SCADA Network-Aware Control Using Traffic Monitoring Feedback

The patent applies parameter changes by dynamically adjusting communication parameters (transmission rate, latency, packet size, protocol selection) based on monitored network conditions. The control device modifies these parameters in real-time to adapt to varying network quality, thereby maintaining reliable operation across geographically distributed networks without sacrificing coverage or flexibility.

The patent implements feedback mechanisms where network performance parameters are continuously monitored and measured, then fed back to the control device. This feedback loop enables the system to automatically adjust communication strategies based on actual network conditions, resolving the contradiction between wide coverage and operational reliability by making the system responsive to real-time network status.

The patent introduces a monitoring device as an intermediary component that separates the complex tasks of network parameter measurement and analysis from the control device. This intermediary handles the sophisticated monitoring and evaluation functions, while the control device focuses on decision-making and actuation, thereby improving transmission quality without excessively increasing the complexity of the core control system.

The patent segments the control system into distinct functional components: a monitoring device responsible for measuring and analyzing network parameters, and a control device responsible for making decisions and executing control actions. This segmentation allows each component to specialize in specific tasks, improving overall reliability while managing system complexity through functional decomposition.

The patent applies dynamics by making the observation period duration adaptive rather than fixed. The system dynamically adjusts the length of observation periods based on current network conditions, traffic patterns, and the specific control task requirements. This allows the system to capture sufficient data for accurate measurements when needed while minimizing observation time when rapid response is required, thereby balancing measurement precision with response time.

The patent implements preliminary action by performing machine learning-based forecasting of network conditions in advance. The system analyzes historical network data to predict future network states, allowing control decisions to be prepared beforehand. This preliminary forecasting reduces the need for extended real-time observation periods, improving both measurement accuracy and response time by anticipating network conditions before they fully manifest.