32 results about "Control quality" patented technology

The invention relates to the technical field of heat pump drying control, and discloses a dynamic identification control method of a closed heat pump drying system based on waste heat recovery. The method comprises the following steps: establishing a multi-level parameter monitoring network by arranging a sensor array, and continuously collecting system operation parameters to form time sequence data; constructing a waste heat distribution characteristic spectrum, and mapping the collected parameters to a thermodynamic state space to identify a coupling relationship among the parameters; analyzing a heat transfer path to determine a key control variable, and establishing a dynamic weight adjustment mechanism to generate a parameter priority sequence; and a multi-target coordination control strategy is made based on the sequence and the real-time operation state, and an actuator control instruction is output. According to the method, through global energy state recognition and dynamic priority adjustment, adaptive optimization operation of the system under different working conditions is realized, and the energy utilization efficiency and the control quality are improved.

The invention discloses a pressurized water reactor steam generator liquid level control method and system, and belongs to the technical field of nuclear power station control. The method comprises the steps that a liquid level set value and a measured value of the steam generator are obtained, and deviation is calculated; inputting the set value, the measured value, the deviation and the bias constant into a preset BP neural network; the network dynamically outputs three coefficients of proportion, integral and differential through forward propagation to serve as real-time parameters of a PID (Proportion Integration Differentiation) controller; the PID controller calculates and outputs a water supply flow demand value, and a water supply valve is adjusted after conversion; and calculating a root-mean-square error of liquid level control as a loss function, and updating the weight of the neural network on line by using the new liquid level deviation through a back propagation algorithm to realize adaptive optimization of PID parameters. According to the invention, the self-learning ability of the BP neural network is combined with the reliability of PID control, the control quality and stability of the liquid level of the steam generator under various power levels are significantly improved, the system can be deployed in a power plant DCS through modular packaging, and the engineering practicability is high.

The invention relates to the technical field of flow control, in particular to an oxygen inhalation terminal flow balance control system which comprises a pressure distribution monitoring module, a flow regulation execution module, a terminal state judgment module, an instruction optimization adaptation module and an operation record acquisition module. According to the method, the pressure of multiple points in the oxygen supply pipeline is periodically collected, the change amplitude is analyzed, sensitive identification and state marking of flow abnormity are achieved, the accuracy and execution matching performance of flow adjustment are improved by combining the time point of equipment operation with time window matching of pressure fluctuation, and the flow adjustment accuracy is improved through a forward insertion rearrangement mechanism of a control instruction. According to the method and the system, priority issuing is guaranteed in a lagging scene, the adjustment response speed and the execution stability are enhanced, an adjustment process is recorded, accurate traceability and regulation and control quality evaluation of an oxygen supply terminal operation track are supported, and the regulation and control precision, the abnormity recognition capability and the overall controllability of system operation are effectively improved in a multi-terminal concurrent oxygen consumption scene.

Embodiments may be generally directed to techniques to cause communication of a registration request between a first end-point and a second end-point of an end-to-end path, the registration request to establish resource load monitoring for one or more resources of the end-to-end path, receive one or more acknowledgements indicating resource loads for each of the one or more resources of the end-to-end path, at least one of the acknowledgements to indicate a resource of the one or more resources is not meeting a threshold requirement for the end-to-end path, and perform an action for communication traffic utilizing the one or more resources based on the acknowledgement.

The invention relates to a multi-energy power system active power dynamic distribution method based on a GRU and a DRL, and belongs to the technical field of active power dynamic distribution of various power sources in a power system. And scheduling strategies for units with different operation characteristics during dynamic power distribution are optimized accurately and quickly. Meanwhile, the deep reinforcement learning performs optimization of a dynamic power distribution algorithm based on historical operation data actually acquired by the multi-energy power system, and considers the nonlinear and uncertain problems, which are difficult to describe in a traditional scheduling mode, existing in the multi-energy power system in an off-line training part of a model by using a soft action-Critic, SAC algorithm. And the power distribution real-time calculation efficiency of the multi-energy power system is improved.

The application discloses a kind of gas boiler main steam temperature self-disturbance decoupling control methods, comprising: signal detection device measures various signal data involved in the present application, then data is processed;Using the data after processing, the system model of double-input double-output is established, and the system model is identified;The system model after identification is decoupled, and the system is decoupled into two single-input single-output systems;For system, design linear self-disturbance controller to realize boiler main steam temperature stable control;Firefly algorithm is used to optimize linear self-disturbance controller parameter, and the control quality of main steam temperature control system is improved, the present application is aimed at the problems, such as nonlinearity, multiple disturbance and the like existing in the process of gas power generation boiler main steam temperature control, adopts the control strategy that linear self-disturbance control and decoupling control are combined, and the mutual coupling action between furnace gas flow and low-temperature superheater, desuperheating water flow and high-temperature superheater outlet temperature is decoupled and eliminated using Gauss elimination method.

