186 results about "Random optimization" patented technology

The invention relates to the technical field of flour processing, and discloses an intelligent control method and system for a flour workshop, and the method comprises the following steps: collecting raw material characteristic data and equipment operation state data in real time through a sensor, calculating the state variable of the current system according to the collected data, and calculating the state variable of the current system according to the current state variable. Performing rolling optimization on a control strategy in a future time domain, dynamically adjusting grinding parameters according to a rolling optimization result, correcting a prediction model according to a data deviation between real-time acquired data and nonlinear model prediction data, and calculating an optimal control parameter combination based on a stochastic optimization theory; the system comprises a sensing module, a control module, an execution module, a feedback correction module and a random optimization module. By adopting the technical scheme that a high-precision sensor is combined with intelligent control, comprehensive automation and accurate control of the flour production process are achieved. The stability and consistency of flour quality are remarkably improved, and quality fluctuation is avoided.

The invention discloses a new energy medium and long term stochastic programming method and system coupled with an electro-hydrogen water regulation power supply, and the method comprises the steps: building a power supply planning model with the minimum planning operation cost as an optimization target; bringing the new energy power supply and energy storage adjusting equipment operation model and the new energy consumption rate upper and lower limit constraint expression into a power supply planning model; in consideration of the randomness of fluctuation at two sides of a source and a load in power supply planning, based on the obtained power system operation scene set S and the power supply planning model, dividing into an investment decision-making layer and an operation simulation layer according to a time period influenced by a decision, and constructing a power supply planning two-stage stochastic optimization model based on a typical scene; and solving the power supply planning two-stage stochastic optimization model under a medium and long term planning framework, and determining the investment planning installed capacity of the adjusting power supply and the new energy in the planning level year. According to the invention, economy of medium and long term planning and power supply capacity configuration of new energy can be improved, and reasonable consumption and flexible adjustment of new energy electric power are promoted.

The invention relates to the technical field of data processing, and particularly provides a low-altitude aircraft power supply facility cooperative control method, system and device and a medium, and the method comprises the steps: predicting the renewable energy power generation power of a power supply facility and the charging load power of a low-altitude aircraft based on obtained multi-source information, and generating a prediction scene set representing uncertainty; based on the prediction scene set, through a multi-time scale optimization strategy, solving a scheduling model with optimal operation cost as a target, and successively obtaining a pre-scheduling plan of a day-ahead stage and a real-time control instruction of an intra-day stage; the multi-time scale optimization strategy comprises random optimization based on a scene and feedback optimization based on a rolling time domain; according to the real-time control instruction, power distribution is conducted on the hybrid energy storage system, and a power type energy storage unit preferentially responds to the transient power requirement for charging of the low-altitude aircraft. The comprehensive operation cost is effectively reduced, and particularly, the service life of equipment is prolonged through quantitative energy storage degradation.

The invention discloses an optimization method and system for aviation equipment storage and a medium, and the method comprises the steps: collecting multi-dimensional dynamic data, carrying out the fusion cleaning and structural feature extraction, and generating a high-dimensional feature set; outputting a dynamic demand prediction value and uncertainty measurement by using a hybrid intelligent prediction model fusing time sequence prediction and ensemble learning; calculating an optimal inventory control parameter through a stochastic optimization model in combination with the aviation material key grade and the stockout loss cost; and generating an inventory operation instruction according to the optimal inventory control parameter and the real-time inventory state, and outputting a strategy report with confidence evaluation and key influence factor analysis. According to the method, multi-dimensional accurate prediction of the aerial material demand and dynamic optimization of the inventory strategy are realized, the inventory control precision and the resource utilization efficiency are remarkably improved, the stockout risk and the overstocked cost are effectively reduced, and the reliability and the economical efficiency of aviation equipment guarantee are enhanced.

The invention relates to the technical field of array antennas, in particular to a sparse linear array beam forming method and system with unit rotation and mutual coupling considered. Array elements are obtained, Euler angle derivation is carried out on rotation of the array elements around the z axis, and a radiation electric field after rotation is obtained; setting a beam forming target according to the electric field of the radiation field; according to a beam forming target, optimizing the position, the rotation angle and the excitation phase of an array element by adopting a random optimization algorithm, and generating an initial beam scheme; an active directional diagram is extracted from the initial beam scheme according to full-wave simulation, the active directional diagram is combined with a CAL-SAPSO algorithm to optimize the array element position, the rotation angle and the excitation phase, and a final array element directional diagram is generated; according to the invention, by introducing array element rotation and considering a mutual coupling effect, efficient beamforming of the sparse wiring array is realized, the radiation characteristic of the antenna array can be adjusted more finely, the peak side lobe level and the cross polarization level are effectively reduced, and the overall performance of the system is improved.

