Data Modeling Studio

Abstract

The data modeling studio provides a structured environment for graphically creating and executing models that can 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 the graphical construction of the model and a runtime engine for executing the data model, for example in an offline or online environment. The configuration engine includes interface routines that generate user interface elements, a plurality of model templates stored in memory that serve as building blocks for the model, and a model compiler that converts the graphical model into a data format executable by the runtime engine. The runtime engine executes the model to generate desired output and may include a retrieval routine for retrieving data corresponding to the template 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

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