238 results about "Non linear model" patented technology

The invention provides a thermal power plant flexibility transformation and deep peak regulation optimization control method and system, and relates to the technical field of control engineering, and the method comprises the steps: collecting the omnibearing operation data of a thermal power plant to construct a multi-level thermal power plant dynamic nonlinear model, carrying out the real-time identification of model parameters, generating an identification result, and building a multi-dimensional thermal power plant operation characteristic database; based on the thermal power plant operation characteristic database, designing a transformation scheme, and performing visual simulation and optimization to obtain a flexible transformation result; constructing a multi-objective optimization function, formulating an adaptive deep peak regulation optimization control strategy, carrying out short-term and medium-term prediction on a power grid load, generating an initial control strategy based on a prediction result, carrying out online optimization and adjustment, and continuously evaluating and optimizing the control strategy.

The invention provides a multi-mode intelligent propulsion control method, device and equipment for a short vertical aircraft and a storage medium, and the method comprises the steps: firstly obtaining flight state data in real time, and classifying the flight state data according to a state classification model of a decision tree to generate a current flight mode; next, calling a control strategy group and performing parallel processing on the current flight mode by taking the lowest energy consumption as an optimization target so as to generate a plurality of control instructions, and calling a fusion device weight network to process the current flight mode so as to generate a fusion weight of each control instruction, and generating a final control quantity according to the plurality of control instructions and the fusion weight, and sending the final control quantity to a pre-constructed hybrid propulsion structure nonlinear model to control the short vertical aircraft, thereby solving the problem that the short vertical aircraft cannot be adaptively adjusted when a plurality of propulsion units have differential dynamic characteristics.

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 provides an intelligent control method and system based on electro-hydraulic linkage. The method comprises the steps that multi-mode environment parameters are collected, a digital twin environment model is constructed, and data are synchronized with an electro-hydraulic servo system in real time. And establishing a nonlinear model, identifying parameters by using a least square method and a recursive least square method, verifying the precision through a step response test, and updating. And constructing an adaptive LSTM neural network inverse model to output a control voltage signal. And the signal is subjected to weighted fusion with the output of a PID controller, and the PID controller dynamically adjusts a gain parameter according to an error integral term to generate a control instruction to drive the system. According to the invention, the LSTM inverse model provides feed-forward compensation, the PID controller realizes feedback correction, and the LSTM inverse model and the PID controller are complementary. Meanwhile, based on a related nonlinear model and a parameter self-adaptive updating mechanism, the influence of working condition change is accurately described, and high-precision control of the electro-hydraulic servo system under the complex working condition is achieved.

The invention discloses an aircraft control surface oscillation fault monitoring method based on multi-scale wavelet transformation, and the method comprises the steps: carrying out the calculation through a PID (Proportion Integration Differentiation) control law through an autopilot according to an input flight path command, and obtaining a control surface control instruction deltadepth; a control surface deflection prediction value delta est is generated based on the nonlinear model and the control surface control instruction, the control surface deflection prediction value and an actual deflection value delta mea collected by the sensor module are subtracted to generate a residual signal Rn, and the residual signal is decomposed into a plurality of sub-band signals through wavelet decomposition; wavelet energy calculation is carried out on the sub-bands possibly containing the control surface oscillation fault signals, whether a fault occurs or not is judged according to the magnitude of an energy comparison threshold value, and the judgment threshold value is determined through Monte Carlo simulation. According to the method, instruction signals are separated by generating residual errors, wavelet transform processing is carried out on residual error signals, interference of noise and irrelevant frequency signals on monitoring performance is reduced, meanwhile, small-amplitude oscillation signals can be effectively monitored according to sub-band wavelet energy serving as a fault criterion, and the monitoring coverage rate of oscillation faults is remarkably increased.

The invention relates to the technical field of power system testing, in particular to a transformer simulation test current optimization method based on dynamic impedance matching, which comprises the following steps: performing low-power scanning on a tested transformer to establish a nonlinear model capable of predicting harmonic waves; actively synthesizing the virtual impedance of the target power grid; and a feed-forward compensation current is generated by using a harmonic prediction model and is injected into the transformer after being superposed with a fundamental wave instruction. According to the method, accurate identification modeling is carried out on nonlinear characteristics of a tested unit, active synthesis of virtual impedance of a target power grid is combined, and harmonic compensation current is generated in real time by utilizing a feedforward control thought so as to optimize finally injected test current. According to the method, the problems of inaccurate power grid simulation and insufficient nonlinear response coupling of the transformer in a traditional test are solved, high-fidelity current injection is realized, real electrical behaviors of the transformer under various complex power grid working conditions can be accurately simulated, and the flexibility, accuracy and safety of the test are remarkably improved.