The invention discloses a day-ahead optimal scheduling method considering a system inertia supply out-of-limit risk, and belongs to the technical field of day-ahead scheduling. Comprising the following steps: calculating to obtain a system inertia demand lower limit according to system frequency change rate constraint and frequency lowest point constraint, and calculating to obtain a system inertia demand upper limit by making a new energy station and an energy storage power station equivalent to a thermal power generating unit when the system is in the maximum load; the method comprises the following steps: considering inertia supporting capacities of a conventional unit, a wind power plant station and network construction energy storage, and constructing a risk driving index considering insufficient maximum inertia supply and excess minimum inertia supply of a system; constructing a two-stage stochastic optimization model according to the objective function and the constraint condition of the day-ahead stage and the objective function and the constraint condition of the real-time stage; and solving the two-stage stochastic optimization model, and verifying the economical efficiency of the scheduling scheme and the supply capability of the system inertia. According to the invention, the system is guaranteed to have sufficient inertia supply capability in the day-ahead stage, and the safety and economy of the system are guaranteed.

The invention discloses a self-adaptive photovoltaic energy storage comprehensive optimization method and system, and belongs to the technical field of photovoltaic energy storage optimization control. According to the method, a system operation model is established based on historical and real-time operation data of a photovoltaic unit, an energy storage unit and a load side, a hybrid robust-stochastic optimization framework is introduced, a collaborative optimization model with energy balance and system stability as targets is constructed, and an initial control parameter set is solved; in the system operation process, a power fluctuation mode and energy storage response characteristics are identified through a multi-layer agent algorithm, and a dynamic energy storage control instruction is generated and corrected; and according to the cross-cycle change trend of the operation performance index, adaptively adjusting the weight and step length of the control parameter, and realizing strategy evolution and dynamic closed-loop optimization. According to the invention, cooperative operation of photovoltaic and energy storage can be realized under complex operation conditions, and the energy utilization rate and the operation stability are improved.

The invention discloses a 9Ni steel process optimization method based on artificial intelligence, and relates to the technical field of steel production, and the 9Ni steel process optimization method comprises the following steps: collecting multi-source process parameters and optimization target parameters; performing feature processing on the collected data to generate target feature data; constructing a 9Ni steel performance multi-target prediction model based on the target feature data, and training the model until convergence; and constructing a multi-objective optimization function by utilizing the converged 9Ni steel performance multi-objective prediction model, and solving the multi-objective optimization function by adopting a stochastic optimization algorithm under an operation constraint condition and outputting an optimal process parameter combination. According to the method, deep learning modeling, cluster cleaning, feature engineering construction, deep learning and a random optimization strategy are comprehensively applied, a data-driven intelligent process optimization system for the 9Ni steel manufacturing process is formed, the achievement rate of target performance indexes can be remarkably increased, the parameter trial and error period is shortened, and the implementation efficiency of the 9Ni steel manufacturing process is improved. And intelligent adjustment and cost optimization of a process path can be realized.

Random key optimization is used to generate sequencing plans. An optimization problem is obtained to generate a sequencing plan. The optimization problem may be described according to nodes that represent decision variables of the optimization problem. A random optimization key technique may be applied to determine a vector of respective values for the nodes. The vector of respective values of the nodes may be decoded according to a sequence planning constraint to generate the sequencing plan and determine a fitness score for the sequencing plan.

The invention relates to a polarization reconfigurable array antenna beam forming method using a polarization selection dictionary matrix, and aims to solve the problems that an existing reconfigurable array polarization selection algorithm is low in calculation efficiency, high in parameter sensitivity and incapable of achieving beam scanning. The method comprises the following steps: firstly, giving an expected main polarization direction and a cross polarization direction; then, according to the polarization state which can be provided by each unit, constructing a polarization selection dictionary matrix used for selecting a unit polarization mode; then, according to the polarization selection dictionary matrix and a given polarization direction, constructing a corresponding main polarization beam matrix and a cross polarization beam matrix; and finally, converting a non-convex polarization selection problem into a convexity problem for solving. Compared with an existing stochastic optimization algorithm in the field of polarization reconfigurable array beam forming, the method has higher efficiency and better stability, so that the method has higher practical value.

