Enable an Industrial Dataspace on Azure
What is an Industrial Dataspace? An industrial dataspace is an environment designed to enable the secure and efficient exchange of data between different organizations within an industrial ecosystem. Developed by the International Data Spaces Association, it focuses on key principles such as data sovereignty, interoperability, and collaboration. These principles are crucial in the context of Industry 4.0 where interconnected systems and data-driven decision-making optimize industrial processes and create resilient supply chains. A tutorial with step-by-step instructions on how to enable an industrial dataspace on Azure is available here. Use Case: Providing a Carbon Footprint for Produced Products One of the most popular use cases for industrial dataspaces is providing the Product Carbon Footprint (PCF), an increasingly important requirement in customers' buying decisions. The Greenhouse Gas Protocol is a common method for calculating the PCF, splitting the task into scope 1, scope 2, and scope 3 emissions. This example solution focuses on calculating scope 2 emissions from simulated production lines using energy consumption data to determine the carbon footprint for each product. Accessing the Reference Implementation The Product Carbon Footprint reference implementation can be accessed here and deployed to Azure with a single click. During the installation workflow, all the required components are deployed to Azure. This reference implementation supports data modelling with IEC standard Open Platform Communication Unified Architecture (OPC UA), aligned with the OPC Foundation Cloud Initiative. It also uses the IEC standard Asset Administration Shell (AAS) to provide product semantics, creating a Product Carbon Footprint AAS for simulated products and storing it in an AAS Repository. Finally, the implementation uses the IEC/ISO standard Eclipse Dataspace Components (EDC) to establish the trust relationship between the manufacturer and the customer, enabling the actual PCF data transfer via an OpenAPI-compatible REST interface. Conclusion Enabling an industrial dataspace on Azure can help manufacturers meet regulatory requirements, optimize industrial processes, and improve customer engagement by leveraging modern cloud technologies and standards to provide a secure and efficient data exchange environment, ultimately driving transparency and sustainability in the manufacturing industry.Partners accelerating industrial transformation with Azure IoT Operations
In the digital age, the essence of innovation lies not only in groundbreaking technology but also in the power of collaboration. At Microsoft, we have always recognized that our success is intertwined with the success of our partners. Our platform products, including the newly released Azure IoT Operations, are designed to be the foundation upon which our partners can build transformative solutions. These collaborations are more than just business arrangements; they are the bedrock of a thriving ecosystem that drives innovation, addresses customer needs, and propels industry standards forward. Partnerships enable us to extend our reach and impact far beyond what we could achieve alone. By combining our technological prowess with the domain expertise and creativity of our partners, we create a dynamic synergy that fosters groundbreaking advancements. This collaborative spirit is vital as we navigate the complexities of the Internet of Things (IoT) landscape, where diverse applications and specialized knowledge are paramount. Our partners bring unique perspectives and capabilities to the table, ensuring that Azure IoT Operations can cater to a broad spectrum of industries and use cases.
can't use create_from_edge_environment with proxy options - client doesn't connect to azure iot hub
Hello, I am trying to configure a client connection towards azure iot hub from a module. I've tried creating the client with create_from_edge_environment method and I get the following error: Traceback (most recent call last): File "/usr/local/lib/python3.10/site-packages/azure/iot/device/common/mqtt_transport.py", line 391, in connect rc = self._mqtt_client.connect( File "/usr/local/lib/python3.10/site-packages/paho/mqtt/client.py", line 914, in connect return self.reconnect() File "/usr/local/lib/python3.10/site-packages/paho/mqtt/client.py", line 1044, in reconnect sock = self._create_socket_connection() File "/usr/local/lib/python3.10/site-packages/paho/mqtt/client.py", line 3683, in _create_socket_connection return socks.create_connection(addr, timeout=self._connect_timeout, source_address=source, **proxy) File "/usr/local/lib/python3.10/site-packages/socks.py", line 209, in create_connection raise err File "/usr/local/lib/python3.10/site-packages/socks.py", line 199, in create_connection sock.connect((remote_host, remote_port)) File "/usr/local/lib/python3.10/site-packages/socks.py", line 47, in wrapper return function(*args, **kwargs) File "/usr/local/lib/python3.10/site-packages/socks.py", line 814, in connect raise GeneralProxyError("Socket error", error) socks.GeneralProxyError: Socket error: 407: Proxy Authentication Required This problem doesn't appear if I create the client using create_from_connection_string method. This adds complexity to my script and I would like to use create_from_edge_environment method. Does anybody have any insights on why one method works and the other doesn't? Thanks!