The invention relates to the technical field of non-electrical variable control, and discloses an industrial water chiller temperature intelligent adjusting system, which comprises a refrigeration circulation loop and a control unit, the control unit calculates a steady state control component based on the water outlet temperature deviation, performs differential processing on the refrigerant pressure to extract the gradient, and outputs the gradient. Generating a transient compensation component when the gradient exceeds a dynamic noise threshold; according to the method, the problem of thermal lag is solved by utilizing the pressure wave transmission speed advantage, aggressive response is prevented from triggering low-pressure shutdown through a safety margin constraint mechanism, the pressure safety margin is calculated in real time, a gain coefficient is determined according to a nonlinear model, and a final driving instruction is generated. And the dynamic balance of the control response speed and the system physical security boundary is realized.

The invention particularly relates to a robot outdoor large-scale scene autonomous navigation method based on an open digital map, which comprises the following steps: extracting a road network structure, constructing a road network topological graph, and modeling a road network space by using an R tree; associating a starting point with a target point according to the road network topological graph of the R-tree structure, and performing global path planning based on a Dijkstra algorithm to obtain path points from the starting point to the target point; acquiring laser radar sensor point clouds, and generating a local voxel map by using the point clouds after the ground points are filtered out; performing local target point mapping by combining the global path line segment and a local voxel map to generate a local target point; performing local front-end path search based on a JPS algorithm to obtain a local front-end path from the current position of the robot to a local target point; a safety corridor is constructed according to the local front-end path, and trajectory optimization is carried out; and tracking the optimized trajectory, and planning the speed by considering the nonlinear model of the robot and the actual motion state. The method provided by the invention can realize outdoor large-range long-distance autonomous navigation.

The invention belongs to the technical field of fine chemical reaction safety testing and automatic chemistry, and particularly discloses a nonlinear model predictive temperature control method of a semiconductor temperature control type synthesis reactor. The method comprises the following steps: constructing a dynamic heat transfer model of the system based on the semiconductor temperature control type synthesis reactor; designing a nonlinear model prediction controller according to the heat transfer model of the synthesis reactor; constructing a multi-constraint objective function for nonlinear predictive control according to the characteristics of the controlled object; and solving an optimal control sequence for the nonlinear model prediction controller by adopting a projection gradient method. Compared with a traditional PID temperature control algorithm, the temperature of the reactor can be accurately predicted and controlled through the designed nonlinear model prediction temperature control algorithm, it is ensured that the chemical reaction is conducted at the optimal temperature, and the robustness and adaptability of a control system are improved.

The invention provides a predetermined time distributed optimal inclusion control method for an unmanned aerial vehicle cluster, and belongs to the technical field of collaborative decision of an unmanned cluster system. The method comprises the steps that unmanned aerial vehicles in an unmanned aerial vehicle cluster are divided into virtual leader unmanned aerial vehicles and follower unmanned aerial vehicles; according to the single integral linear model of the virtual leader unmanned aerial vehicle, generating a preset formation pattern through a preset time distributed optimization theory; constructing a nonlinear model of a follower unmanned aerial vehicle in the unmanned aerial vehicle cluster; and designing an inclusion control protocol of the follower unmanned aerial vehicle based on a distributed inclusion control algorithm according to the nonlinear model of the follower unmanned aerial vehicle, so that the follower unmanned aerial vehicle converges to a preset formation. Through a distributed optimal formation control algorithm and a distributed inclusion control algorithm, a virtual leader unmanned aerial vehicle formation generation strategy within a preset time is realized, follower unmanned aerial vehicles converge to a preset formation, and thus unmanned aerial vehicle cluster security cooperative control under a complex sea condition is completed.

