2603 results about "Optimal control" patented technology

The invention relates to the technical field of servo motor control, and discloses an intelligent servo motor control method and device and computer equipment. The method comprises the following steps: carrying out multi-dimensional data acquisition and preprocessing on a position loop, a speed loop and a current loop of the intelligent servo motor to obtain standard structured parameter data; identifying a working condition characteristic parameter set of the intelligent servo motor; establishing a two-stage optimization architecture comprising a motor parameter layer and a control algorithm layer, and generating a dynamic parameter adjustment mapping table; a depth deterministic strategy gradient network is trained based on the characteristic parameter set and the dynamic parameter adjustment mapping table, and an optimal control strategy model for different working conditions is generated; and the optimal control strategy model is utilized, control parameters are automatically switched according to the working condition types recognized in real time, and smooth transition control of the intelligent servo motor among different working conditions is achieved. Smooth switching among different working conditions is achieved, system oscillation caused by sudden change of control parameters is avoided, and the disturbance influence in the working condition switching process is greatly reduced.

The invention relates to the technical field of production line control, and discloses an automatic control method and system for a plastic processing production line. The method comprises the steps of collecting technological parameters of a plastic processing production line and transmitting the technological parameters to a central control system to generate a database; multi-dimensional parameter correlation analysis is executed, a parameter and quality mapping relation is established through a CNN-LSTM hybrid network, and an optimization model is formed; calculating an optimal control parameter, generating a control strategy and issuing the control strategy to an execution unit; and monitoring a response result, updating the model in real time, and forming closed-loop adaptive control. According to the method, the mapping relation between the process parameters and the product quality is accurately established through the deep learning model, the optimal control parameters are automatically calculated, and closed-loop adaptive control based on production feedback is realized.

The invention belongs to the technical field of circuit breaker management, and discloses a control system for managing a primary and secondary fusion circuit breaker, and the system collects the electrical parameters, mechanical states and environmental conditions of the circuit breaker through multiple channels, and constructs a standardized operation data set; establishing a circuit breaker health characteristic spectrum based on deep characteristic learning; establishing an environmental adaptability control parameter library through environmental factor correlation analysis and multi-scene simulation; performing fault mode identification and predictive diagnosis in combination with the health characteristic spectrum, and generating a fault risk early warning matrix; optimizing a multi-circuit-breaker cooperative control strategy based on the early warning matrix, and generating an optimal control instruction sequence; and reliable execution and effect feedback of the control instruction are realized through security encryption verification and a hierarchical execution mechanism. The problems that a traditional circuit breaker control system is difficult in data integration, insufficient in environment adaptability, weak in fault prediction capacity, incomplete in cooperative control and the like are solved, and the safety and reliability of power grid operation are remarkably improved.

The invention provides a power system operation state intelligent monitoring method and system, and relates to the technical field of power data monitoring, and the method comprises the steps: obtaining multi-source heterogeneous operation data, obtaining a standard operation data set, adaptively extracting high-order multi-modal feature representation through a confidence weighted fusion method, and obtaining an operation state multi-modal feature set. Constructing an intelligent monitoring cognitive map by learning topological association characteristics and semantic association rules of the running state; mapping the real-time operation data into an intelligent monitoring cognitive map, dynamically sensing an operation state spatio-temporal evolution rule through a spatio-temporal dependence modeling unit and a graph feature propagation unit in an operation state graph learning model, and generating a prediction and diagnosis result; and learning and generating an optimal control strategy of the power system through a depth deterministic strategy gradient algorithm, decoupling the optimal control strategy of the power system into a plurality of local sub-strategies, issuing the local sub-strategies to corresponding power control equipment, and obtaining a distributed cooperative control scheme through a federated learning algorithm.

The invention relates to the technical field of refrigeration equipment control, and discloses an artificial intelligence-based refrigeration equipment operation energy-saving control method and equipment, and the method comprises the following steps: collecting multi-dimensional data in a refrigeration environment in real time through a multi-sensor system, the data including environment variable data and equipment operation data; preprocessing the collected multi-sensor data, and generating a comprehensive environment variable through credibility evaluation and data fusion; carrying out load prediction on the current environment variable and the equipment operation state based on a deep learning model, and predicting a future refrigeration load demand; and according to the load prediction result, an optimization objective function is constructed, and an optimal control strategy of the refrigeration equipment is generated. According to the invention, through a deep learning load dynamic prediction technical scheme, a technical effect of high-precision prediction of a future refrigeration load demand is achieved, so that the change trend of an environment variable and an equipment operation state can be accurately captured, and the reliability of load prediction is improved.

