Siemens Aktiengesellschaft

Baidu Online Network Technology (Beijing) Co., Ltd.

Bo Wen

Shun Jie Fan

Zhu Niu

Lei Zhang

Jian Qiao Sun

Jianfeng Kang

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. As such, prediction accuracy and training efficiency of the process control prediction model of the process controller can be improved.

Process controller and method and system therefor

The present disclosure relates to a method, device, and system for configuring parameters, a computer device, a medium, and a product. A configuration device for configuring parameter sampling with respect to an edge device includes: one information acquiring unit, configured to acquire information related to the purpose and use environment of the edge device; one transmitting unit, configured to transmit the information to a cloud platform; and one configuration information determining unit, configured to receive configuration information for parameter sampling with respect to the edge device from the cloud platform, where the configuration information is configuration information determined as matching the information by the cloud platform utilizing a configuration model stored thereby.

Methods, devices, systems, and non-transitory machine-readable storage mediums for configuring parameter sampling of devices

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.

Multi-parameter dynamic sampling method and multi-parameter dynamic sampling device

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.

Method and apparatus for adjusting process control prediction model and process controller

A data processing system and method are provided. The data processing system includes: a data acquisition unit, configured to acquire a plurality pieces of data related to a target object; and a data processing unit, configured to receive the plurality pieces of data and set a plurality of adjacent regions in a two-dimensional spatial representation of the plurality pieces of data according to a tolerable compression error. The plurality of regions include an adjacent first region and second region, respectively covering a plurality pieces of data. The data processing unit is configured to forwardly expand the second region to obtain the expanded second region overlapping the first region, calculate a compression error of data covered by the expanded second region, reset the first region and compress the data covered by the reset first region. The data processing system can reduce or minimize the data compression error.

Data processing system and method

A method and apparatus for generating information are provided. An embodiment of the method includes: collecting characteristic information of a target user, and dividing the collected characteristic information into at least two dimensions, the characteristic information including user information and user behavior information; selecting the dimension associated with a target service type from the plurality of dimensions; and generating a target number of characteristic information sets based on cluster analysis on the characteristic information in the selected dimension. The embodiment can generate a characteristic information set corresponding to a user of any service type using the characteristic information of the target user, thereby improving the universality of the method for generating information.

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Beijing, China

Bo Wen