19 results about "Optimal control system" patented technology

The invention provides a greenhouse water, fertilizer and pesticide optimization control system and method based on environment-crop-input multi-element coupling, and the method comprises the steps: deploying a sensor network, collecting greenhouse data, processing the greenhouse data, generating a space-time continuous data set, and carrying out the optimization control of the greenhouse water, fertilizer and pesticide on the basis of the generated space-time continuous data set. And quantifying a dynamic causal chain of the environment, the crops and the input, generating a decision rule base, fusing the decision rule base with a real-time simulation result of the three-dimensional digital twinborn model of the root zone, and generating a water, pesticide and fertilizer regulation and control instruction. Space-time continuous acquisition and fusion processing of greenhouse environment-crop-input product data are realized through a multi-modal sensor network, a decision rule base containing a dynamic causal chain is constructed, real-time simulation of a root zone three-dimensional digital twinborn model and Bayesian causal reasoning are combined, an accurate water, fertilizer and pesticide regulation and control instruction is generated, and the accuracy of the water, fertilizer and pesticide regulation and control is improved. The nonlinear causal relationship among multiple elements is effectively quantified, and the decision scientificity is improved.

The invention relates to the technical field of source network load storage cooperative control, in particular to an area source network load storage cooperative optimization control system based on an intelligent fusion terminal, and the system comprises an acquisition module which acquires energy characteristic parameters and cooperative response characteristic parameters of a target area in a historical period; analyzing an energy characteristic characterization value based on the energy characteristic parameter through an analysis module, and calculating a collaborative response characteristic characterization value based on the collaborative response characteristic parameter; determining a corresponding control strategy based on the energy feature representation value through a control module; through a verification module, whether the stability of the transformer area source network load storage meets the standard is judged based on the collaborative response characteristic representation value; and through a feedback module, based on a difference value between the energy feature representation value and a predetermined energy feature representation threshold, determining an adjustment amplitude of the cooperative response feature representation threshold, and performing collection and verification again. According to the invention, through the verification module and the feedback module, the response rate of cooperative control of each link of source network load storage is improved.

The invention relates to the technical field of magneto-rheological damper regulation and control, and discloses a reinforcement learning adaptive optimal regulation and control system of a magneto-rheological damper, which comprises a magneto-rheological damper body, the body is provided with a magnet exciting coil, a magneto-rheological fluid channel, a piston rod and a cylinder barrel, after the magnet exciting coil is electrified, a controllable magnetic field is established in the magneto-rheological fluid channel; the magnetic field intensity is continuously adjustable, the sensing network is composed of a displacement sensing unit, a speed sensing unit, an acceleration sensing unit, a temperature sensing unit and a magnetic field intensity sensing unit, and all the sensing units are fixed to the outer wall of the cylinder barrel in a non-contact mode and are coaxially arranged with the movement axis of the piston rod. A sensing network, an edge calculation node, a reinforcement learning decision-making unit and an online evolution engine are embedded in a heat conduction substrate and a shielding cover on the outer wall of the same cylinder barrel, the displacement angle and acceleration response between structural layers are remarkably reduced, and advanced protection that vibration is not displayed and damping is changed is achieved.

The invention discloses a multi-agent-based optimal control system for building equipment. The system comprises an equipment layer, an edge layer and a cloud platform layer, the invention relates to the technical field of building energy equipment adjustment. According to the multi-agent-based optimal control system for the building equipment, through node division and acquisition of a personnel thermal image, judgment that the current node needs to be heated, cooled or maintained at the current temperature is achieved, a reference is provided for indoor temperature control, and through small-gradient and large-gradient temperature change control, the optimal control effect is achieved. The energy consumption analysis model is used for visually analyzing the energy consumption generated by adjusting the indoor environment temperature while effectively adapting to the indoor environment temperature change and reducing the irritation of the environment temperature change to the human body, flexible energy consumption regulation and control are performed in cooperation with the electricity price time period, flexible control conditions are provided for energy conservation and comfort level balance, and the energy-saving effect is improved. And optimal control of the intelligent equipment on the building is realized.