The invention relates to a control method and device for wind load resistance of a mooring unmanned aerial vehicle and a storage medium. The method comprises the following steps: collecting and modeling a mooring unmanned aerial vehicle to obtain a system dynamics model; state estimation is carried out to obtain system wind load disturbance data and expected attitude data; performing optimal control solution on the plurality of mooring points to obtain tension distribution control data of each mooring point; inputting into a discrete time neural network controller for attitude compensation calculation to obtain thrust vector compensation control data; performing adaptive robust control on the attitude of the mooring unmanned aerial vehicle to obtain a rotating speed control instruction of each motor of the four rotors; and performing safety threshold judgment, and when any monitoring data exceeds a preset threshold, outputting a working mode switching instruction and executing safe landing control. According to the invention, a multi-stage safety protection mechanism is established, and reliable operation of the mooring unmanned aerial vehicle under extreme conditions is ensured through real-time monitoring and mode switching.

The invention provides a servo hydraulic system optimization control method and system for a press machine test platform, and relates to the technical field of press machines, and the method comprises the steps: building a state space expression and building a mathematical model of a servo hydraulic system based on dynamic characteristics by deducing mathematical expressions of all components of the system; building a predictive control algorithm based on the mathematical model, solving a quadratic programming problem by adopting a rolling optimization strategy through building a state space predictive model and determining a multi-objective optimization function to obtain an optimal control sequence, and taking a first element of the optimal control sequence as control input at the current moment; feedforward compensation and pressure feedback compensation are introduced, the feedforward compensation constructs a compensation model based on an inverse system of a press transfer function and pre-corrects a control signal, and the pressure feedback compensation designs a digital filter based on a pressure error, fuses a feedforward compensation signal with a pressure feedback compensation signal and superposes the fused signal with the control signal; and obtaining a final control signal.

The invention relates to the technical field of gold ore flotation full-process monitoring and control, and discloses a gold ore flotation full-process intelligent monitoring and optimal control method and system. The method comprises the following steps: collecting flotation process parameters, and carrying out abnormal value detection and grading treatment; the graded data are fused, and a digital twinborn model is established; designing a negotiation control framework based on the model, and adjusting parameters by using a contract network protocol; and constructing a soft measurement model and an optimization strategy, and optimizing control parameters through a self-learning mechanism. The technical problems that global optimization is difficult to achieve due to unit independent control in an existing gold ore flotation control system, control decision is inaccurate due to simple data processing, key quality indexes are difficult to estimate due to lack of soft measurement means, and adaptability is poor due to limited system learning ability are solved.

The invention relates to the technical field of wind turbine generator control, and discloses a wind turbine generator control optimization method based on dynamic changes of wind speed and wind direction. According to the method, real-time wind speed time sequence data and three-dimensional wind direction vector field data of a target wind field are received, a wind field dynamic analysis model is constructed by using a space-time convolutional neural network, and a wind field energy density distribution matrix and a turbulence intensity probability graph are generated. And constructing a multi-target adaptive optimization model on the basis, generating a unit control parameter instruction set, optimizing a cooperative adjustment coefficient according to a preset unit load-power generation efficiency balance equation, and outputting an optimal control action sequence to a wind turbine generator master control system through Bayesian optimization framework iterative updating. The method can accurately sense the wind field change, effectively balance the unit load and power generation efficiency, realize multi-unit cooperative control, improve the wind energy capture efficiency, and improve the operation stability and economic benefits of the wind turbine generator.

The invention belongs to the field of energy storage cluster auxiliary thermal power peak regulation, and particularly provides an energy storage cluster auxiliary thermal power generating unit peak regulation layered optimization control method for a wind power high-permeability area. The invention provides a double-layer optimization regulation and control strategy for assisting thermal power generating unit peak regulation by an energy storage cluster in a high-proportion wind power system, the upper layer is a fire storage peak regulation optimization control strategy based on each energy storage peak regulation surplus coefficient, and optimal distribution of peak regulation resources and system flexibility between the energy storage cluster and the thermal power generating unit in each time period is obtained; and the lower layer is an optimal control strategy of each energy storage power station under the multi-target constraint of considering the energy storage life, and the optimal output of each energy storage power station is obtained under triple targets. Simulation analysis and comparison of dynamic regulation and control effects are performed on the basis of the method, and orderliness and scientificity of the method are shown.

