Abstract: Various embodiments of the teachings herein include a method for knowledge recommendation. The method may include: obtaining current searching information of a user for certain knowledge in factory production, job characteristic information of the user, and historical feedback information of the user for at least one knowledge item and/or knowledge resource in the past; using a first prediction algorithm model for learning based on historical searching information, job feature information, and historical feedback information of the user and other users to analyze obtained information, to obtain first prediction information including a first number of knowledge items; using a second prediction algorithm model to perform fusion sorting on the first prediction information and thereby obtain second prediction information including a second number of knowledge items; providing the second number of knowledge items to a knowledge recommendation model; and recommending a knowledge resource output from the model.

Abstract: The present disclosure describes multi-parameter dynamic sampling methods and systems. An example method includes: configuring two sampling rates for each parameter in a set of sampling parameters; selecting one of the sampling rates for each parameters to sample it; calculating a load for each parameter at the selected sampling rate, sorting the parameters according to the loads, and determining high-loaded parameters according to a sorting result; and determining whether each parameter has data loss, and if at least one of the sampling parameters has data loss, performing down-sampling on the high-loaded sampling parameters within a range of the configured at least two sampling rates.

Abstract: The present disclosure describes multi-parameter dynamic sampling methods and systems. An example method includes: configuring two sampling rates for each parameter in a set of sampling parameters; selecting one of the sampling rates for each parameters to sample it; calculating a load for each parameter at the selected sampling rate, sorting the parameters according to the loads, and determining high-loaded parameters according to a sorting result; and determining whether each parameter has data loss, and if at least one of the sampling parameters has data loss, performing down-sampling on the high-loaded sampling parameters within a range of the configured at least two sampling rates.

Abstract: Provided are a data acquisition system and method. The system includes: data acquisition units, to acquire a plurality of pieces of data related to a target object within a data acquisition period; and a controller, communicatively connected to the data acquisition units, to set, according to an ith data acquisition period, a sampling interval time of the ith piece of data, and to set a polling interval time according to a minimum data acquisition period of the data acquisition units. Upon the controller starting to perform polling on the data acquisition units through n_1 polling interval times until a condition is satisfied for the first time, the controller performs first polling, for the ith piece of data, on a data acquisition unit of the data acquisition units for acquiring the ith piece of data.

Abstract: The present disclosure provides a method and an apparatus for adjusting a process control prediction model, and a process controller. In an embodiment, the method includes: determining, based on controlled variable data in process control data obtained through real-time monitoring, whether a prediction performance of the process control prediction model is lower than a reference performance; and when the prediction performance is determined to be lower than the reference performance, using manipulated variable data in the process control data monitored to adjust the process control prediction model. By way of the method, a re-test does not need to be executed to re-identify a model so as to eliminate a mismatch of the process control prediction model, thereby eliminating an influence of fluctuation introduced by addition of an excitation signal during the re-testing.

Abstract: Provided are a data acquisition system and method. The system includes: data acquisition units, to acquire a plurality of pieces of data related to a target object within a data acquisition period; and a controller, communicatively connected to the data acquisition units, to set, according to an ith data acquisition period, a sampling interval time of the ith piece of data, and to set a polling interval time according to a minimum data acquisition period of the data acquisition units. Upon the controller starting to perform polling on the data acquisition units through n_1 polling interval times until a condition is satisfied for the first time, the controller performs first polling, for the ith piece of data, on a data acquisition unit of the data acquisition units for acquiring the ith piece of data.

Abstract: A processor controller includes: a deep neutral network, for extracting, based upon feature information of process control data, from a process control data storage device, process control data available to a production device to be controlled, the feature information of the process control data including at least production device feature parameters and a production device load; and an enhanced neural network, for performing, based upon a process control prediction model, process control prediction by using real-time process control data of said production device. In an embodiment, the process control prediction model is trained by using the extracted available process control data. The process controller further includes a process control decision unit, for determining an operation control instruction for the production device based upon the result of process control prediction.

Abstract: The present disclosure provides a method and an apparatus for adjusting a process control prediction model, and a process controller. In an embodiment, the method includes: determining, based on controlled variable data in process control data obtained through real-time monitoring, whether a prediction performance of the process control prediction model is lower than a reference performance; and when the prediction performance is determined to be lower than the reference performance, using manipulated variable data in the process control data monitored to adjust the process control prediction model. By way of the method, a re-test does not need to be executed to re-identify a model so as to eliminate a mismatch of the process control prediction model, thereby eliminating an influence of fluctuation introduced by addition of an excitation signal during the re-testing.