponding control nodes in the topological structure are updated according to the gate dam opening degree control quantity, and the endpoint water flow parameter prediction data of the control section is predicted through a target water flow parameter prediction model. According to the endpoint water flow parameter prediction data of the global control section and the preset target function, the target function value is calculated. The target opening degree control quantity combination is obtained by solving the target function value, and the gate dam opening degree control quantity of the global to-be-controlled gate station is controlled according to the target opening degree control quantity combination. Mainly used for controlling the gate dam opening degree.

Description

Technical Field

[0001] This invention relates to the field of data processing technology and can be used in the field of water conservancy. In particular, it relates to a method and device for controlling the opening of dams and gates, a storage medium, and a terminal. Background Technology

[0002] As key water conservancy engineering facilities for regulating the water volume of rivers, lakes, and reservoirs, the control of sluice gate openings is a core element in ensuring the effective functioning of these systems and maintaining urban water security. Precise sluice gate opening control enables accurate allocation of water resources in rivers, lakes, and reservoirs. In terms of flood control and drainage, during extreme weather events such as torrential rains and floods, the reasonable adjustment of sluice gate openings can promptly discharge upstream water and surface runoff, effectively lowering river levels, mitigating the impact of floods on urban infrastructure such as dikes and bridges, preventing urban flooding, and protecting the lives and property of urban residents and maintaining normal production and living order. Simultaneously, sluice gate opening control can scientifically regulate downstream flow based on urban water demand and river, lake, and reservoir water levels, ensuring a stable and reliable water source for the city during droughts and other water-scarce periods, meeting the water needs of residents, industrial production, and agricultural irrigation, and maintaining urban water supply security.

[0003] Currently, traditional methods for regulating river, lake, and reservoir water volume mainly rely on comparative analysis of similar control plans and human experience-based judgment. Comparative analysis compares the current control situation with historical control plans, selecting similar cases as references for water volume regulation. Human experience-based judgment relies primarily on the experience and intuition of technical personnel for decision-making. However, with climate change and the accelerated pace of urban construction, urban hydrological cycles have undergone significant changes. The total amount and timing of upstream water inflows have become difficult to predict, and regional rainfall exhibits greater uncertainty. This exposes key control bases upon which traditional methods rely, such as historical control plans and human experience-based judgment, to a high degree of uncertainty, thus compromising the accuracy and timeliness of dam and gate control. Summary of the Invention

[0004] In view of this, the present invention provides a method and device for controlling the opening of dams and gates, a storage medium, and a terminal, the main purpose of which is to solve the problem of low accuracy in the control of dams and gates in existing complex water systems.

[0005] According to one aspect of the present invention, a method for controlling the opening degree of a dam or gate is provided, comprising: Multiple initial opening control quantity combinations are generated for each gate station to be controlled on each water system within the target water network area, and the topology of the target water network area is constructed. The initial opening control quantity combination includes the gate dam opening control quantity corresponding to each gate station to be controlled. The topology includes multiple nodes and control segments between each node. The nodes include control nodes corresponding to each gate station to be controlled and constraint nodes corresponding to each water intake point on each water system. For any of the initial opening control quantities, the attributes of the corresponding control nodes in the topology are updated according to the gate and dam opening control quantities, so that for any control segment, the endpoint flow parameter prediction data of the control segment is predicted by matching the target flow parameter prediction model of the control segment according to the gate and dam opening control quantities. Based on the predicted data of the final flow parameters of the global control segment and the preset objective function, the objective function value is calculated to obtain the objective function value of each combination of the initial opening control quantities; Using the combination of opening control quantities as optimization variables, the target opening control quantity combination is obtained by solving based on the objective function value, and the opening control quantity of the gate and dam of the global gate station to be controlled is controlled based on the target opening control quantity combination.

[0006] Furthermore, the construction of the topology of the target water network area includes: Obtain the location distribution of each gate station to be controlled and the location distribution of each water intake point in each water system; The initial topology is constructed by taking the gate station to be controlled as the control node, the water intake point as the constraint node, the water flow path between adjacent control nodes or between adjacent control nodes and constraint nodes as the control segment, the gate opening control quantity as the attribute of the control node, and the water intake constraint as the attribute of the constraint node. By assigning multidimensional parameter values ​​to each control segment of the initial topology, the topology of the target water network area is obtained.

[0007] Further, the step of assigning multi-dimensional parameter values ​​to each control segment of the initial topology to obtain the topology of the target water network region includes: Acquire basic data, hydrological monitoring data, and meteorological monitoring data for each control segment, wherein the hydrological monitoring data and meteorological monitoring data include both historical data and real-time data; For each control segment, the basic attribute parameters of the control segment are assigned values ​​based on the basic data, and the monitoring attribute parameters of the control segment are assigned values ​​based on the hydrological monitoring data and the meteorological monitoring data, so as to obtain the topology of the target water network area; The basic attribute parameters include at least one of the flow length, cross-sectional area, and water consumption coefficient along the control section, and the monitoring attribute parameters include at least one of the historical hydrological parameters, real-time hydrological parameters, historical meteorological parameters, and real-time meteorological parameters of multiple monitoring points in the control section.

[0008] Further, the step of predicting the endpoint flow parameter prediction data of the control section based on the dam opening control amount and matching the target flow parameter prediction model of the control section includes: From the relationship curve between the gate opening control quantity and the discharge flow, find the discharge flow corresponding to the gate opening control quantity; The expected flow rate at the starting point of the downstream control section is calculated based on the discharge flow rate, wherein the downstream control section is the control section starting from the gate station to be controlled; Retrieve the target flow parameter prediction model matching the control section, and extract the real-time hydrological monitoring data, real-time meteorological monitoring data, and basic attribute parameters of the control section; The target flow parameter prediction model is used to predict the expected flow at the starting point, the real-time hydrological monitoring data, the real-time meteorological monitoring data, and the basic attribute parameters to obtain the predicted flow parameter data at the end of the control section.

[0009] Furthermore, before predicting the predicted flow parameters of the endpoint of the control section by matching the target flow parameter prediction model of the control section based on the dam opening control amount, the method further includes: Construct an initial flow parameter prediction model, wherein the initial flow parameter prediction model includes an input layer, a hidden layer, and an output layer; For each control segment, historical hydrological monitoring data, historical meteorological monitoring data, and historical basic attribute parameters at the starting point of the control segment under different dam opening control values ​​are used as sample data, and historical water flow data at the end point of the control segment are used as sample markers to construct training samples, thereby obtaining the training samples corresponding to each control segment. Based on the training samples corresponding to different control sections, the initial flow parameter prediction model is trained respectively to obtain the flow parameter prediction model corresponding to each control section.

[0010] Furthermore, the endpoint flow parameter prediction data includes the endpoint water level prediction value, water volume prediction value, and flow velocity prediction value, and the key parameters of the preset objective function include water resource utilization efficiency, flood control risk index, and water demand satisfaction. The calculation of the objective function value based on the predicted endpoint flow parameters of the global control segment and the preset objective function includes: The flood control risk index is calculated based on the real-time water level and predicted water level of each control section. Water resource utilization efficiency and water demand satisfaction are calculated based on real-time hydrological monitoring data, real-time meteorological monitoring data, water volume prediction values, and flow velocity prediction values ​​of each control section. Retrieve real-time hydrological data and determine the weighting coefficients of each key parameter based on the real-time hydrological data. The objective function value is obtained by weighting and summing the weight coefficients of each key parameter, the water resource utilization efficiency, the flood control risk index, and the water demand satisfaction.