The invention relates to a four-rotor unmanned aerial vehicle pose control method based on a global sliding mode. The invention relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: carrying out the modeling of a dynamic model of a quad-rotor unmanned aerial vehicle, and obtaining the nonlinear state space description of the quad-rotor unmanned aerial vehicle; secondly, respectively designing global sliding mode control laws for the position subsystem and the attitude subsystem according to the nonlinear model; according to the output of a position subsystem controller, the expected acceleration is mapped into expected lift force and attitude angle by designing control distribution; and finally, constructing a nonlinear programming problem corresponding to a nonlinear model prediction controller by taking a previously designed global sliding mode control law as a contraction constraint, and solving the problem to obtain a control quantity. According to the method, the quadrotor unmanned aerial vehicle can effectively perform trajectory tracking under the conditions of output constraint and convergence guarantee, and the control precision, the response speed and the safety in the flight process are improved.

The invention relates to an electric vehicle wireless charging system LPV-Hammerstein model identification method based on an alternating least square iteration strategy, and belongs to the technical field of power electronic system modeling and parameter identification. The method comprises the following steps: firstly, constructing an LPV-Hammerstein model structure based on a WPT system circuit topology and a working mechanism, and then collecting discrete data of a system input control signal U, an output DC voltage Vo and a scheduling variable Req; then, alternately fixing other parameters by adopting an alternate least square iterative algorithm, and converting a to-be-estimated parameter problem into a linear least square sub-problem to be solved; and when a convergence condition is satisfied, a final LPV-Hammerstein model parameter estimation result is obtained. According to the method, the common nonlinear and parameter change coupling characteristics in the wireless charging system of the electric vehicle can be effectively processed, a systematized and efficient way is provided for obtaining a high-precision system dynamic model, and the limitation that the system cannot be accurately described by a traditional linear model or a simple nonlinear model is overcome.

Systems and methods for enabling unconstrained demand forecast based on accurate lost sales estimation using collective consumer behavior are disclosed. In some embodiments, a disclosed method includes: obtaining raw sales data of an item in a store for a past time period, wherein the item is offered for sale in the store; detecting at least one out-of-stock (OOS) time period within the past time period based on the raw sales data, wherein sale of the item is impacted by an OOS status of the item in the store in the at least one OOS time period; computing, based on a non-linear model, lost sales estimate of the item in the store for each of the at least one OOS time period; generating, based on the raw sales data and the lost sales estimate, recommended inventory for the item in the store for a future time period; and transmitting the recommended inventory to a computing device associated with the store for inventory arrangement in the future time period.

The invention discloses an efficient self-cleaning method for a heat exchanger of an air conditioner, relates to the technical field of efficient air conditioners, and designs the steps of multi-source data acquisition and residual dirt identification, residual cumulative effect prediction and formula optimization decision, directional deep cleaning, dynamic parameter control and the like according to the self-cleaning requirements of the heat exchanger of the air conditioner. Accurate positioning and grading of stubborn dirt are realized by utilizing data of multiple types of sensors; on the basis of machine learning or a nonlinear model, predicting an accumulative effect of residual dirt in subsequent operation, dynamically generating a cleaning formula and optimizing cleaning parameters; strengthening treatment is carried out on key parts through mechanical vibration, high-pressure ultrasound and real-time feedback control; and performing secondary detection on the cleaning effect, comparing the cleaning effect with a predicted value, and if the deviation is obvious, correcting the weight of the model to form a closed-loop self-learning lifting strategy. Damage to materials and energy waste caused by excessive cleaning can be effectively reduced, and long-term efficient operation of the heat exchanger can be kept in various complex scenes.

The application discloses a kind of atmospheric environment ozone concentration prediction system based on chemical reaction mechanism, including obtaining weather forecast parameter, historical observation data cleaning analysis, analysis screening suitable model, model training and prediction, prediction result analysis, data visualization and so on functional module composition.This system is based on Lagrangian coordinate system, establishes the response relationship between meteorological parameter, pollutant concentration and ozone concentration, using thermodynamic equilibrium principle, chemical reaction equilibrium principle and photochemical reaction principle and other basic chemical reaction engineering theory, deduces the non-linear model of meteorological parameter, pollutant concentration and other parameters on ozone concentration influence, by the independent design of each software function module, with low energy consumption, low carbon emission, low operating cost, data automatic processing, easy to deploy, can be connected ecological cloud platform, system expansibility is strong, maintenance is easy, and computing efficiency is high.Therefore, it has broad market application prospect and good social and economic benefits.