The application discloses an LSTM-MPC collaborative closed-loop multivariable constraint optimal control method based on a green ammonia production system, and relates to the related technical field of multivariable collaborative optimal control of a green ammonia preparation process. The scheme adopts LSTM to perform multistep prediction on an ammonia generation rate, and utilizes process feedback to perform online correction on prediction errors. Accordingly, an MPC solves a constrained optimization problem in a rolling time domain, and generates optimal control instructions acting on a feed valve opening degree and electric heater power. Model parameters are recursively updated based on MPC execution results and process measurement data, a closed-loop collaborative mechanism of prediction optimization-correction is constructed, accurate tracking of a set value by the ammonia generation rate and suppression of overshoot are realized, and the control quality of the green ammonia preparation process is improved.

Systems and methods for controlling quality of content is provided. A confidence tool of an automated quality control system may receive a request to analyze a tag indicating content to be presented by a content presentation service. The tag may be indicative of a link to the content and a tracking pixel associated with the content. The confidence tool may determine whether the tag meets criteria (e.g., pixel whitelisting criteria, specification of a content presentation service). The confidence tool may notify a user whether the tag meets the criteria to prevent problematic content from being presented by the content presentation service.

The application discloses a control instruction recognition and multi-system collaborative control method and device based on a Petri net and a storage medium. The method comprises the following steps: standardizing an engine test time card, adding a structured label to generate a parseable text; extracting the label content to construct a discrete event sequence and perform visual display; constructing a Petri net model based on the discrete event sequence, defining a library, a transition and a token; designing a multi-system collaborative control strategy comprising a PID controller and a consistency controller; generating a control instruction sequence according to a current identification state of the Petri net, combining the collaborative control strategy to drive the linkage of a gas supply, air intake and exhaust subsystem model, and automatically executing a test process. The application realizes automatic recognition of a test process and dynamic generation of a control instruction, improves the response speed and control quality through multi-system collaborative control, and significantly improves the automation level and execution efficiency of an aero-engine test.

1. The name of the design product: air quality detector. 2. The use of the design product: for monitoring and controlling quality. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

This invention provides a trajectory planning and intelligent control method for a three-stage swing-type overhead crane, relating to the technical field of intelligent overhead crane systems. The method includes: defining a planar coordinate system and determining the position coordinates of the crane's trolley, hook, frame, and load within the planar coordinate system; obtaining a dynamic model using the Lagrange equation based on the position coordinates; designing a controller based on the dynamic model; setting up a two-layer radial basis function neural network and determining the control force; and determining the overall control law based on the position coordinates, the controller, and the control force. According to this invention, intelligent anti-sway control of the three-stage swing system improves control quality, enhances the system's anti-interference capability, and improves the overall system safety level.

The application discloses a plant factory light intensity integrated control system, which comprises an environment sensing module, a central control unit and a partition light adjusting driving module. The core of the application is that a Bayesian optimization module and a digital PID controller are integrated in the central control unit. The Bayesian optimization module takes PID parameters as optimization variables, and a comprehensive performance index as a target function, and automatically optimizes through a Bayesian optimization algorithm. The target function comprises a function for improving response speed and accuracy, a function for suppressing light overshoot, and a function for protecting equipment and reducing energy consumption. The application realizes adaptive setting of PID parameters, solves the problem of insufficient control precision of a traditional method, and improves the control quality of a plant factory light environment.

The invention relates to the technical field of ethylene oxide filling, in particular to an ethylene oxide filling system which comprises an ethylene oxide storage tank, a filling pump, a mass flow meter, an electronic scale, a control unit, a filling gun and a plurality of connecting pipelines, and all the components are communicated with one another through the connecting pipelines. According to the ethylene oxide filling system, by means of the Coriolis mass flow meter, the high-precision electronic scale and a PID algorithm in the control unit, the filling amount is accurately controlled. The mass flow meter is not only high in precision, but also capable of compensating temperature and pressure; the electronic scale measures the mass of the receiving container in real time, and the control unit adjusts the rotating speed of the filling pump based on feedback data of the electronic scale and the receiving container.

