46 results about "Control quality" patented technology

The invention relates to the technical field of electric power automation control, and discloses a coordinated control system for a generator set, which adopts an intelligent DCS integrated design architecture and performs real-time data interaction with a set decentralized control system through hard wiring or a communication protocol. According to the system, through deep fusion of a model prediction control algorithm and intelligent DCS integrated design, a boiler-steam turbine dynamic coordination model is constructed, and a control instruction is generated in real time in combination with a rolling optimization strategy. By means of the design, the main steam pressure deviation is controlled within + / -0.2 MPa, and the load response rate reaches 1.5% Pe / s and is improved by more than 40% compared with a traditional system. Particularly, under the working condition of deep peak regulation, by means of decoupling control over a fuel-air supply-water supply loop through a neural network algorithm, main steam temperature fluctuation (within + / -5 DEG C) and reheater spray desuperheating water quantity sudden change (the change rate is smaller than or equal to 10 t / min) are effectively restrained, the control quality of a unit under the conditions of variable loads and variable coal quality is remarkably improved, and the control efficiency of the unit is improved. And the high-standard requirement of the power grid on the AGC performance is met.

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 thermal power generating unit fly ash quality control method, and belongs to the technical field of control optimization, and the method comprises the steps: collecting and integrating working condition data from a thermal power generating unit system to form a standardized data set, and building a prediction model based on the standardized data set to predict the key indexes of fly ash in the future, then, a multi-objective optimization engine containing an expert rule and a particle swarm optimization algorithm and a digital twinborn simulation technology are utilized to generate and optimize a control instruction covering electric field operation, boiler combustion organization, graded conveying path switching and flue gas conditioning; and finally, the model is updated through instruction execution and an online incremental learning algorithm to form closed-loop adaptive control. According to the method, the dynamic prediction model is constructed, the problems that a traditional control method depends on artificial experience and response lags behind are solved, the perspectiveness and initiative of quality control are improved, and meanwhile the contradiction that the high-quality fly ash output rate and the system operation energy consumption are difficult to consider at the same time is solved through multi-target collaborative optimization.

The invention relates to a method for operating a power device (3), wherein: - in a first control mode, the power device (3) is controlled by means of non-model-based control; - in a second control mode, the power device (3) is controlled by means of model-predictive control; - switching between the first control mode and the second control mode is carried out on the basis of at least one switchover criterion; - in both control modes, system values (33) of the power device (3) are transferred to an estimation module (27) which is based on the model on which the model-predictive control is based, the system values (33) comprising at least measured values (41) measured at the power device (3); - control quality of the model-predictive control is evaluated by means of the estimation module (27) on the basis of the system values (33); - the at least one switchover criterion comprises the evaluation of the control quality; - at least in the second control mode, a state (35) of the power device (3) is estimated by the estimation module (27) on the basis of the system values (33); and - in the second control mode, the power device (3) is operated by means of the model-predictive control on the basis of the estimated state (35).

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.

The invention discloses a thermal power plant coal pulverizing system load adaptive time sequence control method, and particularly relates to the technical field of thermal power plant intelligent control, and the method comprises the steps: S1, collecting multi-mode operation data of a coal pulverizing system, S2, carrying out the multi-step prediction of numerical time sequence data, generating a key parameter prediction sequence, and carrying out the calculation of the key parameter prediction sequence. The method comprises the steps of S1, predicting data, real-time data and text working condition data, S3, fusing the predicted data, the real-time data and the text working condition data to generate a structured natural language description, S4, inputting a state description into a large language model special for the coal pulverizing system subjected to field fine adjustment, and outputting a time sequence control instruction containing a decision reason, S5, carrying out safety verification on the control instruction, S6, executing control or feedback correction according to a verification result, and S5, outputting a control result. And closed-loop intelligent control is formed. According to the method, through deep fusion of time sequence prediction, natural language processing and physical modeling technologies, cognitive-level adaptive control of the coal pulverizing system from perception to decision is realized, and the control quality and economic safety of the system under complex working conditions are effectively improved.

The present invention provides a model-free control method for a human-in-the-loop multi-agent system under a denial of service attack, belonging to the field of multi-agent system control technology. This control method is based on human-in-the-loop technology and introduces external human experts to supervise the system to improve the security and reliability of the system; at the same time, the DoS attack between the intelligent agents is modeled as a time-varying switching topology, and a fully distributed designated time leader output observer is designed, which effectively reduces the adverse effects of the DoS attack jump on the observation effect and achieves the designated time convergence performance of the observation error; in addition, an optimization performance function is designed and a model-free Q learning algorithm is derived. This control method realizes model-free optimization control of a human-in-the-loop multi-agent system under the influence of DoS attacks, while taking into account the designated time convergence performance of the consistency error, effectively improving the control quality.