The invention provides an underwater-water surface unmanned system cooperative control method based on NMPC, belongs to the technical field of robot control, and particularly provides an underwater terrain prediction method based on sonar information feedback by using multi-beam sonar data, and obtains underwater terrain coordinate data by fusing a robot state predicted by a non-linear model in NMPC. And according to the predicted underwater terrain information, an underwater terrain following control problem is converted into an optimization problem with constraints by using NMPC, and terrain following control of the underwater robot can be realized by solving the optimization problem. On the basis, a task-triggered underwater-water surface unmanned system cooperative control strategy is constructed, and the tracking of the underwater robot by the water surface unmanned ship is realized. And finally, the cooperative control problem of the underwater-water surface unmanned system for unknown sea area exploration is successfully solved.

The invention relates to the technical field of semiconductor chips, in particular to a buffer insertion method and a storage medium. The method comprises the following steps: acquiring position information of a fan-out point and all corresponding fan-in points of the integrated circuit, and calculating a Manhattan distance between the fan-out point and each fan-in point based on the position information; calculating a corresponding first delay between the fan-out point and the fan-in point when the buffer is not placed and a corresponding second delay after the buffer is placed based on a nonlinear model; obtaining a corresponding buffer insertion candidate position based on the first delay, the second delay and the Manhattan distance between the corresponding fan-out point and the fan-in point; and carrying out calculation by taking the buffer insertion candidate position as a candidate point of a Van Ginneken algorithm to obtain a buffer placement scheme. The problems of inaccurate long conductive path delay estimation and inaccurate calculation of a buffer insertion scheme are solved.

Aspects relate to signaling relating to a non-linearity model for power amplifier circuitry of a transmitting device. The power amplifier circuitry may apply digital pre-distortion (DPD) to a signal prior to amplification and transmission of the signal. A receiving device may apply digital post-distortion (DPoD) to a signal received from the transmitting device where the DPoD is based on the non-linearity model. The transmitting device may send to the receiving device non-linearity parameters for the non-linearity model.

The invention discloses a power amplifier digital pre-distortion optimization method based on a complex convolutional neural network, and the method comprises the following steps: S1, collecting a plurality of groups of digital signals before and after passing through a power amplifier PA, and forming a sample set; s2, preprocessing the data in the sample set; s3, constructing a complex number time delay convolutional neural network containing a linear auxiliary branch and a nonlinear model as a digital pre-distortion model of the power amplifier; s4, training the digital pre-distortion model of the power amplifier by using the preprocessed data in the sample set to obtain maturely trained digital pre-distortion model parameters; and S5, when the digital pre-distortion of the power amplifier is actually carried out, sending a to-be-sent digital signal to the maturely trained digital pre-distortion model for processing. According to the method, the linearization capability is remarkably improved by introducing complex field processing and auxiliary linear branches while the low complexity characteristic is kept.

The invention discloses a ship control method and device in an inland river scene, electronic equipment and a storage medium, and can be applied to the technical field of intelligent navigation of ships. According to the method, the ship traveling wave energy distribution rule is calculated according to the first state information and the second state information which are acquired in real time, then the first following distance is determined, and then path planning is performed by combining the ship energy consumption, the ship traveling wave energy distribution rule, the safety margin and the total navigation time which are acquired in real time to obtain the first navigation path of the target ship; a first sailing control instruction of the target ship is predicted by adopting a nonlinear model control method so as to control the sailing process of the target ship in the inland river scene, and meanwhile, the sailing process of the target ship in the inland river scene is controlled by a second sailing control instruction generated by dynamically adjusting the first sailing path according to the channel environment change information. Therefore, the balance between the energy efficiency and the sailing efficiency can be realized in the whole sailing process of the target ship, and emergencies in the sailing process can be effectively coped with.