[0011] Further, the step of using the combination of opening control variables as optimization variables and solving for the target opening control variable combination based on the objective function value includes: From the relationship curve between the opening control quantity and the discharge flow, the discharge flow corresponding to each initial control quantity in the initial control quantity combination is retrieved to form a discharge flow combination, and each discharge flow in the discharge flow combination is encoded with a real number to obtain the initial population. The fitness of the initial control variable combination is determined based on the objective function value, and a new generation population is generated by sequentially performing selection, crossover, and mutation operations on the initial population based on the fitness. The new generation population is subjected to non-dominated sorting and crowding calculation, and the population is iteratively updated based on the calculation results until the termination condition is met, so as to obtain the target opening control quantity combination. In the process of solving the target opening control quantity combination, the pre-constructed water balance constraints and water level and flow velocity constraints of each control section, the gate operation constraints of each control node, and the water intake constraints of each constraint node are used as constraints.

[0012] According to another aspect of the present invention, a gate / dam opening control device is provided, comprising: The construction module is used to generate multiple initial opening control quantity combinations for each gate station to be controlled on each water system in the target water network area, and to construct the topology of the target water network area. The initial opening control quantity combinations include the gate and dam opening control quantities corresponding to each gate station to be controlled. The topology includes multiple nodes and control segments between each node. The nodes include control nodes corresponding to each gate station to be controlled and constraint nodes corresponding to each water intake point on each water system. The prediction module is used to update the attributes of the corresponding control nodes in the topology based on the gate and dam opening control quantities for any initial opening control quantity combination, so as to predict the endpoint flow parameter prediction data of the control segment based on the gate and dam opening control quantities by matching the target flow parameter prediction model of the control segment for any control segment. The calculation module is used to calculate the objective function value based on the predicted data of the endpoint water flow parameters of the global control segment and the preset objective function, so as to obtain the objective function value of each combination of the initial opening control quantities; The optimization module is used to obtain the target opening control quantity combination by using the opening control quantity combination as the optimization variable and the objective function value, and to control the gate and dam opening control quantity of the global gate stations to be controlled according to the target opening control quantity combination.

[0013] Furthermore, the building module includes: The acquisition unit is used to acquire the location distribution of each gate station to be controlled and the location distribution of each water intake point in each water system; The first construction unit is used to construct an initial topology structure with the gate station to be controlled as the control node, the water intake point as the constraint node, the water flow path between adjacent control nodes or between adjacent control nodes and constraint nodes as the control segment, the gate opening control quantity as the attribute of the control node, and the water intake constraint as the attribute of the constraint node. The second construction unit is used to assign multi-dimensional parameters to each control segment of the initial topology to obtain the topology of the target water network area.

[0014] Furthermore, in a specific application scenario, the second construction unit is specifically used to acquire basic data, hydrological monitoring data, and meteorological monitoring data for each control segment, wherein the hydrological monitoring data and meteorological monitoring data both include historical data and real-time data; for each control segment, the basic attribute parameters of the control segment are assigned values ​​based on the basic data, and the monitoring attribute parameters of the control segment are assigned values ​​based on the hydrological monitoring data and meteorological monitoring data, thereby obtaining the topology of the target water network area; wherein the basic attribute parameters include at least one of the flow length, cross-sectional area, and water consumption coefficient along the line of the control segment, and the monitoring attribute parameters include at least one of the historical hydrological parameters, real-time hydrological parameters, historical meteorological parameters, and real-time meteorological parameters of multiple monitoring points in the control segment.

[0015] Furthermore, the prediction module includes: The query unit is used to query the discharge flow corresponding to the dam opening control quantity from the relationship curve between the opening control quantity and the discharge flow; The first calculation unit is used to calculate the expected starting flow of the downstream control section based on the outflow, wherein the downstream control section is the control section starting from the gate station to be controlled; The retrieval unit is used to retrieve the target flow parameter prediction model that matches the control section, and extract the real-time hydrological monitoring data, real-time meteorological monitoring data and basic attribute parameters of the control section. The prediction unit is used to perform prediction processing on the expected flow at the starting point, the real-time hydrological monitoring data, the real-time meteorological monitoring data, and the basic attribute parameters through the target flow parameter prediction model to obtain the predicted flow parameter data at the end of the control section.

[0016] Furthermore, the device also includes: The model building module is used to build an initial flow parameter prediction model, wherein the initial flow parameter prediction model includes an input layer, a hidden layer and an output layer; The sample construction module is used to construct training samples for each control segment, using historical hydrological monitoring data and historical meteorological monitoring data of the starting point of the control segment under different dam opening control quantities, and historical basic attribute parameters as sample data, and using historical water flow data of the ending point of the control segment as sample markers, so as to obtain the training samples corresponding to each control segment. The training module is used to train the initial flow parameter prediction model based on the training samples corresponding to different control sections, so as to obtain the flow parameter prediction model corresponding to each control section.

[0017] Furthermore, the computing module includes: The second calculation unit is used to calculate the flood control risk index based on the real-time water level and predicted water level of each control section. The third calculation unit is used to calculate the water resource utilization efficiency and the satisfaction of water intake and water demand based on the real-time hydrological monitoring data, real-time meteorological monitoring data, water volume prediction value and flow velocity prediction value of each control section. A determining unit is used to retrieve real-time hydrological stages and determine the weighting coefficients of each key parameter based on the real-time hydrological stages. The fourth calculation unit is used to calculate the objective function value by weighted summation based on the weight coefficients of each of the key parameters, the water resource utilization efficiency, the flood control risk index, and the water demand satisfaction.

[0018] Furthermore, the optimization module includes: The encoding unit is used to query the discharge flow corresponding to each initial control quantity in the initial control quantity combination from the relationship curve between the opening control quantity and the discharge flow to form a discharge flow combination, and to encode each discharge flow in the discharge flow combination with a real number to obtain the initial population. The generation unit is used to determine the fitness of the initial control variable combination based on the objective function value, and generate a new generation population by sequentially performing selection, crossover and mutation operations on the initial population based on the fitness. The iterative update unit is used to perform non-dominated sorting and crowding calculation on the new generation population, and iteratively update the population according to the calculation results until the termination condition is met, so as to obtain the target opening control quantity combination. In the process of solving the target opening control quantity combination, the pre-constructed water balance constraints and water level and flow velocity constraints of each control section, the gate operation constraints of each control node, and the water intake constraints of each constraint node are used as constraints.

[0019] According to another aspect of the present invention, a storage medium is provided, wherein at least one executable instruction is stored therein, the executable instruction causing a processor to perform an operation corresponding to the above-described gate opening control method.

[0020] According to another aspect of the present invention, a terminal is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described dam opening control method.

[0021] By employing the above-described technical solutions, the technical solutions provided by the embodiments of the present invention have at least the following advantages: This invention provides a method, device, storage medium, and terminal for controlling the opening degree of sluice gates and dams. In embodiments of this invention, multiple initial opening degree control quantity combinations are generated for each sluice gate to be controlled on each water system within a target water network area, and a topology structure of the target water network area is constructed. The initial opening degree control quantity combinations include sluice gate and dam opening degree control quantities corresponding to different sluice gates to be controlled. The topology structure includes multiple nodes and control segments between the nodes. Each node includes a control node corresponding to each sluice gate to be controlled and a constraint node corresponding to each water intake point on each water system. For any given initial opening degree control quantity combination, the attributes of the corresponding control node in the topology structure are updated according to the sluice gate and dam opening degree control quantity, so that for any control segment, the sluice gate and dam opening degree control is applied. The method involves predicting the endpoint flow parameters of the control section by matching the target flow parameter prediction model of the control section; calculating the objective function value based on the endpoint flow parameter prediction data of the global control section and the preset objective function to obtain the objective function value of each initial opening control quantity combination; using the opening control quantity combination as the optimization variable, solving for the target opening control quantity combination based on the objective function value, and controlling the gate and dam opening control quantity of the global gate stations to be controlled based on the target opening control quantity combination. This greatly reduces the uncertainty of manual regulation, reduces the inaccuracy of fixed preset value control, and ensures the timeliness and accuracy of gate station control in complex water system scenarios, thereby improving the scientificity, rationality and overall operational efficiency of water network resource allocation.

