An industrial device matching method and apparatus are used for acquiring a corresponding relationship between industrial devices in different industrial data sources to provide basis for industrial data analysis. The method, in an embodiment, includes collecting data of at least two industrial data sources; determining a first relationship between various industrial devices in each industrial data source, and determining a first relationship topology between the industrial devices in the industrial data source; and comparing the first relationship topologies corresponding to various industrial data sources, to determine a first corresponding relationship between industrial devices in industrial data sources, the first corresponding relationship enabling the first relationship topologies corresponding to at least two industrial data sources to be similar. A relationship between industrial devices in different industrial data sources is determined by comparing a relationship topology between devices according to the similarity of relationships between the devices in different industrial data sources.

Some implementations of the disclosure are directed to tapping input/output (I/O) data from an industrial control system (ICS) or applying the tapped I/O data to a learned model to perform predictive or prescriptive maintenance. In one implementation, a method comprises: tapping I/O data from a controller of an ICS while the controller executes first control code to control one or more devices of the ICS; transmitting the tapped I/O data over a network to a second system; and executing, via the second system, second control code comprising an original or modified version of all or a subset of the first control code of the ICS, wherein the second control code executes in response to receiving the tapped I/O data. The output of executing the second control code may be provided to a model to predict a future event involving the ICS or to prescribe maintenance of the ICS.

Computer-implemented systems and methods for the sale of consumer services. Predictions are made based on the behaviors, preferences, assets, identifying characteristics, and other attributes associated with customers and merchants. In one implementation, a prediction is made as to whether a customer is likely to request a service and whether a merchant is likely to be selected by the customer to provide the service. In another implementation, the calendars of a merchant and customer are automatically updated to account for the customer's late arrival to an appointment at the merchants location. In yet another implementation, a customer purchases an appointment for a service from another customer that has the appointment scheduled with a merchant providing the service.

Example implementations described herein are directed to a system for manufacturing dispatching using reinforcement learning and transfer learning. The systems and methods described herein can be deployed in factories for manufacturing dispatching for reducing job-due related costs. In particular, example implementations described herein can be used to reduce massive data collection and reduce model training time, which can eventually improve dispatching efficiency and reduce factory cost.

A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components/nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and/or simulations thereof. Virtual components may include virtual run-time nodes and/or simulated nodes. The MPDSC includes an I/O Switch which delivers I/O data between virtual and/or physical nodes, e.g., by using publish/subscribe mechanisms, thereby virtualizing physical I/O process data delivery. Nodes serviced by the I/O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and/or simulations may be manipulated as desired (speed, values, administration, etc.). The platform simultaneously supports simulation and run-time operations and interactions/intersections therebetween.

A system for rule management, predictive maintenance and quality assurance of a process using automatic rule formation comprising a plurality of sensors capable of being attached to at least one machine for measuring at least one information about the process and machine operation. The system comprises a server connected to the sensors over a wireless communication network and running a reconfigurable rule management program for identifying and processing the particular process and machine information related to at least one process received from the plurality of sensors. A controller in communication with the server capable of controlling the process based on a rule set by the rule engine. The rule engine automatically detects the normal process data, classifies the received data based on the dynamic rule formed by the rule engine and finds anomalies in the process or machine operation for predictive maintenance and process quality assurance.

A position sensing tool for enabling topographical measurements of a working surface is provided. The tool includes sensors for mapping the tool environment and for positioning of the tool within the environment. The tool enables tracking of tool activity within the environment. The tool enables design and fabrication collaboration with other computer systems. The tool enables user and tool environment safety using tool positional, user position and tool environment awareness. Certain embodiments of the tool permit automated guidance of tasks in the tool environment.

A method for producing a well includes receiving production information associated with wells within a field; deriving a field specific model from the production information; receiving production information associated with the well; projecting production changes associated with installing artificial lift at the well at a projected date, the projecting using a production analysis engine applied to the field specific model, the projecting including determining a set of artificial lift parameters; and installing the artificial lift at the well in accordance with the artificial lift parameters.

In a plasma processing apparatus, occurrence of unplanned maintenance is predicted in advance so that a time when the work should be incorporated into planned maintenance can be determined. An apparatus diagnostic apparatus including a degradation score estimation unit that receives an output of a sensor mounted on a plasma processing apparatus and estimates a degradation score of the plasma processing device; a maintenance work occurrence probability estimation unit that calculates a probability of occurrence of an unplanned maintenance work that is not included in an original maintenance plan based on the degradation score; an actual maintenance cost calculation unit that calculates an actual maintenance cost; and a plan incorporated maintenance cost calculation unit that outputs a correction plan of maintenance plan in which the original maintenance plan is corrected by incorporating the unplanned maintenance work based on the probability of occurrence of the unplanned maintenance work.

A computer-implemented method for the monitoring of a plurality of cable-processing machines for the processing of cables includes the following steps: transmission of production parameters from one cable-processing machine, or a plurality of cable-processing machines, to a control server with a central database, wherein the production parameters include settings for the cable-processing machine for the processing of the cables by the cable-processing machine in question; reception of the production parameters by the control server; and storage of the production parameters in the central database.