89results about How to "Optimal Control Strategy" patented technology

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 discloses a road traffic signal control system and method, and the method comprises the steps: collecting real-time data through a sensor, a camera and a geomagnetic device, combining historical data with emergency analysis, and predicting a flow mode and a change trend; green light time distribution is dynamically optimized, a fixed timing, inductive and adaptive control strategy is adopted, and traffic light duration and priority adjustment is achieved; through regional cooperation and linkage control, green wave bands and branch intersection red light optimization are set, and peak period traffic signals are intelligently dispatched. A machine learning model is used to predict a flow peak value, environment and accident detection data are combined, a signal lamp period is dynamically adjusted, vehicle waiting time, traffic efficiency, carbon emission and traffic satisfaction are evaluated, and a traffic control strategy is comprehensively optimized. According to the road traffic signal control system and method, the traffic flow in different time periods is predicted by adopting the flow prediction model based on big data analysis and machine learning, and the signal period is adjusted in advance.

The invention discloses an electrical automatic control method and system. The method comprises the following steps: acquiring temperature data and load current data in an electrical cabinet; denoising the temperature data by adopting a wavelet transform algorithm; filtering the load current data by adopting a Kalman filtering algorithm; establishing a temperature rise compensation model based on a long short-term memory (LSTM) neural network, wherein the output of the temperature rise compensation model is a temperature rise predicted value in the electrical cabinet; setting a multi-objective function of temperature rise control in the electrical cabinet; solving by adopting a multi-target ant colony algorithm to obtain an optimal control strategy of the cooling fan; and starting, stopping and speed adjustment of the cooling fan are controlled according to the optimal control strategy of the cooling fan. Aiming at frequent starting and stopping of a cooling fan of an electrical cabinet in the prior art, a temperature rise compensation model is established based on a long short-term memory (LSTM) neural network, a multi-objective optimization function including temperature rise deviation and starting and stopping times of the cooling fan is set, an improved multi-objective ant colony algorithm is adopted for solving, and starting and stopping of the cooling fan are reduced.

The invention discloses an energy consumption optimization control method and system for heating and ventilation equipment, and the method comprises the steps: carrying out the comprehensive evaluation of the energy consumption condition of the heating and ventilation equipment, and recognizing the current energy consumption condition and energy-saving potential of the equipment; the system is connected with heating and ventilation equipment through the Internet of Things technology, and collects parameters of the heating and ventilation equipment in real time. Based on the preprocessed data, establishing energy consumption models of the water chilling unit, the cooling tower, the freezing pump and the cooling pump; building an energy consumption prediction regression model by adopting a decision tree algorithm, and predicting the total energy consumption of the heating and ventilation system in real time; according to the predicted energy consumption data, an optimization control strategy of the heating and ventilation equipment is formulated in combination with a set energy consumption control objective function; and applying the formulated optimization control strategy to the heating and ventilation equipment, carrying out real-time monitoring and feedback, and carrying out iterative optimization on the control strategy according to an actual effect. According to the method, comprehensive evaluation, real-time monitoring and optimal control of the energy consumption of the heating and ventilation equipment can be realized, and a powerful guarantee is provided for energy conservation and emission reduction of a building.

The invention discloses a self-adaptive optimization method for a fan control strategy. The method comprises the following steps: acquiring real-time data of a fan, health state data of equipment in the fan and meteorological data around the fan; performing data preprocessing and fusion on the acquired real-time data, the equipment health state data and the meteorological data to generate processed data; based on the processing data, constructing a deep neural network model, and correcting a power curve of the traditional fan according to the real-time working condition through the constructed deep neural network model; adjusting control parameters of the fan according to the corrected power curve by using a particle swarm optimization algorithm; through a machine learning algorithm, real-time analysis is carried out on the operation data of the fan, and early warning is carried out on potential faults; according to the actual operation effect and the potential fault early warning information of the draught fan, the feedback mechanism is combined, the draught fan control strategy is optimized, the draught fan control strategy can be accurately and dynamically adjusted, and it is ensured that the draught fan is always kept in the optimal working state.