[0022] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention, it can be implemented according to the contents of the specification. Furthermore, in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0023] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart of a gate / dam opening control method provided by an embodiment of the present invention is shown; Figure 2 A schematic diagram of a topology provided by an embodiment of the present invention is shown; Figure 3 This diagram illustrates a block diagram of a gate / dam opening control device provided in an embodiment of the present invention. Figure 4 A schematic diagram of the structure of a terminal provided in an embodiment of the present invention is shown. Detailed Implementation

[0024] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0025] To address the problem of low accuracy in dam and gate control within complex water systems, this invention provides a method for controlling dam and gate opening, such as... Figure 1 As shown, the method includes: 101. Generate multiple initial opening control quantity combinations for each gate station to be controlled on each water system within the target water network area, and construct the topology of the target water network area.

[0026] In this embodiment of the invention, the target water network area refers to the specific water conservancy system range requiring optimization and regulation, encompassing multiple interconnected water systems (such as rivers, lakes, reservoirs, and canals) and their ancillary facilities (such as sluice gates, pumping stations, and water intakes). The sluice gates to be controlled are those whose opening degree requires control. The initial opening degree control quantity combination is the set of sluice gate opening degree control quantities corresponding to different sluice gates to be controlled. By randomly generating the initial opening degree control quantity combination as the initial solution for subsequent optimization, a diverse starting point is provided for the subsequent optimization process, avoiding local optima.

[0027] Simultaneously, the physical connectivity of the target water network area is abstracted using a graph structure approach. Each gate to be controlled is designated as a control node, and each water intake point on the water system is designated as a constraint node, collectively forming the topological structure nodes of the target water network area. Water system segments without functional attributes between nodes are designated as control segments. In other words, the topological structure includes multiple nodes and control segments between them. Nodes include the control nodes corresponding to each gate to be controlled and the constraint nodes corresponding to each water intake point on each water system. Describing the physical connectivity of the water network through topological structure effectively solves the node partitioning problem in scenarios with "multiple gates / multiple water intake points on a single river," ensuring the model's adaptability to complex water systems. Compared to traditional methods, the accuracy of describing complex water systems is improved by more than 40%.

[0028] 102. For any of the initial opening control quantities, update the attributes of the corresponding control nodes in the topology according to the gate and dam opening control quantities, so that for any control segment, predict the endpoint flow parameter prediction data of the control segment by matching the target flow parameter prediction model of the control segment according to the gate and dam opening control quantities.

[0029] In this embodiment of the invention, the attributes of the control node include the gate opening degree of the corresponding gate to be controlled. After determining the initial opening control quantity combination, for each initial opening control quantity combination, the attributes of the corresponding control node in the topology are updated with the gate dam opening value of each gate to be controlled, so as to predict the water flow parameter prediction data under the current initial opening control quantity combination through the target water flow parameter prediction model. The target water flow parameter prediction model is constructed separately for each control segment; that is, each control segment in the topology corresponds to a separate target water flow parameter prediction model. During the prediction process, different control segments make predictions based on their target water flow parameter prediction models. The endpoint water flow parameter prediction data refers to the predicted water flow parameters when the water is about to flow out of the current control segment and enter the next control segment, which may include water flow parameters such as flow rate, water level, and flow velocity. The endpoint of the control segment can be selected as follows: when the downstream node of the control segment is a constraint node (water intake point), the water intake point can be used as the endpoint; when the downstream node of the control segment is a control node (gate to be controlled), the location adjacent to the gate to be controlled where the water is about to flow can be used as the endpoint.

[0030] By independently constructing a segmented neural network prediction model for each control segment in the topology, which consists of "starting point hydrological parameters - control segment environmental parameters - ending point flow parameters," real-time monitoring data can be integrated into the prediction process, enabling dynamic and accurate prediction of the ending flow parameters for each control segment. Furthermore, the neural network model reduces the prediction response time to the minute level, meeting the rapid control requirements in scenarios such as heavy rainfall and sudden water intake demands.

[0031] 103. Calculate the objective function value based on the predicted data of the endpoint water flow parameters of the global control segment and the preset objective function to obtain the objective function value of each combination of the initial opening control quantities.

[0032] In this embodiment of the invention, an objective function is pre-constructed for the target water network area. This objective function uses water resource utilization efficiency, flood control risk index, and water demand satisfaction as key indicators. Each key indicator has a different weight to reflect differences in response level. These key indicators change with variations in water flow parameters. After predicting the terminal water flow parameters of each control segment under different combinations of initial opening control quantities using the target water flow parameter prediction model, the predicted terminal water flow parameters corresponding to each combination of initial opening control quantities are substituted into this objective function to calculate the objective function value for each combination of initial opening control quantities, providing a quantitative evaluation benchmark for subsequent optimization.

[0033] 104. Using the combination of opening control quantities as optimization variables, the target opening control quantity combination is obtained by solving based on the objective function value, and the opening control quantity of the gate and dam of the global gate station to be controlled is controlled based on the target opening control quantity combination.

[0034] In this embodiment of the invention, after calculating the objective function value of the initial opening control quantity combination, the opening control quantity combination is used as the optimization variable. An optimization algorithm (such as genetic algorithm, particle swarm optimization, etc.) is employed to globally optimize the objective function value under preset constraints (such as water balance constraints, gate operation constraints, water intake constraints, etc.). Through iterative calculation, the opening control quantity combination that optimizes the objective function value (e.g., minimizes cost) is selected as the objective solution. Finally, this target opening control quantity combination is applied to all gates to be controlled globally. By adjusting the gate opening in real time, the flow parameters are controlled, thereby achieving the engineering optimization objective. The target opening control quantity combination is obtained by optimizing the prediction results of the global control segment.

[0035] By employing a multi-module collaborative optimization strategy, the water intake compliance rate at constrained nodes is increased to ≥95%, while reducing the dam opening adjustment range by 20%, thereby lowering dam control energy consumption and equipment wear, achieving the dual technical objectives of accurate water intake compliance and optimal control costs.

[0036] In one embodiment of the present invention, for further explanation and limitation, the step of constructing the topology of the target water network area includes: Obtain the location distribution of each gate station to be controlled and the location distribution of each water intake point in each water system; The initial topology is constructed by taking the gate station to be controlled as the control node, the water intake point as the constraint node, the water flow path between adjacent control nodes or between adjacent control nodes and constraint nodes as the control segment, the gate opening control quantity as the attribute of the control node, and the water intake constraint as the attribute of the constraint node. By assigning multidimensional parameter values ​​to each control segment of the initial topology, the topology of the target water network area is obtained.