The invention relates to a multi-target control method for a fuel cell hydrogen supply system based on switching type model prediction. The method comprises the following steps: S1, constructing a high-order nonlinear model of the fuel cell hydrogen supply system; s2, obtaining a high-load linear sub-model and a low-load linear sub-model; s3, converting the high-load linear sub-model and the low-load linear sub-model into a high-load model prediction controller and a low-load model prediction controller respectively; and S4, acquiring the actual load current of the electric pile, judging the adopted model prediction controller based on a switching strategy judged by a load current threshold, outputting a control variable by the adopted model prediction controller, controlling the fuel cell hydrogen supply system based on the control variable, and repeating the step S4 until the control is finished. Compared with the prior art, the hydrogen supply system has the advantages that the combined precise adjustment of the anode pressure and the hydrogen passing ratio is realized, so that the dynamic response performance of the hydrogen supply system and the overall operation stability of the system are improved, and the like.

The invention relates to the technical field of intelligent control in the converter steelmaking process, and particularly discloses a converter steelmaking furnace mouth pressure intelligent balancing method with self-adaptive image segmentation, which comprises the following steps: image acquisition: acquiring a furnace mouth flame image in real time through an industrial camera; image preprocessing: adopting a dynamic gamma correction algorithm to adaptively adjust a correction coefficient according to an R channel mean value of the image; in the flame feature extraction stage, the corrected image is subjected to bilateral filtering to remove noise, and then an improved OTSU segmentation algorithm is input to extract a flame main reaction area; in the pressure intelligent control stage, a flame characteristic-furnace pressure nonlinear model is constructed, and finally the rotating speed of an ID fan is dynamically adjusted through a fuzzy self-adaptive controller; the furnace mouth flame image is intelligently monitored, then the furnace mouth pressure is analyzed, the system pressure fluctuation standard deviation is controlled to be reduced, the gas recovery rate is improved, the false detection rate is reduced, the energy efficiency of converter steelmaking is improved, and the production safety is improved.

The invention is suitable for the technical field of viscosity monitoring and control, and provides a viscosity monitoring and control system for the blockage risk of a cyclized rubber solution conveying pipeline, the viscosity monitoring and control system comprises a tank body for storing a cyclized rubber solution, a hydraulic pump and a conveying pipeline, a liquid inlet of the hydraulic pump is communicated with the tank body, and a liquid outlet of the hydraulic pump is communicated with the conveying pipeline; a stirring device for stirring the cyclized rubber solution is arranged in the tank body, and the flow channel state evaluation module is used for constructing a flow channel state evaluation model according to the cross sectional area of the conveying pipeline, the density of residual solidified colloid on the inner wall of the conveying pipeline and the sealing performance of a connector of the conveying pipeline and outputting a flow channel state coefficient. According to the method, the pipeline blockage risk degree can be dynamically evaluated in real time, the influences of different inducements such as hydraulic pump load abnormity, pipeline structure degradation and solution fluidity reduction are accurately distinguished, and quantitative output of the risk level is achieved through the nonlinear model.

The invention discloses an alkaline electrolytic cell model for power system frequency analysis, and belongs to the field of power system modeling. The model is firstly based on an electrochemical semi-empirical formula of the electrolytic cell, and a first-order equivalent circuit model containing ohm voltage, inverse voltage and activation voltage is established; secondly, according to an equivalent circuit model, deriving an analytic relation between activation voltage and current of the electrolysis cell, and converting the analytic relation into a transfer function of a Laplacian domain; and finally, a PI control link is adopted to replace an inverter control link, the initial power, the power change value and the operation electrolysis power are used as input, the cell voltage is used as output, and a complete electrolytic cell frequency analysis model is constructed. According to the method, by simplifying the electrochemical process and performing frequency domain linearization processing, the application limitation of a traditional nonlinear model in frequency response analysis is overcome while the key dynamic characteristics of the electrolytic cell are reserved, and a practical and reliable tool is provided for frequency analysis and operation management of a power system.

The invention discloses an adhesion type ducted flight operation robot and a control method thereof, and relates to the field of flight operation robot control. The operation task planning module identifies an operation task target according to a ground station instruction, and plans an expected task trajectory of an operation mechanism in a three-dimensional space according to the position of the operation task target; the tail end track integrated control module determines the position of a flight platform, a yaw angle reference value and all joint angle reference values of a mechanical arm through a nonlinear model. The flight platform pose control module estimates and compensates the coupling effect of the operation mechanism acting on the flight platform in real time, and the operation mechanism pose control module compensates the nonlinear coupling effect between the joints of the mechanical arm and between the mechanical arm and the flight platform in real time; and the tail end physical contact control module performs admittance control in an adhesion performance space of the operation mechanism according to a ground station instruction after the operation mechanism reaches a task target position. The invention aims to improve the stability control precision of the robot.