The invention discloses a waterproof composite material production intelligent control system, and relates to the technical field of control systems, the system comprises a data acquisition layer, a standard template generation module, a data processing layer, a Transform-based model core layer and an output layer, the data acquisition layer acquires production data in real time; a data processing standard template is generated through a standard template generation module, standardization processing is conducted on real-time data through a data processing layer, production process modeling and production state analysis are conducted through a Transform model core layer, an analysis result is decoded into a control instruction through an output layer, and production line process parameters are adjusted. The production process is intelligently and optimally controlled by applying the artificial intelligence technology, the intelligent level, the operation efficiency and the product quality of the production line are remarkably improved, meanwhile, the production goals of energy conservation, efficiency improvement and intrinsic safety are achieved through refined management and control and whole-process safety protection, and intelligent and green development of the waterproof composite material industry is promoted.

The invention belongs to the field of thermal management systems, and provides a multi-heat-source thermal management system self-adaptive control method based on reinforcement learning model predictive control, which comprises the steps of environmental state receiving, reward function calculation, strategy optimization, decision correction, objective function optimization and control law construction, temperature error calculation, action optimization and device regulation and control. By fusing strategy optimization of the intelligent agent and action optimization in traditional model predictive control, real-time optimization of control strategies of equipment such as a fan, a water pump and a valve in the cooling system is achieved, and adaptability of reinforcement learning and stability of model predictive control are achieved; by arranging the intelligent agent and the feedback correction module, the prediction model parameters are adjusted according to the actual state of the system, and the robustness and the adaptive capacity of the control system are improved; the control law of rolling optimization output is combined with multiple times of real-time feedback and prediction correction, and efficient temperature control of the system in dynamic change is achieved.

The embodiment of the invention provides a drainage system real-time optimization control method and device, equipment and a medium, and relates to the technical field of drainage systems. The method comprises the steps of obtaining historical data of a drainage system, wherein the historical data comprises historical state data of the drainage system, historical rainfall data of an area where the drainage system is located and historical control data of a plurality of control devices in the drainage system; on the basis of the historical control data, the historical state data, the historical rainfall data and the multiple future random scenes, the overflow quantity of the drainage system in the multiple scenes is determined; determining candidate control strategies of a plurality of control facilities of the drainage system based on the overflow quantity of the various scenes and the occurrence probability corresponding to each scene; and performing optimization processing on the candidate control strategy to obtain an optimized target control strategy. According to the method, the robustness of a real-time control strategy is improved, the phenomena of urban inland inundation and abrupt increase of discharge port overflow when the drainage facility breaks down suddenly are effectively relieved, and better comprehensive benefits are achieved.

The invention discloses an intelligent equipment control system and method based on cloud computing, and relates to the technical field of equipment control. The system comprises a data acquisition and processing module, a user behavior analysis and preference modeling module, a standby energy consumption identification module and a real-time monitoring and adjusting module. The data acquisition and processing module collects historical power consumption data and external environment data of the smart home equipment, obtains a power consumption mode by analyzing the historical power consumption data, and performs historical energy efficiency evaluation in combination with the environment data to form an energy consumption portrait; the user behavior analysis and preference modeling module obtains user historical behavior data and constructs a user preference model; the standby energy consumption identification module identifies equipment with standby energy consumption and generates an energy-saving control scheme in combination with the use time period of the equipment; and the real-time monitoring and adjusting module implements the energy-saving control scheme, acquires the power consumption data and the environment data in real time, performs energy efficiency evaluation, and adjusts the energy-saving scheme according to the evaluation result.

The invention relates to the technical field of heat management, in particular to a multi-objective optimization heat management system, and aims to solve the problems that traditional heat management systems often have limitations in design, mostly can only focus on a single heat dissipation or preheating function, and the capability of comprehensively processing various heat management requirements is lacked. Comprising a temperature sensor array, a heat management execution module, an intelligent control module and a data interaction module, temperature data of key areas in a vehicle are collected in real time, and the system can accurately adjust the temperature environment in the vehicle by combining the real-time working condition of the vehicle, so that the vehicle temperature is controlled. According to the method, the engine, the battery and other key components are always in the most suitable working temperature range, the multi-objective optimization algorithm not only pays attention to the accuracy of temperature adjustment, but also gives consideration to the maximization of the energy utilization efficiency, and the system can reduce the energy consumption as much as possible and reduce the energy consumption of the thermal management system while meeting the temperature adjustment requirement.