The invention relates to a power grid side energy storage two-stage locating and sizing method in a sea wind access scene considering the influence of temperature on a tide safety domain. The method comprises the following steps: adopting a temperature trend prediction model combining LSTM and TCN; a Gaussian mixture model is adopted to calculate a temperature variable capacitance expectation coefficient, and a power flow safety domain is reconstructed; establishing a random optimization scene set of source-load space-time coupling by adopting a typical source-load daily trajectory clustering method; establishing a power distribution network energy storage locating and sizing planning layer model taking the maximum comprehensive utility as a target; a power distribution network energy storage locating and sizing operation layer model with the minimum voltage fluctuation quadratic sum as the target is established; constructing a DistFlow multi-period optimal power flow model under the sea wind access scene; and solving the planning layer by adopting second-order cone relaxation, and carrying out secondary optimization on a charge-discharge and capacity configuration strategy on the operation layer by adopting a target cascade algorithm on the basis of anchoring a locating and sizing decision variable of the planning layer. According to the method, planning and operation targets can be more effectively coordinated, and comprehensive optimal configuration of power grid side energy storage is realized.

The invention relates to the technical field of intelligent building control, in particular to a sunshade optimization method based on environmental parameter reverse modeling, and the method comprises the steps: 1, collecting dynamic parameters; 2, light field reverse modeling is carried out; 3, reversely solving a sunshade configuration, constructing an optimization function by taking improvement of an indoor photo-thermal environment as a target, inverting a regulation and control action region of a sunshade component according to the position of a highlight region, and iteratively solving a target state set of the sunshade component by adopting a random optimization algorithm; step 4, interference effect compensation: detecting light intensity deviation of interference areas of adjacent components, and generating dynamic compensation amount of the sunshade component; and 5, generating a control instruction, fusing the target state set and the dynamic compensation amount to generate a final control parameter, and converting the final control parameter into an execution mechanism driving instruction according to mechanical transmission characteristics. According to the sunshade optimization method, the energy efficiency of the building is improved, the comfort and adaptability of the building are enhanced, and the method has high practical application prospects and market value.

The invention provides a power distribution network mobile energy storage optimization configuration method and equipment considering fault evolution under typhoon disasters, and belongs to the field of power distribution network mobile energy storage optimization configuration. The method comprises the following steps: firstly, establishing a typhoon disaster space-time evolution model according to typhoon weather prediction information, and constructing a space-time characteristic matrix of a distribution line fault rate; then determining a fragile line based on the space-time characteristic matrix of the line fault rate, and building a pre-disaster mobile energy storage pre-scheduling model to prepare for rapid recovery of a post-disaster system; then, a mobile energy storage optimal configuration and operation strategy in a probability scene is searched by adopting random optimization to minimize the economic loss of the affected power distribution network during a typhoon disaster, and a random optimization model of mobile energy storage optimal configuration and operation is established; the flexibility of mobile energy storage is fully utilized to provide staged power support for the power distribution network during typhoon disasters, the problem that safe and reliable power utilization of the power distribution network is difficult to guarantee under typhoon extreme disasters is effectively solved, and the elasticity of the power distribution network is improved.

The invention discloses an unmanned aerial vehicle cruise path planning method for multi-priority dynamic coverage, and the method comprises the steps: carrying out the sorting of the detection priorities of different parts of a region according to the traversal period constraint, carrying out the high-frequency cruise detection of key parts, and carrying out the low-frequency cruise detection of general parts; secondly, according to the priority ranking, recursively constructing a series of cyclic cruise path planning schemes by adopting a stochastic optimization algorithm, so that the region is dynamically covered and meets corresponding traversal period constraints; and finally, analyzing and evaluating the cruise path planning scheme from two aspects of redundancy and priority, and outputting an optimal planning scheme. Compared with a traditional path planning method, cyclic cruise detection is carried out according to the priority, the whole area can be dynamically covered with emphasis, the cooperative cruise detection efficiency of the unmanned aerial vehicle is effectively improved, and the area change is accurately mastered; in addition, the cooperative cruise path can be adaptively adjusted according to the priority change, unmanned detection resources are efficiently utilized, and the method flexibly adapts to the dynamic coverage demand change.

