1822 results about "Optimal control" patented technology

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 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 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 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.

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 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 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 discloses a multi-unmanned-ship cooperative target tracking game control method, and relates to the field of unmanned-ship cooperative tracking control, and the method comprises the steps: obtaining an estimation value of an unmanned ship after the offset of a tracking target through building a preset-time target state observer, obtaining a reference signal of the unmanned ship through a preset-time guidance law, and obtaining a target state estimation value of the unmanned ship; obtaining a speed error vector of the auxiliary system considering compensation input saturation; and then obtaining an optimal control law of the unmanned ships through the established optimal cost function, and obtaining an approximation optimal control law satisfying Nash equilibrium based on an adaptive law of the evaluation network, thereby realizing control of cooperative target tracking of the multiple unmanned ships. Through the establishment of the predetermined time guidance law, the problem of relatively long convergence time is solved, and the dependence of the convergence time on control parameters is reduced. And meanwhile, the influence of neighbor members in the formation is fully considered, a solution meeting Nash equilibrium is obtained by adopting an evaluation network, and the multi-unmanned ship Nash game problem of a nonlinear continuous time system with unknown interference is solved.

The invention provides a compressor energy-saving operation control method and system based on reinforcement learning, and belongs to the technical field of compressor control. The method comprises the steps that multi-dimensional data in the operation process of a compressor are collected through a multi-parameter sensor network; preprocessing the multi-dimensional data to obtain target feature data; inputting the target characteristic data into a state prediction model, and predicting an operation parameter prediction value in a future control period; splicing and fusing the target feature data and the operation parameter predicted value, and constructing state representation of the reinforcement learning model; inputting the state representation into a target reinforcement learning model based on a near-end strategy optimization framework to obtain an optimal control action; safety verification is conducted on the optimal control action based on preset compressor safety operation constraints, and an execution instruction is determined; and adjusting operation parameters of the compressor based on the execution instruction. According to the compressor energy-saving operation control method and system based on reinforcement learning, the energy-saving performance and the operation stability of the compressor are improved.

The invention relates to the technical field of food processing and intelligent control, and discloses a pear juice production system and an intelligent control method thereof.The system comprises a raw material processing module used for recognizing, sorting and cleaning pears entering a production line based on a visual sensor, image recognition equipment and automatic cleaning equipment; the juicing control module is used for controlling the working state of the juicer according to the real-time data; the invention further discloses a method which comprises the following steps: acquiring raw material information: acquiring physical characteristic data of types, maturity and hardness of pears entering a production line through a sensor and an image recognition technology, and classifying and preprocessing the pears; by combining an optimal control theory, a robust control algorithm, a system identification and adaptive adjustment algorithm and a deep learning and big data analysis technology, real-time optimization and adjustment of operating parameters of the juicer are realized, changes of different pear varieties and maturity are automatically adapted, potential problems are found in advance, and early warning is provided.

The invention relates to the field of power system frequency stability control, and discloses a virtual synchronous machine energy storage frequency modulation method and system based on model prediction and adaptive control, and the method comprises the steps: constructing a prediction model of a virtual synchronous machine, wherein a virtual inertia coefficient and a virtual damping coefficient are included; designing a cost function, wherein the cost function is a weighted sum of squares of the frequency increment and the input power increment; solving the optimal control sequence by using quadratic programming, and further correcting the input power of the VSG in real time; and in combination with the dynamic characteristics of the energy storage device, the virtual inertia coefficient and the damping coefficient are adaptively adjusted according to the frequency deviation and the frequency change rate. The method focuses on the combination of model prediction control and adaptive control, is applied to the energy storage frequency modulation of the virtual synchronous machine, and aims to effectively solve the problem of frequency stability caused by the access of high-proportion renewable energy to a power system.

The invention discloses a transient optimization control method based on a parallel system of a virtual synchronous machine and a grid-following converter. The method comprises the following steps: 1, constructing a topological structure of the parallel system of the virtual synchronous machine and the grid-following converter; 2, based on the topological structure, establishing a mathematical model of a parallel system, including an active loop equation and a reactive loop equation of a virtual synchronous machine and a phase-locked loop equation of a grid-following converter; 3, constructing an equivalent circuit, a network equation and a transient analysis model of the parallel system based on the mathematical model; 4, on the basis of the equivalent circuit, the network equation and the transient analysis model, determining conditions met by respective balance points of the virtual synchronous machine and the grid-following converter by using a balance point method; and 5, when the power grid has a fault, constructing a transient optimization strategy according to a condition satisfied by the balance point so as to realize transient optimization control of the parallel system. According to the method, the control modes of a virtual synchronous machine power loop and a grid-following type converter phase-locked loop can be improved, so that the transient stability of a parallel system converter is improved.