The invention provides an air water production intelligent prediction and adjustment method and system based on meteorological big data, and relates to the technical field of air water production, and the method comprises the steps: collecting data through a meteorological sensor, constructing an enhanced feature space, fusing multi-domain knowledge through a migration cross-domain learning network, and predicting the water production through combining with a deep neural network. A control strategy is generated based on a deep reinforcement learning model, optimal control parameters are selected through multi-target Bayesian optimization and a dynamic decision algorithm, the air water production amount can be accurately predicted, intelligent adjustment is achieved, the water production efficiency is improved, energy consumption is reduced, and high environmental adaptability and robustness are achieved.

The invention relates to a mechanical arm safe obstacle avoidance control method and system based on a high-order control obstacle function, aiming at a multi-degree-of-freedom mechanical arm system with an obstacle in a task trajectory space, the high-order control obstacle function suitable for a high-relative-order system is used, and the method comprises the following steps: establishing a dynamic model of a multi-degree-of-freedom mechanical arm; designing a stable tracking controller based on the task trajectory in combination with a backstepping control method; an obstacle collision detection model is established, and safety constraints are provided for a control system through a high-order control obstacle function; and in combination with the control constraint and the optimal control problem, solving the optimal control input through quadratic programming. According to the method, the optimal task trajectory tracking can be completed on the premise that the tail end of the mechanical arm meets the safety constraint, the calculation complexity is effectively reduced, and the real-time performance is guaranteed.

The invention discloses an integrated energy system model prediction control method based on deep reinforcement learning, which is used in the field of energy prediction control, and comprises the following steps: constructing a state prediction model according to the state, output increment and disturbance quantity of each unit in integrated energy equipment in a park; predicting the output state of each device of the system at each moment in the prediction time domain within the day, establishing a rolling optimization objective function based on a prediction value and a day-ahead plan value, and calculating an intra-day scheduling optimal control quantity; and constructing an intelligent agent, and learning weight parameters in the rolling optimization objective function by using a double-delay depth deterministic strategy gradient algorithm to realize optimization of model prediction control of the integrated energy system. According to the invention, through learning of the neural network on different scenes, the optimization efficiency and quality of the algorithm on the integrated energy system are improved.

The invention provides a liquid cooling fusion energy consumption adjustment optimization method and system based on dynamic adjustment, and the method comprises the steps: S1, obtaining the thermal load data of calculation equipment, the state data of a liquid cooling system, the state data of an air cooling system, and the constraint data of the system, and constructing a cooling fusion real-time state map; s2, performing calibration operation on the cooling prediction model based on the cooling fusion real-time state atlas, after calibration is completed, extracting cooling key dynamic feature vectors, obtaining short-time cooling prediction data based on the cooling prediction model and the cooling fusion real-time state atlas, and constructing a cooling conflict objective function; s3, obtaining a cooling random sample set, performing initialization operation on a PSO algorithm based on the cooling random sample set, and generating an optimal control parameter combination; and S4, the cooling system is controlled in real time based on the optimal control parameter combination, the step S1 to the step S4 are executed in a circulating mode, and by means of the method, optimal energy consumption control can be found in real time under the condition that the cooling capacity of the cooling system is kept.

The invention provides a one-key start-stop operation monitoring method and system for a gas-steam combined cycle unit, and relates to the technical field of equipment state monitoring, and the method comprises the steps: obtaining operation data, carrying out the data cleaning, noise reduction and standardization, and extracting the performance characteristics of the unit; constructing a component correlation model based on the spatial-temporal characteristics, and optimizing operation efficiency parameters; and performing fault diagnosis by using a multi-modal feature enhancement network, constructing a layered optimization control system, and performing control strategy optimization to obtain an optimal control strategy. According to the invention, intelligent monitoring, fault diagnosis and optimal control of the gas-steam combined cycle unit can be realized, the operation efficiency and reliability of the unit are improved, the fault risk is reduced, and the service life of equipment is prolonged.

