OPC UA with Python 

Time-Sensitive Networking (TSN) is important in many applications. I typically use MODBUS when speed is important. It is an older standard, but specializes in high-speed numerical data.

I am facing the similar isssue while connecting a codesys softplc controller, Please reply to this thread if you find any solution, will be really greatful to your help.

It looks like this is a common issue that everyone is facing with the current version. I recommend contacting the developers or else use an earlier version of the library.

When working with OPC servers, identifying and managing stale or outdated data values is important in industrial applications where data integrity is vital for operation safety and efficiency. Here are strategies to detect stale values with OPC UA: 1. Timestamp Checking Each value read from an OPC UA server includes a timestamp that can be used to detect when data becomes stale if the timestamp remains unchanged over an extended period. 2. Subscription and Monitored Items Instead of polling data (repeatedly reading values), OPC UA supports subscriptions where the server notifies the client of changes. You can specify a publishing interval and a "deadband" to filter out unnecessary updates. Monitored items can be set to notify only on significant changes or after a certain time of inactivity. 3. Setting Data Quality Flags OPC UA servers can be configured to include data quality flags with each value. These flags can indicate issues such as communication errors, sensor malfunctions, or configuration errors. By checking these flags, you can determine if the data should be considered reliable. 4. Implement Heartbeat Signals For critical variables that must be updated regularly, implementing a "heartbeat" signal can be effective. This is a write operation performed by the data source at a regular interval to a specific variable, indicating that the source is still operational. The absence of recent updates to this heartbeat variable can signal an issue with the data source. 5. Analyzing Historical Data Patterns In some cases, analyzing the historical patterns of data updates can help identify anomalies in data freshness. If certain variables typically update at regular intervals, deviations from this pattern can be flagged for further investigation.

It looks like this is a common issue that everyone is facing with the current version. I recommend contacting the developers or else use an earlier version of the library.

@@apm okay!! can you please recommend the versions you have used in your video ?

Here is more on managing Python environments: apmonitor.com/dde/index.php/Main/InstallPythonPackages

The error message "The requested operation has no match to return." (BadNoMatch) from the Python OPC UA package generally indicates that the operation you attempted to perform did not find a valid target or match in the server's address space or data structures.