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 provides a control risk assessment method for vehicle remote control driving, a medium and a product, which are applied to a remote driving platform, and the method comprises the following steps: obtaining a control quality assessment index of a vehicle, the control quality evaluation index is calculated at least based on communication process related data between the vehicle and the remote driving platform; and inputting the control quality evaluation index into a control risk evaluation model, and obtaining a control risk evaluation result for the vehicle output by the control risk evaluation model.

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.

The invention relates to the technical field of flight simulation, in particular to a flight simulation equipment control quality goodness of fit calculation method based on incomplete test flight data. Comprises: determining a test item; acquiring real flight data of the test item and simulation data of flight simulation equipment; preprocessing the real flight data and the simulation data; complementing the missing part of the real flight data; and comparing the real flight data with the simulation data, and calculating the control quality goodness of fit. According to the invention, automatic testing of flight test data and simulated flight data can be realized, and the defect of inaccuracy caused by subjective artificial evaluation is overcome.

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 discloses a method and system for evaluating the deviation degree between software requirements and code development, and relates to the technical field of software engineering analysis, and the method comprises the steps: carrying out functional requirement matching on the contents of chapter titles, tables and list items in a software requirement specification, positioning to the corresponding text content according to a matching result, and carrying out functional requirement matching on the corresponding text content; main bodies and actions of the functions and objects and conditions of the actions are extracted, and required function point statements are obtained through assembling according to a set sentence pattern rule; extracting a grammar unit and a function calling relation from the code product, and matching the code product with words in a preset code feature library to obtain a code function point statement; and calculating a deviation degree according to the demand function point statement and the code function point statement, and obtaining an evaluation result in combination with a preset deviation threshold. The code development deviation degree is objectively evaluated by quantifying the difference between the code and an expected demand target, and the problems of low cooperation efficiency, out-of-control quality, demand disjunction and the like existing in large-scale code development are solved.

The present invention discloses a multi-model predictive control method based on multi-model gap measurement PGNN feedforward dynamic compensation, which relates to the technical field related to coordinated control of generator sets. The predictive model estimates the future state of the system and guides the controller to make adjustments in advance to solve the deviation and overshoot problems that are easy to occur in the main steam temperature when the unit is running at a wide load, and realizes accurate and rapid response to load changes through feedforward compensation. This solution integrates technologies such as PGNN predictive model, feedforward dynamic compensation and multi-model predictive control to improve the flexibility of the control system. Through accurate prediction of the predictive model and optimization of valve opening feedforward dynamic compensation, the control system can better respond to changes in the main steam temperature, improve the performance of the control system, eliminate deviations, maintain stability, and enable the main steam temperature to more accurately follow the set value, thereby achieving better control effects and improving control quality.

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 preset index control method based on intelligent adaptive boundary adjustment, and the method is as follows: a preset performance boundary function basic form is designed, and a control equation under a constrained state is obtained according to a dynamic equation; a state variable capable of affecting control performance is selected, and a full feedback adaptive recurrent neural network model is combined to exert weight distribution on parameter values in the preset performance boundary function and subsequent control gain; an equivalent unconstrained conversion is performed on the constrained control model based on a barrier function, so that an unconstrained equivalent control model based on an auxiliary variable is obtained; a controller is designed by using a preset time stability theory and a backstepping method, so that a preset index controller design which can only adjust a control response speed by using one parameter and can constrain control performance is completed, and control gain can be set in advance according to a flight task and corresponding performance requirements. The method can fully exert the capability of the aircraft, and improve the control quality in any flight stage under a full flight profile.

The application relates to a method for correcting single-valve and sequence-valve parameters based on DEH valve flow characteristics, and belongs to the technical field of thermal automatic control of thermal power plants. The system comprises a data acquisition module, a data processing module, a parameter correction module of each branch high regulating valve in a single-valve control mode, and a parameter correction module of each branch high regulating valve in a sequence-valve control mode. The application provides turbine regulating valve nonlinear compensation data. After linearization correction of DEH control, the comprehensive valve position instruction change of the turbine under load, the generated steam flow and the unit load are linearly corresponding changes, the rapidity and stability of the unit load control are improved, the oscillation of the unit load control is prevented, the control quality of the unit AGC and primary frequency modulation is improved, and the safe, stable, economic and reliable operation of the thermal power unit is of great significance.

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.