Author: Goran Appelquist, CTO of Crosser Technologies.
A Yokogawa survey found that more than half of decision makers in the global process industry are increasing their investment in industrial autonomy. After a recent analysis of how its customers use its visual drag-and-drop Flow Studio, Goran Appelquist, CTO of the edge analytics software company Crosser, has an in-depth look at the five most popular analysis modules used to create data flows in its platform.
One of Crosser’s goals is to use simplicity to combat complexity, which is obvious in Flow Studio1Generally, monitoring advanced data flow requires various skills to manage each input, but Flow Studio minimizes the need for additional software developers and data science teams. Pre-built modules do not need to write code, the process flow can be viewed together and managed by one person. Since the combined user group is performing extensive testing, the low-code solution also ensures higher code quality.
Let’s take a look at the five most popular analysis modules.
Attribute mapper
Described as the “Swiss Army Knife” of data conversion, Property Mapper is the most commonly used module in Windows Studio.
It is unlikely to receive the available data immediately, so the module reorganizes the data into the required format, and if the data is presented in an unstructured form, introduces structure, aligns naming conventions, and adds metadata. It only operates on the structure without changing the value.
Property Mapper simplifies data processing by coordinating data from multiple sources and treating specified data as one stream. It also supports scenarios where a large amount of data output is sent to multiple destinations, all of which require data in different formats, by reorganizing the data on the way out.
Mangqiao
All programmers are familiar with Python, a high-level programming language that optimizes code readability. However, this module enables Python code to run as part of the process and install any third-party libraries. It also makes it easier to write Python than searching for ideal pre-built modules to perform what you need. Most Python modules can be used with conversions supported by Property Mapper.
Python Bridge is great for running machine learning (ML) models because most models are built and trained using one of the Python ML frameworks-to execute these models, you need to copy the same ML framework as a resource at the edge. This is an important part of the Edge MLOps strategy.
Text template
If you can’t communicate the actions related to that information, then creating a data stream is meaningless. The text template creates dynamic text messages, combining static text with data in the message. It has two key functions in a process.
The first is that it creates human-readable notifications to monitor conditions. When the condition is triggered, the notification will notify the team member, who can take action on the trigger.
Its second function is to combine multiple stream values into one value. For example, communication via API requires specific values based on multiple input values and data (including numeric values), which must be sent as strings.
Timestamp
When data is received, there is no record to confirm when it was captured. In the data flow, it is very important to monitor the waiting time during code execution and measure the efficiency of the code. This is where the TimeStamp module is used to stamp data with the capture time.
Sometimes a timestamp is reported, but the format is incorrect. TimeStamp can also convert the incoming timestamp into the format required by the target.
Array split and join
Array Split is the fifth most commonly used analysis module in Crosser Flow Studio, although Array Join is used with Split. Array is a common format used when retrieving data from multiple sensors of PLC or API. Using arrays is a very common operation in the Industrial Internet of Things.
This multi-sensory data is presented in one big message, where sensor values are presented in the form of an array. The Array Split module splits the array into individual messages and applies some processing, while Join does the opposite, recombining the message flow into an array.
Analyzing how customers use Flow Studio can provide insights into the features that are most important to them. The operation of the Internet of Things is complex, but by analyzing the perfect recipe of the module, our Flow Studio eliminates common complexities and allows a comprehensive understanding of how to create advanced processes-without unnecessary staff.
1 Crosser Flow Studio allows professionals working in any asset-rich data generation environment (such as the factory floor) to build advanced data flows. Choose from hundreds of pre-built modules and build flows using simple drag and drop capabilities. Flow Studio is used to assemble and configure modules into data streams that collect and process data close to the data source. This could come from machines, mobile assets, local data centers or the cloud. The first step in building a flow involves the input module, which collects data from sources including programmable logic controller (PLC) sensors, databases, or application programming interfaces (APIs). It is not common to receive data in the required format, so you usually need to transform, coordinate, and structure the data before applying the specified operations and integration. The output module then transmits the results back to the machine, system, service, database or cloud.
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