Aviato & Quantify
Aviato Aviato
Quantify Quantify
Aviato Aviato
Hey, I’ve been sketching out an idea for a new drone that streams live sensor data straight into a dashboard you can tweak in real time. Think weather patterns, crowd flow, even office snack drawer trends—anything that turns raw footage into actionable insights. Want to help me map out the data flow and pull some predictive charts?
Quantify Quantify
Sure, let’s chart the data pipeline: camera feed → edge compute for object detection → sensor fusion layer (temperature, motion, maybe snack drawer weight) → ingest into time-series DB → real-time analytics engine → dashboards with drill-downs. We can set up a predictive model on crowd flow using ARIMA or a simple moving average, and for weather patterns a Kalman filter would do. For the snack drawer, just a bin‑count and trend line. Don’t expect any “good vibes” to show up in the metrics, though. Let’s get the schema drafted and start pulling sample data.
Aviato Aviato
Nice, that’s the roadmap I was picturing. I’ll pull a quick ER diagram for the edge nodes and the DB, and we can hack together a mock payload from the snack drawer sensor—just a few kilos, a weight reading, timestamp, and that’s a hit. I’m thinking of adding a quick‑look UI that flips from a heatmap of the crowd to a little pie chart of snack consumption. Once the data’s flowing, we can run that Kalman filter on the weather feed and compare the ARIMA predictions to the live crowd counts—maybe even feed the crowd model back into the drone’s flight plan to avoid bottlenecks. Let’s sprint on the schema first, then grab some sample data from the test field. Ready to dive in?
Quantify Quantify
Sounds like a solid sprint. ER diagram first, then mock payloads. I’ll watch for any anomalies in the snack weight—those can skew the chart if you don’t flag them. Once the Kalman and ARIMA are humming, we can feed the crowd model back into the flight plan. Let’s get that schema on the table and then run a few test payloads. I’m ready, just don’t expect any “vibes” to make it into the analytics.
Aviato Aviato
Let’s fire up the ER diagram—camera node, edge compute unit, sensor fusion hub, time‑series DB table, analytics engine, and dashboard view. I’ll draft the tables: Cameras(id, location, feed_url), Edge(id, camera_id, obj_det_model), Fusion(id, edge_id, temp, motion, snack_weight, ts), TSDB(id, sensor_id, value, ts), Dashboards(id, user_id, view_type). Then we’ll ship a fake payload: 10.3kg, 22°C, motion=0.4, ts=now. Once the Kalman on temp and ARIMA on crowd counts are running, we can loop the crowd forecast back to the drone’s path planner. All set—let’s hit sprint 1 and watch those numbers dance!
Quantify Quantify
Nice, that table list looks good—just remember the Fusion table needs a foreign key to the TSDB if you want to join directly, otherwise you’ll have to pull the snack_weight separately. The fake payload you gave will seed the Fusion row, and you can then push that into TSDB for time‑series analysis. Once you run the Kalman filter on temp and the ARIMA on the crowd counts, loop the forecast back to the flight plan, and the drone should avoid those high‑density zones. Let’s sprint on the schema first, then test‑drive the payload. I’ll keep an eye on any outliers that might hide in the snack data; you’ll thank me later.