1459 results about "Hydropower" patented technology

The invention relates to the technical field of power equipment monitoring, and discloses a hydropower station high-altitude equipment fault intelligent identification system based on multi-sensor fusion, which collects multi-modal data in real time and evaluates data quality by deploying multi-source sensors at key parts of high-altitude equipment. Extracting multi-scale features of each modal, performing normalization processing, calculating a fusion weight based on feature saliency and data credibility, and performing weighted fusion and dimension reduction on the multi-modal features; based on the fusion feature vector, intelligent matching analysis of fault features and intelligent identification of fault types are carried out; a fault identification result is obtained; in addition, the system also comprises safety monitoring of overhead working personnel, and realizes closed-loop management from fault identification to safety maintenance. According to the invention, early weak faults can be accurately identified, and the safe operation level of equipment and the intelligent degree of operation safety management are improved.

The invention discloses a BIM-based hydropower station full life cycle design, construction, operation and maintenance integrated control system, and the system comprises an intelligent sensing layer which integrates 5G + Beidou positioning, a LoRa gateway and a sensor, collects multi-source heterogeneous data in real time, and transmits the multi-source heterogeneous data to a digital twinborn layer after the multi-source heterogeneous data is filtered by an edge node; a digital twinborn layer: constructing a parameterized model library through laser point cloud and BIM automatic registration, integrating a geological parameter dynamic correction algorithm, mapping a construction period stress field in real time, and updating model parameters based on sensing data self-evolution; the intelligent decision-making layer performs equipment fault prediction by using an LSTM neural network, optimizes multi-machine load distribution in combination with an improved PSO algorithm, and automatically adjusts a start-stop strategy when the load fluctuates; and the security execution layer is used for triggering equipment operation after virtual twinborn deduction verification through a block chain evidence storage instruction, realizing virtual-real dual verification in combination with an industrial firewall, and finally feeding back a running state to the sensing layer to calibrate and update a model, and supporting intelligent decision.

The invention relates to the technical field of hydropower station optimization scheduling, in particular to a hydropower station multi-target scheduling decision-making method and system, and the method comprises the steps: obtaining the multi-source heterogeneous data of a target cascade hydropower station, and constructing and dynamically updating a scheduling knowledge graph fusing the cascade hydraulic coupling and collaborative operation association relationship; identifying a reference scheduling time period and a non-reference scheduling time period and establishing a differential output constraint; performing feature compression on the scheduling knowledge graph, extracting a key feature sub-graph influencing a scheduling decision, and predicting a state evolution path of related scheduling elements of the target cascade hydropower station in a future scheduling time domain; constructing and solving a multi-target dynamic decision model, and generating a candidate scheduling scheme set; and performing cross-scale conflict detection based on the candidate scheduling scheme set, performing hierarchical re-optimization on the candidate scheduling scheme set according to a detection result, and outputting and executing a scheduling decision result. The objective of the invention is to adapt to the dynamic demand of the power market for cascade hydropower station scheduling and realize rapid and accurate collaborative scheduling decision.

The invention belongs to the technical field of hydropower station monitoring and early warning, and particularly relates to a hydropower station intelligent inspection and fault early warning linkage method, which comprises the following steps: arranging a redundant sensor network at key equipment parts, synchronously acquiring pressure, flow, vibration and water turbidity data, establishing a flow-pressure loss reference relationship, and calculating real-time pressure-flow ratio deviation; performing spectral analysis on the vibration signals, extracting characteristic frequency band energy, performing collaborative study and judgment in combination with a turbidity change trend, and ensuring data credibility through data cross validation of main and auxiliary sensors; performing time sequence difference operation on the pressure-flow ratio deviation to obtain a change rate, fusing vibration-turbidity abnormal performance, and comprehensively diagnosing water diversion blockage or foreign matter invasion; early warning levels are divided according to the deviation and the change rate, corresponding response measures are triggered, response actions are executed according to a preset risk priority sequence, and linkage control of the whole process from state sensing, anomaly recognition, fault diagnosis to intelligent response is achieved.