The invention belongs to the field of wind power, and discloses a floating wind turbine generator thrust constraint robust adaptive pitch control method, which comprises the following steps: establishing a floating wind turbine generator nonlinear model considering coupling between rotor rotation dynamics and platform pitching motion; setting a control target based on comprehensive consideration of power adjustment, platform stability and limit load constraint; designing a self-adaptive propeller pitch control method based on a fast self-adaptive law; and designing a thrust envelope protection system based on thrust limit detection and a self-adaptive thrust avoidance strategy, and integrating the thrust envelope protection system into the self-adaptive pitch control method, so that the wind turbine generator can operate within an allowable thrust range. The self-adaptive propeller pitch control method can achieve efficient power adjustment and platform pitching motion suppression at the same time, is superior to a traditional method in the aspects of transient response and robustness, and is particularly suitable for the severe marine environment.

A method includes determining a portion of a flight path of an aerial vehicle. The method also includes determining an attribute value representing an operating condition expected to be experienced by the aerial vehicle at the portion of the flight path. The method additionally includes determining, based on the attribute value and using a non-linear model, a power value representing an amount of power expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method further includes determining, based on the power value, an energy value representing an amount of energy expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method yet further includes determining the flight path based on the energy value.

The invention discloses a cross arm and insulator installation robot motor control method based on a T-S fuzzy model, and the method comprises the following steps: building a permanent magnet synchronous motor nonlinear model according to electromagnetism and motor dynamics; based on a sector nonlinear method, establishing a permanent magnet synchronous motor T-S fuzzy model; designing an observer to obtain a load torque estimated value; designing an L-infinity robust controller to ensure that the angular velocity of the motor can track a reference instruction; and an anti-control gain matrix solving criterion is formulated, and it is ensured that the system meets performance indexes. According to the method, the nonlinear control problem of the permanent magnet synchronous motor system is effectively solved, the influence of continuous interference signals on the system can be effectively suppressed, and the robustness of the system is enhanced.

The invention provides an anti-interference control method for the concentration of a main reactant in a continuous stirring reaction kettle process, belongs to the technical field of chemical process control, and solves the problem that in the chemical process of producing cyclopentanol by catalyzing electrophilic water with cyclopentadienoic acid, the concentration of the main reactant is not influenced. The technical problem that the concentration of a main reactant is difficult to track a set value without steady-state errors under the conditions of fluctuation of feeding concentration, uncertainty and external disturbance of the continuous stirring reaction kettle is solved. According to the technical scheme, the method comprises the steps of constructing a model pre-estimation compensator and an anti-interference control structure, designing a four-dimensional decision model, carrying out parameter collaborative optimization by adopting a multi-target genetic optimization algorithm, and carrying out optimal solution selection based on a TOPSIS method. Under the condition of feed concentration fluctuation, the dynamic response speed and the anti-interference capability of the system can be remarkably improved, and faster adjustment time, smaller overshoot and stronger robustness can be realized in linear and nonlinear models.

The application provides a photovoltaic power station photovoltaic power prediction method and system. Among them, a cloud layer movement time sequence library is constructed based on historical cloud charts, and a nonlinear model of cloud speed and output attenuation is established. The light sensor array monitors the cloud layer thickness and aerosol density in real time, combines the model to calculate the shadow area light intensity attenuation gradient, and predicts the remaining time of the cloud group reaching the core area. According to the remaining time, the power prediction confidence interval is divided, the satellite data is input into the multi-scale meteorological fusion architecture, and the meteorological feature extraction path of the attention mechanism is adjusted based on the cloud density. The ground reflectivity correction value calculated by fusing the light sensor scattering data is used to generate a short-term power prediction sequence of the sudden cloud layer scene. The sequence is input into the energy management system, combined with the reactive power compensation parameters of the grid-connected point voltage deviation and the grid rules, and the dual-objective scheduling instructions of active and reactive power are output. The technical scheme provided by the application improves the cooperative scheduling accuracy of the photovoltaic power station under the sudden change of the cloud layer.