22 results about "Process output" patented technology

The invention provides an industrial sensor intermittent fault detection method and device and a medium. The method comprises the following steps: acquiring a process input vector of a target industrial process at a current moment and a process output measurement value measured by a sensor to be measured; determining a process output predicted value according to the process input vector and the virtual sensor model; calculating a current residual between the process output measured value and the process output predicted value; if the current residual error exceeds a target early warning threshold value, marking an early warning signal, and generating a first fault mark under the condition that the continuous triggering times of the early warning signal on a time sequence reach a first preset times; based on the residual sequence from the historical starting moment to the current moment, the posterior probability that the sensor to be detected is in a fault state at the current moment is calculated through a hidden Markov model, and a second fault mark is generated under the condition that the posterior probability is larger than or equal to a preset probability threshold value; and if at least one of the first fault mark and the second fault mark is established, determining that the to-be-detected sensor has an intermittent fault.

The invention provides a task flow dynamic optimization method and system based on data feedback, and the method comprises the steps: obtaining process feedback data, determining the process execution time consumption and a processing result through a production monitoring system log, recognizing the current processing capability of a process, and obtaining the to-be-processed workpiece amount of a downstream assembly node and the frequency fluctuation amplitude of an upstream process output workpiece; acquiring the queuing workpiece quantity and the real-time load quantity of each process corresponding to the rearranged process processing execution sequence, analyzing to obtain a process processing capacity matching degree index, and determining a global workpiece circulation stability evaluation result; and obtaining total number change data of workpieces completed in each process from the dynamic optimization implementation scheme, judging the system processing throughput improvement degree, and determining the process load balancing state of workpiece circulation.

The application relates to the motor winding technology field and provides a motor winding process automatic control method and system.The method comprises the following steps: obtaining motion state information, stress state information and material characteristic information of a flat wire; inputting the motion state information, the stress state information and the material characteristic information into a preset flat wire mechanical simulation model to obtain a predicted torsion angle of the flat wire in a bending process output by the preset flat wire mechanical simulation model; obtaining an actual torsion angle of the flat wire; adjusting a parameter value of an equivalent shear modulus of the flat wire in the preset flat wire mechanical simulation model according to the predicted torsion angle and the actual torsion angle to obtain an adjusted preset flat wire mechanical simulation model; and performing winding control on the flat wire based on the adjusted preset flat wire mechanical simulation model. The accuracy of motor winding can be controlled.

The invention relates to the technical field of industrial control, in particular to a self-adaptive cutting control method based on a continuous feeding scene of a cutting machine, which comprises the following steps: acquiring process input parameters of a material sample of the cutting machine, process constraint parameters under different processing tracks and corresponding actual process output parameters; respectively constructing a first training data set and a second training data set, and training to obtain a constraint process model and a state process model; a process input parameter flow of a to-be-processed material area is obtained online and input to the constraint process model, and a dynamic process constraint is generated; creating a candidate control trajectory based on the dynamic process constraint; inputting the candidate control trajectory into the state process model, outputting a prediction performance index, and selecting and determining an optimal control trajectory; and executing the optimal control track to realize self-adaptive cutting control, and carrying out online updating on the constraint process model and the state process model to realize optimal control on the machining quality and efficiency of the cutting machine.

The present application relates to the technical field of industrial process dynamic characteristic monitoring, and discloses a kind of injection molding process real-time monitoring method based on multi-sensor data fusion, comprising: firstly, the dynamic response characteristics of drive source itself are identified and compensated to obtain corrected process output signal;Further, process input signal and the corrected process output signal are cooperatively processed to generate process gain and time constant capable of overall representing process dynamic transfer characteristics, and process stability is judged accordingly, the present application separates the disturbance source of drive system performance fluctuation from the monitoring signal, realizes the direct quantitative characterization of the dynamic transfer process connecting input and output itself, converts a complex industrial process stability problem into the monitoring of process gain and time constant two-dimensional coordinate point, and improves the reliability of monitoring.