The invention discloses a hydraulic power plant equipment full life cycle health management system and method based on the Internet of Things, and relates to the technical field of hydraulic power plant equipment management, and the method comprises the steps: collecting operation parameters through a sensor network disposed on various types of equipment of a hydraulic power plant, and forming a data sequence after time synchronization and analog-to-digital conversion; the data sequence is subjected to noise reduction processing at the edge computing node, whether an abnormal state exists or not is judged on the basis of the data analyzed and processed by the lightweight anomaly detection model, the data is graded and marked according to a judgment result, and only statistical characteristics of abnormal data or non-abnormal data marked as high priorities are uploaded to a cloud data center; and the cloud data center identifies the type of the equipment component according to the uploaded data, calls the corresponding physical degradation evolution model, dynamically calculates the degradation degree of each component in combination with the real-time operation state, determines the weight relationship of each degradation dimension according to the service stage of the equipment, and generates a comprehensive health index.

The invention provides a hydropower station fusion computing power intelligent server system, and relates to the technical field of carbon emission. The method comprises the following steps: an acquisition correction module acquires hydropower station generation power data and server cluster instantaneous energy consumption data in real time, establishes a time sequence corresponding relation between a power generation side and a power utilization side, and obtains clean energy supply quantity data; the prediction scheduling module generates a non-tampering carbon footprint tracking chain number, and predicts a clean energy supply fluctuation trend sequence by adopting a long short-term memory network algorithm; training a random forest model to predict a computing power demand trend sequence; optimizing task scheduling according to a carbon emission minimization target, and generating dynamic balance configuration data of a computing power task and clean energy supply; and the monitoring evaluation module outputs computing power task level carbon emission and a clean energy use ratio in real time through an application programming interface according to the dynamic balance configuration data, and generates quantitative evaluation data of a computing power service carbon neutralization target by adopting a data visualization technology monitoring technology.

The invention relates to the technical field of power system dispatching automation control, and discloses an AGC load distribution control method and system based on a multi-region greedy hydropower station, and the method comprises the steps: generating dynamic boundary thresholds of a high-efficiency region, a medium-efficiency region and a low-efficiency region based on a real-time performance curve and a dispatching set value of a hydroelectric generating set; judging whether to start a cross-interval distribution strategy or not by utilizing the generated dynamic boundary threshold value; when the number of the units participating in distribution is lower than a set scale, an enumeration algorithm is automatically switched to carry out global optimization, and generation of an optimal solution under a small-scale unit combination is ensured; calculating a deviation value between the actual total load and a target value based on the obtained cross-interval distribution strategy; and preferentially adjusting the output of the high-efficiency area unit through a dynamic compensation mechanism. The system comprises an operation interval dynamic calibration module, a region selection and load pre-distribution module and a deviation compensation and adaptive adjustment module. The power grid frequency adjusting precision and the overall operation efficiency of the hydroelectric system are remarkably improved.

The invention discloses a hydropower line economic dispatching and load collaborative optimization system and method, and relates to the technical field of hydropower dispatching, and the system comprises a data collection module which is used for collecting hydropower station unit water consumption characteristic curve data, power grid real-time load demand data, reservoir water level dynamic data and unit vibration area constraint data; the model construction module is used for preprocessing the data acquired by the data acquisition module and constructing a multi-target optimization model with the minimum total water consumption and the minimum line transmission loss as targets in combination with constraint conditions; the improved optimization module is used for receiving the target function and the constraint condition output by the multi-target optimization model module and dynamically calculating the optimal load distribution scheme of each unit; and the result output module is used for receiving the optimal load distribution scheme output by the improvement and optimization module and realizing visual output and decision assistance of the scheme. The problem that a traditional scheduling scheme is low in traditional scheduling decision-making efficiency is solved, and the scheduling decision-making efficiency and engineering practicability are improved.

The invention discloses a hydroelectric equipment anomaly detection method based on physical mechanism guidance and sequential topological entropy fluctuation characteristics, and belongs to the technical field of hydroelectric equipment monitoring and fault diagnosis. The method comprises the steps that multi-source sensor data of hydroelectric equipment is collected and preprocessed; constructing a physical weighted distance function in combination with an equipment physical mechanism, and embedding time sequence data into a high-dimensional point cloud space; extracting persistent homology features through a sliding window, generating a persistent graph sequence and calculating topological feature indexes; a persistence graph entropy fluctuation index is provided, and anomaly detection is realized by quantifying time sequence fluctuation of topological entropy; and finally, visual output and an alarm mechanism are combined to assist diagnosis. According to the method, equipment physical characteristics and topological data analysis are fused, the problems that a traditional method is insufficient in nonlinear system modeling, insensitive to dynamic evolution and the like are solved, the early warning capacity and detection precision of early faults are improved, and the method is suitable for anomaly detection application of core equipment such as a water turbine and a generator.