[0037] In this embodiment of the invention, each gate station to be controlled in the water system, i.e., the section of the water system equipped with gate and dam control devices, is simplified as a control node; water systems with water intake needs are simplified as constraint nodes; and the remaining sections without functional attributes are simplified as connection control sections. Based on the actual hydraulic connections and flow direction in the water system, the connection relationships between control nodes, constraint nodes, and control sections are determined, forming an overall topological connection structure. Taking a city water system including River A, Lake B, and Canal C as an example, its topological structure is divided as follows: Figure 2 As shown. All sluice gate locations on River A are designated as control nodes (denoted as...). , ,..., Each control node is associated with a curve showing the relationship between the opening control amount of the corresponding sluice gate and the downstream flow rate, used for precise regulation of the water flow distribution in River A. Control nodes for Lake B and Canal C are set according to functional requirements (e.g., the inlet and outlet sluice gates of Lake B are denoted as...). The control gate of Canal C is denoted as Similarly, the relationship curve between the gate opening control quantity and the discharge flow is also related. River A has three gate stations, denoted as follows: - The sluice gate dam at the inlet of Lake B is designated as The starting gate of Canal C is denoted as The operational characteristics of the dams at each control node were documented using measured data. Furthermore, all water intake points along River A, Lake B, and Canal C were designated as constraint nodes. , ,..., Including the agricultural water intake of River A C Canal Industrial Water Intake Point Each constraint node specifies the minimum and maximum water consumption thresholds, such as... Minimum water intake flow rate: 2 cubic meters per second; maximum: 8 cubic meters per second. The average daily water intake does not exceed 50,000 cubic meters. After determining the nodes, the non-functional sections between adjacent control nodes and between control nodes and constraint nodes in River A are simplified into control sections, such as... and Between , and Between , and Between Control node at the inlet of Lake B. Starting from, As the endpoint, the non-functional section is divided into a control section. , From the starting control node of Canal C ( Upon reaching the endpoint, the non-functional section is divided into a control section. , All control sections possess basic attributes such as length, cross-sectional area, shoreline extent (River A, Canal C), or water area (Lake B). Based on the actual hydraulic connections and flow directions of River A, Lake B, and Canal C, a cross-water body topology network is constructed. The main chain structure of the topology is the upstream starting point of River A → → → → → → → →Downstream terminus of River A; branch link is → →Entrance to Lake B This forms a branch line from River A to Lake B; the outlet of Lake B is through... →C Channel starting point This forms a branch line from Lake B to Canal C. The starting point of Canal C... pass →C Canal Industrial Water Intake Point → →The lower reaches of River A form a local water cycle system, thus yielding the initial topological structure. Furthermore, the three-tiered topological structure of "river-lake-channel" can be visualized using a geospatial information system, clearly identifying each control node (…). - ), constraint nodes ( - ) and control section ( - Spatial connections and water flow transmission paths.

[0038] In one embodiment of the present invention, for further explanation and limitation, the step of assigning multi-dimensional parameter values ​​to each control segment of the initial topology to obtain the topology of the target water network area includes: Acquire basic data, hydrological monitoring data, and meteorological monitoring data for each of the control sections; For each control segment, the basic attribute parameters of the control segment are assigned values ​​based on the basic data, and the monitoring attribute parameters of the control segment are assigned values ​​based on the hydrological monitoring data and the meteorological monitoring data, thereby obtaining the topology of the target water network area.

[0039] In this embodiment of the invention, both hydrological monitoring data and meteorological monitoring data include historical data and real-time data. Basic attribute parameters include the flow length, cross-sectional area, and water consumption coefficient along the control section. Monitoring attribute parameters include at least one of historical hydrological parameters, real-time hydrological parameters, historical meteorological parameters, and real-time meteorological parameters from multiple monitoring points within the control section. After completing the spatial distribution structure construction, i.e., the initial topology construction, parameter values ​​are assigned to each control section in the initial topology. The basic attribute parameters of the control section are assigned based on the basic data, specifically including the flow length (corresponding to the control section length L_L, in km), the cross-sectional area A_L (in square meters), and the water consumption coefficient k_loss along the line (this coefficient is dynamically determined based on vegetation cover and soil type, with a value range of 0.01-0.05). These parameters collectively constitute the basis for water balance calculation. Furthermore, based on hydrological and meteorological monitoring data, values ​​are assigned to the monitoring attribute parameters of the control section. Emphasis is placed on monitoring points near key nodes such as sluice gates and water intake points. This requires recording historical hydrological parameters such as historical water levels and flows, as well as historical meteorological parameters such as historical rainfall and temperatures, to establish a time-series database. Real-time dynamic data such as current water levels and flows also need to be updated. Additionally, the regional runoff coefficient φ (determined by the underlying surface type; 0.3-0.5 for farmland and 0.7-0.9 for urban paved roads) and the shoreline area S_L are used to calculate the precipitation-to-water conversion volume Q_L, where rain = S_L × P_L × φ, and P_L is the rainfall in the corresponding area of ​​the control section (in meters). This process assigns the precipitation-to-water conversion volume to the control section. For areas with multiple water intake points and sluice gates, the influence range and degree of each point on the precipitation-to-water conversion volume are comprehensively considered. Finally, by integrating basic attribute parameters and monitoring attribute parameters, a topology structure with both spatial distribution characteristics and temporal dynamic characteristics is formed, providing data support for subsequent water balance analysis, such as calculating the water consumption along the control section endpoint Q_L,loss=Q_L,start×k_loss×L_L, where Q_L,start is the water consumption along the control section start point, and for predicting water flow parameters.

[0040] The hydrological monitoring data can include water level, flow rate, and velocity data for each river section; designed water intake and real-time demand adjustments for water intake areas (agriculture, industry, and urban life) (such as increased water volume during peak agricultural irrigation periods and temporary reductions in water volume for industrial production); historical data on dam control, including dam opening degree, control time, and changes in upstream and downstream water level and flow rate before and after control; and water intake data for constrained nodes, including water intake period, water intake volume, and corresponding river section water level changes during the water intake period. The data collection frequency can be once per hour, with a historical data span of no less than 3 years. The above data is acquired through evenly distributed water level stations and flow meters within the basin, with increased monitoring equipment density upstream and downstream of dams and around water intake points to ensure data acquisition accuracy in key areas. Meteorological monitoring data includes rainfall data for the riverbank area, acquired through rain gauges covering the entire region and meteorological satellite remote sensing technology, with a spatial resolution of 500m × 500m and a collection frequency of once per hour. For areas with dense dams and water intake points, additional meteorological monitoring stations are added to improve the accuracy of meteorological data acquisition in local areas.

[0041] In one embodiment of the present invention, for further explanation and limitation, the step of predicting the predicted flow parameters of the endpoint of the control section based on the dam opening control amount by matching the target flow parameter prediction model of the control section includes: From the relationship curve between the gate opening control quantity and the discharge flow, find the discharge flow corresponding to the gate opening control quantity; The expected flow rate at the starting point of the downstream control section is calculated based on the discharge flow rate, wherein the downstream control section is the control section starting from the gate station to be controlled; Retrieve the target flow parameter prediction model matching the control section, and extract the real-time hydrological monitoring data, real-time meteorological monitoring data, and basic attribute parameters of the control section; The target flow parameter prediction model is used to predict the expected flow at the starting point, the real-time hydrological monitoring data, the real-time meteorological monitoring data, and the basic attribute parameters to obtain the predicted flow parameter data at the end of the control section.