The invention relates to the field of artificial intelligence, and provides a multi-target control method and device for energy consumption optimization, electronic equipment and a storage medium, and the method comprises the steps: building large-scale and small-scale standard vectors in real time, adaptively adjusting the weights of different optimization targets according to the operation state, solving the problem that a fixed weight cannot adapt to a complex working condition, and improving the optimization efficiency. The energy consumption is remarkably reduced while the temperature control precision is ensured, a hierarchical optimization architecture is provided, the control quality is improved, a candidate scheme is generated by adopting large-scale global optimization, and an optimally executed double-layer decision-making mechanism is determined through small-scale local screening. The method has the beneficial effects that the long-term energy efficiency stability and the short-term dynamic response characteristic are considered, the balance between the global performance and the local performance is realized, the optimization decision is automatically completed in a data driving mode, the comprehensive energy efficiency and the control quality are remarkably improved while the operation safety is guaranteed, and the intelligence and the user experience of the electric fire stove are enhanced.

The embodiment of the invention provides a partition control method, system and equipment for a vertical furnace environment and a medium, and belongs to the field of semiconductor equipment. The method comprises the following steps: according to different requirements of a process on environmental parameters, dividing the interior of the vertical furnace into a plurality of functional areas which are physically communicated and have gradually increased control levels along a material conveying direction in sequence; independently setting an environmental parameter target value for each functional area; establishing and maintaining a stable air pressure gradient which is gradually reduced from the highest control grade area to the lowest control grade area among the functional areas based on the air pressure gradient; and an isolating device is arranged at a material transfer interface between the adjacent areas and is used for isolating environments on two sides of the adjacent functional areas. According to the method, the interior of the vertical furnace equipment is partitioned, different control strategies are selected for different regions, the cost is saved, the region with high control quality is reduced, the environment control precision is improved, the product quality is better, all the parts are connected and separated, and the transfer efficiency is improved.

The application discloses a self-adaptive energy-saving control system for stirring of high-viscosity chemical materials, and particularly relates to the technical field of energy-saving control, wherein the state estimation module is arranged to deduce the most real state of the system in real time through a process model and a nonlinear state transfer function; the digital twin model is arranged to continuously update itself using the latest data through data assimilation technology, so that the digital twin model is always consistent with the physical entity; the energy consumption optimization module is arranged to define an energy consumption target function, a stirring effect target function and a stirring time target function, and the NSGA-II multi-objective optimization algorithm is applied to find a Pareto optimal solution set, and the optimal energy-saving path under the current specific conditions is recalculated and generated every time a decision is made; the intelligent control module is arranged to generate stirring speed control instructions by model predictive control, and to generate material temperature control instructions by fuzzy logic control; the application realizes energy saving under the premise of ensuring control quality, and realizes predictive operation and maintenance.

An autonomy computing system of an autonomous vehicle for controlling quality in planning a trajectory of the autonomous vehicle is provided. The at least one processor of the autonomy computing system is programmed to process sensor data received from one or more sensors of an autonomous vehicle, and generate a trajectory of the autonomous vehicle based on the sensor data and a plurality of rules. At least one of the plurality of rules corresponds to at least one of a plurality of costs. The at least one processor is further programmed to generate a log of violations of the plurality of rules at one or more nodes along the trajectory, and transmit the log over a wireless communication channel to a remote computing device. The log includes explanations of the violations in a human readable language.

The application provides a regional interconnected power system load frequency control method based on event triggering and preset performance, aiming at solving the problem of frequency stability and control quality decline caused by actuator saturation, unknown disturbance and limited communication resources in a multi-regional power system containing a hybrid energy storage system. The method builds a multi-regional load frequency control model containing a hybrid energy storage system, and establishes a state space equation considering input saturation and composite disturbance; the transient and steady-state performance of the regional control error is constrained by using a preset performance function, and it is converted into an unconstrained form through error transformation; the unknown disturbance of the system is estimated and compensated online by using a projection adaptive law, and a dynamic event triggering mechanism is combined to reduce the update frequency of the control signal, thereby saving network communication resources. Through Lyapunov theory analysis, the control method proposed in this paper can not only ensure the boundedness of the system signal, but also constrain the tracking error within the preset range, and there is no Zeno behavior. Simulation results show that compared with traditional PID control and model predictive control (MPC), the strategy can better suppress frequency and power fluctuations, and reduce communication burden.

