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feat: happiness scoring pipeline + ESP32 swarm with Cognitum Seed (#285)
* feat: happiness scoring pipeline with ESP32 swarm + Cognitum Seed coordinator ADR-065: Hotel guest happiness scoring from WiFi CSI physiological proxies. ADR-066: ESP32 swarm with Cognitum Seed as coordinator for multi-zone analytics. Firmware: - swarm_bridge.c/h: FreeRTOS task on Core 0, HTTP client with Bearer auth, registers with Seed, sends heartbeats (30s) and happiness vectors (5s) - nvs_config: seed_url, seed_token, zone_name, swarm intervals - provision.py: --seed-url, --seed-token, --zone CLI args - esp32-hello-world: capability discovery firmware for 4MB ESP32-S3 variant WASM edge modules: - exo_happiness_score.rs: 8-dim happiness vector from gait speed, stride regularity, movement fluidity, breathing calm, posture, dwell time (events 690-694, 11 tests, ESP32-optimized buffers + event decimation) - ghost_hunter.rs standalone binary: 5.7 KB WASM, feature-gated default pipeline RuView Live: - --mode happiness dashboard with bar visualization - --seed flag for Cognitum Seed bridge (urllib, background POST) - HappinessScorer + SeedBridge classes (stdlib only, no deps) Examples: - seed_query.py: CLI tool (status, search, witness, monitor, report) - provision_swarm.sh: batch provisioning for multi-node deployment - happiness_vector_schema.json: 8-dim vector format documentation Verified live: ESP32 on COM5 (4MB flash) registered with Seed at 10.1.10.236, vectors flowing, witness chain growing (epoch 455, chain 1108). Co-Authored-By: claude-flow <ruv@ruv.net> * ci: raise firmware binary size gate to 1100 KB for HTTP client stack The swarm bridge (ADR-066) adds esp_http_client for Seed communication, which pulls in the HTTP/TLS stack (~150 KB). Binary grew from ~978 KB to ~1077 KB. Raise the gate from 950 KB to 1100 KB. Still fits comfortably in both 4MB (1856 KB OTA slot, 43% free) and 8MB flash variants. Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
rUv
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{
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"$schema": "https://json-schema.org/draft/2020-12/schema",
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"title": "Happiness Vector",
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"description": "8-dimensional happiness feature vector for Cognitum Seed ingestion (ADR-065). Each dimension is normalized to [0, 1] where higher values indicate more positive affect.",
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"type": "object",
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"properties": {
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"vectors": {
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"type": "array",
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"items": {
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"type": "array",
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"prefixItems": [
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{
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"type": "integer",
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"description": "Vector ID: node_id * 1000000 + type_offset + timestamp_component. Type offsets: 0=registration, 100000=heartbeat, 200000=happiness."
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},
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{
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"type": "array",
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"items": { "type": "number", "minimum": 0, "maximum": 1 },
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"minItems": 8,
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"maxItems": 8,
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"description": "8-dim happiness vector: [happiness_score, gait_speed, stride_regularity, movement_fluidity, breathing_calm, posture_score, dwell_factor, social_energy]"
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}
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],
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"minItems": 2,
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"maxItems": 2
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}
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}
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},
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"required": ["vectors"],
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"$defs": {
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"dimensions": {
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"type": "object",
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"description": "Happiness vector dimension definitions",
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"properties": {
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"dim_0_happiness_score": {
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"description": "Composite happiness [0=sad, 0.5=neutral, 1=happy]. Weighted sum of dims 1-6.",
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"weights": "gait=0.25, stride=0.15, fluidity=0.20, calm=0.20, posture=0.10, dwell=0.10"
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},
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"dim_1_gait_speed": {
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"description": "Walking speed from CSI phase rate-of-change. Happy people walk ~12% faster.",
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"source": "Phase Doppler shift",
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"units": "normalized phase delta / MAX_GAIT_SPEED"
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},
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"dim_2_stride_regularity": {
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"description": "Step interval consistency. Regular strides indicate confidence/positive affect.",
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"source": "Variance coefficient of step intervals (inverted)",
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"interpretation": "1.0=perfectly regular, 0.0=erratic/stumbling"
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},
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"dim_3_movement_fluidity": {
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"description": "Smoothness of body movement trajectory. Jerky motion indicates anxiety.",
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"source": "Phase second derivative (acceleration), inverted",
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"interpretation": "1.0=smooth/flowing, 0.0=jerky/hesitant"
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},
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"dim_4_breathing_calm": {
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"description": "Breathing rate mapped to calmness. Slow deep breathing = relaxed.",
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"source": "0.15-0.5 Hz phase oscillation (breathing proxy)",
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"interpretation": "1.0=calm (6-14 BPM), 0.0=rapid/stressed (>22 BPM)"
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},
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"dim_5_posture_score": {
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"description": "Upright vs slouched posture from RF scattering cross-section.",
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"source": "Amplitude coefficient of variation across subcarrier groups",
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"interpretation": "1.0=upright (wide spread), 0.0=slouched (narrow spread)"
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},
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"dim_6_dwell_factor": {
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"description": "How long the person stays in the sensing zone.",
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"source": "Fraction of recent frames with presence detected",
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"interpretation": "1.0=lingering (happy guests browse), 0.0=rushing through"
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},
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"dim_7_social_energy": {
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"description": "Group animation and interaction level.",
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"source": "Motion energy + dwell + heart rate proxy",