[0042] In this embodiment of the invention, during the prediction of the final flow parameters of the control section, firstly, based on the dam opening control quantity, the corresponding discharge flow value is accurately retrieved from the pre-established opening-discharge flow relationship curve. Using this discharge flow as input, and combining the principle of flow continuity, the expected starting flow of the downstream control section, originating from the dam to be controlled, is calculated. This curve is constructed based on long-term water conservancy engineering monitoring data, through the collection, organization, and analysis of operational data of different types of dams under various operating conditions, using mathematical methods such as data fitting and regression analysis. In actual operation, based on the dam opening in the initial opening control quantity combination, the corresponding discharge flow can be accurately retrieved by querying this relationship curve, thereby determining the flow value in the starting hydrological parameters used to input the neural network model. Next, the system automatically retrieves the target flow parameter prediction model that matches the current control section, and simultaneously extracts real-time hydrological monitoring data (such as real-time water volume, real-time flow velocity, and real-time water level), real-time meteorological monitoring data (such as real-time rainfall and precipitation-to-water conversion), and basic attribute parameters (such as flow length, cross-sectional area, and water consumption coefficient along the route) from the starting point of the control section. After the dam is opened, water flows out from the reservoir or upstream river channel through the dam openings, forming an instantaneous flow pulse. The starting flow refers to the inflow at the cross-section of the adjacent river channel upstream of the dam, which is the initial value of the water flow entering the model prediction range. The outflow will inversely affect the upstream water level, thereby changing the starting flow; the two are dynamically correlated through changes in river channel storage. Therefore, the expected starting flow can be derived based on this correlation.

[0043] Finally, the expected flow rate at the starting point, real-time monitoring data, and basic parameters are input into the target flow parameter prediction model. The input layer of the model performs structured processing on the real-time hydrological parameters and the conversion of precipitation into water volume to obtain initial hydrological characteristics. Low-order combined features and high-order abstract features of the initial hydrological characteristics are extracted through nonlinear transformation to obtain hydrological features. The output layer performs prediction processing on the hydrological features to obtain the predicted results of the endpoint flow parameters (such as endpoint water level, flow rate, and flow velocity), providing data basis for the subsequent optimization of dam opening control.

[0044] In one embodiment of the present invention, for further explanation and limitation, before the step of predicting the predicted data of the final flow parameters of the control section by matching the target flow parameter prediction model of the control section based on the dam opening control amount, the method further includes: Construct an initial flow parameter prediction model; For each control segment, historical hydrological monitoring data, historical meteorological monitoring data, and historical basic attribute parameters at the starting point of the control segment under different dam opening control values ​​are used as sample data, and historical water flow data at the end point of the control segment are used as sample markers to construct training samples, thereby obtaining the training samples corresponding to each control segment. Based on the training samples corresponding to different control sections, the initial flow parameter prediction model is trained respectively to obtain the flow parameter prediction model corresponding to each control section.

[0045] In this embodiment of the invention, the initial flow parameter prediction model includes an input layer, a hidden layer, and an output layer. When constructing a flow parameter prediction model adapted to each control section, firstly, an initial flow parameter prediction model framework with a general architecture of input layer-hidden layer-output layer needs to be built. Subsequently, customized training was conducted for each control section: based on historical data accumulated under different dam opening conditions for that control section, multi-dimensional historical data at the starting point of the control section (including hydrological monitoring data such as water level and flow rate, meteorological monitoring data such as rainfall and precipitation-to-water conversion, and basic attribute parameters such as flow length, cross-sectional area, and water consumption coefficient along the line) were used as model input features. The flow data at the end of the control section recorded at the same time (such as the end water level and flow rate) were used as the target label for supervised learning. A dedicated training sample set was constructed by combining spatiotemporal data sequences under different opening conditions. Finally, based on the independent sample set of each control section, algorithms such as error backpropagation were used to optimize the parameters of the initial model, enabling the model to gradually learn the nonlinear mapping relationship between the input features and the end flow parameters, thereby forming a customized flow parameter prediction model that is highly matched with the hydrological characteristics of the control section, providing accurate algorithmic support for subsequent real-time prediction.

[0046] The input layer can be configured with 7 neurons, integrating key variables such as the starting point water volume, flow velocity, water level, and precipitation-converted water volume of the control section, and strengthening the collection of hydrological parameters around the sluice gate and water intake point. The hidden layer can adopt a two-layer activation function structure (10+8 neurons) to extract the nonlinear coupling features of the input variables layer by layer. The output layer can directly output the continuous predicted values ​​of the endpoint water volume, flow velocity, and water level through 3 linear neurons. During the training phase, a sample set (total samples ≥1000 groups) is constructed using historical data from the past 3 years, with 20% more data added for areas with dense sluice gates (sluice gates and water intake points in rivers). After dividing the training set and validation set into a 7:3 ratio, the Adam optimizer and mean squared error loss function are used for 500 iterations of training. The training is terminated when the mean squared error of the validation set is stably below 0.001, and the optimized weight parameters are saved. Finally, a lightweight prediction model that can be called in real time and supports dynamic input updates is formed, providing high-precision endpoint flow parameter support for sluice gate and dam regulation.

[0047] In one embodiment of the present invention, for further explanation and limitation, the step of calculating the objective function value based on the predicted data of the endpoint flow parameters of the global control segment and the preset objective function includes: The flood control risk index is calculated based on the real-time water level and predicted water level of each control section. Water resource utilization efficiency and water demand satisfaction are calculated based on real-time hydrological monitoring data, real-time meteorological monitoring data, water volume prediction values, and flow velocity prediction values ​​of each control section. Retrieve real-time hydrological data and determine the weighting coefficients of each key parameter based on the real-time hydrological data. The objective function value is obtained by weighting and summing the weight coefficients of each key parameter, the water resource utilization efficiency, the flood control risk index, and the water demand satisfaction.

[0048] In this embodiment of the invention, the predicted data of the endpoint flow parameters includes the predicted values ​​of water level, water volume, and flow velocity. The key parameters of the preset objective function include water resource utilization efficiency, flood control risk index, and water demand satisfaction. In the joint optimization scheduling of the dam group, the calculation of the objective function value achieves global cost minimization by dynamically integrating multi-dimensional parameters. Its core logic is as follows: First, based on the real-time water level, water volume, and flow velocity data of each control section, combined with the predicted data of the endpoint flow parameters (such as predicted water level, water volume, and flow velocity), three key parameters are calculated: water resource utilization efficiency (reflecting the degree of water resource utilization), flood control risk index (quantifying flood control risk), and water demand satisfaction (the degree to which water demand is met). Then, weight coefficients are automatically allocated according to the real-time hydrological stage (such as dry season, flood season, etc.). For example, during the flood season, the weight of the flood control risk index is significantly increased to prioritize safety, while during the dry season, the focus is on water resource utilization efficiency to improve water supply efficiency. Finally, the three parameters are integrated into the objective function value through weighted summation. The flood control risk index, to be minimized, is calculated using its complement (1 - risk index), ensuring that a larger objective function value indicates a better overall scheduling effect. This process achieves the coupling of real-time data and forecast information, and the dynamic coordination of multi-objective conflicts, providing a quantitative decision-making basis for the coordinated control of dam and sluice gate groups.

[0049] The objective function can be expressed as: Min F=α×(1 η)+β×R+γ×(1 S); Where η is the water resource utilization efficiency (0≤η≤1), η=(∑actual water intake at each water intake point) / (expected inflow + ∑conversion of precipitation); R is the flood control risk index (0≤R≤1), R=∑(H_L,end / H_L,max) / k, where H_L,end is the predicted water level of each control section, H_L,max is the upper limit of the safe water level of each control section, k is the number of control sections, and R=1 when H_L,end>H_L,max; S is the water demand satisfaction degree (0≤S≤1), S=(∑actual water intake at each water intake point) / (∑designed water intake at each water intake point); α, β, γ are weighting coefficients (α+β+γ=1), during the flood season β=0.5, α=0.2, γ=0.3, during the dry season α=0.4, γ=0.4, β=0.2. The water level of each control section can be calculated based on the actual water level at the starting point and the predicted water level. For example, the larger of the actual water level and the predicted water level can be taken as the current water level of the control section. Flow rate and velocity, as core parameters characterizing the flow capacity of a water body, are the basis for deriving the actual water intake. Water resource utilization efficiency and the satisfaction of water intake and demand are directly related to the actual water intake and can be calculated using commonly used theories in technical water resource assessment.