The invention discloses a hydropower station signal source side data anti-interference acquisition method and system. The method comprises the following steps: constructing a multi-source interference feature recognition network according to an environment state of a hydropower station, obtaining an interference source distribution feature spectrum, and obtaining an original signal through space-frequency domain joint filtering preprocessing based on the spectrum; performing recursion processing on the original signal by adopting an adaptive Kalman filtering algorithm to obtain a high-fidelity target signal; processing the high-fidelity target signal based on a multi-scale feature extraction and verification mechanism to obtain a credibility quantitative purification signal; and processing the credibility quantitative purification signal based on a compensation strategy to obtain a high-reliability output signal. According to the method, the influence of interference signals is gradually removed from interference source identification, filtering processing, signal reconstruction and quality evaluation to signal compensation, and finally, a result subjected to multiple anti-interference processing is obtained, so that various interferences in a complex environment of a hydropower station can be effectively resisted, and high-reliability data acquisition is realized.

The invention discloses a rock blasting load transfer and throwing simulation method which comprises the following steps: S1, acquiring field parameters of a target blasting project, the field parameters comprising rock mass mechanical parameters, explosive parameters, blast hole parameters and charging structure parameters; and S2, based on the field parameters, constructing a three-dimensional explosive-rock calculation model by adopting pretreatment software, dividing grids, importing the grids into finite element software, setting a material model, a state equation and boundary conditions, and simulating an explosive detonation process. The limitation of a single numerical value method is solved; quantitative analysis and accurate prediction of rock blasting lumpiness are achieved, inclined joints are adopted in the discretization process, actual rock mass joint distribution is better fitted, and the reliability of a simulation result is improved; bulk rocks and excessive crushing phenomena are effectively reduced, the explosion energy utilization rate is improved, the engineering cost is reduced, and a scientific basis is provided for explosion design of projects such as mining and water conservancy and hydropower.

The invention provides a hydraulic power plant medium leakage identification method and system, and relates to the technical field of hydropower leakage identification, and the method comprises the steps: deploying a high-definition video monitoring device and an operation state sensor module, carrying out the synchronous registration, configuring infrared channel enhancement and polarized light interference suppression, and carrying out the spatial filtering noise reduction and adaptive adjustment; and configuring a multi-modal enhanced input tensor. High-definition video monitoring equipment and an operation state sensor module are deployed in an operation area of a hydraulic power plant, and technologies of infrared channel enhancement, polarized light interference suppression, spatial filtering noise reduction and the like are combined, so that a multi-mode enhanced input tensor is constructed; according to the method, a clear leakage area identification effect can still be kept in a night low-illumination environment, water vapor and fog interference and metal reflection conditions, so that the problem that the identification precision is reduced due to the fact that the prior art depends on single-mode input is effectively solved, and stable distinguishing of oil medium leakage and water medium leakage is achieved.

The invention discloses a hydropower station unit state monitoring system based on deep learning, and relates to the technical field of hydropower station unit state monitoring and intelligent diagnosis. Comprising a multi-modal acquisition module, an electromagnetic common-mode texture anchor point generation module, an event transient skeleton generation module, a sparse anchor point index retrieval module, a continuous time joint inversion module and an analysis domain prior correction module. Generating an event stream from the mechanical side signal, and outputting an aligned candidate set to define a time mapping feasible region and a candidate time window; solving a unified time axis continuous potential signal under the constraint of missing mask information, and outputting an alignment uncertainty field; generating an alarm permission result based on the precedence relation and the delay window; and updating index parameters, threshold value strategies, priori condition parameters and iteration schedule parameters according to permission results to realize long-term stable monitoring and reliable alarm.