A central air-conditioning system energy consumption simulation method based on a multi-region coupling model comprises the steps that basic data and initial parameters of a cold-chain logistics center site are obtained, and the basic data and the initial parameters are preprocessed; and constructing a multi-region coupling physical mechanism model based on a heat and mass transfer equation, and outputting a physical model state through a finite difference method by taking the basic data and the initial parameters as boundary conditions. And taking the physical model state as the input of a convolutional neural network, extracting short-period penetration load characteristics through time sequence convolution, and outputting a short-period penetration load prediction result. And combining the short-term penetration load prediction result with the cargo thermal parameters and the partition state as the input of a Transform model, and outputting multi-region energy consumption distribution prediction data. And performing strategy optimization processing on the multi-region energy consumption distribution prediction data through MPC, and calculating a preliminary control strategy. And searching a global optimal solution in the high-dimensional nonlinear parameter space through a myxomycete optimization algorithm, and outputting an optimal control strategy.

The invention discloses a multi-intersection signal cooperative control method based on deep reinforcement learning, and the method comprises the steps: firstly constructing a comprehensive reward function which comprises waiting time, queuing length and the influence of adjacent intersections, and constructing a mathematical model of a multi-intersection signal control system based on the reward function, so as to achieve the maximization of a comprehensive reward; and secondly, designing a deep Q network cooperative control algorithm for explicit strategy adjustment, and optimizing and adjusting adjustable strategy function parameters by introducing an explicit strategy adjustment function and utilizing a stochastic gradient descent method. And finally, dynamically adjusting the signal control strategy of each intersection in the road network in a complex and changeable traffic environment by designing a parameter sharing mechanism, thereby realizing collaborative optimization control of multiple intersections. According to the invention, the cooperative control efficiency of a multi-intersection signal system is obviously improved, traffic delay is reduced, and the traffic capacity of a road network is improved.

The invention provides a self-adaptive control method and system for an automatic driving truck, and the method comprises the steps: carrying out the filtering and structural processing of multi-dimensional data collected by a vehicle-mounted sensor network, and generating a standardized data package containing vehicle state parameters and environment information; and according to the standardized data packet, retrieving a deep learning neural network model library, selecting a currently applicable basic model, and dynamically adjusting parameters of the basic model to generate an optimized adaptive control model. And inputting the standardized data packet into an adaptive control model for operation, generating a candidate control instruction, and generating an optimal control strategy through rapid simulation and multi-objective optimization calculation. And finally, the optimal control strategy is executed by the vehicle controller, vehicle response is monitored in real time, and controller parameters are adaptively adjusted based on deviation. According to the method, the dynamic adaptability and strategy optimization of the control model are realized, and the safety and efficiency of the automatic driving truck in a variable environment are improved.

The invention provides a high-efficiency ship automatic control system, and the system comprises a comprehensive control platform which carries out the unified integration of subsystems, sets a target function, and carries out the data sharing, collaborative optimization and global control among the subsystems. The intelligent sensing network module is used for collecting historical data and real-time data; the self-learning control module is used for constructing a control strategy generation model; the ship digital model module is used for constructing a digital model for simulating ship navigation so as to test different ship control strategies; the decision execution module is used for generating and executing a control instruction; and the safety monitoring module detects abnormal behaviors of the ship and gives an alarm. According to the invention, a plurality of subsystem modules can be integrated in a unified manner, data sharing, collaborative optimization and global control are realized, an optimal control strategy can be provided, the cost and risk of actual navigation are reduced, and the control precision, response speed and operation efficiency of a ship are improved.

The invention relates to the field of heat energy engineering and automatic control, and discloses a multi-mode optimization system for combustion efficiency of a thermal power boiler. The system comprises a multi-modal data perception and space-time alignment module, a tensor manifold modeling and physical constraint feature extraction module, a space-time coupling dynamic prediction and uncertainty quantification module, a quantum optimization decision and DCS cooperative control module and a combustion state derivative early warning and optimization feedback module. Through multi-modal data space-time alignment, five-order tensor physical constraint modeling, PDE deep network prediction, quantum optimization decision and a closed-loop feedback mechanism, space-time unified fusion and physical feature extraction of combustion data are realized, the reliability of combustion state prediction is improved, an optimal control instruction is efficiently solved, system parameters are dynamically corrected, and the reliability of combustion state prediction is improved. The problems that in the prior art, data fusion is difficult, modeling physical constraints are lacked, optimization real-time performance is poor, and adaptivity is weak are solved, and the combustion efficiency and the intelligent control level of the thermal power boiler are remarkably improved.