The invention relates to an aircraft high-speed throwing control method and device and electronic equipment, and relates to the technical field of aircraft flight control, and the method comprises the steps: calculating an optimal control instruction sequence in a preset time domain in a rolling manner based on an expected throwing trajectory, a predicted throwing trajectory model and a multi-objective optimization function, and generating a main control quantity, the main control quantity is a control instruction in the optimal control instruction sequence; performing feedforward compensation on the main control quantity based on the disturbance observation result, and performing superposition processing of the sliding mode control quantity on the main control quantity after feedforward compensation based on the disturbance observation result to obtain a superposed main control quantity; high speed throwing of the aircraft cargo is performed based on the superimposed master control quantity. Through application of the scheme, the problem of throwing track deviation caused by posture change and airflow disturbance is effectively solved, it is ensured that the goods can accurately reach the target position according to expectation, and the accuracy of goods throwing is remarkably improved.

The invention relates to a multi-cell control method for hydrogen production alkaline electrolytic cells based on nonlinear model predictive control, which belongs to the technical field of hydrogen production and comprises the following steps of: performing data acquisition on key positions of a hydrogen production alkaline electrolytic cell system in real time; based on the dynamically determined priority of each electrolytic cell, dynamic load distribution of each electrolytic cell is carried out, and a multi-dimensional cooperative regulation strategy is implemented to obtain a hydrogen yield target value of each electrolytic cell; constructing a nonlinear model comprising an electrolytic reaction model, a heat transfer model and an inter-tank coupling model, and performing parameter identification and verification; and designing a nonlinear model predictive control algorithm, performing discretization processing on the nonlinear model, introducing Kalman filtering to process the uncertainty of the model, taking a corresponding hydrogen yield target value reached by each electrolytic cell on the premise of meeting the constraint as a final control target, constructing an optimized target function, and performing solving to obtain an optimal control scheme. Compared with the prior art, the control precision and the system stability can be effectively improved.

The invention discloses a BIM-based hoisting construction supervision optimization management system. The system comprises a sensing layer which collects multi-source heterogeneous data in real time; according to the decision-making layer, a bottom layer utilizes an incremental RRT # algorithm to generate candidate paths meeting crane kinematics constraints, a distributed Q-learning framework is embedded in an upper layer, all cranes serve as independent agents, collaborative learning is carried out through a shared experience pool, a path planning strategy is dynamically optimized according to environment sensing data and construction progress requirements, and a path planning strategy is established. Meanwhile, a fuzzy sliding mode control algorithm is developed to be combined with an LSTM-Transformer crane cart and trolley walking speed, a hook crane cart and trolley walking speed, a hook lifting speed and a steel wire rope disturbance prediction model to calculate crane motion compensation parameters, and an optimal control instruction is generated in advance based on predicted crane moving walking and lifting data; the execution layer is used for issuing the instruction generated by the decision-making layer to construction equipment; and the optimization layer is used for constructing a BIM model and feeding back an equipment execution result and structure safety monitoring data to the decision-making layer.

The invention belongs to the field of robot control, and particularly relates to a reinforcement learning tracking control method of a mobile robot based on event triggering, and the method employs an Actor-Critic synchronous learning algorithm to solve an optimal control strategy, employs interaction data to carry out strategy iteration updating, and introduces an event triggering mechanism. The controller updates the control signal only when the trigger threshold value is met; based on the optimal control theory, the consistent final bounded stability of the four-Mecanum-wheel mobile robot system is analyzed, and the reinforcement learning tracking control method of the four-Mecanum-wheel mobile robot based on event triggering is completed. According to the invention, the trajectory tracking problem of the mobile robot when external disturbances such as sliding exist is solved, and the trajectory tracking precision is ensured under the condition that the calculation burden is effectively reduced.