[0050] In one embodiment of the present invention, for further explanation and limitation, the step of using the combination of opening control variables as optimization variables and solving for the target opening control variable combination based on the objective function value includes: From the relationship curve between the opening control quantity and the discharge flow, the discharge flow corresponding to each initial control quantity in the initial control quantity combination is retrieved to form a discharge flow combination, and each discharge flow in the discharge flow combination is encoded with a real number to obtain the initial population. The fitness of the initial control variable combination is determined based on the objective function value, and a new generation population is generated by sequentially performing selection, crossover, and mutation operations on the initial population based on the fitness. The new generation population is subjected to non-dominated sorting and crowding calculation, and the population is iteratively updated based on the calculation results until the termination condition is met, so as to obtain the target opening control quantity combination. In this embodiment of the invention, during the solution of the target opening control quantity combination, the pre-constructed water balance constraints and water level and velocity constraints of each control section, the gate operation constraints of each control node, and the water intake constraints of each constraint node are used as constraints. During the solution of the target opening control quantity combination, the fitness of each initial opening control quantity combination in the initial population is evaluated based on the objective function value (i.e., fitness is determined by the objective function value corresponding to the initial opening control quantity combination (which can be based on a preset relationship matching); the smaller the fitness, the higher the fitness). Subsequently, a roulette wheel selection strategy is used to select individuals with high fitness based on the fitness, and a new generation population is generated through single-point crossover (probability 0.8) and mutation (probability 0.01). The new generation population is then subjected to non-dominated sorting (dividing into levels 1, 2, ..., with level 1 being optimal) and crowding calculation (maintaining solution set diversity). Based on the calculation results, high-quality solutions are selected and the population is iteratively updated until the termination condition is met (100 iterations or a fitness change of <0.001 for 10 consecutive generations).

[0051] The entire optimization process strictly follows pre-built constraints, including water balance constraints in the control section (ensuring water supply and expenditure balance), water level and velocity constraints (maintaining ecological / navigable water levels and scour prevention flow velocities), gate operation constraints at control nodes (limiting the discharge flow range to ensure safe operation), and water intake constraints at constraint nodes (meeting the minimum / maximum water demand at the water intake point). Finally, a combination of target opening control quantities that meets the actual needs of the basin (such as prioritizing flood control during the flood season and focusing on water supply during the dry season) is selected from the Pareto optimal solution set. For example, a discharge flow of 50 cubic meters per second corresponds to a gate opening of 30%; a discharge flow of 30 cubic meters per second corresponds to a gate opening of 45%.

[0052] In a specific instance, the constraints can be constructed as follows: a) Water balance constraint: Q_L,start+Q_L,rain=Q_L,end+Q_L,loss+∑Q_Y; where Q_L,start is the real-time water volume at the starting point, Q_L,rain is the water volume converted from precipitation, Q_L,end is the predicted water volume at the ending point, Q_L,loss represents the water consumption along the route, and Q_Y is the water consumption at each constraint node, i.e., the water intake point. This constraint ensures the water balance of each control section.

[0053] b) Control node (gate) operation constraints: Q_Z,min≤Q_Z≤Q_Z,max; where Q_Z is the gate discharge flow, Q_Z,min is the minimum discharge flow, which can be 10 cubic meters per second to avoid siltation; Q_Z,max is the maximum discharge flow, which can be 80 cubic meters per second to control flood risk. This constraint ensures the safe operation of the river.

[0054] c) Constraint node (water intake point) demand constraints: Q_Y,min≤Q_Y≤Q_Y,max; where Q_Y is the water intake, Q_Y,min is the minimum water intake, and Q_Y,max is the maximum water intake. For example, for agricultural water intake, Q_Y,min=2 cubic meters per second and Q_Y,max=8 cubic meters per second; for industrial water intake, the daily average Q_Y,max=50,000 cubic meters.

[0055] d) Water level and flow velocity constraints: H_L,min≤H_L,end≤H_L,max; where H_L,end is the predicted water level, H_L,min is the minimum ecological or navigable water level, and H_L,max is the maximum ecological or navigable water level. v_L,min≤v_L,end≤v_L,max; where v_L,end is the predicted flow velocity, v_L,min is the minimum flow velocity to avoid siltation, and v_L,max is the maximum flow velocity to avoid riverbank erosion.

[0056] This invention provides a method for controlling the opening degree of sluice gates and dams. In embodiments of this invention, multiple initial opening degree control quantity combinations are generated for each sluice gate and dam to be controlled on each water system within a target water network area, and a topology structure of the target water network area is constructed. The initial opening degree control quantity combinations include sluice gate and dam opening degree control quantities corresponding to different sluice gates and dams. The topology structure includes multiple nodes and control segments between nodes. Each node includes a control node corresponding to each sluice gate and a constraint node corresponding to each water intake point on each water system. For any given initial opening degree control quantity combination, the attributes of the corresponding control nodes in the topology structure are updated based on the sluice gate and dam opening degree control quantity. For any control segment, based on the sluice gate and dam opening degree control quantity, the method is used to match... The target flow parameter prediction model of the control section predicts the endpoint flow parameter prediction data of the control section; based on the endpoint flow parameter prediction data of the global control section and the preset objective function, the objective function value is calculated to obtain the objective function value of each initial opening control quantity combination; using the opening control quantity combination as the optimization variable, the target opening control quantity combination is obtained based on the objective function value, and the gate and dam opening control quantity of the global gate to be controlled is controlled based on the target opening control quantity combination. This greatly reduces the uncertainty of manual regulation, reduces the inaccuracy of fixed preset value control, and at the same time ensures the timeliness and accuracy of gate control in complex water system scenarios, thereby improving the scientificity, rationality and overall operational efficiency of water network resource allocation.

[0057] Furthermore, as a response to the above Figure 1 The implementation of the method shown in this invention provides a gate / dam opening control device, such as... Figure 3 As shown, the device includes: The construction module 31 is used to generate multiple initial opening control quantity combinations for each gate station to be controlled on each water system in the target water network area, and to construct the topology of the target water network area. The initial opening control quantity combination includes the gate dam opening control quantity corresponding to each gate station to be controlled. The topology includes multiple nodes and control segments between each node. The nodes include control nodes corresponding to each gate station to be controlled and constraint nodes corresponding to each water intake point on each water system. Prediction module 32 is used to update the attributes of the corresponding control nodes in the topology based on the gate and dam opening control quantities for any initial opening control quantity combination, so as to predict the endpoint flow parameter prediction data of the control segment based on the gate and dam opening control quantities by matching the target flow parameter prediction model of the control segment for any control segment. Calculation module 33 is used to calculate the objective function value based on the predicted data of the endpoint water flow parameters of the global control segment and the preset objective function, so as to obtain the objective function value of each combination of the initial opening control quantities; The optimization module 34 is used to obtain the target opening control quantity combination by using the opening control quantity combination as the optimization variable and the objective function value, and to control the gate and dam opening control quantity of the global gate station to be controlled according to the target opening control quantity combination.

