11 results about "Reservoir optimization" patented technology

The invention discloses a cascade reservoir scheduling method and system based on an artificial bee colony algorithm, and the method comprises the following steps: the system collects multi-source hydrological data in real time through a hydrological perception and preprocessing module, and introduces a large language model to carry out the semantic judgment and anomaly labeling of an abnormal hydrological time sequence; inputting the processed high-quality data into a reservoir model construction module, establishing a cascade reservoir optimal scheduling system, and setting corresponding boundary conditions and operation constraints in combination with reservoir scheduling regulations; the scheduling optimization module receives model input, adopts a variable structure taking a water level as a core to construct an optimization individual, and completes population initialization, disturbance generation and fitness evaluation based on a potential solution guide mechanism in an improved artificial bee colony algorithm; the system transmits the scheduling sequence optimized and output by the scheduling optimization module into an LLM intelligent auxiliary module; and the intelligent text interpretation generated by the LLM and the scheduling optimization solution enter a result evaluation and visualization module together. According to the scheduling method and system, a high-quality and physically feasible scheduling scheme can be output within reasonable calculation time.

The invention belongs to the technical field of water resource optimal scheduling, and discloses a reservoir optimal scheduling method based on hybrid swarm intelligent optimization, which comprises the following steps: S1, collecting data required by reservoir historical inflow, a reservoir capacity curve, a water supply demand and a scheduling constraint condition; s2, setting a constraint condition and a scheduling target, and establishing a reservoir scheduling optimization model; s3, constructing a candidate solution group on the basis of combining reservoir scheduling decision variables, performing initial fitness calculation by utilizing an objective function of the reservoir scheduling optimization model, introducing a hybrid group intelligent optimization algorithm, performing multi-objective optimization scheduling calculation on the reservoir scheduling optimization model, and performing iterative updating; s4, completing all set iterations, and outputting a reservoir optimization scheduling scheme meeting constraint conditions; according to the method, the premature convergence problem of a traditional swarm intelligence optimization algorithm is effectively avoided, the convergence speed and the solution stability are improved, and the method is suitable for optimization solution of single-library and multi-library systems in single-target or multi-target scheduling problems of water supply, flood control, power generation and the like.

The invention relates to a reservoir optimization scheduling system and scheduling method for generating multiple objectives based on AI, the reservoir optimization scheduling system comprises a data input module, a multi-objective optimization module, a constraint processing module, a result output module and a visualization module, the data input module is configured to obtain basic data of a reservoir; the multi-objective optimization module is configured to construct an optimization model based on a particle swarm optimization algorithm, minimize the maximum discharged flow, minimize the flood exceeding storage capacity and minimize the discharged flow change rate as objective functions, and search the optimal discharged flow process through particle iteration; the constraint processing module is configured to perform constraint check on the discharged flow and the water level of the reservoir and apply punishment to solutions violating constraints so as to ensure the feasibility of the scheduling scheme; the result output module is configured to output the optimized discharge flow process, the optimized water level process and the optimized flood limit exceeding storage capacity process; the visualization module is configured to draw a water level-flow process chart and optimize a target convergence curve.

The invention discloses equipment and a method for predicting, controlling and dispatching the scouring and silting amount of a downstream river channel of a dam, which are taken into account the influence of a flood process on the scouring and silting amount of the river channel and comprehensively consider the influence of water and sediment elements and flood characteristics on the scouring and silting amount of the downstream river channel of the dam. Water surface gradient, flow, peak pattern coefficient, jacking coefficient, sediment content and sediment grading elements are incorporated to predict the erosion and deposition amount of the downstream river channel of the dam, a calculation formula structure of the erosion and deposition amount of the downstream river channel of the dam and a matched calculation method are constructed, a corresponding table of the erosion and deposition amount of the downstream river channel of the dam is compiled according to the method, and reverse derivation can be achieved through table query. When the scouring and silting amount of the downstream river channel of the dam is controlled to be a fixed value, key dispatching parameters such as reservoir outlet flow and peak pattern coefficient required to be regulated and controlled by an upstream reservoir; a reservoir management department can formulate a scientific reservoir optimization scheduling scheme through the method, technical guarantee is provided for river regime stability and bank slope protection of a downstream river channel of a dam, flood control standard improvement and shipping safety maintenance are effectively supported, and a decision basis is provided for exerting comprehensive benefits of the reservoir.

The application discloses a kind of arid region reservoir water and sand prediction and coordinated regulation potential evaluation method and system, belong to water resources, silt treatment and reservoir optimization scheduling technical field.The technical problems of traditional model prediction accuracy insufficient caused by arid region hydrology data scarcity, water and sand prediction process is fragmented and reservoir desilting and silt reduction and water storage benefit difficult to be synergistically optimized, its gist is that: long short-term memory network is used to predict future runoff sequence by fusing attention mechanism, and the sediment concentration is calculated by combining the water and sand relationship formula;By setting the critical sediment concentration threshold, the high sediment concentration period is identified and the available water for dredging is calculated;At the same time, the flood resource potential is evaluated by combining reservoir capacity and water demand constraints;Finally, the coordinated scheduling scheme is generated by comparing the two potentials.The application realizes the integrated accurate prediction of water and sand process in arid region and the simultaneous quantitative evaluation of regulation potential, and provides intelligent decision support for multi-objective optimization scheduling of reservoir.

