Cloud Infrastructure for Remote Industrial Data Analytics
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Solution Overview
Problem
Industrial automation systems generate vast amounts of data that are typically limited to local access, restricting the ability to leverage this data for broader analytics and remote monitoring across geographically diverse facilities, and existing remote monitoring solutions are often expensive, inflexible, and require significant reprogramming for expansions.
Innovation Solution
A cloud-based infrastructure with agent-based communication channels and analytics frameworks that collect, sort, and process industrial data from various sources, enabling remote storage, intelligent analytics, and scalable data management, allowing for dynamic integration of new data types and flexible expansion without reprogramming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If industrial data is stored and processed locally on-site, then data accessibility and response time are improved, but device complexity and costs increase significantly for multiple facilities
Solution Approach 1:
The patent introduces a cloud-based intermediary platform that acts as a mediator between industrial controllers and user applications. The cloud infrastructure receives data from controllers via agents, processes it remotely, and makes it accessible to applications without requiring local processing infrastructure at each facility. This resolves the contradiction by maintaining data accessibility while eliminating the need for complex local systems.
Solution Approach 2:
The patent transitions the system from a two-dimensional local architecture (controller to local application) to a three-dimensional distributed architecture (controller to cloud to application). By adding the cloud dimension, the system enables remote access to industrial data across multiple facilities without requiring physical proximity or complex point-to-point connections, thereby reducing overall system complexity while maintaining accessibility.
2Device complexity
If cloud-based remote monitoring is implemented, then device complexity and costs are reduced, but data transmission time and potential loss increase
Solution Approach 1:
The patent implements preliminary action by having agents continuously collect and pre-process data at the source before transmission to the cloud. Data is buffered and prepared in advance, so when transmission occurs, it is already organized and ready for immediate processing. This reduces the effective transmission time and prevents data loss by ensuring data is captured and prepared beforehand rather than reacting to events after they occur.
Solution Approach 2:
The system maintains continuous data collection and transmission operations through persistent agent processes that run continuously on controllers and cloud services. This continuous operation ensures no data gaps occur during transmission, eliminating data loss while maintaining the efficiency benefits of cloud-based processing. The uninterrupted data flow keeps transmission times minimal while preserving data integrity.
3Adaptability or versatility
If cloud infrastructure is used for remote monitoring, then adaptability and scalability are improved, but initial device complexity increases
Solution Approach 1:
The patent creates a universal cloud-based platform that serves multiple facilities and applications through a single infrastructure. The cloud service acts as a multi-functional hub that can handle data from any number of controllers, support various types of industrial data, and serve different user applications simultaneously. This universal approach eliminates the need for separate systems at each facility, reducing initial complexity while providing unlimited scalability and adaptability as new facilities or applications are added to the existing platform.
Data Source
AI summary
A cloud-based infrastructure facilitates gathering, transmitting, and remote storage of control and automation data using an agent-based communication channel. The infrastructure collects the industrial data from an industrial enterprise and intelligently sorts and organizes the acquired data based on selected criteria. Message queues can be configured on the cloud platform to segregate the industrial data according to priority, data type, or other criteria. Behavior assemblies stored in customer-specific manifests on the cloud platform define customer-specific preferences for processing data stored in the respective message queues. An agent-based analytics framework in the cloud platform performs desired analytics on the data using distributed parallel processing.