The invention discloses a building environment dynamic regulation and control method and system based on Internet of Things driving, particularly relates to the field of building environment intelligent regulation and control, and is used for solving the problems of multi-region environment dynamic regulation and control and fine management. The environment data of each sub-region of the building is accurately acquired and synchronized, real-time region division is realized in combination with an adaptive clustering algorithm, and the accurate capture of environment features and the reasonability of space division are ensured. On the basis, the independence and interaction strength of the sub-regions are comprehensively evaluated by calculating an environmental independence index, and a scientific basis is provided for refined and coordinated regulation and control. Based on a rule engine and a decision tree reasoning algorithm, an optimal regulation and control strategy is automatically generated, and continuous optimization is performed in combination with a Q learning algorithm, so that the system can adapt to environmental changes, dynamically adjust regulation and control actions, realize intelligent optimization and self-learning of the regulation and control strategy, and remarkably improve the regulation and control efficiency and accuracy of the building environment.

The invention relates to the technical field of robot control, in particular to a time delay double-arm collaborative robot control method based on Udwadia-Kalaaba and a cooperative game theory, and the method mainly comprises the following steps: obtaining the control input of a double-arm collaborative robot system obeying servo constraint according to the uncertainty of the system; a dynamic model is simplified by adopting a time delay estimation method so as to realize time delay estimation and double-mechanical-arm cooperative impedance control, a self-adaptive robust controller is established according to time-varying uncertainty existing in a system in combination with a self-adaptive updating law, and optimal control parameters of the robust controller are obtained through a cooperative game method. By the adoption of the method, control parameters can be optimized, performance maximization is achieved, it is guaranteed that the robot can be effectively balanced among different performance targets, and optimization of the overall control performance of the system is promoted.

The invention relates to the technical field of road construction quality control, in particular to an asphalt mixing station intelligent monitoring method and system based on Internet of Things data, and aims to solve the problems that in the prior art, technological parameters of an asphalt mixing station cannot be dynamically optimized, state vectors cannot be structured and defined as action spaces, and the working efficiency of the asphalt mixing station cannot be improved. The stability and convergence efficiency of strategy updating cannot be ensured, and long-term optimal control cannot be realized; the state and action space is constructed through the reinforcement learning strategy construction module, the multi-target reward function is combined, the reinforcement learning model is trained through the PPO algorithm, dynamic optimization of the technological parameters of the asphalt mixing station is achieved, the environment perception and regulation and control capacity of the model is enhanced through the structured state and the executable action, and the dynamic optimization of the technological parameters of the asphalt mixing station is achieved. The weighted reward mechanism overall plans quality, energy consumption and stability, and the PPO algorithm ensures efficient and stable training and supports long-term optimal control.

The invention discloses a liquid chromatogram flow velocity real-time detection and optimal control method based on multi-point sensing. The method comprises the following steps: S1, constructing a distributed acquisition network; s2, constructing a fluid transmission topological graph and generating a multi-point flow velocity time sequence; s3, inputting the fluid transmission topological graph and the multi-point flow velocity time sequence into a FlowFormer model to generate a flow velocity evolution prediction map; s4, executing local gradient scanning, and generating an optimization control objective function; s5, calling a self-adaptive fox swarm algorithm controller, inputting a flow velocity evolution prediction map and an optimization control objective function, and generating an optimal adjustment strategy; s6, the control parameters of the liquid chromatography system are adjusted in real time and fed back to the FlowFormer model; and S7, when abnormal flow velocity change is detected, triggering an adaptive fox swarm algorithm controller to execute a local escape strategy. According to the invention, multi-point sensing and an intelligent optimization algorithm are fused, and real-time detection and optimal control of the liquid chromatogram flow velocity are realized.

The invention discloses a robot dynamic path tracking method and system, and the method comprises the steps: constructing a nonlinear model prediction control model for a differential robot chassis; building a target cost function by considering the obstacle environment information around the robot; adjusting a control input vector in the nonlinear model predictive control model, performing real-time tracking control on the robot, calculating a target cost function value, taking the corresponding control input vector as an optimal control input vector when the target cost function value is minimum, and performing real-time tracking control on the robot. Performing dynamic path tracking control on the robot by using the optimal control input vector; the method has the advantages that real-time obstacle avoidance is reliably carried out while the robot tracks the path, complicated path planning calculation steps are not needed, the calculation process is simple, and the efficiency is high.