The invention discloses a ship fuel cell cooling system energy efficiency management method based on deep reinforcement learning, and relates to the technical field of ship energy management, and the method comprises the steps: constructing a digital simulation model of a ship fuel cell cooling system, which is used for representing the dynamic characteristics of the cooling system under different working conditions; constructing a multi-time scale prediction and thermal load prediction model, and respectively outputting cooling system state and working condition prediction parameters and thermal load prediction parameters under different time scales; a reinforcement learning framework based on the dual deep Q network is built, a state space, an action space and a reward function are defined, and the state space comprises current state parameters of the cooling system and output parameters of all prediction models; the action space comprises seawater pump rotating speed, fresh water pump rotating speed and regulating valve opening; and an optimal control strategy is generated based on a training result of deep reinforcement learning, and global collaborative optimization operation of execution equipment of the cooling system is realized, so that the system energy efficiency and environmental adaptability are improved.

The invention provides a high-speed magnetic levitation suspension system control method and system based on edge calculation and Transform prediction, and the method comprises the steps: constructing a Transform prediction model with a space-time attention mechanism and an autoregression mechanism based on obtained local low-delay calculation resources and train real-time sensing data, and deploying the Transform prediction model in a vehicle-mounted edge calculation unit; performing short-term high-precision prediction on the gap, the acceleration and the disturbance trend at a plurality of sampling moments in the future through a Transform prediction model to obtain a prediction result; processing actuator current saturation and gap safety threshold hard constraints in a limited prediction domain by using a model prediction controller, and solving an optimization control sequence in real time in combination with a prediction result; and overlapping a control barrier function as a safety filter of the model prediction controller, correcting the optimized control sequence to obtain an optimal control sequence, and controlling the high-speed magnetic suspension system. According to the method, the cloud communication delay and jitter are reduced, and the robustness and security of the system under uncertain disturbance are improved.

The invention relates to the field of aircraft intelligent control, and discloses a sensor fault resistant low-altitude aircraft multi-mode propulsion control method, which comprises the following steps: constructing a nonlinear model of a short-vertical aircraft hybrid propulsion system, taking deep exploration optimization reinforcement learning as a main control, and combining a fuzzy controller and a model prediction controller to form a strategy fusion group; the method comprises the following steps: introducing a residual perception mechanism and a GIRLS-EKF health estimation module, extracting residual signal features by using CNN and Transform, and carrying out real-time diagnosis and dynamic weight adjustment on an abnormal measurement state of a sensor in combination with generalized residual least square filtering and an extended Kalman structure; the controller fusion module outputs the optimal control quantity in a self-adaptive manner according to the health state and the flight mode, and the fault-tolerant performance and the energy efficiency scheduling capability of the multi-propulsion system under the complex working condition are improved; the method is suitable for low-altitude economy and urban air traffic task scenes, and has good engineering integration and intelligent control application prospects.

The invention belongs to the field of mechanism control, and provides a mechanical arm control method and system based on adaptive adjustment, and the method comprises the steps: coding a plurality of mechanical arm control behaviors into memetic fragments, endowing each memetic fragment with survival fitness, and carrying out the selection, intersection and mutation operation on the memetic fragments based on the survival fitness, generating a control behavior evolution graph; constructing a task semantic parameter map, and dynamically activating or disabling the semantic nodes by a monitoring mechanism according to the state of the current task to generate real-time working condition description of the current task; according to the similarity between the working condition embedding vector and the semantic embedding of each memetic fragment, extracting the memetic fragment most relevant to the current working condition from the control behavior evolution graph to form a candidate memetic factor graph; selecting the control path with the highest score as an optimal control path; and performing fine tuning on the control parameters through a local disturbance mechanism, and taking the fine-tuned control parameters as final optimal motion control parameters.

The invention provides a multi-parameter cold source intelligent control system and method based on big data, and the system comprises a data collection and control module which is used for collecting related parameters of a cold source system; the comprehensive energy consumption standardization module is used for converting the energy consumption parameters into unified standardized energy consumption indexes; the comfort degree algorithm construction module is used for generating a dynamic comfort degree index; the association rule mining module is used for mining a multi-dimensional association rule from historical data; the intelligent decision module is used for generating an optimal control strategy based on the index, the index and the association rule; according to the embodiment of the invention, based on a multi-parameter fusion cold source intelligent control technology of deep reinforcement learning and an improved FP-Growth algorithm, through deep mining and intelligent decision making of multi-source data, the optimal control strategy is converted into equipment regulation and control parameters, and the equipment regulation and control parameters are obtained. And high efficiency and energy conservation of the cold source system and accurate regulation and control of indoor environment comfort are realized.