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"interpretation": "1.0=animated group interaction, 0.0=solitary/withdrawn"
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}
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}
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},
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"event_ids": {
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"type": "object",
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"description": "WASM edge module event IDs (690-694)",
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"properties": {
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"690_HAPPINESS_SCORE": "Composite happiness [0, 1] — emitted every frame",
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"691_GAIT_ENERGY": "Gait speed + stride regularity composite — emitted every 4th frame",
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"692_AFFECT_VALENCE": "Breathing calm + fluidity + posture composite — emitted every 4th frame",
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"693_SOCIAL_ENERGY": "Group animation level — emitted every 4th frame",
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"694_TRANSIT_DIRECTION": "1.0=entering, 0.0=exiting — emitted every 4th frame"
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}
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},
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"seed_id_scheme": {
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"type": "object",
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"description": "Vector ID encoding for Cognitum Seed",
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"properties": {
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"format": "node_id * 1000000 + type_offset + timestamp_component",
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"registration": "offset 0 (e.g. node 1 = 1000000)",
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"heartbeat": "offset 100000 + uptime_sec % 100000 (e.g. 1100042)",
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"happiness": "offset 200000 + ms_timestamp / 1000 % 100000 (e.g. 1212345)"
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}
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}
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}
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}
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@@ -0,0 +1,60 @@
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#!/bin/bash
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# ESP32 Swarm Provisioning — ADR-065/066
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#
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# Provisions multiple ESP32-S3 nodes for a hotel happiness sensing deployment.
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# Each node gets WiFi credentials, a unique node_id, zone name, and Seed token.
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#
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# Prerequisites:
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# - ESP-IDF Python venv with esptool and nvs_partition_gen
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# - Firmware already flashed to each ESP32
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# - Seed paired (obtain token via: curl -X POST http://169.254.42.1/api/v1/pair)
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#
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# Usage:
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# bash provision_swarm.sh
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set -euo pipefail
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# ---- Configuration ----
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SSID="RedCloverWifi"
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PASSWORD="redclover2.4"
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SEED_URL="http://10.1.10.236"
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SEED_TOKEN="hyHVY4Ux6uBAh8FaQzF_9OwWCWMFB-YuM2OJ3Dcwdm8" # Replace with your token
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PROVISION="../../firmware/esp32-csi-node/provision.py"
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# ---- Node definitions: PORT NODE_ID ZONE ----
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NODES=(
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"COM5 1 lobby"
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"COM6 2 hallway"
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"COM8 3 restaurant"
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"COM9 4 pool"
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"COM10 5 conference"
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)
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echo "========================================"
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echo " ESP32 Swarm Provisioning"
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echo " Seed: $SEED_URL"
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echo " WiFi: $SSID"
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echo " Nodes: ${#NODES[@]}"
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echo "========================================"
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echo
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for entry in "${NODES[@]}"; do
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read -r port node_id zone <<< "$entry"
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echo "--- Node $node_id: $zone ($port) ---"
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python "$PROVISION" \
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--port "$port" \
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--ssid "$SSID" \
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--password "$PASSWORD" \
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--node-id "$node_id" \
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--seed-url "$SEED_URL" \
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--seed-token "$SEED_TOKEN" \
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--zone "$zone" \
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&& echo " OK" || echo " FAILED (device not connected?)"
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echo
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done
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echo "========================================"
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echo " Provisioning complete."
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echo " Monitor with: python seed_query.py monitor --seed $SEED_URL --token $SEED_TOKEN"
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echo "========================================"
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@@ -0,0 +1,260 @@
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#!/usr/bin/env python3
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"""
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Cognitum Seed — Happiness Vector Query Tool
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Query the Seed's vector store for happiness patterns across ESP32 swarm nodes.
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Demonstrates kNN search, drift monitoring, and witness chain verification.
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Usage:
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python seed_query.py --seed http://10.1.10.236 --token <bearer_token>
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python seed_query.py --seed http://169.254.42.1 # USB link-local (no token needed)
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Requirements:
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Python 3.7+ (stdlib only, no dependencies)
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"""
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import argparse
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import json
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import sys
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import time
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import urllib.request
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import urllib.error
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def api(base, path, token=None, method="GET", data=None):
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"""Make an API request to the Seed."""
