Here’s a short speculative-fiction story based on your code-like prompt: .
| ID | Requirement | Priority | Description | | :--- | :--- | :--- | :--- | | | Signal Smoothing | High | The system shall filter transient voltage drops using a prediction window rather than immediate threshold triggers. | | FR-02 | State Persistence | High | If the ML prediction confidence is > 85%, the V2L link shall remain active despite minor signal fluctuations. | | FR-03 | Link Update Broadcast | Medium | The system shall broadcast LINK_UP or LINK_DOWN events to the HMI (Human Machine Interface) only after the prediction stabilizes for 100ms. | | FR-04 | Fallback Mode | Critical | If the ML inference engine fails or hangs, the system must revert to legacy static threshold logic within 50ms. | v2l ml 39link39 upd
However, V2L has a glaring problem: it is dumb . Most systems simply output AC power until the battery hits a user-defined minimum (say, 20%). It has no context. It doesn’t know if you’re powering a life-saving CPAP machine or just a decorative string of lights. It doesn’t learn your patterns. Enter ML. Here’s a short speculative-fiction story based on your