The invention discloses a ship power system optimization control method and system based on a simulated annealing algorithm, and relates to the technical field of ship power control, and the method comprises the steps: obtaining operation parameters and historical energy consumption data in real time, and constructing a multi-objective optimization function of dynamic weight distribution; generating an initial temperature parameter and a solution set in combination with the navigation state and the environment data; a neighborhood search strategy disturbance solution set is improved, a new solution is evaluated by using a dynamic acceptance probability function, temperature parameters are adaptively adjusted for iterative optimization, and an optimal control parameter combination is output; and an adjustment instruction set is generated after multi-dimensional efficiency verification, and a propulsion device, a generator set and an energy storage module are cooperatively controlled, so that global energy consumption optimization is realized. According to the method, through data driving and intelligent algorithm fusion, energy efficiency and environmental adaptability are improved, and stable operation under complex working conditions is guaranteed. According to the ship power system optimization control method and system based on the simulated annealing algorithm, the energy efficiency level and the environmental adaptability of the ship power system are improved.

The invention discloses a racing car racing control method based on Gaussian process regression enhancement model predictive control, and belongs to the field of unmanned driving system trajectory planning and tracking control. The method mainly comprises the following steps: firstly, constructing a racing car racing random optimal control problem model comprising a state equation, a measurement equation and probability constraints, and secondly, respectively modeling system uncertainty and measurement links by adopting a Gaussian process regression model; the method comprises the following steps: firstly, designing prudent model predictive control of racing car racing and an extended Kalman filter GP-EKF based on Gaussian process regression to realize high-precision state estimation, finally, fusing the GP-EKF and the prudent model predictive control, constructing learning model predictive control GP-EKF-LMPC based on GP-EKF enhancement, and feeding back posterior state estimation to an optimization problem, so as to realize high-precision state estimation. And solving an optimal control instruction of racing car racing in real time. Experiments show that the method can significantly improve the control precision and robustness in a complex scene.

The invention discloses a metallurgical waste gas purification system prediction and optimization method based on artificial intelligence. The method comprises the steps that waste gas component concentration, temperature, pressure, flow and reaction time data in the operation process of a waste gas purification system are collected in real time, and an initial multi-dimensional data sample set is constructed; performing adaptive preprocessing on the initial multi-dimensional data sample set to obtain a standardized input data set; inputting the standardized input data set into a neural operator model for real-time dynamic modeling to obtain state real-time prediction features; performing real-time collaborative optimization by using an improved FOX optimization algorithm based on the prediction features to obtain preliminary optimization control parameters; and implementing dynamic iterative optimization of a self-feedback enhanced optimization strategy to obtain an optimal control parameter set, and feeding back the optimal control parameter set in real time and performing closed-loop regulation. Efficient and accurate optimization control of the waste gas purification system is achieved, the waste gas purification efficiency and stability are remarkably improved, and the system dynamically adapts to complex production working conditions.

The invention discloses a second-level regulation and control method and system for a refrigeration system of a data center, and the method comprises the steps: firstly carrying out the validity verification, type recognition and scale evaluation of a data request, outputting a request type and a task scale, distributing a hardware number, predicting the pre-refrigeration capacity through a pre-refrigeration model, and regulating and controlling the equipment combination of the refrigeration system to complete the pre-refrigeration; a load-energy consumption prediction model is utilized to predict a load curve of each component of a server and convert the load curve into a thermal power consumption curve, a cold load conversion model is utilized to predict a cold load of a data center, a real-time cold quantity difference value is combined to dynamically supplement and adjust a refrigeration system, and multi-stage verification and correction are performed according to temperature, cold quantity and energy efficiency data of a measuring point to generate an optimal control strategy. Finally, the optimal strategy is fed back to the pre-refrigeration model, the load-energy consumption prediction model and the cold load conversion model, and iterative optimization of the models is achieved. The response speed, the cooling capacity distribution precision and the overall energy efficiency of the refrigeration system are improved through task perception preposed pre-cooling, component-level precise regulation and control and closed-loop feedback optimization.