The invention discloses a differential early warning method for a water turbine speed regulating system based on data driving, and aims to solve the problems of poor real-time performance, low accuracy, weak adaptability and lack of differential early warning of a traditional method. The method comprises the following steps: collecting data such as guide vane opening and unit frequency in real time, and preprocessing the data through median filtering and Kalman filtering; identifying working conditions by using a rule engine and fuzzy logic and processing according to the working conditions; extracting time domain and frequency domain characteristic parameters, and constructing statistical indexes; integrating statistics, nonlinearity and twinborn simulation to build a threshold model, and combining a production rule, a fault tree and a Bayesian network to build a rule model; and three types of early warning models are constructed, early warning levels are divided according to an expert scoring method, information is pushed, and a differentiation strategy is formulated. The method improves the early warning real-time performance and accuracy, adapts to complex working conditions, and guarantees the safe operation of the hydropower station.

The invention discloses a water conservancy and hydropower construction resource scheduling method and system, and relates to the technical field of construction resource scheduling, and the method comprises the following steps: calculating a vehicle utilization rate and team productivity according to construction data, constructing a three-section code according to a preset construction task, and forming a construction resource scheduling scheme based on the three-section code. Constructing a conflict evaluation function and a target function, randomly generating an initial population based on a construction resource scheduling scheme, and sequentially performing selection operation, crossover operation and mutation operation on the initial population based on a genetic algorithm to obtain a first generation population, and selecting an optimal fitness value from individual fitness values calculated from the first-generation population based on the conflict evaluation function and the target function, presetting an iteration condition, executing an iteration operation when the iteration condition is met, and decoding the individuals with the optimal fitness value in the first-generation population and executing conflict resolution when the iteration condition is not met. The optimal construction resource scheduling scheme is obtained, and the overall benefit of the scheduling scheme is improved.

The invention discloses a hydropower engineering electromechanical equipment installation detail control method based on a three-dimensional laser scanning technology, and the method comprises the steps: S1, setting and determining the instrument parameters of a three-dimensional laser scanner, and collecting the point cloud data of a target region through the three-dimensional laser scanner; s2, remotely reading the point cloud data through a computer, uploading the point cloud data to a cloud database in a file stream form, and storing the point cloud data; s3, performing iterative fitting on the plane model by using a random sample consensus algorithm RANSAC and removing inner points, fitting a target ball model by multiplexing the RANSAC algorithm in the residual point cloud data after the plane model is removed, and screening target ball parameters with the maximum number of inner points; s4, constructing a combined reference model based on the electromechanical equipment to be installed and the virtual target ball, and performing coordinate deviation value comparison with the actually measured target ball model to obtain a deviation value; and S5, generating an installation detail adjustment scheme according to the coordinate deviation value, and sending the adjustment scheme to a mobile client of the field installation personnel.

The invention discloses a hydropower station time-sharing power generation income dynamic control method fusing a power grid load demand, and the method comprises the steps: obtaining hydropower station multi-dimensional operation data and power grid time-sharing load and electricity price data, building a data association model based on an attention mechanism, generating a fusion feature data set through dynamic weight distribution, and carrying out the dynamic control of the hydropower station time-sharing power generation income. Time-sharing power generation income maximization serves as an objective function, reservoir water balance, unit output limitation and downstream ecological flow constraint are combined, a deep reinforcement learning algorithm is introduced to construct a dynamic control model, historical and simulated data are utilized to train the model and optimize parameters, and then real-time data are input to generate an optimal time-sharing power generation control scheme. Issuing an instruction and monitoring output deviation to realize dynamic control; according to the method, the power grid load and the time-of-use electricity price can be accurately matched, the power generation income is improved while multiple constraints are met, and the method is suitable for various hydropower station dispatching scenes needing to consider power grid response and income optimization.

The invention discloses a high-rotating-speed hydraulic generator rotor bolt looseness detection method and system, belongs to the technical field of hydropower station high-rotating-speed rotating mechanical equipment detection, and aims at solving the technical problems that hydraulic generator rotor bolt looseness detection is low in efficiency and poor in precision, real-time monitoring cannot be achieved, and safe and stable operation of a hydropower station is seriously affected. According to the invention, the image of the rotor bolt area is collected, and the image is accurately processed by using an image correction algorithm, so that image distortion is eliminated; thirdly, fusing an improved YOLOv8 target detection technology, a DeepLabV3 + semantic segmentation technology and a DeepSort multi-target tracking technology, and accurately extracting angle features of the bolt marking line; and then, a looseness identification objective function is constructed based on the angle change of the bolt, the function is solved in real time, and a detection result is output. According to the invention, high-precision, non-contact and real-time bolt looseness detection is realized, the detection efficiency and reliability are effectively improved, and a solid guarantee is provided for safe operation of a hydropower station.