The invention discloses a machine tool vibration suppression optimization method and system. The optimization method comprises the steps that a control strategy is generated according to machining parameters; collecting vibration data of key parts of the numerical control machine tool; inputting the vibration data and the current working parameters into a vibration prediction model; and judging whether a vibration result predicted by the vibration prediction model meets machining requirements or not, and if not, adjusting machine tool working parameters in the control strategy by adopting a reinforcement learning algorithm. According to the method, the vibration is suppressed by adopting a method of combining the vibration prediction model and the reinforcement learning algorithm, so that real-time vibration prediction and self-adaptive control are realized, the vibration can be effectively suppressed, the machining precision can be remarkably improved, equipment wear and faults can be reduced, and the service life of a machine tool can be prolonged.

The invention discloses a digital twin city energy management system based on the Internet of Things, which relates to the technical field of intelligent management, comprises a construction module, an analysis module and a management module, constructs a heat supply topological structure, constructs a node flow balance equation and a pressure difference balance equation, obtains a heat supply flow equation set and a theoretical historical load value of a corresponding node, and obtains the theoretical historical load value of the corresponding node; and constructing an urban heat supply digital twinborn model, comparing the theoretical current load value with the corresponding theoretical historical load value, executing a first operation, and sending a solution strategy. Through the digital twin technology, the efficiency and accuracy of energy management are improved, virtual testing and verification are carried out through a digital twin model, operation decisions and control strategies are optimized, through prediction and simulation, energy waste is reduced, the operation cost is reduced, the dynamic adaptive capacity of the system is improved, and through optimization of energy use, the energy management efficiency is improved. And sustainable development and environmental protection targets of cities are supported.

The invention discloses a power transmission line construction equipment parallel control method and device based on Bi-LSTM and DDPG algorithms, and the method comprises the steps: optimizing the weight of a time sequence attention mechanism based on a differential evolution algorithm of a stage mutation strategy, and constructing a Bi-LSTM network model through a feature attention mechanism and the optimized time sequence attention mechanism; inputting the construction equipment state data into the Bi-LSTM network model to obtain a construction equipment state prediction result; and improving a DDPG algorithm based on priority post experience playback, constructing a decision control model by using the improved DDPG algorithm, and inputting a construction equipment state prediction result into the decision control model to obtain an optimal control strategy of the power transmission line construction equipment. According to the method, more accurate construction equipment state prediction is provided through the improved Bi-LSTM network model, and the decision control model constructed by using the improved DDPG algorithm makes a more optimized control strategy by using a state prediction result in combination with deep learning and a strategy gradient method, so that the solution of an optimal decision is realized.

The invention discloses an opening and closing control method and system based on a circuit breaker, relates to the technical field of circuit breakers, and adopts a Python technology to construct a quadratic polynomial function so as to accurately analyze the relationship between an expansion coefficient and temperature, and the process enables the deformation influence of temperature change on a bimetallic strip in the circuit breaker to be comprehensively grasped. The bending deformation of the bimetallic strip at different temperatures can be evaluated in real time based on the electric power data set and the temperature and deformation relation obtained through analysis. By setting a bending deformation threshold and comparing the bending deformation with the threshold, whether an opening mechanism is triggered or not can be judged, after the opening mechanism is triggered, the deformation process of the bimetallic strip is traced back, the upper limit temperature of the bimetallic strip is analyzed and determined, and the thermal response duration is calculated in combination with related material characteristic change data. The step can help to judge whether the thermal response of the bimetallic strip in the circuit breaker is stable or not, and a temperature compensation mechanism is started under the condition that the temperature is unstable, so that the thermal response process is optimized.