This invention relates to the field of water resource optimization scheduling technology in water conservancy and hydropower engineering, specifically to a reservoir optimization scheduling method and related products based on intertemporal sensitivity sparse repair. The method involves constructing and solving implicit equations for power generation and water volume, establishing an evaluation system and calculating intertemporal sensitivity to obtain a set of key time periods. Then, sparse repair is implemented on the initial power generation plan within this set, and the repair operation is embedded in an iterative optimization framework. This invention avoids the use of empirical estimation or coarse trial calculations in traditional reservoir scheduling, enhances the water resource safety net of reservoirs, suppresses the oscillation phenomenon of traditional intelligent evolutionary algorithms, and maintains the scheduling intent and temporal consistency of the initial predetermined plan on the power grid side.

The invention discloses a reservoir optimization scheduling method based on uniform design, relates to the technical field of water conservancy projects, and aims to solve the problem that an existing reservoir scheduling method may generate an inaccurate or low-efficiency scheduling result when facing a complex hydrological environment and multi-target demands.The reservoir optimization scheduling method comprises the following steps that S1, basic data of a reservoir are collected, and the basic data are stored in a database; comprise historical hydrological data, real-time hydrological data, reservoir characteristic parameters and flood control requirements, abnormal values are removed through data cleaning, data of different dimensions are uniformly converted into a [0, 1] interval by adopting a standardization method, and a high-quality data basis is provided for subsequent analysis; s2, determining the decision variables of the drainage flow, the reservoir level and the scheduling time period according to the drainage scheduling scene in the feasible region of each variable; the method has the advantages of maximizing the utilization efficiency of water resources, improving the scheduling capability of the reservoir and reducing the waste of the water resources through the uniformly designed optimal scheduling method.

The application discloses a kind of high-efficiency solving methods of cascade reservoir joint power generation optimization scheduling model, comprising: determining the storage capacity of cascade reservoir at the beginning and end of scheduling period, and the inflow of leading reservoir in whole scheduling period, interval inflow between each reservoir, various constraints of cascade reservoir;The storage capacity value of each reservoir is discretized within the upper and lower limits of storage capacity according to the discrete accuracy requirement;In time period t , for all possible storage capacity combinations of cascade reservoir at the beginning of time period, find the corresponding optimal storage capacity combination of cascade reservoir at the end of time period, and store such a correspondence as optimal state transition relationship;Let t = t -1, repeat step 3 until all time periods in scheduling period are traversed;According to the stored optimal state transition relationship, start backtracking from the beginning of the first time period, and obtain the optimal storage capacity process of cascade reservoir in the whole scheduling period;The application can improve the solving efficiency of cascade reservoir optimization scheduling model, while ensuring the quality of solution, so that the calculation result is close to the global optimal solution.

The embodiment of the invention discloses a deep learning short-term and medium-term runoff forecasting method fusing a physical mechanism, and relates to the technical field of hydrological forecasting, and the method comprises the steps: obtaining the hydrometeorological data and weather forecasting data of a target drainage basin; inputting the hydrometeorological data and the weather forecast data into the trained multivariable mixed downscaling model to obtain model weather forecast data of a forecast period; inputting model weather forecast data and hydro meteorological data into the trained hybrid model to obtain forecast runoff data of a forecast period; determining a posterior probability density function through back calculation forecasting, and determining a probability distribution result of the actual runoff data based on the posterior probability density function; and performing flood early warning based on a probability distribution result. The method is suitable for scenes of runoff forecasting of large and medium-sized drainage basins, mountain torrent early warning of small and medium-sized drainage basins, reservoir optimization scheduling, hydropower station operation management and the like, and can provide key technical support for water safety, water resource management and water ecological protection.

This invention discloses a reservoir scheduling method and system based on hydrological simulation, belonging to the field of reservoir optimization scheduling methods. The method comprises: constructing a hydrological environment simulation model based on hydrological environment data and a pre-set soil and water conservation model; obtaining initial watershed runoff data based on the hydrological environment simulation model; obtaining initial watershed drought characteristics based on the initial watershed runoff data and a pre-set drought assessment model; constructing a reservoir balance model based on reservoir characteristic information; constructing a multi-objective reservoir scheduling model based on the initial watershed runoff data, initial watershed drought characteristics, and the reservoir balance model; and solving the multi-objective reservoir scheduling model to obtain an optimized reservoir scheduling scheme, thereby achieving multi-objective reservoir scheduling. Therefore, by implementing this invention, the limitations of existing reservoir scheduling optimization schemes in practical applications are solved. By constructing a model, flexible scheduling and real-time control of multi-objective reservoirs are achieved, avoiding hydrological drought problems.

The invention discloses a water engineering multi-target robust optimization scheduling method in an uncertain environment, and belongs to the technical field of reservoir optimization scheduling, and the method comprises the following steps: S1, constructing a water engineering multi-target robust optimization scheduling model; s2, introducing two types of risk disturbance variables; s3, a robust double-population collaborative evolution algorithm is adopted for optimizing and solving; and S4, executing a dynamic robust fitness evaluation mechanism. According to the multi-target robust optimization scheduling method for the underwater engineering in the uncertain environment, a double-population elite cooperation strategy, a multi-strategy cooperation updating mechanism, an optimization population archiving and updating mechanism and a dynamic robust fitness evaluation mechanism are integrated, so that the optimality and robustness can be ensured while high-dimensional complex problems are solved; and a robust solution set with excellent comprehensive performance is output for the complex problem.