The invention discloses a chemical process fault diagnosis method. A special calculation model matched with an actual device is constructed and corrected, and a baseline data set is generated; generating a simulation data set containing normal and various fault working conditions by using the model; on the basis of the data set, a reverse diagnosis reasoning model with process output deviation as input and fault types as output is established; the model is applied to factory real-time data, and automatic online diagnosis is achieved. According to the method, the diagnosis problem caused by sparse fault samples in a traditional diagnosis method is solved, and rapid and accurate fault positioning of complex processes such as double-effect rectification is realized through combination of mechanism simulation and data driving.

The invention discloses a control method of wire harness production equipment, and relates to the technical field of intelligent manufacturing. The method comprises the following steps: acquiring preset process parameter curves of all N procedures in a complete wire harness production process; inputting all the process parameter curves and the estimated environment parameters into a virtual production system integrated with a defect-process correlation model; executing continuous production simulation from the first process to the last process in the virtual system, and generating virtual product quality evaluation based on the output of the last process; and according to an evaluation result, if the evaluation result is qualified, issuing all the process parameter curves to physical equipment to execute production, and if the evaluation result is not qualified, adjusting the process parameter curves and re-simulating until the evaluation result is qualified. According to the invention, through full-process virtual simulation and beforehand quality prediction, conversion from post-process detection to beforehand prevention is realized, the quality and efficiency of wire harness production are effectively improved, and meanwhile, the production trial and error cost is remarkably reduced through a closed-loop optimization mechanism.

The invention relates to the field of furniture manufacturing, in particular to a rapid modeling system for digital pattern making of a sofa soft bed. The system comprises a modeling platform module, a three-dimensional modeling and two-dimensional expansion module, a component library and material database, a structure disassembly and consumable statistics module and a process output module. Parametric modeling is carried out on parts such as a seat bag, a backrest, armrests and a bottom frame, a three-dimensional structure is automatically generated, a complex curved surface is unfolded through an energy optimization algorithm, and leather cloth version data are output in combination with splicing optimization. Calling the standard component library by the system, matching material parameters, calculating the consumption of consumables and a loss coefficient, and generating a consumable list; and forming a structure decomposition graph based on the assembly relationship, automatically adding a process label, and finally outputting a digital process document which can be directly used for production. According to the invention, full automation of the plate making process is realized, the development period is shortened, the cost is reduced, and the consistency and production efficiency are improved.

The application provides a production cycle determination method, device, equipment, medium and program product, and relates to the oil and gas well mining process technical field.The method comprises the following steps: obtaining target production time sequence data of a plunger gas lift process; the target production time sequence data comprises tubing pressure data and casing pressure data corresponding to a plurality of time points respectively; inputting the target production time sequence data into a recognition model, recognizing the target production time sequence data through the recognition model, and obtaining a running cycle recognition result of the plunger gas lift process output by the recognition model; and determining a production cycle corresponding to the plunger gas lift process based on the running cycle recognition result.The production cycle determination method, device, equipment, medium and program product provided by the application improve the recognition efficiency and recognition accuracy of the production cycle of the plunger gas lift process, reduce the labor cost, and improve the automation level and production efficiency of the plunger gas lift process.

To configure the control agent (POL), pre-given training data is read in, specifying the state dataset (S), action dataset (A), and the obtained performance values ​​(R) of the technical system (TS). Based on this training data, a data-driven dynamic model (NN) is trained to reproduce the obtained performance values ​​(R) based on the state dataset (S) and action dataset (A). Furthermore, a action evaluation process (VAE) is trained to reproduce the action dataset (A) based on the state dataset (S) and action dataset (A) after information reduction, where reproduction errors (DR, D0, D1) are determined. To train the control agent (POL), training data is fed to the trained dynamic model (NN), the trained action evaluation process (VAE), and the control agent (POL). Here, the performance values ​​(R1, R2) output by the trained dynamic model (NN) are fed into a pre-given performance function (P). Additionally, the reproduction errors (D0, D1) output by the trained action evaluation process (VAE) are fed into the performance function (P) as parameters affecting performance reduction. In this way, the control agent (POL) is trained to output an action dataset (A) that optimizes the performance function (P) based on the state dataset (S).

Machine learning systems and methods for extreme weather event modeling using generative diffusion models are provided. The system includes a weather modeling processor and a weather modeling engine executed by the processor. The weather modeling engine causes the processor to: receive a dataset including a plurality of vorticity samples; process the dataset using a deterministic mean model having a temporal attention unit to model spatial, cross-channel, and temporal dependencies using dynamical attention units; and process output of the deterministic mean model using a reverse diffusion model to capture stochastic fine scale features and to generate a denoised output. A downscaling pipeline can also be executed by the weather modeling engine to downscale outputs of the system.