The invention discloses a shared energy storage and new energy station cooperative operation method and system, and relates to the field of power market and energy storage optimization management in a power system, and the method comprises the following steps: extracting historical wind and light scene data of a new energy station; according to historical wind and light scene data, a wind and light prediction error probability model is established, and after sampling and scene reduction, a limited typical scene is extracted and an uncertainty set is constructed; and on the basis of the limited typical scene and the uncertainty set, by taking the benefit maximization of the new energy station as a target and combining a virtual energy storage operation mode, constructing a shared energy storage and new energy station collaborative operation framework, and generating a double-track system optimal collaborative strategy. The method is used for solving the problems that stochastic optimization calculation is complex, traditional robust optimization benefits are limited, and energy storage resource configuration efficiency is low.

The application provides a mobile energy storage optimal configuration method and equipment for distribution network under typhoon disaster considering fault evolution, and belongs to the field of mobile energy storage optimal configuration for distribution network. The method firstly establishes a typhoon disaster space-time evolution model according to typhoon meteorological prediction information, and constructs a space-time characteristic matrix of distribution line failure rate; then determines the vulnerable line based on the space-time characteristic matrix of the line failure rate, and builds a pre-disaster mobile energy storage pre-scheduling model to prepare for the post-disaster system rapid recovery; then, the optimal configuration and operation strategy of the mobile energy storage under the probability scenario are found by using random optimization to minimize the economic loss of the disaster distribution network during the typhoon disaster, a random optimization model of the mobile energy storage optimal configuration and operation is established, and the flexibility of the mobile energy storage is fully utilized to provide phased power support for the distribution network during the typhoon disaster, effectively solving the problem that the distribution network is difficult to guarantee safe and reliable power supply under the typhoon extreme disaster, and improving the flexibility of the distribution network.

The invention relates to the technical field of data center optimization, and particularly discloses a data center two-stage stochastic optimization method and system considering wind and light uncertainty, and the method comprises the steps: carrying out the modeling of a data center energy supply system, generating a wind and light output sample based on Monte Carlo simulation, enabling the generated random sample to meet wind power and photovoltaic output complementation through Corisky decomposition; introducing a first-order autoregression model, generating random scenes in combination with multivariate normal distribution, and obtaining a wind and light output curve in each scene; a two-stage stochastic optimization model containing computing power scheduling and multi-energy coordination is constructed by taking the minimum expected operation cost of a system as a target, the model is solved, and an optimal time sequence operation strategy of a computing power task is decided. According to the two-stage random optimization, the adjustment cost caused by prediction errors is covered with low risk premium, and effective support is provided for reliable operation of the data center under new energy output fluctuation and computing power load time sequence mismatch.

The invention discloses a port integrated energy system cooperative scheduling method and device, and relates to the technical field of power system optimization scheduling, and the method comprises the steps: receiving equipment operation parameters of a port logistics system and an integrated energy system and ship operation related data, the method comprises the following steps of: inputting equipment operation parameters of a port logistics system and an integrated energy system and ship operation related data into a pre-established port integrated energy system collaborative scheduling model fusing hydrogen energy and electric energy, and modeling uncertainty by utilizing a Markov chain according to a multi-stage stochastic optimization theory; based on a random dual dynamic integer programming algorithm, solving the port integrated energy system collaborative scheduling model fusing the hydrogen energy and the electric energy, and outputting to obtain an optimal collaborative scheduling result of the port integrated energy system under the multi-source uncertainty condition; and the operation flexibility and stability of the energy system are enhanced.

The application discloses a kind of spark flashover point generation position prediction method and system of pulse high voltage power supply, obtain spark flashover characteristic database from initial time to the total number of spark flashover stored during current time, determine the spark flashover number of each partition in total number of spark flashover, based on total number of spark flashover and the spark flashover number corresponding to each partition, obtain the current occurrence probability of spark flashover of each partition, determine the instantaneous impedance variation law of each partition when flue gas working condition parameter changes, based on the current occurrence probability corresponding to each partition spark flashover and instantaneous impedance variation law of spark flashover, using random optimization algorithm, obtain the predicted occurrence position and predicted occurrence probability of spark flashover point next time.The application realizes the predicted occurrence position and predicted occurrence probability of spark flashover point next time, to improve the intelligent control level of pulse high voltage power supply.

