Custom Limits Via Code Implementation In OpenC3/COSMOS

Introduction

Hey guys! Today, we're diving into a cool feature request for OpenC3 and COSMOS: implementing custom limits using code. Currently, we can create custom conversions and limits responses with code. But, a user has asked for the ability to define custom limits on telemetry items by implementing code that returns the limit values themselves. This enhancement would provide greater flexibility and control over how telemetry data is evaluated and acted upon. Let's explore how this could be implemented and the benefits it would bring. The current system allows for custom conversions, but this goes a step further by allowing the limit values themselves to be dynamically determined through code. This is particularly useful in scenarios where limits are not static but depend on other factors or complex calculations. With custom limits via code, you gain the ability to set thresholds based on any arbitrary logic you can implement in code. This means limits can react to changes in the system, external conditions, or even historical data. Imagine setting limits that tighten during critical operations or loosen during maintenance periods. This level of adaptability is a game-changer for real-time monitoring and control. This feature aligns perfectly with the existing capabilities of OpenC3 and COSMOS, enhancing the overall flexibility and power of the platform. By empowering users to define limits through code, we are giving them the tools to tailor the system to their specific needs and use cases. This ultimately leads to more effective monitoring, faster response times, and improved overall system performance.

Proposed Implementation

So, how can we make this happen? A logical place to implement this would be within the Packet's check_limits method. Instead of directly specifying high/low values, the code would return a state like RED, YELLOW, GREEN, or NONE (for black). This approach aligns well with how states are currently handled. By returning a state, we abstract away the need to know the specific limit values within the check_limits method. This simplifies the logic and allows for more complex limit evaluations. The states RED, YELLOW, GREEN, and NONE provide a clear and concise way to represent the severity of a limit violation. This makes it easy to integrate with existing alerting and visualization systems. Returning a state also allows for more flexibility in how limits are defined. For example, you could define a limit that is only active during certain times of day or when certain conditions are met. This would be difficult to achieve with static high/low values. Consider a scenario where you want to monitor the temperature of a critical component. Instead of setting a fixed high limit, you could use code to dynamically adjust the limit based on the component's operating history and current workload. This would allow you to detect anomalies and prevent failures more effectively.

Benefits of This Approach

Guys, think about the possibilities! This approach lets you evaluate your telemetry based on anything you can code. You can return whatever state you want based on this evaluation. Imagine you want to set a "limit" on a counter. You could make it RED if it increments by more than 1. It might be very temporal, but at least it would be logged. This dynamic approach opens up a whole new world of possibilities for monitoring and control. By allowing users to define limits through code, we are empowering them to create highly customized and responsive monitoring systems. This level of flexibility is essential for dealing with complex and dynamic systems. Furthermore, the ability to log these temporal limit violations provides valuable insights into system behavior. This data can be used to identify trends, predict failures, and optimize performance. For instance, imagine monitoring the power consumption of a server. You could set a limit that triggers if the power consumption spikes unexpectedly. This could indicate a hardware issue, a software bug, or even a security breach. By logging these events, you can quickly identify and address the root cause of the problem.

Use Cases

Here are a few use cases to illustrate the power of custom limits via code:

• Adaptive Thresholds: Dynamically adjust limits based on environmental conditions, system load, or time of day.
• Anomaly Detection: Identify unusual patterns or deviations from expected behavior by comparing current telemetry data to historical data.
• Complex Calculations: Implement limits based on complex mathematical formulas or algorithms that take multiple telemetry values into account.
• State-Based Limits: Define limits that are only active when the system is in a specific state or mode of operation.
• Temporal Limits: Monitor changes in telemetry values over time and trigger alerts when predefined thresholds are exceeded.

Example: Monitoring Battery Health

Let's say you want to monitor the health of a battery in a satellite. You could implement a custom limit that takes into account the battery's voltage, current, and temperature. The code could then calculate the battery's state of charge (SOC) and state of health (SOH) and trigger alerts if either falls below a certain threshold. This would allow you to proactively identify and address potential battery issues before they lead to a failure.

Example: Monitoring Network Traffic

Consider a scenario where you want to monitor network traffic on a server. You could implement a custom limit that tracks the number of packets received per second. The code could then compare the current traffic rate to historical data and trigger alerts if there is a sudden spike in traffic. This could indicate a denial-of-service attack or a network configuration issue.

Conclusion

Implementing custom limits via code in OpenC3 and COSMOS would provide users with unprecedented flexibility and control over their telemetry monitoring. By allowing users to define limits through code, we are empowering them to create highly customized and responsive monitoring systems that can adapt to the ever-changing demands of their operations. This feature would not only enhance the capabilities of the platform but also unlock new possibilities for data analysis, anomaly detection, and proactive system management. This enhancement promises to be a significant step forward in making OpenC3 and COSMOS even more powerful and versatile tools for aerospace and other industries. By providing developers with the ability to define custom limits through code, they can tailor the monitoring system to their specific needs and gain deeper insights into their systems' behavior. This feature will enable more effective anomaly detection, faster response times, and improved overall system performance. For more information on COSMOS, check out the Ball Aerospace COSMOS Wiki website.