57 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 invention relates to an engineering detection supervision method and system based on a building information model, and is applied to the technical field of engineering quality detection, and the method comprises the steps: obtaining a target engineering project and to-be-detected entrusting information of the target engineering project; determining a detection delegation type based on the to-be-detected delegation information; generating an electronic delegation book based on the detection delegation type and a preset building information model, and sending the electronic delegation book to a target detection unit; obtaining an entrusted detection result generated by the target detection unit; and monitoring and managing the target project based on the entrusting detection result. The multi-link collaborative management effect of improving the management and control quality is achieved.

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 application belongs to the field of aeroengine nozzle control, and particularly relates to a method for generating dynamic characteristic evaluation indexes of a nozzle control loop actuator. It includes: Step 1, based on the nozzle control loop actuator of an aeroengine, construct a nozzle control loop model; Step 2, perform integration processing on the nozzle control loop model to obtain an equivalent model of the nozzle control loop, extract key parameters affecting dynamic characteristics from the equivalent model of the nozzle control loop, and construct dynamic characteristic evaluation indexes based on the key parameters; Step 3, conduct experimental verification on the dynamic characteristic evaluation indexes. This application uses the indexes of dynamic characteristic evaluation as new product indexes to constrain the product dynamic characteristics, which can improve the consistency of the product characteristics of the double-flow actuator, achieving the purpose of unifying control parameters and improving control quality.

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 invention relates to an iterative guidance method independent of time compensation. The method comprises the following steps: (1) constructing an optimal control state equation; (2) establishing an initial state of optimal control; (3) establishing a terminal constraint condition of optimal control; (4) designing an optimal control performance index function; (5) selecting an optimal control quantity; and (6) calculating the optimal control quantity. According to the method, the direction of the current expected thrust vector can be calculated according to the preset target orbit parameters and the current motion parameters of the aircraft, so that the method is very suitable for some small overload conditions with relatively large fluctuation, and the influence of quick change of time-related correction caused by severe change of overload can be reduced; a more stable program instruction angle is provided for input of an attitude control system, and the control quality is improved.

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 application of improved active disturbance rejection control in superheated steam temperature control of a supercritical thermal power generating unit. Firstly, a superheated steam temperature system of a supercritical unit serves as a research object, and the control difficulty of the superheated steam temperature system is analyzed; and then, designing an improved active-disturbance-rejection control strategy. And finally, verifying the feasibility of the control strategy based on a simulation platform, and carrying out quantitative statistic analysis on the effectiveness of the control strategy by adopting performance indexes. A research object is simplified into a cascade control system, and the dynamic characteristics of the cascade control system are described more accurately. According to the improved active-disturbance-rejection control strategy designed by the invention, the control quality of a superheated steam temperature system can be remarkably improved, and rapid, safe and stable control of the superheated steam temperature is realized.

The invention discloses an overload control method based on acceleration information parameter adjustment, and belongs to the field of aircraft stability control, and the method comprises the steps: (1) in a flight airspace, fitting parameter adjustment rules of a pitching channel and a yaw channel by taking a small synthesis overload state as a reference; (2) calculating a synthetic overload according to the acceleration; and (3) combining the parameter adjusting rules of the pitching channel and the yaw channel, synthesizing overload according to the flight speed of the aircraft, and adjusting the control parameters of the pitching channel and the yaw channel. According to the method, in a relatively small flight airspace, namely, the height change is small, the overload instruction is limited within a certain range, and under the condition that parameter adjustment cannot be carried out due to lack of synthetic attack angle information or large synthetic attack angle error, the pitching bias loop control parameters can be set through the acceleration information, and the control quality under different overload instructions is improved.

The present invention discloses a method for tuning the parameters of a three-phase model predictive controller. A penalty term for the increment of the predicted output is added to the optimization proposition compared with the traditional MPC algorithm, improving the control quality and enhancing the robustness of the closed-loop system at the same time. The parameter tuning steps are as follows: conduct an identification experiment on the controlled object, collect input and output data, perform model identification calculations to obtain the object model; determine the model time domain, prediction time domain, and control time domain according to the model of the controlled object; normalize the input and output variables of the controlled object; determine the prediction error weighting coefficient and the predicted output increment weighting coefficient from the desired closed-loop first-order step response time; given the upper and lower bounds of the input overshoot, determine the input increment weighting coefficient through multiple closed-loop step test simulations to ensure that the overshoot of the control quantity is within the constraint range. The parameter tuning method of the present invention is simple, intuitive and effective, greatly simplifying the complex MPC tuning work and realizing high-quality and high-robustness MPC control.

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 discloses an environment simulation system pressure specified time safety control method, and relates to the technical field of environment simulation system control, and the environment simulation system pressure specified time safety control method mainly comprises the steps: building a header pipe pressure regulating valve model, obtaining an air inlet valve model and a compressor pressure ratio model through a Greitzer model and a Gravdahl model, and calculating the pressure ratio of a compressor according to the air inlet valve model and the compressor pressure ratio model; constructing a header pipe pressure regulating valve designated time control law based on a designated time stability theory, designing a reference control law, constructing a quadratic programming problem by using a control barrier function, obtaining a control strategy by using a flexible item, and controlling an environment simulation system by using the control strategy. By implementing the environmental simulation system pressure specified time safety control method provided by the invention, the control quality and robustness of the environmental simulation system can be improved, and the experiment cost can be reduced.

The invention discloses a VCU control-based pure electric vehicle data integration and test method, and particularly relates to the technical field of vehicle control testing, the VCU control strategy function, the VCU control safety, the VCU control performance and the VCU control intelligent level are marked as test targets, the test requirements are analyzed, the test requirements are decomposed to generate test points, and the test points are integrated and tested. Test cases are written based on test points, multi-type test environments under different working conditions are established based on test targets, and the test cases are applied to test a VCU control strategy function, VCU control safety, VCU control performance and a VCU control intelligent level in different test environments. According to the method, the VCU control function completion degree coefficient, the VCU control safety coefficient, the VCU control performance coefficient and the VCU control intelligence coefficient are calculated, then the VCU control quality index is calculated, the function safety of the vehicle control unit can be further guaranteed, the fault probability is reduced, and meanwhile the performance and the intelligence level of the vehicle control unit can be guaranteed.

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.