The invention discloses a secondary ripple optimization method based on star cascade H bridge injection three-time zero sequence voltage. The method comprises the following steps: acquiring real-time voltage and current data of a system, performing dynamic harmonic analysis on the acquired data, generating a harmonic compensation signal, and generating an optimized control signal; constructing a multi-objective optimization model based on the optimized control signal and pre-stored system parameters, solving the model to obtain a Pareto optimal solution set, and selecting an optimal cubic zero-sequence voltage parameter from the Pareto optimal solution set; based on the optimal cubic zero-sequence voltage parameters, constructing a system dynamic model, constructing an adaptive prediction controller, generating an optimal control sequence, and performing closed-loop feedback adjustment; the ripple suppression effect is evaluated, the ripple suppression proportion is calculated by comparing the secondary ripples before and after the third zero-sequence voltage is injected, and the control parameters are dynamically adjusted according to the result. And the adaptability and precision of harmonic analysis are greatly improved. The problem that secondary ripples rise when the star cascade device compensates reactive power is effectively solved.

The invention relates to the technical field of satellites, and discloses an artificial intelligence aerospace infrastructure agent system, which comprises a satellite agent, and the satellite agent comprises an energy management module, a calculation module, a task execution module and a communication module. The artificial intelligence aerospace infrastructure agent method comprises the following steps: acquiring real-time states of a plurality of satellite agents, performing task scheduling and resource allocation by using an optimal control theory, optimizing a task allocation strategy in combination with a game theory model, and optimizing a communication path according to a channel capacity theorem, so as to obtain an artificial intelligence space infrastructure agent; and the task sequence and the resource allocation strategy are adjusted in real time in the task execution process, so that efficient collaboration and task completion are ensured. According to the method, task scheduling is optimized by combining the optimal control theory and the game theory, a communication path is optimized by adopting the channel capacity theorem, the task execution sequence is adjusted in real time, adaptive resource allocation is performed, efficient cooperation between satellite agents is ensured, and the task execution efficiency, the resource utilization rate and the system stability are effectively improved.

The invention discloses a four-rotor unmanned aerial vehicle model prediction control method based on a deep Q-network (DQN). The method comprises the following steps: firstly, establishing an inertial coordinate system and a body coordinate system, and establishing a kinematic equation and a kinetic equation of the four-rotor unmanned aerial vehicle based on a Newton second law and an Euler-Lagrange equation; according to the method, an unmanned aerial vehicle robust controller based on model predictive control (MPC) is designed, optimal control input is generated through dynamic optimization, and the trajectory tracking performance of the unmanned aerial vehicle is improved. For various disturbances existing in the whole tracking process, a DQN reinforcement learning algorithm and an MPC method are combined to design a flight control system, control rate errors caused by the disturbances are compensated, the stability of attitude control is enhanced, and the tracking precision and the anti-interference capability are improved. And finally, verifying the robust performance of the flight control system through a simulation experiment.

The invention provides a DLH-IHBA-based adaptive cycle engine control law optimization method, and belongs to the technical field of aero-engine control, and the method comprises the steps: building an adaptive cycle engine model, and determining an optimization variable based on the adaptive cycle engine model; according to the control mode of each self-adaptive cycle engine, constraint conditions and an objective function based on optimization variables are determined, the control modes comprise a steady state control mode and a transition state control mode, and the steady state control mode comprises a minimum fuel consumption control mode, a maximum thrust control mode and a minimum turbine front temperature control mode; and based on the objective function and the constraint condition, optimizing the control rule of each control mode by adopting a multi-dimensional learning prey strategy fused with a badger optimization algorithm to obtain an optimal control variable, and adopting Tent mapping as initial particle swarm position mapping. According to the scheme, the convergence speed of engine control rule optimization is increased, and the optimization effect and the engine performance are improved.

The invention relates to a batch rectification system based on intelligent control, and relates to the technical field of batch rectification. A data acquisition module; the data acquisition module is used for acquiring temperature, pressure, flow and material concentration data in real time; the control module is used for receiving various data of the data acquisition module; a model prediction control algorithm is built in the control module; according to the model prediction control algorithm, a rectification process dynamic model is established, a current system state and a set control target are combined, an optimal control strategy is obtained through rolling optimization calculation, and a control instruction is sent by an execution mechanism based on the optimal control strategy; an execution mechanism; the touch display screen is connected with the control module; the operation parameters of the rectification system are displayed in real time.