Systems and methods for input evaluations through model inversion are described herein. In certain embodiments, a system includes a memory configured to store a model of a physical process, wherein the model receives input values and provide output values, wherein the input values represent potential input parameters for the physical process and the output values represent potential measures of process outputs. The system also includes an interface capable of receiving desired output values for the physical process. Further, the system includes processors executing computer executable instructions associated with an application that cause the processors to receive the desired output values; test a plurality of values for at least one of the input values; identify a combination of the input values that is associated with the desired output values; and provide the combination for output through the interface.

The invention relates to a system for modelling the dependency of process output variables on process variables of a process in an automation system, comprising: a server platform (SP) with a computer unit (CE) and a control unit (SE); first measuring devices (M1) for detecting measured values of the process variables (W1); second measuring devices (M2) for detecting measured values of the process output variables (W2); a closed communication network (KN) for connecting the measuring devices (M1, M2) and server platform (SP); wherein the first measuring devices (M1) detect measured values of the process variables (W1) and transmit them as input values (I) to the server platform (SP) by means of the closed communication network (KN); wherein the second measuring devices (M2) detect measured values of the process output variables (W2) and transmit them as output values (O) to the server platform (SP) by means of the closed communication network (KN); wherein the server platform (SP) stores transmitted input values (I) and output values (O) at least partially as time series (ZR); wherein the server platform (SP) trains an algorithm (ML) with stored time series (ZR) by means of computer unit (CE); wherein the server platform (SP) calculates values (WO) for the process output variables by means of the trained algorithm (ML) on the basis of the transmitted input values (I).

Machine learning systems and methods for extreme weather event modeling using generative diffusion models are provided. The system includes a weather modeling processor and a weather modeling engine executed by the processor. The weather modeling engine causes the processor to: receive a dataset including a plurality of vorticity samples; process the dataset using a deterministic mean model having a temporal attention unit to model spatial, cross-channel, and temporal dependencies using dynamical attention units; and process output of the deterministic mean model using a reverse diffusion model to capture stochastic fine scale features and to generate a denoised output. A downscaling pipeline can also be executed by the weather modeling engine to downscale outputs of the system.

A model predictive control (MPC) device includes an input interface configured to receive an industrial process input associated with at least one component of a process automation plant, an output interface configured to transmit a control instruction to control the component, memory configured to store first and second MPC process models corresponding to different states, and a processor configured to identify a current state parameter of an industrial process, and predict a future industrial process output using the first or second MPC process model, based on the current state parameter being associated with the first or second MPC process model. The processor is configured to calculate a target operating point according to the predicted future industrial process output, determine a control signal to drive the industrial process to the calculated target operating point, and output the determined control signal to control operation of the component of the industrial process plant.

The invention provides a troubleshooting process determination method and device, equipment and a storage medium, and the method comprises the steps: obtaining production problem information; matching is carried out based on the production problem information and a knowledge graph, service system information is determined, and the service system information comprises service process information and system architecture information; the knowledge graph comprises a corresponding relation between the production problem information and the business system information; and inputting the business system information into the troubleshooting process determination model to obtain a target troubleshooting process output by the troubleshooting process determination model. By means of the method, when a production problem occurs, the troubleshooting process can be automatically generated, the efficiency and accuracy of determining the troubleshooting process are improved, the manual analysis and judgment time is shortened, and the efficiency and accuracy of troubleshooting are improved.

This invention discloses a method and system for designing robust operating conditions for industrial production processes. Considering various uncertainties in industrial production, and addressing scenarios where process mechanism models are unknown or simulation costs are high, this method utilizes only the input and output data of the production process. By constructing a robust Bayesian optimization model, it can determine nominal operating condition setpoints with strong disturbance resistance for the production process. The implementation process is as follows: sampling and testing within the operable range of the operational variables to obtain process output data; defining constraints, constructing the robust operating condition design problem as an optimization proposition that minimizes the maximum constraint value; establishing a Gaussian process regression surrogate model based on the sampled data, and constructing a sampling function that integrates the predicted mean, uncertainty, and constraint satisfaction probability; employing a two-stage Bayesian optimization framework, iteratively sampling and updating the model until convergence is achieved to obtain the robust operating condition setpoints.