The invention discloses a self-adaptive air conditioning system and method based on environment detection, and belongs to the technical field of air conditioning systems, the system comprises a multi-source environment sensing module, an edge calculation and data fusion module, a central intelligent decision and control module and an actuator module; the system comprehensively collects environment and personnel data through a sensor network, and after edge node fusion, the environment and personnel data are input to a central decision module integrating a dynamic prediction model, a deep reinforcement learning controller and a neural network decoupler; the controller performs online optimization and decision making by taking energy conservation, comfort and equipment service life as multiple targets, outputs a decoupled control instruction, and drives an actuator to act. A data driving mode is adopted, the dependence of a traditional system on a fixed mathematical model is overcome, global optimal, self-learning and self-adaptive control in a multivariable, nonlinear and time-varying environment is achieved, and the control quality, the energy efficiency level and the user experience of the system are remarkably improved.

The invention provides a self-adaptive fault-tolerant control method and system for a ship steam turbine based on a digital twinborn predictive model, and the method comprises the steps: constructing a high-fidelity predictive digital twinborn body which is synchronous with a physical steam turbine in real time, and can predict the state of the steam turbine in a short-time domain in the future; using the output of the twin as virtual sensor data, and performing real-time residual analysis and fault diagnosis on the virtual sensor data and physical sensor data; and when a fault of the sensor or the execution mechanism is diagnosed, a control law of the steam turbine is dynamically reconstructed based on a prediction simulation result of the digital twinborn body, and a self-adaptive fault-tolerant control loop fused with virtual sensor data is formed. According to the method, the problems that traditional fault-tolerant control depends on fixed redundancy and response lags are solved, conversion from passive fault tolerance to predictive fault tolerance is achieved, and the control quality, safety and reliability of the ship steam turbine under the fault working condition are remarkably improved.

The invention discloses a control method of an automobile maintenance lifting platform, which comprises the following steps that: when the lifting platform lifts an automobile, a pressure sensor acquires pressure data suffered by four lifting points, and calculates a deviation angle and a deviation amount of a gravity center projection point in a lifting plane; constructing a state vector of an automobile lifting process, constructing a prediction model of time when the automobile is separated from the ground, and calculating a prediction moment when the automobile is separated from the ground; estimating a state vector of a lifting process discrete time step based on extended Kalman filtering, and calculating predicted power of a lifting motor in a future discrete time step; and based on the predicted state vector and in combination with the control time domain of the lifting process, constructing a target function of the lifting control process, calculating a control quality target coefficient of a future time step, and correcting the control quality. The method can effectively adapt to lifting control when the automobile weight distribution is uneven or the load suddenly changes, the problem that the lifting process is unstable due to uneven load is avoided, and the safety, stability and precision of the lifting process are ensured.

The application discloses a kind of thermal power unit fly ash quality control method, belong to the technical field of control optimization, including from thermal power unit system acquisition and integration condition data form standardized data set, and based on standardized data set construction prediction model, to predict the key indicators of future fly ash, then utilize the multi-objective optimization engine including expert rule and particle swarm optimization algorithm and digital twin simulation technology, generate and optimize the control instruction covering electric field operation, boiler combustion organization, hierarchical conveying path switching and flue gas conditioning, finally through instruction execution and online incremental learning algorithm update model form closed loop adaptive control.The application solves the problem that traditional control method depends on artificial experience and response lag by constructing dynamic prediction model, improves the foresight and initiative of quality control, and solves the contradiction between high-quality fly ash output rate and system operation energy consumption through multi-objective collaborative optimization.

The present application relates to a kind of iterative guidance methods not dependent on time compensation, comprising the following steps: (1) constructing the state equation of optimal control;(2) establishing the initial state of optimal control;(3) establishing the terminal constraint condition of optimal control;(4) designing optimal control performance index function;(5) selecting optimal control quantity;(6) calculating optimal control quantity.The present application can calculate the direction of current desired thrust vector according to predetermined target orbit parameters and current motion parameters of vehicle, is very suitable for some small overload conditions with fluctuation, can reduce the influence of time-dependent correction quantity fast change caused by the violent change of overload, provide a more stable program instruction angle for the input of attitude control system, improve control quality.

This invention discloses a control method for an automotive maintenance lifting platform. The method includes: collecting pressure data from four lifting points using pressure sensors during vehicle lifting; calculating the offset angle and offset of the center of gravity projection point within the lifting plane; constructing a state vector for the vehicle lifting process and building a prediction model for the vehicle's liftoff time to calculate the predicted liftoff moment; estimating the state vector for discrete time steps of the lifting process using extended Kalman filtering and calculating the predicted power of the lifting motor for future discrete time steps; constructing an objective function for the lifting control process based on the predicted state vector and combining it with the control time domain of the lifting process; calculating the control quality target coefficient for future time steps; and correcting the control quality. This method effectively adapts to lifting control when there is uneven vehicle weight distribution or sudden load changes, avoiding instability caused by uneven load and ensuring the safety, stability, and accuracy of the lifting process.

