File size: 4,016 Bytes
3ee46b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | /**
* ALWAS ML-Enhanced Bottleneck Scanner
* Drop-in replacement for the existing node-cron bottleneck scanner.
* Uses ML models instead of simple 48h threshold.
*
* Place in: server/cron/mlBottleneckScanner.js
*
* Setup in server/app.js:
* const cron = require('node-cron');
* const mlScanner = require('./cron/mlBottleneckScanner');
* cron.schedule('0 * * * *', () => mlScanner(io)); // hourly
*/
const ml = require('../utils/alwas-ml-client');
const Block = require('../models/Block');
const Notification = require('../models/Notification');
const User = require('../models/User');
async function mlBottleneckScanner(io) {
console.log('[ML Scanner] Starting hourly bottleneck scan...');
try {
// Get all in-progress blocks
const blocks = await Block.find({
status: { $nin: ['Not Started', 'Completed'] }
}).populate('assignedTo');
let highRisk = 0;
let mediumRisk = 0;
let alerts = [];
for (const block of blocks) {
try {
// Calculate days in current stage
const lastTransition = block.transitions?.[block.transitions.length - 1];
const daysSince = lastTransition
? (Date.now() - new Date(lastTransition.timestamp)) / (1000 * 60 * 60 * 24)
: 0;
const riskData = ml.constructor.formatForBottleneck(block, daysSince);
const risk = await ml.predictBottleneck(riskData);
if (risk.risk_level === 'High') {
highRisk++;
// Create notification for assigned engineer
if (block.assignedTo) {
const notification = await Notification.create({
user: block.assignedTo._id || block.assignedTo,
type: 'stuck',
message: `⚠️ ML Alert: ${block.name} has HIGH bottleneck risk`,
data: {
blockId: block._id,
risk: risk.risk_level,
confidence: risk.confidence,
recommendations: risk.recommendations,
hours_over_budget: risk.hours_over_budget_ratio,
}
});
// Real-time socket notification
io.emit('newNotification', {
userId: block.assignedTo._id || block.assignedTo,
notification: notification,
});
}
// Also notify managers
const managers = await User.find({ role: 'manager' });
for (const manager of managers) {
await Notification.create({
user: manager._id,
type: 'stuck',
message: `🔴 ML Bottleneck Alert: ${block.name} (${block.status}) — ${risk.recommendations[0] || 'High risk detected'}`,
data: {
blockId: block._id,
risk: risk.risk_level,
confidence: risk.confidence,
recommendations: risk.recommendations,
engineer: block.assignedTo?.name || 'Unassigned',
}
});
}
alerts.push({
block: block.name,
stage: block.status,
risk: risk.risk_level,
confidence: risk.confidence,
reason: risk.recommendations[0] || 'High risk',
});
} else if (risk.risk_level === 'Medium') {
mediumRisk++;
}
} catch (blockError) {
console.error(`[ML Scanner] Error scanning block ${block._id}:`, blockError.message);
}
}
console.log(`[ML Scanner] Scan complete: ${blocks.length} blocks scanned, ${highRisk} high risk, ${mediumRisk} medium risk`);
if (alerts.length > 0) {
console.log('[ML Scanner] High-risk blocks:');
alerts.forEach(a => console.log(` - ${a.block} (${a.stage}): ${a.reason}`));
}
return { scanned: blocks.length, highRisk, mediumRisk, alerts };
} catch (error) {
console.error('[ML Scanner] Fatal error:', error.message);
return { error: error.message };
}
}
module.exports = mlBottleneckScanner;
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