The invention provides an inertia adaptive optimization control method for electric propulsion of a hydrogen fuel cell ship, and the method comprises the steps: building an equivalent output model of each power source during the steady-state operation of the hydrogen fuel cell ship, taking the equivalent model as a mixed potential function, and analyzing a system signal stability criterion; inertial parameters and damping parameters required by the system based on the mixed potential function are obtained by inputting ship working conditions and hydrogen fuel cell working conditions; power output is realized by performing frequency division control on demanded power in the system; and an energy storage unit in the system is controlled by a virtual direct current machine, so that the inertia requirement of the system is realized, and the dynamic characteristic of the system is improved.

The invention discloses a control method and system for a self-suction lock of an electric tail door and a storage medium, and the method comprises the steps: respectively obtaining the motor operation duration of the self-suction lock of the electric tail door and the motor torque and motor current at each collection moment, calculating the torque deviation, carrying out the first wear clamping stagnation analysis in combination with the motor operation duration, and obtaining a second wear clamping stagnation analysis result; calculating current deviation, carrying out second wear clamping stagnation analysis by combining the motor current at the current acquisition moment and the motor operation duration to obtain a motor current compensation coefficient, and carrying out comparative analysis on the motor torque compensation coefficient and the motor current compensation coefficient to obtain a self-suction lock compensation coefficient; therefore, the state control parameters of the self-suction lock can be adjusted. According to the control method and system for the self-suction lock of the electric tail door and the storage medium provided by the embodiment of the invention, the control effect on the self-suction lock of the electric tail door is improved.

The invention relates to the technical field of flue gas treatment, and particularly provides a coal-fired power plant intelligent environmental protection island platform management system, which comprises a sensing layer, an application layer and a decision-making layer, and is characterized in that the sensing layer comprises a plurality of data acquisition devices and is used for collecting data of all working devices in an environmental protection island; the application layer comprises a flue gas management and control module, and the flue gas management and control module establishes a flue gas management and control prediction model based on the data collected by the sensing layer and historical data records; and the decision-making layer determines an optimal collaboration strategy of the environmental protection island based on the model and data established by the application layer. According to the environment-friendly island platform management system provided by the invention, parameter information is obtained through the sensing layer, modeling is performed through the application layer, emission prediction is realized, the application layer uploads a prediction model and sensing layer parameters to the decision-making layer, the decision-making layer performs particle swarm global optimization algorithm on output parameters and each model, and the minimum cost is taken as a target. And the range of the parameters and the pollutant emission limit value are adjusted as constraint conditions, and a control strategy of the optimal system is obtained.

The invention belongs to the field of wastewater treatment and industrial automatic control, and particularly relates to an intelligent dosing control method based on water quality online monitoring. The method comprises the following steps: capturing a water body spectrum fingerprint spectrum by using a full-spectrum scanning module to obtain a characteristic signal sequence; a chemical informatics molecular algorithm is introduced for preprocessing and feature enhancement, and molecular fingerprint information is recognized; a convolutional neural network model is constructed to decouple chemical oxygen demand, ammonia nitrogen, total phosphorus and water toxicity indexes from the spectral signals, and multi-parameter parallel inversion is achieved; and based on the inversion index and the spectral dynamic trend, the dosage is calculated through an intelligent decision-making algorithm, and accurate dosing of the medicament is driven. According to the invention, through fusion of full spectrum scanning and deep learning, high-precision anti-interference water quality perception is realized, potential risks can be identified and preventive intervention can be carried out, and the intelligent level and economical efficiency of dosing control are improved.

The invention discloses a power grid frequency cooperative control method and system. The method comprises the steps that a first target model is established, wherein the first target model at least comprises one or more sets of traditional units, wind turbine units or energy storage systems; establishing a target function and a constraint condition based on a first improved algorithm controller; and solving the first target model according to the target function and the constraint condition. According to the method, the advantages of reinforcement learning can be utilized, the control strategy is continuously optimized through interactive learning of the intelligent agent and the environment, and the adaptability and robustness of the power grid are improved. By setting the environment and the intelligent agent based on the Markov decision process, the actual power grid operation condition can be simulated, the intelligent agent can effectively deal with various load disturbances through learning and decision making, and stable operation of the power grid is guaranteed.