The application discloses an online optimization control system for realizing optimal efficiency of a motor in a wide rotating speed range, and relates to the technical field of motor control. The application discloses an online optimization control system for realizing optimal efficiency of a motor in a wide rotating speed range, and relates to the technical field of motor control. The application discloses an online optimization control system for realizing optimal efficiency of a motor in a wide rotating speed range, and relates to the technical field of motor control. The application discloses an online optimization control system for realizing optimal efficiency of a motor in a wide rotating speed range, and relates to the technical field of motor control. The application discloses an online optimization control system for realizing optimal efficiency of a motor in a wide rotating speed range, and relates to the technical field of motor control. The application discloses an online optimization control system for realizing optimal efficiency of a motor in a wide rotating speed range, and relates to the technical field of motor control. The application discloses an online optimization control system for realizing optimal efficiency of a motor in a wide rotating speed range, and relates to the technical field of motor control. The application discloses an online optimization control system for realizing optimal efficiency of a motor in a wide rotating speed range, and relates to the technical field of motor control. The application discloses an online optimization control system for realizing optimal efficiency of a motor in a wide rotating speed range, and relates to the technical field of motor control. The application discloses an online optimization control system for realizing optimal efficiency of a motor in a wide rotating speed range, and relates to the technical field of motor control. The application discloses an online optimization control system for realizing optimal efficiency of a motor in a wide rotating speed range, and relates to the technical field of motor control. The application discloses an online optimization control system for realizing optimal efficiency of a motor in a wide rotating speed range, and relates to the technical field of motor control. The application discloses an online optimization control system for realizing optimal efficiency of a motor in a

The invention discloses a sewage treatment process optimization control system based on ASM2 and BP models. The system comprises a data acquisition module for acquiring sewage parameters; the ASM2 mechanism modeling module is used for constructing an ASM2 dynamic mechanism model to simulate organic matter degradation, nitrification and denitrification, biological phosphorus removal and sludge generation and attenuation processes, and determining key state variables; the BP neural network prediction module obtains a joint prediction value of the effluent quality and the sludge yield according to the key state variable; the sludge reduction prediction and control module dynamically generates a sludge reduction control strategy on the basis of the joint prediction value in combination with a sludge generation mechanism of an ASM2 dynamic mechanism model; and the optimization control module optimizes the treatment process through a control strategy. According to the invention, the effluent quality prediction precision can be improved, stable and standard reaching is ensured, the strategy of dynamically optimizing the aeration rate, the reflux ratio and the sludge discharge rate is realized, the energy consumption is reduced, a sludge yield prediction model is established, and sludge reduction is realized.

The invention discloses an intelligent analysis system for public health decision and trend prediction. The intelligent analysis system comprises the following steps: establishing a random optimal control system state equation containing a drift term, a diffusion term and a control function, and an objective function; a disturbance term is introduced into the state equation, and an original optimal control problem is converted into a random differential game problem; constructing a smooth trajectory estimation function based on the observation data, and defining a joint objective function; solving a random differential game problem to obtain an optimal control function and a system parameter estimator; the method is applied to a random control SIRD infectious disease model, and dynamic estimation of time-varying parameters is achieved; and on the basis of an estimation result, predicting a future disease transmission trend, and providing decision support for prospective planning and accurate configuration of public health resources. According to the method, a system with high noise and time-varying parameters can be effectively processed, the prediction precision and robustness are improved, and a quantitative basis is provided for prevention and control of infectious diseases.

The application provides an optimization control method and an optimization control system for a refrigerating device of a liquid flow energy storage system. The optimization control method comprises: obtaining historical operation data of the liquid flow energy storage system under multiple complete charge-discharge cycles; constructing a regression model based on the historical operation data, the regression model comprising an output parameter and multiple input parameters, the multiple input parameters comprising a starting temperature of electrolyte in a charging stage, a set frequency of the refrigerating device in a constant-power charging stage, a set frequency of the refrigerating device in a constant-voltage charging stage, and a set temperature of electrolyte in a discharging stage, and the output parameter comprising an overall energy conversion efficiency of the liquid flow energy storage system; in the historical operation data, obtaining a data combination of the multiple input parameters corresponding to the highest overall energy conversion efficiency; obtaining an optimized combination of the input parameters based on the data combination and the regression model; and determining a control strategy of the refrigerating device according to the optimized combination.

This invention provides a greenhouse water, fertilizer, and pesticide optimization control system and method based on multi-factor coupling of environment, crop, and inputs. The system includes deploying a sensor network to collect greenhouse data, processing the data to generate a spatiotemporally continuous dataset, quantifying the dynamic causal chain of environment, crop, and inputs based on the generated dataset, generating a decision rule base, and fusing the decision rule base with the real-time simulation results of a root zone three-dimensional digital twin model to generate water, fertilizer, and pesticide control commands. This invention achieves continuous spatiotemporal acquisition and fusion processing of greenhouse environment, crop, and input data through a multimodal sensor network, constructs a decision rule base containing dynamic causal chains, and combines real-time simulation of the root zone three-dimensional digital twin model with Bayesian causal inference to generate precise water, fertilizer, and pesticide control commands. This effectively quantifies the nonlinear causal relationships between multiple factors and improves the scientific nature of decision-making.