The invention relates to a comprehensive energy system optimization scheduling method considering indirect carbon emission uncertainty. The method comprises the following steps: acquiring basic data; quantitative modeling is carried out on the uncertainty factors, and an uncertainty set used for describing the fluctuation range of the uncertainty factors is constructed; establishing a carbon emission and transaction cost calculation model of the system, firstly accounting the free carbon emission quota of the system, secondly calculating the actual net carbon emission of the system, and finally calculating the carbon transaction cost required to be paid or obtained by the system; constructing a stochastic optimization scheduling model, taking a minimum actual total net carbon emission amount of the system as a target function, and setting constraint conditions including a system total operation cost constraint, a power balance constraint and an equipment operation constraint; and converting the stochastic optimization scheduling model into a mixed integer linear programming problem for solving to obtain an optimal output plan and an energy scheduling scheme of each device, which enable the actual total net carbon emission of the system to be minimum. According to the method, the net carbon emission of the system can be minimized in multiple uncertain environments.

The application discloses a kind of multi-stage random optimization scheduling method and system of photovoltaic-thermal power system, comprising: step one: obtaining unit parameter and new energy output prediction curve;Step two: based on prediction curve, generate at least five kinds of scene, construct the scene tree structure of multi-stage;Step three: on the basis of scene tree structure, with multi-stage random programming model as framework, construct the multi-stage random optimization scheduling model of photovoltaic-thermal power system;Step four: stage 3 is optimized using load aggregation, photovoltaic-thermal unit aggregation two methods, and construct acceleration optimization model;Step five: solve acceleration optimization comprehensive model.The system includes data acquisition module, scene tree construction module, model construction module, model optimization module and model solution module.The application can make up the slow speed of multi-stage random optimization scheduling model solution of photovoltaic-thermal power system, and it has certain significance for large-scale medium and long term power system planning.

The present application relates to the technical field of power grid and electrical equipment operation and maintenance, and discloses a power grid safety control device opportunity maintenance decision method based on multi-layer random optimization. The method comprises the following steps: according to the state transition process after the safety control device is put into operation, the correlation between the internal hardware of the safety control device, between the safety control devices, and between the safety control device and the primary equipment is considered, the functional correlation, the economic correlation and the random correlation of the safety control device are planned from three levels, a nested compound objective function is first constructed, a multi-level safety control device opportunity maintenance decision model is established, and thus the best maintenance strategy of the safety control device is formulated, the maintenance efficiency of the safety control device can be improved, and the effective balance between the safety control device maintenance and the power system operation is realized.

The invention discloses an airport operation robustness decision-making method and device based on stochastic optimization. The method comprises the following steps: constructing a low-altitude intrusion situation feature space through multi-source heterogeneous sensing data; on the basis of the feature space, constructing a runway recovery time Wasserstein uncertainty set capable of dynamically adjusting the boundary so as to describe uncertainty; establishing a distribution robust optimization model for coupling aircraft position distribution and ground support facility scheduling; a column constraint generation algorithm is used for solving, and decision making and closed-loop feedback are executed in a rolling time domain mode. The method does not need to depend on prior probability distribution of interference events, adaptive balance of decision robustness and economical efficiency can be achieved, the problem of space-time mismatching is effectively avoided through resource coupling scheduling, and improvement of the operation recovery efficiency and safety of an airport under high uncertainty is facilitated.

The invention provides a service demand-oriented 5G network slice dynamic arrangement method, which belongs to the technical field of 5G communication, and comprises the following steps of: establishing a network optimization model comprising a differential transmission rate model, a system utility model and a queue delay model; in combination with the network optimization model, proposing a random optimization problem of meeting a queue stability condition, a bandwidth allocation scheme, a time delay maximum threshold value and a minimum total cost in a multi-service coexistence scene of the power internet of things; and solving a stochastic optimization problem, decomposing the randomness of the problem into joint optimization of a queue drift amount and a cost penalty term, and carrying out collaborative optimization on time delay and throughput based on a dual-time scale mechanism dynamic decision. The method has the advantages that efficient collaborative management of network resources in a multi-service scene is realized; through dynamic weight adjustment and real-time resource allocation, the time-varying characteristics of the service priority and the channel state are considered, and a solution which is high in adaptability and stable is provided for network slice management.

The application discloses a building energy consumption real-time control method based on DQN, generates multiple groups of scenes based on building photovoltaic power generation and building rigid load prediction data, optimizes building energy consumption behavior by using a DQN algorithm, reasonably designs a DQN reward function by using the concept of random optimization, and makes the optimization result have good robustness to the uncertainty of building power generation and load. The application makes the building maintain high comfort all day, increases the flexibility of building energy consumption by scheduling energy storage equipment, and reduces the building energy consumption cost.