The invention discloses a multi-source heterogeneous data quality security assurance method for unified management and control of a power grid, which comprises the following steps of: firstly, performing multi-dimensional data reputation calculation, performing reputation calculation on data of different data sources from three dimensions of credibility, contribution degree and freshness in combination with a multi-weight subjective logic model, and setting a corresponding threshold value; then the reputation value is compared with a threshold value, if the reputation value is lower than the threshold value, it is indicated that the quality of the data source is low, use of the type of data should be avoided in the management and control process, and the management and control quality is guaranteed; secondly, performing data access permission evolution control, defining authorization rules, access control strategies and access records, clustering behavior records by using an enhanced self-organizing neural network to obtain daily behavior classes of a user, and finally mining candidate rules by using an association mining algorithm and updating the existing authorization rules, the access security of the control software and the user experience are improved, and safe and stable operation of power control is ensured.

The invention belongs to the field of two-dimensional thrust vectoring nozzle control, and particularly relates to a collaborative decision-making method and system based on instruction planning. The method comprises the following steps: acquiring an actual throat area and a target throat area; processing the actual throat area and the target throat area through a cooperative control strategy based on instruction planning to generate a throat area scattering instruction; and according to the throat area scattering instruction, generating an actuating cylinder instruction of a spray pipe actuating mechanism. In order to ensure that the lower-layer executing mechanism can stably execute the instruction and ensure the control quality of the spray pipe in the transition process, instruction planning is carried out on the original instruction, the instruction preprocessing scheme accurately plans an actuating instruction signal of the executing mechanism, and the control efficiency is greatly improved on the premise that the final control target is not changed. The dynamic response of the system is obviously optimized, it is ensured that the spray pipe moves stably in the transition process, the control quality is guaranteed, and meanwhile impact and influence on an executing mechanism are reduced.

The application relates to a high-efficiency program turning trajectory design method, which comprises the following steps: arranging two turning engines on a carrier rocket to sequentially provide lateral thrust; designing a program angle mode in a program turning section and setting a handover point; before the handover point, the program angle mode is a pitch angle mode; after the handover point, the program angle mode is converted from the pitch angle mode to an attack angle mode. The application provides a high-efficiency program turning trajectory design method, two turning engines are arranged on a carrier rocket to provide a control impulse moment, the two turning engines work in relay, sequentially provide lateral thrust for a program turning section, and assist in completing program turning; a program angle mode with a handover point is designed, the working mode of the turning engine is accurately matched, various constraints such as rocket turning overload, turning angular velocity, attack angle change rate and the like are met, the turning is smooth throughout, and the control quality is effectively improved. The method reduces the design pressure of a control system and guarantees the attitude control precision of the carrier rocket.

The invention relates to the field of optimization control, and discloses an adaptive optimization control method for a thermal process of a thermal power generating unit, which is used for improving the control quality and adaptability of the thermal process of the thermal power generating unit. The method includes calculating deviation between a measured value and a set value of a controlled variable; generating a total disturbance real-time estimated value of the system by using a quasi-continuous disturbance observer, and performing feedforward calculation to generate a feedforward compensation signal according to the total disturbance real-time estimated value; a self-adaptive compensation signal is generated by combining the real-time disturbance estimation value and deviation integral learning; and finally, fusing the output of the basic controller, the feedforward compensation signal and the adaptive compensation signal to form a final control instruction. According to the method, the operation load is monitored to call the parameters, the parameter mapping table is constructed and maintained, and the feed-forward gain coefficient and the adaptive learning rate are optimized, so that the adaptability to working condition change, the anti-disturbance capability and the control effect on a large delay link are effectively improved.

This invention relates to the field of distributed control technology, solving the technical problem that the lack of a mechanism combining model prediction and neural network self-learning in existing technologies leads to a decline in control quality over operating time, severely restricting the safety and economy of generator sets. Specifically, it relates to a distributed control system and method for generator sets. The method involves: collecting the physical quantities of the target generator set; obtaining a standardized dataset of the generator set through multi-channel standardization based on these physical quantities; using a nonlinear dynamic model for rolling time-domain prediction to improve the speed of regulation and control, significantly reducing dynamic deviation; and enabling weak generator sets to maintain stable operation even when faults worsen. By adaptively adjusting the output error weighting matrix based on health status, early warning and protection are achieved, avoiding sudden damage. This method significantly reduces the rate of unplanned downtime.