The invention discloses an optimal control system and method for the hydrogen content in oxygen of an alkaline water electrolysis hydrogen production system. According to the optimal control system, a cooling water flow regulating valve, an alkali liquor heat exchanger, an alkali liquor circulating pump, a heater, an alkali liquor flow meter, an alkali liquor flow regulating valve, a temperature transmitter, a pressure transmitter, a hydrogen-in-oxygen detection instrument, an oxygen outlet film regulating valve, a vacuum degassing device and a controller are correspondingly arranged on an alkali water electrolysis hydrogen production system; key technological parameters such as current density, alkali liquor flow, tank temperature and system pressure in the operation process of the alkaline water electrolysis hydrogen production system are integrally regulated and controlled, and meanwhile hydrogen bubbles dissolved in alkali liquor in an alkali liquor circulation system are cooperatively treated, so that the hydrogen content in finally output oxygen is further greatly reduced, the efficiency of reducing the hydrogen content in the oxygen by the system is remarkably improved, and the energy consumption of the system is reduced. The safety of the system is ensured, and the performance of the system is improved.

The invention provides an optimal control system and a decision-making method for thermal power generating unit peak regulation, and relates to the technical field of machine learning, and the method comprises the steps: collecting and analyzing the operation characteristic value and peak regulation performance data of a thermal power generating unit, training a neural network model capable of predicting the peak regulation performance of the thermal power generating unit through machine learning, thermal power units with similar operation characteristics are clustered by using a clustering algorithm, a data mining algorithm is used to analyze association reasons of poor peak load regulation performance of the thermal power units, implicit rules and modes between the peak load regulation performance of the thermal power units and operation of the thermal power units are mined, and control management of the thermal power units is realized; the system combines a working condition data acquisition module, a peak regulation performance prediction module, a working condition mode clustering module and a peak regulation decision mining module, collects thermal power generating unit data in real time, rapidly predicts the peak regulation performance and analyzes association reasons; the performance association reason of the thermal power generating unit is mined, and a thought is provided for optimization decision making.

The invention discloses an unmanned aerial vehicle / unmanned vehicle cluster following formation search method based on protopigeon foraging. The method comprises the steps of 1, modeling and parameter initialization of an unmanned aerial vehicle and unmanned vehicle heterogeneous system; 2, designing a disturbance observer under the unmanned aerial vehicle and unmanned vehicle heterogeneous system pilot following mode formation; 3, designing a controller under the unmanned aerial vehicle and unmanned vehicle heterogeneous system pilot following mode formation; 4, designing a formation search reference trajectory of the unmanned aerial vehicle and unmanned vehicle heterogeneous system; and the like. According to the method, the problem of unified modeling of different dimensions of the heterogeneous system is solved; the method comprises the following steps of: designing a state observer and a formation controller in a pilot following mode by considering possible disturbance of an unmanned aerial vehicle and an unmanned vehicle system in an actual condition; aiming at the formation coverage search problem, designing a heterogeneous system S-shaped formation search reference trajectory; a pilot following mode formation target constraint equation is established, a protopigeon foraging mechanism is provided to solve the established target constraint equation, and optimal control system parameters are obtained.

The invention discloses a power distribution network fault rapid recovery optimization control system based on a low-voltage flexible interconnection device, and relates to the technical field of power grid fault optimization control, and the system comprises the following modules: a main network operation state prediction module which respectively obtains the voltage, current, phase and frequency of each line of a main network, and obtains a power grid operation state prediction module; a fault risk line is captured in main network lines through parameter periodicity judgment in each line of a main network, an off-network island data adjustment module is executed according to the fault risk line, a involved line is obtained according to a fault result of the fault risk line, and a involved database is established through the involved line; according to the scheme, the difference of voltage, phase and frequency of a grid-connected line in a main network and an off-grid island is calculated in an off-grid island data adjustment module, and an adjustment strategy is formulated according to a predicted fault line risk level; therefore, quick adjustment is facilitated, off-network switching is facilitated, and meanwhile, off-network island electronic equipment damage caused by frequent and large-amplitude adjustment of the catastrophe is avoided.

The invention relates to the technical field of ventilation equipment, in particular to a temperature control method and device, and the method comprises the steps: taking the temperature of a heating sheet of ventilation equipment as a control target temperature value, taking the real-time temperature of the heating sheet as the input of a model-free adaptive control system MFAC, and obtaining the output control power of the heating sheet; the temperature control system is an optimal control system and aims to converge the system output to zero, so that the temperature error is used as the input of the incremental compensation system, the system output is used for compensating the model-free control system MFAC, and the current total control quantity is the sum of the MFAC control quantity and the incremental compensation control quantity. According to the method, the advantage that the MFAC does not need to construct a precise mathematical model and the advantage of real-time compensation of the incremental compensation controller are combined, the change of the set temperature of the temperature control system can be quickly tracked, the temperature of the temperature control system reaches the temperature set value range, and the control precision and the response speed of temperature control of the ventilation equipment are improved.