The invention provides a wind and light storage micro-grid optimization scheduling method and system containing small hydropower storage, and the method comprises the steps: obtaining historical operation data, and carrying out the prediction according to the historical operation data, and obtaining a full-dimension prediction data set; according to the full-dimension prediction data set, constructing a multi-objective optimization model with the goal of minimizing residual load, minimizing power abandoning cost, minimizing cascade water abandoning amount, maximizing green energy collaborative utilization efficiency and minimizing time weighted voltage-power comprehensive error; and solving the multi-objective optimization model by adopting a hybrid optimization algorithm fused by a Hanniac optimization algorithm and an improved sparrow search algorithm to obtain a final output. According to the invention, efficient consumption of green energy can be realized, the electricity abandoning phenomenon is reduced, carbon emission is reduced, and the stability, economical efficiency and environmental protection property of a power system are improved.

The invention discloses a hydropower station corridor autonomous mobile robot path navigation method, system and device and a storage medium, and the method comprises the steps: collecting hydropower station multi-source environment data, constructing a laser two-dimensional map, and fusing image semantic segmentation labels to generate a two-dimensional semantic map; the current sensor data is matched with the two-dimensional semantic map, and the initial pose of the robot is determined; a navigation path is generated on the basis of the initial pose and the target point, and the pose of the robot is dynamically adjusted and updated in the navigation process; path planning control is carried out based on the updated pose, obstacles are recognized in real time, and a driving path is optimized; recording path driving navigation data, updating the two-dimensional semantic map, and returning to the starting point based on the updated map after the task is completed. According to the method, a two-dimensional semantic map with space consistency and semantic definition is constructed, key geometric structure extraction and navigation structure index generation are combined, accurate recognition and path topology understanding of passable areas in the hydropower station gallery are achieved, and the accuracy and robustness of autonomous path navigation are improved.

The invention relates to the technical field of computer simulation, and provides an ecological restoration measure carbon sink effect quantitative simulation prediction method and system based on digital modeling. The method comprises the following steps: collecting ecological element information of a target drainage basin, extracting a multi-dimensional spatial distribution feature set, and establishing an ecological environment response simulation model under engineering disturbance in combination with digital baseline data; constructing a carbon sink effect quantitative model based on construction operation situation information, and embedding the carbon sink effect quantitative model into associated nodes of the ecological environment response simulation model to realize coupling; the dynamic influence of engineering construction operation on the drainage basin carbon sink capacity is simulated based on the coupling model, the multi-target optimization decision matrix is generated, the pre-optimization decision suggestion is output, collaborative simulation of ecological environment response and carbon sink effect quantification is achieved, and the accuracy of hydropower engineering carbon sink influence prospective evaluation and the scientificity of decision suggestion are improved.

The invention relates to the technical field of hydropower station optimal dispatching, in particular to a hydropower station reservoir dispatching decision-making method, system and device and a medium, and the method comprises the steps: obtaining a multi-source data set of a target hydropower station reservoir, and carrying out the preprocessing; adopting a clustering algorithm to carry out meteorological and hydrological mode typing on the drainage basin where the target hydropower station reservoir is located, and extracting a typical meteorological and hydrological typing result; according to a typical meteorological and hydrological typing result, constructing a scheduling macro-variable for quantifying a collaborative imbalance state among meteorological, hydrological and scheduling subsystems; identifying a current scheduling mode based on the real-time storage flow, and extracting a multi-scale spatial-temporal feature corresponding to the current scheduling mode; and according to the current scheduling mode, the typical meteorological and hydrological typing result and the multi-scale spatial-temporal characteristics, dynamically selecting and fusing multiple types of pre-training models, and generating a reservoir scheduling strategy by using scheduling macro-variable rolling optimization. The objective of the invention is to improve the efficiency and robustness of hydropower station reservoir resource decision and the reliability of a reservoir scheduling strategy in an uncertain factor scene.