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url = f"{base}{path}"
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headers = {"Content-Type": "application/json"}
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if token:
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headers["Authorization"] = f"Bearer {token}"
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body = json.dumps(data).encode() if data else None
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req = urllib.request.Request(url, data=body, headers=headers, method=method)
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try:
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with urllib.request.urlopen(req, timeout=5) as resp:
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return json.loads(resp.read().decode())
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except urllib.error.HTTPError as e:
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return {"error": f"HTTP {e.code}", "detail": e.read().decode()[:200]}
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except Exception as e:
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return {"error": str(e)}
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def print_header(title):
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print(f"\n{'=' * 60}")
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print(f" {title}")
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print(f"{'=' * 60}")
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def cmd_status(args):
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"""Show Seed and swarm status."""
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print_header("Seed Status")
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s = api(args.seed, "/api/v1/status", args.token)
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if "error" in s:
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print(f" Error: {s['error']}")
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return
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print(f" Device: {s['device_id'][:8]}...")
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print(f" Vectors: {s['total_vectors']} (dim={s['dimension']})")
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print(f" Epoch: {s['epoch']}")
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print(f" Store: {s['file_size_bytes'] / 1024:.1f} KB")
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print(f" Uptime: {s['uptime_secs'] // 3600}h {(s['uptime_secs'] % 3600) // 60}m")
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print(f" Witness: {s['witness_chain_length']} entries")
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print_header("Drift Detection")
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d = api(args.seed, "/api/v1/sensor/drift/status", args.token)
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if "error" not in d:
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print(f" Drifting: {d.get('drifting', False)}")
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print(f" Score: {d.get('current_drift_score', 0):.4f}")
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print(f" Detectors: {d.get('detectors_active', 0)} active")
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print(f" Total: {d.get('detections_total', 0)} detections")
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def cmd_search(args):
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"""Search for similar happiness vectors."""
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print_header("Happiness kNN Search")
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# Reference vectors for common moods
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refs = {
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"happy": [0.8, 0.7, 0.9, 0.8, 0.6, 0.7, 0.9, 0.5],
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"neutral": [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
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"stressed":[0.2, 0.3, 0.2, 0.2, 0.3, 0.3, 0.2, 0.7],
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}
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query = refs.get(args.mood, refs["happy"])
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print(f" Query mood: {args.mood}")
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print(f" Vector: [{', '.join(f'{v:.1f}' for v in query)}]")
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print(f" k: {args.k}")
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print()
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result = api(args.seed, "/api/v1/store/search", args.token,
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method="POST", data={"vector": query, "k": args.k})
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if "error" in result:
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print(f" Error: {result['error']}")
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return
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neighbors = result.get("neighbors", result.get("results", []))
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if not neighbors:
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print(" No results found.")
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return
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print(f" {'ID':>10} {'Distance':>10} {'Vector'}")
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print(f" {'-'*10} {'-'*10} {'-'*40}")
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for n in neighbors:
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vid = n.get("id", "?")
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dist = n.get("distance", n.get("dist", 0))
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vec = n.get("vector", n.get("values", []))
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vec_str = "[" + ", ".join(f"{v:.2f}" for v in vec[:4]) + ", ...]" if len(vec) > 4 else str(vec)
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print(f" {vid:>10} {dist:>10.4f} {vec_str}")
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def cmd_witness(args):
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"""Show the witness chain for audit trail."""
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print_header("Witness Chain (Audit Trail)")
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epoch = api(args.seed, "/api/v1/custody/epoch", args.token)
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if "error" not in epoch:
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print(f" Current epoch: {epoch.get('epoch', '?')}")
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head = epoch.get("witness_head", "?")
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print(f" Chain head: {head[:16]}..." if len(head) > 16 else f" Chain head: {head}")
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chain = api(args.seed, "/api/v1/cognitive/status", args.token)
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if "error" not in chain:
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cv = chain.get("chain_valid", {})
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print(f" Chain valid: {cv.get('valid', '?')}")
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print(f" Chain length: {cv.get('chain_length', '?')}")
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print(f" Epoch range: {cv.get('first_epoch', '?')} - {cv.get('last_epoch', '?')}")
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def cmd_monitor(args):
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"""Live monitor happiness vectors flowing into the Seed."""
