Siemens Aktiengesellschaft

Shun Jie Fan

Ming Jie

Armin Roux

Bo Wen

Qi Sun

Bin Zhang

Zhu Niu

Lei Zhang

Ye Hu

Yunhua Fu

Zi Jian Wang

Xian Tao Meng

Zhong Yang Sun

Qian Wang

Yang Li

Jun Jiang

Yue Zhuo

Ji Dong Li

Tao Wang

Wen Kang Xu

Herbert Grieb

Kurt Dirk Bettenhausen

Wen Bin Cao

Hongwei Yang

Jian Guo Pang

Er Ping Meng

Grieb Herbert

Jun Feng Jiang

Bettenhausen Kurt

Examples of the present disclosure provide a parameter optimization method, device and computer readable storage medium. The method includes: establishing a one-to-one functional relationship between each parameter and each performance index; determining each current correlation coefficient between the parameter and each performance index based on the one-to-one function relationships; obtaining a current weight of each performance index; according to current weights and current correlation coefficients, obtaining a current influence coefficient of each parameter on a comprehensive performance of the performance indexes; and determining important optimization parameters according to the current influence coefficient; for each two parameters, calculating a current correlation coefficient of the two parameters, and determining an adjustment parameter; and performing parameter optimization based on the important optimization parameters and the adjustment parameters. The technical solutions of the present disclosure can improve the parameter optimization efficiency.

Parameter optimization method, device, and storage medium

A programming method is for a numerical control machine tool system including a mobile terminal, a controller, a robot, and a numerical control machine tool. The mobile terminal is wirelessly connected to the controller configured to control the robot and the numerical control machine tool, and the robot and the numerical control machine tool being configured to work cooperatively to process a workpiece. The method includes: loading, at the mobile terminal, a preset motion model of the robot and the numerical control machine tool, the preset motion model being a plurality of graphical functional modules and a connection between them; receiving a graphical programming instruction for a user to configure the preset motion model using the mobile terminal; and converting the graphical programming instruction into G-code, where the G-code is used by the controller to control the robot and the numerical control machine tool to process the workpiece.

Smart programming method for integrated CNC-robot

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 provides a method for remote assistance and device. A method for remote assistance, the method comprising:

Method for remote assistance and device

Some embodiments of the teachings herein include a motion control method. An example includes: creating a motion control model; training an online reinforcement learning model using the model; producing feedback with a controlled object, a model control value, and an initial control value; calculating a reward using the control value and the feedback; generating a residual control value using the online reinforcement learning model based on the reward, the model control value, and the feedback; controlling motion of the object with the residual control value and the model control value; sending the motion control model, the model control value, the feedback, and the reward to the cloud; training an offline model with the motion control model, the model control value, the feedback value, and the reward; and updating the existing online model using the offline model or deploying the offline model in a motion control system without an online model.

Motion control method and apparatus

An example system includes: a production system skill library, with a plurality of skill blocks describing and encapsulating the realization part of the skills involved in the production process; a unified execution engine with a plurality of skill function blocks describing and encapsulating the interface part of the skills involved in the production process; to receive a production procedure programmed by a user based on the skill function blocks, and successively start each skill function block in the production procedure to call at least one corresponding skill block; and device agents for controlling devices in the production system. Each device agent is used to provide a unified interface to control the corresponding device to perform operations according to the operation instructions from the unified execution engine or the skill block.

System, method and storage medium for production system automatic control

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

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.

Data acquisition system and method

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 method, a component, and an electronic device relating to the field of industrial automation technology, particularly relating to the creation of a human machine interface, and are used for automatically creating an HMI of a system. The method for creating an HMI comprises: a first component of a system receives, via an HMI, a first command for creating an HMI for the system; the first component obtains, from each searched and found second component of the system an HMI resource of each of the second components; and the first component creates, on the basis of an HMI resource of the first component and the obtained HMI resource from each of the second components, the HMI for the system. One component of the system collects the HMI resource of another component of the system and creates the HMI for the whole system, providing a method for automatically creating the HMI for the system.

Method, component, and electronic device for creating human machine interface

In the technical field of industrial automation, a fault diagnosis method and apparatus for a numerical control machine tool are disclosed which can improve the efficiency of diagnosing faults of numerical control machine tools by utilizing feedback information provided by users. In a fault diagnosis method of an embodiment, the fault diagnosis apparatus receives a fault symptom to be diagnosed from a user terminal, diagnoses the fault for the fault symptom to be diagnosed, returns a fault diagnosis result to the user terminal, receives feedback on the fault diagnosis result from the user terminal, and adjusts the diagnosis policy for the fault symptom to be diagnosed according to the fault diagnosis result if the feedback of the user on the fault diagnosis result indicates that the fault has been cleared.

Fault diagnosis method and apparatus for numerical control machine tool

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

An interconnection device, a communication method, and a system including a robot are disclosed. In an embodiment, the interconnection device includes a first OPC UA interface, configured to establish connection between the interconnection device and an OPC UA device; and an ROS interface, in communication with the first OPC UA interface. The ROS interface includes: at least one ROS node module, configured to perform communication between the OPC UA device and an ROS device; an ROS client library module, configured to provide a function library to be called when the ROS node module performs the communication; and an ROS core module, configured to manage the ROS node module in the ROS interface unit and a node module in the ROS device. Communication between the OPC UA device and the ROS device is achieved via the interconnection device and the communication method.

Interconnection device, communication method, and system including robot

The present invention related to the technical field of industrial automation, in particular to a method and apparatus for generating a fault diagnosis database of a CNC machine tool, intended to conveniently provide comprehensive fault diagnosis information. The method comprises: setting a fault condition of the CNC machine tool; running a simulation model of the CNC machine tool under each set fault condition to generate fault, and recording a fault phenomenon of the fault; backtracking a possible fault generating each fault phenomenon, to establish an association between the fault phenomenon and a possible fault generating the fault phenomenon; generating a fault diagnosis database of the CNC machine tool according to the association established. Due to the fact that what is being run is a simulation model of the CNC machine tool, various fault conditions can be conveniently set. The fault diagnosis database established by the method has the advantage of comprehensive coverage of fault situations and abundant fault diagnosis information. The result of performing fault diagnosis on the basis of such a fault diagnosis database is more accurate.

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Beijing, China

Shun Jie Fan