Data modeling studio

Abstract

A data modeling studio provides a structured environment for graphically creating and executing models which may be configured for diagnosis, prognosis, analysis, identifying relationships, etc., within a process plant. The data modeling studio includes a configuration engine for generating user interface elements to facilitate graphical construction of a model and a runtime engine for executing data models in, for example, an offline or an on-line environment. The configuration engine includes an interface routine that generates user interface elements, a plurality of templates stored in memory that serve as the building blocks of the model and a model compiler that converts the graphical model into a data format executable by the run-time engine. The run time engine executes the model to produce the desired output and may include a retrieval routine for retrieving data corresponding to the templates from memory and a modeling routine for executing the executable model.

Description

technical field [0001] This patent relates generally to process plants and process control systems, and more specifically to the use of data modeling studios to create and implement process control systems in process plants and / or in process control systems and particularly in implementing big data architectures The data processing model in the system. Background technique [0002] Distributed process control systems, such as those used in chemical, petroleum, or other process plants, generally include devices communicatively coupled to one or more field devices via an analog, digital, or combined analog / digital bus, or via a wireless communication link or network. One or more process controllers. Field devices, which can be such as valves, valve positioners, switches and transmitters such as temperature, pressure, level and flow rate sensors, are located within the process environment and typically perform physical or process control functions such as opening or closing va...

AI Technical Summary

Problems solved by technology

However, there is currently no or very limited ability to analyze or use this data, as collected from process plants or processes, to perform comprehensive or systematic analysis of plant operations, to be able to detect trends, to perform predictive analysis, to detect exceptions, realignment or reconfiguration of plants to operate more economically, etc.

In particular, users must still manually create and test data models or modeling routines to process data in specific ways, which is very time-intensive and difficult to do

Images