1931 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 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 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 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 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.

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 a quantum gate simulation and control parameter optimization method based on numerical calculation, and the method sequentially comprises the following three modules: a model Hamiltonian matrix representation module can calculate the matrix representation of a model Hamiltonian according to a specific problem; a time evolution and fidelity calculation module of the system discretely solves a Schrodinger equation by using a Hamiltonian matrix, gives a corresponding time evolution matrix, and calculates gate operation fidelity by using the obtained time evolution matrix; and the control parameter optimization module calls the first two modules, optimizes control parameters and quantum gate operation time by adopting a local optimization algorithm or a global optimization algorithm, and gives optimal control parameters and gate operation time. The problem that the gate fidelity of a current superconducting quantum computing system cannot meet the requirement for reliable operation of a practical quantum algorithm is solved, the detailed process of quantum gate operation can be simulated through a numerical calculation method, and the quantum gate operation fidelity is improved by optimizing control parameters.

The invention discloses a deep sea deduction scene course path intelligent planning system based on an optimal control theory, and relates to the technical field of course path planning. The data management platform is in communication connection with an environment perception modeling module, a constraint condition definition module, a global path planning module, a simulation verification module, a control execution module and a feedback adjustment module. By receiving and fusing deep sea environment data and equipment state data in real time, position deviation, speed deviation and course deviation in the sailing process can be accurately detected and analyzed, and based on the optimal control theory, the system can quickly formulate an adjustment strategy to ensure that equipment is always kept in a safe sailing range, so that the sailing safety is improved. The real-time feedback and adjustment mechanism greatly improves the safety and reliability of navigation, effectively avoids the risk of colliding with obstacles or entering a dangerous area, and provides a solid guarantee for deep sea operation.

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 an intelligent building equipment control method and system, and relates to the technical field of intelligent building equipment control, and the method comprises the steps: fusing the multi-mode association topology of two groups of data through employing a Kruskal algorithm, constructing a minimum spanning tree, setting the reciprocal of an edge weight as a pheromone value, and selecting an optimal control path through employing a maximum flow algorithm-ant colony algorithm. The method comprises the following steps: converting phase space data by using a Takens embedding method, randomly selecting a pair of adjacent phase space data from a time sequence as initial points, analyzing the state of building equipment by using a Lyapunov index, constructing a hidden Markov model, and dynamically correcting a transition probability matrix and a transmission probability matrix by using the Lyapunov index to obtain the predicted state of the building equipment. The optimal control path is selected by constructing the minimum spanning tree and combining the maximum flow algorithm-ant colony algorithm, the resource utilization rate and the control precision are improved, and the comprehensiveness and adaptability of equipment control are enhanced based on the combination of the hidden Markov model and the Lyapunov index.

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 discloses a heat-pump water heater temperature control method based on artificial intelligence, relates to the technical field of heat-pump water heater temperature control, and aims to solve the problem that the COP (coefficient of performance) is reduced due to a low-temperature environment, the exhaust temperature of a compressor and the opening degree of an expansion valve are optimized in real time through a GA (genetic algorithm), and operating parameters of a heat-pump water heater are dynamically adjusted by taking COP maximization as a target; the operation frequency of a compressor and the flow of a water pump are dynamically adjusted through fuzzy control logic in combination with a GA optimization result, the current water temperature and the temperature set by a user, the stability of the water supply temperature is guaranteed, an optimal control strategy is calculated through defuzzification, and the adaptability under different environment conditions is improved; an ANN is adopted to learn and predict a user behavior pattern, an LSTM network is utilized to analyze time sequences of historical water consumption data, target temperature and environment temperature, future water consumption peak and temperature requirements are predicted, a preheating function is started in advance, and insufficient hot water supply in the water consumption peak is avoided.