[0058] Furthermore, the construction module 31 includes: The acquisition unit is used to acquire the location distribution of each gate station to be controlled and the location distribution of each water intake point in each water system; The first construction unit is used to construct an initial topology structure with the gate station to be controlled as the control node, the water intake point as the constraint node, the water flow path between adjacent control nodes or between adjacent control nodes and constraint nodes as the control segment, the gate opening control quantity as the attribute of the control node, and the water intake constraint as the attribute of the constraint node. The second construction unit is used to assign multi-dimensional parameters to each control segment of the initial topology to obtain the topology of the target water network area.

[0059] Furthermore, in a specific application scenario, the second construction unit is specifically used to acquire basic data, hydrological monitoring data, and meteorological monitoring data for each control segment, wherein the hydrological monitoring data and meteorological monitoring data both include historical data and real-time data; for each control segment, the basic attribute parameters of the control segment are assigned values ​​based on the basic data, and the monitoring attribute parameters of the control segment are assigned values ​​based on the hydrological monitoring data and meteorological monitoring data, thereby obtaining the topology of the target water network area; wherein the basic attribute parameters include at least one of the flow length, cross-sectional area, and water consumption coefficient along the line of the control segment, and the monitoring attribute parameters include at least one of the historical hydrological parameters, real-time hydrological parameters, historical meteorological parameters, and real-time meteorological parameters of multiple monitoring points in the control segment.

[0060] Furthermore, the prediction module 32 includes: The query unit is used to query the discharge flow corresponding to the dam opening control quantity from the relationship curve between the opening control quantity and the discharge flow; The first calculation unit is used to calculate the expected starting flow of the downstream control section based on the outflow, wherein the downstream control section is the control section starting from the gate station to be controlled; The retrieval unit is used to retrieve the target flow parameter prediction model that matches the control section, and extract the real-time hydrological monitoring data, real-time meteorological monitoring data and basic attribute parameters of the control section. The prediction unit is used to perform prediction processing on the expected flow at the starting point, the real-time hydrological monitoring data, the real-time meteorological monitoring data, and the basic attribute parameters through the target flow parameter prediction model to obtain the predicted flow parameter data at the end of the control section.

[0061] Furthermore, the device also includes: The model building module is used to build an initial flow parameter prediction model, wherein the initial flow parameter prediction model includes an input layer, a hidden layer and an output layer; The sample construction module is used to construct training samples for each control segment, using historical hydrological monitoring data and historical meteorological monitoring data of the starting point of the control segment under different dam opening control quantities, and historical basic attribute parameters as sample data, and using historical water flow data of the ending point of the control segment as sample markers, so as to obtain the training samples corresponding to each control segment. The training module is used to train the initial flow parameter prediction model based on the training samples corresponding to different control sections, so as to obtain the flow parameter prediction model corresponding to each control section.

[0062] Furthermore, the computing module 33 includes: The second calculation unit is used to calculate the flood control risk index based on the real-time water level and predicted water level of each control section. The third calculation unit is used to calculate the water resource utilization efficiency and the satisfaction of water intake and water demand based on the real-time hydrological monitoring data, real-time meteorological monitoring data, water volume prediction value and flow velocity prediction value of each control section. A determining unit is used to retrieve real-time hydrological stages and determine the weighting coefficients of each key parameter based on the real-time hydrological stages. The fourth calculation unit is used to calculate the objective function value by weighted summation based on the weight coefficients of each of the key parameters, the water resource utilization efficiency, the flood control risk index, and the water demand satisfaction.

[0063] Furthermore, the optimization module 34 includes: The encoding unit is used to query the discharge flow corresponding to each initial control quantity in the initial control quantity combination from the relationship curve between the opening control quantity and the discharge flow to form a discharge flow combination, and to encode each discharge flow in the discharge flow combination with a real number to obtain the initial population. The generation unit is used to determine the fitness of the initial control variable combination based on the objective function value, and generate a new generation population by sequentially performing selection, crossover and mutation operations on the initial population based on the fitness. The iterative update unit is used to perform non-dominated sorting and crowding calculation on the new generation population, and iteratively update the population according to the calculation results until the termination condition is met, so as to obtain the target opening control quantity combination. In the process of solving the target opening control quantity combination, the pre-constructed water balance constraints and water level and flow velocity constraints of each control section, the gate operation constraints of each control node, and the water intake constraints of each constraint node are used as constraints.

[0064] This invention provides a dam opening control device. In embodiments of this invention, multiple initial opening control quantity combinations are generated for each dam to be controlled on each water system within a target water network area, and a topology structure of the target water network area is constructed. The initial opening control quantity combinations include dam opening control quantities corresponding to different dams to be controlled. The topology structure includes multiple nodes and control segments between nodes. Each node includes a control node corresponding to each dam to be controlled and a constraint node corresponding to each water intake point on each water system. For any given initial opening control quantity combination, the attributes of the corresponding control nodes in the topology structure are updated based on the dam opening control quantity. For any control segment, based on the dam opening control quantity, the device matches the dam opening control quantity. The target flow parameter prediction model of the control section predicts the endpoint flow parameter prediction data of the control section; based on the endpoint flow parameter prediction data of the global control section and the preset objective function, the objective function value is calculated to obtain the objective function value of each initial opening control quantity combination; using the opening control quantity combination as the optimization variable, the target opening control quantity combination is obtained based on the objective function value, and the gate and dam opening control quantity of the global gate to be controlled is controlled based on the target opening control quantity combination. This greatly reduces the uncertainty of manual regulation, reduces the inaccuracy of fixed preset value control, and at the same time ensures the timeliness and accuracy of gate control in complex water system scenarios, thereby improving the scientificity, rationality and overall operational efficiency of water network resource allocation.

[0065] According to one embodiment of the present invention, a storage medium is provided, the storage medium storing at least one executable instruction, which can execute the gate / dam opening control method in any of the above method embodiments.

[0066] Figure 4 The diagram shows a structural schematic of a terminal according to an embodiment of the present invention. The specific implementation of the present invention is not limited to the specific implementation of the terminal.

[0067] like Figure 4 As shown, the terminal may include: a processor 402, a communication interface 404, a memory 406, and a communication bus 408.

[0068] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408.

[0069] Communication interface 404 is used for network communication with other devices such as clients or other servers.

[0070] The processor 402 is used to execute program 410, which can specifically execute the relevant steps in the above-described dam opening control method embodiment.

[0071] Specifically, program 410 may include program code that includes computer operation instructions.

[0072] Processor 402 may be a central processing unit (CPU), a specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The terminal may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0073] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0074] Specifically, program 410 can be used to cause processor 402 to perform the following operations: Multiple initial opening control quantity combinations are generated for each gate station to be controlled on each water system within the target water network area, and the topology of the target water network area is constructed. The initial opening control quantity combination includes the gate dam opening control quantity corresponding to each gate station to be controlled. The topology includes multiple nodes and control segments between each node. The nodes include control nodes corresponding to each gate station to be controlled and constraint nodes corresponding to each water intake point on each water system. For any of the initial opening control quantities, the attributes of the corresponding control nodes in the topology are updated according to the gate and dam opening control quantities, so that for any control segment, the endpoint flow parameter prediction data of the control segment is predicted by matching the target flow parameter prediction model of the control segment according to the gate and dam opening control quantities. Based on the predicted data of the final flow parameters of the global control segment and the preset objective function, the objective function value is calculated to obtain the objective function value of each combination of the initial opening control quantities; Using the combination of opening control quantities as optimization variables, the target opening control quantity combination is obtained by solving based on the objective function value, and the opening control quantity of the gate and dam of the global gate station to be controlled is controlled based on the target opening control quantity combination.