System for modeling the dependence of process output variables on process variables of a process in an automation plant, comprising: server platform (SP) with computer unit (CE) and control unit (SE); first measuring devices (M1) for acquiring measured values ​​of the process variables (W1); second measuring devices (M2) for acquiring measured values ​​of the process output variables (W2); closed communication network (KN) for connecting measuring devices (M1, M2) and server platform (SP); wherein first measuring devices (M1) acquire measured values ​​of the process variables (W1) and transmit them as input values ​​(I) to the server platform (SP) via a closed communication network (KN); wherein second measuring devices (M2) acquire measured values ​​of the process output variables (W2) and transmit them as output values ​​(O) to the server platform (SP) via a closed communication network (KN);wherein the server platform (SP) stores transmitted input values ​​(I) and output values ​​(O) at least partially as time series (ZR); wherein the server platform (SP) trains an algorithm (ML) with stored time series (ZR) using a computer unit (CE); wherein the server platform (SP) uses the trained algorithm (ML) to calculate values ​​(WO) for the process output variables based on the transmitted input values ​​(I).

The application belongs to the technical field of structured process design. The application provides a radar assembly process structured design method based on a process model. The disclosure embodiment realizes multi-specialty collaborative process design of precision assembly, electronic assembly, surface treatment and the like by clearly defining the responsibility boundary of the main process engineer end and the collaborative process engineer end, standardizing the cross-specialty collaborative process, and solves the multi-specialty collaborative integrated process design problem of the radar assembly process. Relying on basic resources such as process library, process model library and typical process resource library, the application reduces repeated design and shortens the process preparation cycle. The standardized process procedure template reduces the dependence on individual experience, improves the quality stability of process design, and ensures the consistency and reliability of process output. The application establishes a perfect process change process, realizes the flexibility and standardization of multi-specialty process collaborative change, ensures the consistency and accuracy of the changed process file, improves the reliability of the radar assembly process, and provides protection for process design optimization and improvement.

The application provides a stepped first-order fuzzy predictive control method and device, wherein the method comprises the following steps: obtaining a plurality of predictive control components according to preset conditions of control action; obtaining process output predictive values of a (t+N)th moment corresponding to the plurality of predictive control components respectively based on a process output value of a tth moment, a control amount output value of the tth moment and the plurality of predictive control components; obtaining a predictive control deviation and a deviation change rate of process output of the (t+N)th moment corresponding to the plurality of predictive control components respectively according to the process output predictive values of the (t+N)th moment corresponding to the plurality of predictive control components respectively; determining a first predictive control component based on the predictive control deviation and the deviation change rate of the process output of the (t+N)th moment corresponding to the plurality of predictive control components respectively; and controlling a thermal first-order inertia system of a coal-fired unit according to the first predictive control component. The application has good adaptability to outer ring disturbance and time-varying characteristics of process control.

Predictive feedforward control whereby past and the planned movements of certain process input variables are used in planning the control of some selected process output variables without allowing for the impact of the certain process input variables on those selected process output variables to impact planned movements of the certain process input variables. Existing model-based predictive control systems can be modified by incorporating a control package that is encoded with the predictive feedforward technique to control industrial multivariable process systems. A method includes (a) identifying a dynamic process model for the multivariable process system; and (b) implementing a dedicated feedforward control whereby within the processing unit certain process input variable signals are used by the processing unit for planning control of selected process signal output variable signals by adjustment of other process input variable signals to one or more manipulated actuators without impact to the certain process input variable signals.

Architectures and techniques for initializing a simulation architecture are described. A platform model and an environment model are run on recorded data during a first period of time. The platform model and the environment model provide information to control modules. The platform model is run on a blended combination of recorded data and data from the set of control modules and the environment model is run on processed output data from the platform model during a second period of time. The platform model is run on data from the set of control modules and the environment model is run on output from the platform model from a previous period of time during a third period of time. Output signals from the platform model can be adjusted based on a subset of the recorded data during the first, second and / or third period of time.