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print_header("Live Happiness Monitor")
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print(f" Polling every {args.interval}s (Ctrl+C to stop)")
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print()
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prev_epoch = 0
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prev_vectors = 0
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try:
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while True:
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s = api(args.seed, "/api/v1/status", args.token)
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if "error" in s:
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print(f" [{time.strftime('%H:%M:%S')}] Error: {s['error']}")
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time.sleep(args.interval)
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continue
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epoch = s["epoch"]
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vectors = s["total_vectors"]
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new_v = vectors - prev_vectors if prev_vectors > 0 else 0
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new_e = epoch - prev_epoch if prev_epoch > 0 else 0
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d = api(args.seed, "/api/v1/sensor/drift/status", args.token)
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drift = d.get("current_drift_score", 0) if "error" not in d else 0
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drifting = d.get("drifting", False) if "error" not in d else False
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ts = time.strftime("%H:%M:%S")
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drift_str = f" DRIFT!" if drifting else ""
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print(f" [{ts}] epoch={epoch} vectors={vectors} (+{new_v}) "
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f"drift={drift:.4f} chain={s['witness_chain_length']}{drift_str}")
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prev_epoch = epoch
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prev_vectors = vectors
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time.sleep(args.interval)
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except KeyboardInterrupt:
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print("\n Stopped.")
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def cmd_happiness_report(args):
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"""Generate a happiness report from stored vectors."""
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print_header("Happiness Report")
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s = api(args.seed, "/api/v1/status", args.token)
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if "error" in s:
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print(f" Error: {s['error']}")
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return
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print(f" Total vectors: {s['total_vectors']}")
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print(f" Store epoch: {s['epoch']}")
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print()
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# Search for happiest and saddest vectors
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happy_ref = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.5]
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sad_ref = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5]
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print(" Happiest moments (closest to ideal happy):")
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happy = api(args.seed, "/api/v1/store/search", args.token,
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method="POST", data={"vector": happy_ref, "k": 3})
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for n in happy.get("neighbors", happy.get("results", [])):
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dist = n.get("distance", n.get("dist", 0))
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vec = n.get("vector", n.get("values", []))
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score = vec[0] if vec else 0
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print(f" id={n.get('id','?'):>10} happiness={score:.2f} dist={dist:.4f}")
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print()
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print(" Most stressed moments (closest to stressed reference):")
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sad = api(args.seed, "/api/v1/store/search", args.token,
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method="POST", data={"vector": sad_ref, "k": 3})
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for n in sad.get("neighbors", sad.get("results", [])):
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dist = n.get("distance", n.get("dist", 0))
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vec = n.get("vector", n.get("values", []))
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score = vec[0] if vec else 0
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print(f" id={n.get('id','?'):>10} happiness={score:.2f} dist={dist:.4f}")
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# Drift status
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print()
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d = api(args.seed, "/api/v1/sensor/drift/status", args.token)
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if "error" not in d:
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if d.get("drifting"):
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print(f" WARNING: Mood drift detected (score={d['current_drift_score']:.4f})")
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print(f" This may indicate a change in guest satisfaction.")
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else:
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print(f" Mood stable (drift score={d.get('current_drift_score', 0):.4f})")
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def main():
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parser = argparse.ArgumentParser(
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description="Happiness Vector Query Tool for Cognitum Seed",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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%(prog)s status --seed http://169.254.42.1
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%(prog)s search --seed http://10.1.10.236 --token TOKEN --mood happy
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%(prog)s monitor --seed http://10.1.10.236 --token TOKEN
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%(prog)s report --seed http://10.1.10.236 --token TOKEN
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%(prog)s witness --seed http://10.1.10.236 --token TOKEN
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"""
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)
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parser.add_argument("--seed", default="http://169.254.42.1",
|
||||
help="Seed base URL (default: USB link-local)")
|
||||
parser.add_argument("--token", default=None,
|
||||
help="Bearer token for WiFi access (not needed for USB)")
|
||||
|
||||
sub = parser.add_subparsers(dest="command")
|
||||
|
||||
sub.add_parser("status", help="Show Seed and swarm status")
|
||||
sub.add_parser("witness", help="Show witness chain audit trail")
|
||||
|
||||
p_search = sub.add_parser("search", help="kNN search for mood patterns")
|
||||
p_search.add_argument("--mood", default="happy",
|
||||
choices=["happy", "neutral", "stressed"])
|
||||
p_search.add_argument("--k", type=int, default=5)
|
||||
|
||||
p_monitor = sub.add_parser("monitor", help="Live monitor incoming vectors")
|
||||
p_monitor.add_argument("--interval", type=int, default=5)
|
||||
|
||||
sub.add_parser("report", help="Generate happiness report")
|
||||
|
||||
args = parser.parse_args()
|
||||
if not args.command:
|
||||
args.command = "status"
|
||||
|
||||
cmds = {
|
||||
"status": cmd_status,
|
||||
"search": cmd_search,
|
||||
"witness": cmd_witness,
|
||||
"monitor": cmd_monitor,
|
||||
"report": cmd_happiness_report,
|
||||
}
|
||||
cmds[args.command](args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user