Industrial Telemetry Streaming with Adaptive Queue and Payload Control
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Solution Overview
Problem
Existing methods for transmitting industrial telemetry data from industrial sites to remote servers require substantial storage capacity and suffer from significant delays between data publications, limiting precise remote monitoring and efficiency.
Innovation Solution
A method for streaming industrial telemetry data using a data elements queue and data frames buffer, with adaptive queuing and buffering mechanisms, including machine learning algorithms to optimize data transmission, ensuring quasi-continuous data flow and minimizing storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data elements are stored in a queue and published periodically according to a schedule, then storage capacity requirements are reduced, but monitoring precision and responsiveness deteriorate due to significant delays between publications
Solution Approach 1:
The patent implements dynamic adjustment of the publishing frequency based on real-time conditions. The system monitors queue depth and adapts the publishing interval accordingly, transitioning from static periodic publishing to dynamic event-driven publishing. This allows the system to maintain low storage requirements while improving monitoring precision by publishing more frequently when needed and less frequently when stable.
Solution Approach 2:
The system incorporates feedback mechanisms where the publishing frequency is adjusted based on actual queue conditions and data flow patterns. The controller monitors the rate of data element arrival and publication, and modifies the publishing schedule in response to changing conditions, thereby optimizing both storage efficiency and monitoring precision simultaneously.
2Quantity of substance
If data elements are extracted and compressed into files for periodic publishing, then network bandwidth usage is controlled, but data transmission speed and responsiveness deteriorate due to batch processing delays
Solution Approach 1:
The patent segments the data transmission process into smaller, more frequent batches rather than large periodic batches. By dividing the data stream into smaller units that can be transmitted more frequently, the system reduces the impact of batch processing delays while maintaining network bandwidth efficiency. The segmentation allows for more flexible timing and size control of data transmissions.
Solution Approach 2:
The system dynamically adjusts the size and timing of data batches based on real-time network conditions and data arrival rates. The publishing frequency and batch size are modified adaptively to optimize both bandwidth utilization and transmission speed, transitioning from fixed periodic batching to dynamic event-driven batching that responds to actual system conditions.
3Device complexity
If a fixed publishing schedule is used to transmit data, then system complexity is reduced, but adaptability to changing data flow patterns and network conditions deteriorates
Solution Approach 1:
The system implements feedback-based control where the publishing schedule is continuously adjusted based on monitored data flow patterns and network conditions. The controller receives information about data arrival rates, queue depths, and transmission status, and modifies the publishing frequency accordingly. This feedback mechanism provides adaptability while maintaining relatively simple system architecture through rule-based decision making.
Solution Approach 2:
The system performs self-adjustment of its publishing schedule based on intrinsic data flow patterns and network conditions. Rather than requiring external control or complex configuration, the system automatically monitors its own performance and modifies its behavior to optimize data transmission. This self-service capability enhances adaptability without significantly increasing system complexity.
Data Source
AI summary
A method of streaming data elements from an industrial site to a remote server, including: receiving data elements from industrial devices on the industrial site, storing the data elements in a queue, periodically extracting a number N of data elements from the queue and aggregating the same into one data frame whose size does not exceed a maximum size, storing each data frame in a buffer, and periodically extracting a data frame from the buffer and sending the same to the remote server. A data element is removed from the queue and stored in a database if the data element's time in the queue exceeds a maximum queuing time. A data frame is stored in the database instead of being stored in the buffer if the buffer is full. The maximum queuing time and the number N are then optimised.