[0075] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0076] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for controlling the opening degree of a dam or gate, characterized in that, include: Multiple initial opening control quantity combinations are generated for each gate station to be controlled on each water system within the target water network area, and the topology of the target water network area is constructed. The initial opening control quantity combination includes the gate dam opening control quantity corresponding to each gate station to be controlled. The topology includes multiple nodes and control segments between each node. The nodes include control nodes corresponding to each gate station to be controlled and constraint nodes corresponding to each water intake point on each water system. For any of the initial opening control quantities, the attributes of the corresponding control nodes in the topology are updated according to the gate and dam opening control quantities, so that for any control segment, the endpoint flow parameter prediction data of the control segment is predicted by matching the target flow parameter prediction model of the control segment according to the gate and dam opening control quantities. Based on the predicted data of the final flow parameters of the global control segment and the preset objective function, the objective function value is calculated to obtain the objective function value of each combination of the initial opening control quantities; Using the combination of opening control quantities as optimization variables, the target opening control quantity combination is obtained by solving based on the objective function value, and the opening control quantity of the gate and dam of the global gate station to be controlled is controlled based on the target opening control quantity combination.

2. The dam opening control method according to claim 1, characterized in that, The construction of the topology of the target water network area includes: Obtain the location distribution of each gate station to be controlled and the location distribution of each water intake point in each water system; The initial topology is constructed by taking the gate station to be controlled as the control node, the water intake point as the constraint node, the water flow path between adjacent control nodes or between adjacent control nodes and constraint nodes as the control segment, the gate opening control quantity as the attribute of the control node, and the water intake constraint as the attribute of the constraint node. By assigning multidimensional parameter values ​​to each control segment of the initial topology, the topology of the target water network area is obtained.

3. The dam opening control method according to claim 2, characterized in that, The process of assigning multi-dimensional parameter values ​​to each control segment of the initial topology to obtain the topology of the target water network region includes: Acquire basic data, hydrological monitoring data, and meteorological monitoring data for each control segment, wherein the hydrological monitoring data and meteorological monitoring data include both historical data and real-time data; For each control segment, the basic attribute parameters of the control segment are assigned values ​​based on the basic data, and the monitoring attribute parameters of the control segment are assigned values ​​based on the hydrological monitoring data and the meteorological monitoring data, so as to obtain the topology of the target water network area; The basic attribute parameters include at least one of the flow length, cross-sectional area, and water consumption coefficient along the control section, and the monitoring attribute parameters include at least one of the historical hydrological parameters, real-time hydrological parameters, historical meteorological parameters, and real-time meteorological parameters of multiple monitoring points in the control section.

4. The dam opening control method according to claim 1, characterized in that, The step of predicting the endpoint flow parameters of the control section based on the dam opening control amount and matching the target flow parameter prediction model of the control section includes: From the relationship curve between the gate opening control quantity and the discharge flow, find the discharge flow corresponding to the gate opening control quantity; The expected flow rate at the starting point of the downstream control section is calculated based on the discharge flow rate, wherein the downstream control section is the control section starting from the gate station to be controlled; Retrieve the target flow parameter prediction model matching the control section, and extract the real-time hydrological monitoring data, real-time meteorological monitoring data, and basic attribute parameters of the control section; The target flow parameter prediction model is used to predict the expected flow at the starting point, the real-time hydrological monitoring data, the real-time meteorological monitoring data, and the basic attribute parameters to obtain the predicted flow parameter data at the end of the control section.

5. The dam opening control method according to claim 1, characterized in that, Before predicting the endpoint flow parameter prediction data of the control section based on the dam opening control amount and matching the target flow parameter prediction model of the control section, the method further includes: Construct an initial flow parameter prediction model, wherein the initial flow parameter prediction model includes an input layer, a hidden layer, and an output layer; For each control segment, historical hydrological monitoring data, historical meteorological monitoring data, and historical basic attribute parameters at the starting point of the control segment under different dam opening control values ​​are used as sample data, and historical water flow data at the end point of the control segment are used as sample markers to construct training samples, thereby obtaining the training samples corresponding to each control segment. Based on the training samples corresponding to different control sections, the initial flow parameter prediction model is trained respectively to obtain the flow parameter prediction model corresponding to each control section.

6. The dam opening control method according to claim 1, characterized in that, The endpoint flow parameter prediction data includes the endpoint water level prediction, water volume prediction, and flow velocity prediction. The key parameters of the preset objective function include water resource utilization efficiency, flood control risk index, and water demand satisfaction. The calculation of the objective function value based on the predicted endpoint flow parameters of the global control segment and the preset objective function includes: The flood control risk index is calculated based on the real-time water level and predicted water level of each control section. Water resource utilization efficiency and water demand satisfaction are calculated based on real-time hydrological monitoring data, real-time meteorological monitoring data, water volume prediction values, and flow velocity prediction values ​​of each control section. Retrieve real-time hydrological data and determine the weighting coefficients of each key parameter based on the real-time hydrological data. The objective function value is obtained by weighting and summing the weight coefficients of each key parameter, the water resource utilization efficiency, the flood control risk index, and the water demand satisfaction.

7. The dam opening control method according to claim 6, characterized in that, The process of obtaining the target opening control variable combination by using the opening control variable combination as the optimization variable and solving for the target opening control variable combination based on the objective function value includes: From the relationship curve between the opening control quantity and the discharge flow, the discharge flow corresponding to each initial control quantity in the initial control quantity combination is retrieved to form a discharge flow combination, and each discharge flow in the discharge flow combination is encoded with a real number to obtain the initial population. The fitness of the initial control variable combination is determined based on the objective function value, and a new generation population is generated by sequentially performing selection, crossover, and mutation operations on the initial population based on the fitness. The new generation population is subjected to non-dominated sorting and crowding calculation, and the population is iteratively updated based on the calculation results until the termination condition is met, so as to obtain the target opening control quantity combination. In the process of solving the target opening control quantity combination, the pre-constructed water balance constraints and water level and flow velocity constraints of each control section, the gate operation constraints of each control node, and the water intake constraints of each constraint node are used as constraints.

8. A dam / sluice gate opening control device, characterized in that, include: A construction module is used to generate multiple initial opening control quantity combinations for each gate station to be controlled on each water system in the target water network area, and to construct the topology of the target water network area. The initial opening control quantity combinations include the gate and dam opening control quantities corresponding to each gate station to be controlled. The topology includes multiple nodes and control segments between each node. The nodes include control nodes corresponding to each gate station to be controlled and constraint nodes corresponding to each water intake point on each water system. The prediction module is used to update the attributes of the corresponding control nodes in the topology based on the gate and dam opening control quantities for any initial opening control quantity combination, so as to predict the endpoint flow parameter prediction data of the control segment based on the gate and dam opening control quantities by matching the target flow parameter prediction model of the control segment for any control segment. The calculation module is used to calculate the objective function value based on the predicted data of the endpoint water flow parameters of the global control segment and the preset objective function, so as to obtain the objective function value of each combination of the initial opening control quantities; The optimization module is used to obtain the target opening control quantity combination by using the opening control quantity combination as the optimization variable and the objective function value, and to control the gate and dam opening control quantity of the global gate stations to be controlled according to the target opening control quantity combination.

9. A storage medium, characterized in that, The storage medium stores at least one executable instruction, which causes the processor to perform the operation corresponding to the gate opening control method as described in any one of claims 1-7.

10. A terminal, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the gate opening control method as described in any one of claims 1-7.

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