The invention discloses a crawler belt tensity control method of a crawler belt type combined harvester for soil compaction and reduction, belongs to the field of agricultural machinery control, and aims to solve the problem that the existing control on the soil compaction degree lacks self-adaptability and flexibility. The method comprises the following steps that 1, the current soil compaction degree is obtained based on monitoring data of a soil compaction monitoring sensor group; 2, according to the error, obtained in the step 1, between the current soil compaction degree and the target soil compaction degree, the crawler belt tension degree adjustment amount is obtained through a PID control algorithm; and step 3, the actuator adjusts the crawler belt in a stepping mode according to the crawler belt tensity adjustment amount obtained in the step 2 until the crawler belt tensity target value is reached.

The invention relates to the technical field of steelmaking automation, in particular to an intelligent control system and method for a high-purity molten steel smelting converter, and the system comprises a data collection module which is used for collecting operation parameters related to the converter during molten steel smelting, and the operation parameters comprise molten steel temperature, gas components, oxygen amount and blowing time; the curve generation module is used for determining a change curve of the corresponding operation parameters during molten steel smelting according to the obtained operation parameters, and the change curve comprises a change curve corresponding to the molten steel temperature, the gas composition, the oxygen amount and the blowing time; the component display module is used for analyzing and processing the change curve according to the identified change curve of the operating parameters, and determining the occurrence probability of the operating parameters and a target value corresponding to the occurrence probability when the purity of the molten steel meets the requirement; and the production efficiency and the stability in the molten steel smelting process are improved.

The invention belongs to the technical field of vibration control, and relates to a sensor arrangement and feedback control algorithm joint optimization method in structural vibration control, which adopts a continuous time recurrent neural network CTRNN as a basic neuron, and adopts an NEAT algorithm to optimize a network structure to obtain an optimal network structure; the optimal network structure serves as a control strategy of the vibration control system, the positions of the sensors are obtained, when external excitation is applied to the structure, the optimal control force is output by the optimal network structure through state response measured by the sensors at the corresponding positions, and then an execution mechanism is controlled to generate the corresponding control force. According to the invention, the position of the sensor can be accurately optimized, a neural network architecture with the simplest structure is evolved, and the overall efficiency of the system is improved.

The invention provides a multi-agent system mean square consistency control method under hybrid attacks, which relates to the technical field of intelligent control, and comprises the following steps: establishing a multi-agent system model, designing a controller, and on this basis, constructing an error system between a follower agent and a leader, and establishing a mean square consistency condition of the multi-agent system under the spoofing attack and the DOS attack, thereby controlling the multi-agent system to realize leader following mean square consistency. According to the method, the control strategy is improved by comprehensively considering the spoofing attack and the DOS attack possibly existing in the working period of the multi-agent system, so that the mean square bounded consistency of the leader-follower is realized, and the control efficiency and the control precision are improved.

The invention discloses an intelligent control method and system for a large-span cross-business-line horizontal swivel, and is applied to the technical field of large-span bridge construction. The method comprises the following steps: S1, a data acquisition step: controlling a bridge to carry out swivel operation and monitoring relevant parameters of bridge swivel in real time through preset sensor equipment; s2, a data processing step: carrying out attitude calculation by utilizing the relevant parameters of the bridge swivel obtained in the S1, determining the state of the current bridge swivel, and judging whether the state of the current bridge swivel is abnormal or not; and S3, a swivel intelligent control step: calculating a bridge swivel control instruction by adopting a preset control algorithm, and accurately controlling the bridge swivel according to the bridge swivel control instruction. The bridge swivel is accurately controlled through an intelligent control algorithm, the control strategy is optimized, and the response speed and the control precision of the system are improved.

This invention discloses an intelligent combustion control method and system based on online fiber optic detection of flame signals. The method monitors the flame light signal emitted by the burner via fiber optic cable, analyzes the signal to predict pollutant concentrations, and performs fuzzy adjustment and defuzzification on the burner's feedback signal based on the predicted values, thereby achieving intelligent and adaptive control of the combustion process. This method can quickly respond to changes in combustion conditions, optimize control strategies, ensure the stability and environmental friendliness of the combustion process, overcome the technical bottlenecks of high cost and poor real-time performance, effectively reduce system hardware costs, and improve the intelligence level of the control process.