The invention discloses a water turbine local turbulence field prediction method, and relates to the technical field of water turbines. The method comprises the following steps: S1, data acquisition and preprocessing; s2, sparse measurement point feature extraction; s3, low-dimensional representation of the turbulent flow field; s4, carrying out double-model joint training; and S5, performing real-time prediction. According to the method, the global turbulent flow field prediction can be realized only by using the pressure / speed time sequence data of a small number of sparse measuring points in the water turbine runner, and an expensive full flow field experiment table does not need to be built or a dense sensor array does not need to be deployed. Compared with the traditional experimental measurement (the equipment cost is more than ten million yuan), the total investment of the sensor and the computing equipment is only ten thousand yuan, and the cost is reduced by more than 99%; meanwhile, the installation difficulty of the sensor in a high-flow-speed and high-pressure area is avoided, the existing monitoring point layout of a hydropower station can be directly adapted, the technology landing feasibility is remarkably improved, and the model architecture does not depend on a specific water turbine type and can be adapted to different types such as a mixed-flow type and an axial-flow type by adjusting grid parameters and training data.

The embodiment of the invention discloses an energy storage control system information transmission and index evaluation method and system. The method comprises the following steps: acquiring signal data of a lower computer of a hydropower station unit and transmitting the signal data to a remote centralized control target center; obtaining the integrity of unit lower computer signal data received by the target center; obtaining the availability of unit lower computer signal data received by the target center; acquiring the transmission safety of the acquired hydropower station unit lower computer signal data to a target center; and based on integrity, availability and transmission security, determining evaluation information of the acquired hydropower station unit lower computer signal data. According to the method, by comprehensively considering integrity, usability and safety, powerful support can be provided for a remote centralized control operation on-duty mode, an operator can accurately monitor the operation state of a unit in a remote centralized control center in real time and perform remote operation and control, centralized management and optimal scheduling of a hydropower station are achieved, and the production efficiency and the management level are improved.

The invention provides an automatic operation control method for a low-dam hydropower station. The method comprises the following steps: preprocessing collected water level and incoming water flow data; a reservoir capacity dynamic calculation model is constructed in combination with the reservoir capacity curve graph and the water level flow data, and dynamic updating of reservoir capacity data is achieved; historical hydrological data, current water level flow and reservoir capacity data are fused to train a machine learning model, and the reliability of short-term inflow prediction is improved; based on the safe operation threshold value, the reservoir capacity data and the incoming water prediction result are input into a multi-objective optimization algorithm, a generator set power adjustment strategy and a flood discharge gate opening degree regulation strategy are automatically generated, and the generator set operation power and the flood discharge gate opening degree are automatically controlled, so that the problems that in the prior art, a low-dam hydropower station is low in automation degree and high in efficiency are solved. And manual adjustment is relied on.

The invention relates to a reservoir hydropower station carbon accounting key information extraction method and system based on a large language model, and belongs to the technical field of artificial intelligence and environmental science. The method mainly comprises the following steps that firstly, domain entities, attributes and relations are extracted through a large language model, and a reservoir hydropower station carbon accounting domain knowledge graph is constructed; secondly, core concept nodes are recognized based on the knowledge graph, retrieval keywords are generated, and target literatures are accurately obtained from the multi-source heterogeneous data; thirdly, performing multi-modal analysis on the literature, extracting unstructured text and table data, and converting the unstructured text and table data into a structured document; secondly, performing semantic partitioning and vectorization processing on the document content, and constructing a vector knowledge base; and finally, responding to user query, realizing enhanced retrieval through multi-path recall and reordering, and accurately obtaining carbon accounting information meeting multiple limitation conditions. According to the method provided by the invention, efficient and accurate extraction of the carbon accounting core parameters can be realized.

The invention discloses a cascade hydropower station inter-station load optimal distribution method, which belongs to the technical field of hydropower dispatching, and comprises the following steps: solving a cascade hydropower station inter-station load distribution model which takes the maximum energy storage increment of a cascade hydropower station as a target and meets multiple constraints by utilizing an A * algorithm; next moment state nodes of the cascade hydropower station are generated according to unit output constraints and added into a candidate set, adjacent state nodes meeting all constraints are screened out, heuristic function values of the adjacent state nodes are calculated, and the adjacent state node with the minimum heuristic function value is selected as a new current moment state node of the cascade hydropower station for repeated iteration; and obtaining a path from the initial state node to the latest state node of the cascade hydropower station at the current moment through a reverse traversal method until an iteration termination condition is met, and taking the path as an optimal inter-station load distribution scheme of the cascade hydropower station. According to the method, the decision-making efficiency can be improved while the decision-making precision is guaranteed.