The invention relates to the technical field of power system operation and control, discloses a mountainous area power distribution network multi-source coordinated optimization scheduling method, system, equipment and medium, and constructs a mountainous area power distribution network multi-source coordinated optimization scheduling framework based on a stochastic optimization theory and a decomposition coordination algorithm. A three-dimensional power grid model is established through a geographic information system, accurate prediction of new energy output is realized in combination with a meteorological prediction technology, and overall consideration of economy, reliability and environmental protection is realized by applying a multi-objective optimization method.

The application discloses a kind of 6G network multidimensional resource AI model dynamic deployment optimization methods based on category theory, belongs to intelligent collaborative optimization technical field;Method is: the cross-layer consistency dependency of end-to-end AI reasoning service is formalized by functor form;Establish the joint optimization model with long-term average end-to-end delay minimization as target, while being constrained by multidimensional resource and service quality;Convert long-term random optimization problem into time-slot online decision problem;Get AI model dynamic deployment and task scheduling result.The application realizes cross-layer consistency description to task scheduling and model deployment through category theory unified modeling and functor composite mechanism, reduces the inconsistency and redundant constraint caused by hierarchical modeling, improves the structured degree and explainability of joint decision;Under the constraint of multidimensional resources such as calculation, memory, storage and bandwidth, dynamic adaptive optimization is realized, node resource over-limit and load imbalance are effectively avoided, and congestion and queuing delay are reduced.

The invention relates to the technical field of EV scheduling, and discloses a multi-zone-area electric vehicle cluster scheduling optimization method and device and electronic equipment, which are used for solving the technical problem of poor scheduling effect of the existing EV cluster scheduling scheme. Comprising the steps that minimization of the sum of power grid operation cost and user comprehensive cost is taken as an optimization target, power grid security constraints, user demand constraints, adjustable potential constraints and power boundary constraints are taken as constraint conditions, and the adjustable potential of a single transformer area is combined to construct a day-ahead initial scheduling model; when a preset condition is triggered, generating an uncertain scene, and based on the uncertain scene, constructing a rescheduling model by taking minimization of rescheduling cost and conditional value-at-risk as an optimization target and taking an adjustment amplitude constraint, a power grid security constraint and a user demand constraint as constraint conditions; generating a two-stage stochastic optimization rescheduling model by adopting the day-ahead initial scheduling model and the rescheduling model; and solving the two-stage stochastic optimization rescheduling model to obtain a multi-zone-area electric vehicle cluster optimization scheduling scheme.

The application provides a kind of intelligence calculation fusion network heterogeneous resource elasticity adaptation and deterministic scheduling system and method, belongs to industrial internet of things technical field, comprising: the system of establishing distributed management and centralized control under the scene of intelligence calculation fusion network;According to the distributed management and centralized control system, the two-stage random optimization problem of different time scales is established;For large-scale resource adaptation problem, a resource elasticity adaptation scheme based on heuristic algorithm is adopted;For small-scale scheduling sub-problems, a deterministic scheduling scheme based on deep reinforcement learning is adopted;Using the resource elasticity adaptation scheme and deterministic scheduling scheme, the optimal resource adaptation and scheduling result is obtained.The application can meet the resource flexible configuration demand under high dynamicity and uncertainty environment, ensure low delay and high reliability in data scheduling process.

The present invention relates to the intersection of renewable energy utilization and reservoir scheduling, and specifically to a long-term and short-term coordinated water-wind-solar integrated scheduling method and system, including obtaining solar radiation, air temperature, and wind speed data to obtain wind power and photovoltaic output; constructing a medium- and long-term optimal scheduling model for water-wind-solar complementarity, using an implicit random optimization method to perform data analysis on medium- and long-term deterministic optimal scheduling samples, and extracting medium- and long-term scheduling rules; determining a daily power generation plan compilation model by constructing a short-term optimal scheduling model for water-wind-solar complementarity; solving the medium- and long-term optimization model to obtain daily average hydropower output and daily discharge as short-term inputs, obtaining a daily power generation plan through daily power generation plan compilation rules, inferring hydropower output through the daily power generation plan and wind power and photovoltaic output, and then considering reservoir characteristic constraints and calculating the flow rate based on the hydropower output to obtain the final water level, which is input into the medium- and long-term model for the next scheduling period, thereby simulating the scheduling process.