This invention discloses an intelligent model predictive control method for intensive mixing processes based on reinforcement learning. Addressing the degradation of control performance caused by model mismatch due to modeling errors, abrupt state changes, and component degradation in traditional model predictive control, this invention introduces reinforcement learning into the model predictive control framework. The method designs an intelligent model predictive control approach based on reinforcement learning to solve a standard quadratic programming problem with respect to optimization variables. Reinforcement learning selects compensation terms to compensate for model deviations caused by model mismatch, combining this with the controller output and applying it to the system. Finally, the reward is calculated and updated to optimize the control compensation selection for future time steps. As the control cycle progresses, new information is continuously integrated, model predictions are updated, and control inputs are optimized to adapt to potential changes in system behavior or external disturbances. This improves system stability and control accuracy, ensuring that the intensive mixing process can track a preset trajectory.

The application discloses a kind of based on edge-end cooperation's continuous ridge greenhouse environment control system and method, system includes: terminal perception execution layer contains multimodal sensor network and intelligent execution mechanism group;Edge intelligent control layer specifically includes: multi-source data fusion module, for fusing the measurement value of multimodal sensor network, output environment state optimal estimation value;Model predictive control module is used as input with environment state optimal estimation value and weather forecast, solves optimal control sequence in future time domain by rolling optimization, and issues control instruction to intelligent execution mechanism group;Fault diagnosis and fault-tolerant control module is used to obtain the current feedback value, position feedback value and / or angle feedback value of execution mechanism in real time, and compare with the instruction reference issued by model predictive control module, execute fault diagnosis and hierarchical fault tolerance.The application is based on the three-module closed-loop architecture of edge-end cooperation, realizes the intelligent fault tolerance of execution mechanism and the accurate control to continuous ridge greenhouse environment.

The invention belongs to the field of flexible interconnection system control, and particularly discloses a control method based on a current type multi-port flexible interconnection system, the method is applied to the flexible interconnection system, and the method comprises the following steps: detecting first electrical load data of each port of the system, transmitting the first electrical load data to an energy management system for storage for user reference; first power utilization load data are read from the energy management system, calculation is carried out based on a control algorithm and the first power utilization load data, control parameters of each port are obtained, and the control algorithm is set by a user based on requirements of a power distribution and utilization system; according to the control parameters, current type converters of all the ports are controlled to operate, so that flexible interconnection between all the ports is achieved; and performing analysis according to the real-time electric energy flexible scheduling data and the control parameters of each port to obtain information required by the power distribution and utilization system. Multi-port electric energy flexible scheduling is achieved through the current type multi-port flexible interconnection system, and the electric energy requirement of a power distribution and utilization system is met.

This invention discloses a fuel cell energy management optimization method, comprising the following steps: 1) The fuel cell energy management system maximizes the utilization of fuel cell energy by adjusting and optimizing system parameters; 2) Modeling the fuel cell energy management system, including the stack, battery management system, and DC / DC converter; 3) Simulating and analyzing the fuel cell energy management system, considering the system's operating state, load requirements, and environmental conditions; 4) Optimizing and analyzing the simulation results to obtain the optimal parameter configuration and control strategy; 4-1) Optimization analysis of the fuel cell energy management system; 4-2) Adjustment analysis of the fuel cell energy management strategy; 4-3) Adjustment analysis of the fuel cell energy management system.

The application discloses a steady-state analysis method for a land lightweight converter station single-end sending-out system, and aims at the problems that the existing DRU-MMC DC power transmission system steady-state analysis cannot realize two-side collaborative modeling, voltage / current / harmonic unified calculation and result connection, constructs a 12-pulse DRU-DC overhead line-MMC point-to-point power transmission system topology, establishes a rectifier-side DRU steady-state mathematical model to solve voltage, current and harmonic characteristics, and constructs an inverter-side MMC steady-state model to analyze sub-module capacitor voltage three components and bridge arm circulating current characteristics; through power balance constraints, two-side model collaborative solving is realized, unified calculation of key electrical quantities is realized, adaptability evaluation is carried out from three aspects of voltage, current and harmonic, and unified expression and quantitative connection of two-side steady-state results are completed.