Abstract: Various embodiments of the teachings herein include a method for predicting service life of a rolling bearing. An example includes: acquiring a first vibration signal of a rolling bearing; extracting a time-domain feature of the first vibration signal, wherein the time-domain feature represents a degradation state of the rolling bearing; entering the time-domain feature into a trained Seq2Seq model comprising an encoder and a decoder, the encoder comprising a bidirectional gated recurrent unit (BIGRU), the decoder comprising a long short-term memory model (LSTM), the BIGRU adapted to output a hidden state based on the time-domain feature, the LSTM is adapted to predict service life of the rolling bearing based on the hidden state; and generating the service life.

Abstract: The present disclosure provides a method for remote assistance and device. 1. A method for remote assistance, the method comprising: determining a state of a machine deployed at a working site; adjusting a digital twin of the machine to be in the same state as the machine deployed at the working site; rendering the digital twin at a remote site using a remote rendering device to enable a remote person at the remote site to perform an operation on the digital twin; and mapping an effect of the operation rendered as visual assistance information onto the machine at the working site using a local rendering device deployed at the working site.

Abstract: 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.

Abstract: Various embodiments of the teachings herein include an interface display method for industrial software. An example method includes: obtaining a present operation sequence by a user in a functional interface of industrial software; using a recurrent neural network (RNN) model to predict a next operation of the user according to the present operation sequence by the user, wherein the RNN model is obtained through history operation sequence of the user and history operation sequence of typical users in industrial domain knowledge; and highlighting a functional component of the predicted operation of the user from the functional interface and/or generating navigation corresponding to the predicted operation of the user.

Abstract: 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.

Abstract: Various embodiments of the teachings herein include a method for measuring valve characteristic parameters of a valve actuator. An example method includes: resetting the push rod to a top end position; driving the push rod; measuring the DC drive current in real time; recording a first position of the push rod at the start of a first abrupt change in the DC drive current when the first abrupt change in the DC drive current is detected and lasts for more than a first preset time period; continuing to drive the push rod and measure the DC drive current in real time; recording a second position of the push rod at the start of a second abrupt change; and calculating the stroke length of the valve based on the first position and the second position.

Abstract: 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.

Abstract: 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.

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 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.

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: A multi-type industrial robot control system may include: an automation controller; and a programmable logic controller (PLC)-robot bridge. The controller stores a general-purpose robot control function block generated using a unified design standard, calls a corresponding robot control function block for a robot and using robot state data and puts out corresponding first command data to the PLC-robot bridge. The PLC-robot bridge virtualizes robot interfaces of corresponding offline programming functions of different types of robot as a unified virtual interface, calls a corresponding offline programming function via the unified virtual interface according to the first command data, and generates second command data for output to a corresponding robot controller. The corresponding robot controller controls a corresponding robot. At the same time, robot state data from a corresponding type of robot is received via the unified virtual interface and the robot state data is fed back to the automation controller.

Abstract: Examples of the present disclosure include methods and/or systems for fault diagnosis and solution recommendation. A method may include: obtaining original data including fault problem of a target device; analyzing the original data including fault problem to obtain problem description information; analyzing the problem description information to obtain a diagnosis report; and, according to the diagnosis report, obtaining a video and/or document solution for the fault based on a cloud knowledge map, and recommending the solution to a user. The knowledge map comprises: nodes representing the fault, video solution and/or document solution, and multiple edges representing the relationship between nodes.

Abstract: 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.

Abstract: The present disclosure provides a method for remote assistance and device. 1. A method for remote assistance, the method comprising: determining a state of a machine deployed at a working site; adjusting a digital twin of the machine to be in the same state as the machine deployed at the working site; rendering the digital twin at a remote site using a remote rendering device to enable a remote person at the remote site to perform an operation on the digital twin; and mapping an effect of the operation rendered as visual assistance information onto the machine at the working site using a local rendering device deployed at the working site.

Abstract: Various embodiments include methods for intelligently providing recommendation information. For example, the method may include: determining user attribute parameters corresponding to a user identifier; determining a score value corresponding to the user identifier by inputting the user attribute parameters into a scoring model; determining recommendation information corresponding to the user identifier based on the score value and a knowledge graph related to the user; and providing the recommended information.

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: Various embodiments of the teachings herein include a method for controlling a plurality of execution mechanisms. The method may include: determining a dependency relationship of synchronous motions among the plurality of execution mechanisms, wherein the dependency relationship represents motions of a second group of execution mechanisms in the plurality of execution mechanisms depending on motions of a first group of execution mechanisms in the plurality of execution mechanisms; determining an execution order of the first group of execution mechanisms and the second group of execution mechanisms according to the dependency relationship; determining rotation angles of joints coupled to corresponding execution mechanisms; and sequentially controlling the joints to respectively rotate by the determined rotation angles according to the determined execution order.

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: Various embodiments include a fault diagnosis method for a rotating motor. The method may include: obtaining a time-domain acceleration signal of the rotating motor along a vibration direction and a rotation period of the rotating motor; converting the time-domain acceleration signal into a time-domain velocity signal, cutting off a plurality of velocity signal segments along the time-domain velocity signal by taking the rotation period as a step size, and arranging the plurality of velocity signal segments in sequence to obtain a velocity signal matrix; converting the velocity signal matrix into an image; and inputting the image into a trained neural network model to obtain a fault diagnosis result.