The invention relates to the technical field of greenhouse environment control, and provides a greenhouse environment man-machine cooperation optimal control system, which comprises a cloud platform, a local controller and a sensor, the cloud platform is used for implementing remote control, control decision support and hosting control of the greenhouse environment adjusting equipment; the local controller is used for implementing manual control and automatic control of the greenhouse environment adjusting equipment; the sensor is used for acquiring environment data in real time; wherein the cloud platform comprises a decision support module, a hosting control module and an optimal control generation module; the decision support module is used for pushing decision support information of greenhouse environment control to the planter; the trusteeship control module is used for sending a trusteeship control instruction aiming at the automatic control environment adjusting equipment to the local controller; and the optimal control generation module is used for deploying a greenhouse environment man-machine cooperation optimal control algorithm. According to the greenhouse environment man-machine cooperation optimal control system, optimal control over the greenhouse environment can be achieved, and the economic performance of greenhouse production is improved.

The present application relates to source network load storage collaborative control technical field, especially to a kind of based on intelligent fusion terminal's table area source network load storage collaborative optimization control system, the present application is collected by acquisition module, collects the energy characteristic parameter and collaborative response characteristic parameter of target table area historical period;Through analysis module, based on the energy characteristic parameter analysis energy characteristic representation value, based on the collaborative response characteristic parameter calculates collaborative response characteristic representation value;Through control module, based on the energy characteristic representation value determines corresponding control strategy;Through verification module, based on the collaborative response characteristic representation value determines whether the stability of table area source network load storage meets standard;Through feedback module, based on the difference between energy characteristic representation value and predetermined energy characteristic representation threshold determines the adjustment range of collaborative response characteristic representation threshold, and reacquire verification.The present application improves the response rate of source network load storage each link collaborative control by verification module and feedback module.

The invention discloses an on-line optimization control system for realizing the optimal efficiency of a motor in a wide rotating speed interval, and relates to the technical field of motor control, and the system comprises the steps: collecting the data of a DC bus and a torsion bar, and calculating the back-and-forth energy storage power of the torsion bar and the proportion index thereof; eliminating a component synchronized with the torsion bar reciprocating energy storage power from the DC side input power by using the projection correlation coefficient to obtain a residual power evaluation quantity; alternating disturbance is applied to the direct-axis current reference value, the optimizing direction is determined according to the difference of the residual power evaluation quantity in the disturbance period, and a self-adaptive weight is generated through a proportion index to restrain the updating amplitude. The method can effectively eliminate the power fluctuation interference caused by the release of the elastic potential energy of the torsion bar, and guarantees the stability and convergence precision of the optimization direction under the dynamic working condition.

The application provides an optimization control method and an optimization control system for a refrigerating device of a liquid flow energy storage system. The optimization control method comprises: obtaining historical operation data of the liquid flow energy storage system under multiple complete charge-discharge cycles; constructing a regression model based on the historical operation data, the regression model comprising an output parameter and multiple input parameters, the multiple input parameters comprising a starting temperature of electrolyte in a charging stage, a set frequency of the refrigerating device in a constant-power charging stage, a set frequency of the refrigerating device in a constant-voltage charging stage, and a set temperature of electrolyte in a discharging stage, and the output parameter comprising an overall energy conversion efficiency of the liquid flow energy storage system; in the historical operation data, obtaining a data combination of the multiple input parameters corresponding to the highest overall energy conversion efficiency; obtaining an optimized combination of the input parameters based on the data combination and the regression model; and determining a control strategy of the refrigerating device according to the optimized combination.

The invention belongs to the technical field of nitriding furnace process control, and particularly relates to an optimal control system for nitriding process gas and a control method thereof. The optimization control system for the nitriding process gas comprises a control unit, a furnace pressure detection unit, a flow meter and an exhaust valve, the control unit is respectively connected with the furnace pressure detection unit, the flow meter and the exhaust valve; the furnace pressure detection unit is connected with the nitriding furnace; the flow meter is connected with a nitriding process gas inlet of the nitriding furnace; and the exhaust valve is connected with a waste gas outlet of the nitriding furnace. According to the optimal control system for the nitriding process gas, the control unit is connected with the furnace pressure detection unit, the flow meter and the exhaust valve, so that the furnace pressure in the nitriding furnace is maintained within the preset range, and waste of the nitriding process gas is reduced.